Exploring Connectivity in Air Transport as an Equity Factor

Exploring Connectivity in Air Transport as an Equity Factor

Frederico Ferreira Valente Nunes

Thesis to obtain the Master of Science Degree in

Civil Engineering

Thesis supervised by

Prof. Maria do Rosário Maurício Ribeiro Macário

Examination Committee

Chairperson: Prof. João Torres de Quinhones Levy

Supervisor: Prof. Maria do Rosário Maurício Ribeiro Macário

Member of the Committee: Doutor Vasco Domingos Moreira Lopes Miranda dos Reis

September 2015

Exploring Connectivity in Air Transport as an Equity Factor| Frederico Valente Nunes |

II


III

And our people shall leave to find a new India,

One that does not exist yet,

On boats built from the same material dreams are made of.

Fernando Pessoa, in Renascença Portuguesa

E a nossa grande raça partirá em busca de uma Índia nova,

Que não existe no espaço,

Em naus que são construídas daquilo de que os sonhos são feitos.

Fernando Pessoa, in Renascença Portuguesa


IV

Exploring Connectivity in Air Transport as an Equity Factor| Frederico Valente Nunes | Abstract (and key-words)

I

Abstract (and key-words)

In this Master Dissertation we analysed the equity in air transportation in the European Union (EU)

regarding routes and ticket prices. This study aimed to analyse whether the European Union policies in

this field of transportation are considering the equity as a factor, whether they ensure the main purposes

of the EU and whether they are improving the cohesion between countries. For this, three different

indicators were created in order to evaluate equity in air transportation in the EU: Availability, the

existence of routes between countries; Affordability, if the prices take into account the purchasing power

of each country; and Business Convenience, to evaluate the cost of travelling by air on business in

Europe. After this analysis, the same procedures were applied to two Federative Nations, the United

States of America and Brazil, in order to analyse the differences and similarities and to develop

recommendations focused on improving EU political measures.

Key-words: equity; air transportation; European Union; transport policies; equity indicators.

Resumo (e palavras-chave)

Nesta tese de mestrado foi analisada a equidade no transporte aéreo dentro da União Europeia (UE),

no que se refere a rotas e preços de viagem. Estes dados tiveram como objetivo servir de base à análise

das políticas europeias e entender se a equidade é um fator chave no desenho destas políticas, se

estão a ser cumpridos os propósitos iniciais da União e se estas políticas permitem uma maior coesão

no espaço europeu. Para isso foram criados três indicadores com o objetivo de avaliar a equidade no

transporte aéreo na EU: Availability (Disponibilidade), se existem rotas entre os estados membros;

Affordability (Esforço Económico), se os preços têm em conta o poder de compra de cada país; e

Business Convenience (Facilidade de Negócio), por forma a avaliar o custo de viajar de avião para

realizar negócios na Europa. Posteriormente a mesma análise foi realizada para duas federações, os

Estado Unidos da América e o Brasil, com o objetivo de descobrir semelhanças e diferenças por forma

a desenvolver recomendações focadas em melhorar as políticas europeias.

Palavras-chave: equidade; transporte aéreo; União Europeia; políticas de transporte; indicadores de

equidade.

Exploring Connectivity in Air Transport as an Equity Factor| Frederico Valente Nunes | Abstract (and key-words)

II

Exploring Connectivity in Air Transport as an Equity Factor| Frederico Valente Nunes | Acknowledgements

III

Acknowledgements

This work is based on a bigger project than me or you, the reader. In fact it is based on

something that has begun many decades ago, when people of Europe, tired of war, had the idea of

building a common ground for improvement and friendship. Therefore my deepest gratitude to everyone

who worked and works to create, build and improve the European Union.

In a more personal level I have to thank all those who, with their friendship, helped me to reach

this point where I am about to get my Master Degree: my parents, who told me what hard work and

persistence are, in order to accomplish my dreams; my grandmother who told me how to be kind, how

to respect the others and how to be the best person possible; my brothers and other family for being

always present and to give me a good and safe environment to build my life; to all my friends that

fortunately I was able to find and accompanied me since childhood, that everyday give me reasons to

smile, laugh and love them; a special thanks to Joana Barbosa, Inês Marques, Patrícia Cabral, João

Paiva and Jorge Miguel for all their support during the writing of this dissertation, for the company and

for all their suggestions that have certainly improved this project.

An even more special thanks to my supervisor, Professor Maria Rosário Macário for all the

dedication and support, the wise words and the suggestions that made this dissertation possible; for her

advice that made my journey in Civil Engineering to reach this point in spite of my passion for

transportation and transportation policies.

Finally my gratitude to two of my schools, Escola Salesiana de Manique and Instituto Superior

Técnico, where I studied from my 10th anniversary until the 23rd, that gave me the hard and soft skills to

be what I am today and to become what I will be tomorrow.

Exploring Connectivity in Air Transport as an Equity Factor| Frederico Valente Nunes | Acknowledgements

IV

Exploring Connectivity in Air Transport as an Equity Factor| Frederico Valente Nunes | List of Abbreviations

V

List of Abbreviations

EAS – Essential Air Service PPP – Purchasing Power Parity

EC – European Council PSO – Public Service Obligations

EP – European Parliament TEA-21 – Transportation Equity Act for the 21st Century

EU – European Union UNSD – United Nations Statistic Division

FAA – Federal Aviation Administration USA – United States of America

GDP – Gross Domestic Product

Countries abbreviations according to Interinstitutional Style Guide (underlined the short name):

AT – Republic of Austria IE – Ireland

BE – Kingdom of Belgium IT – Italian Republic (Italy)

BG – Republic of Bulgaria LT – Republic of Lithuania

CY – Republic of Cyprus LU – Grand Duchy of Luxemburg

CZ – Czech Republic LV – Republic of Latvia

DE – Federal Republic of Germany MT – Republic of Malta

DK – Kingdom of Denmark NL – Kingdom of the Netherlands

EE – Republic of Estonia PL – Republic of Poland

EL – Hellenic Republic (Greece) PT – Portuguese Republic (Portugal)

ES – Kingdom of Spain RO - Romania

FI – Republic of Finland SE – Kingdom of Sweden

FR – French Republic (France) SI – Republic of Slovenia

HR – Republic of Croatia SK – Slovak Republic (Slovakia)

HU – Hungary UK – United Kingdom of Great Britain and

Northern Ireland

States, from United States of America, abbreviations:

AL – Alabama MT – Montana

AK – Alaska NE – Nebraska

AZ – Arizona NV – Nevada

AR – Arkansas NH – New Hampshire

CA – California NJ – New Jersey

CO – Colorado NM – New Mexico

CT – Connecticut NY – New York

DE – Delaware NC – North Carolina

DC – District of Columbia ND – North Dakota

FL – Florida OH – Ohio

GA – Georgia OK – Oklahoma

HI – Hawaii OR – Oregon

Exploring Connectivity in Air Transport as an Equity Factor| Frederico Valente Nunes | List of Abbreviations

VI

ID – Idaho PA – Pennsylvania

IL – Illinois RI – Road Island

IN – Indiana SC – South Carolina

IA – Iowa SD – South Dakota

KS – Kansas TN – Tennessee

KY – Kentucky TX – Texas

LA – Louisiana UT – Utah

ME – Maine VT – Vermont

MD – Maryland VA – Virginia

MA – Massachusetts WA – Washington

MI – Michigan WV – West Virginia

MN – Minnesota WI – Wisconsin

MS – Mississippi WY – Wyoming

MO – Missouri

States, from Federative Republic of Brazil, abbreviations:

AC – Acre PB – Paraíba

AL – Alagoas PR – Paraná

AP – Amapá PE – Pernambuco

AM – Amazonas PI – Piauí

BA – Bahia RJ – Rio de Janeiro

CE – Ceará RN – Rio Grande do Norte

DF – Distrito Federal RS – Rio Grande do Sul

ES – Espírito Santo RO – Rondônia

GO – Goiás RR – Roraima

MA – Maranhão SC – Santa Catarina

MT – Mato Grosso SP – São Paulo

MS – Mato Grosso do Sul SE – Sergipe

MG – Minas Gerais TO - Tocantins

PA – Pará

Exploring Connectivity in Air Transport as an Equity Factor| Frederico Valente Nunes | Index

VII

Index

1. Introduction and Objectives ............................................................................................................. 1

2. Literature Review ............................................................................................................................. 5

2.1. Equity ....................................................................................................................................... 5

2.1.1. Introduction ...................................................................................................................... 5

2.1.2. Existing Equity Indicators in Transport ............................................................................ 7

2.3.1 Transport Equity in today’s society .................................................................................. 9

2.2. Connectivity ........................................................................................................................... 13

2.2.1. Networks ........................................................................................................................ 13

2.2.2. Measuring connectivity in worldwide air transportation ................................................. 14

2.2.3. The Existing Air Transport Network ............................................................................... 15

3. Methodology .................................................................................................................................. 19

3.1. Construction of Indicators for Air Transportation ................................................................... 19

3.2. Study restrictions and data organisation ............................................................................... 20

3.3. Study of the outcomes ........................................................................................................... 24

4. Case of Study – European Union .................................................................................................. 29

4.1. Availability .............................................................................................................................. 30

4.2. Affordability ............................................................................................................................ 38

4.3. Business Convenience .......................................................................................................... 41

5. Brazil and USA – Comparative Analysis ....................................................................................... 45

5.1. Brazil ...................................................................................................................................... 45

5.1.1. Availability ...................................................................................................................... 46

5.1.2. Affordability .................................................................................................................... 51

5.1.3. Business Convenience .................................................................................................. 54

5.2. USA ....................................................................................................................................... 57

5.2.1. Availability ...................................................................................................................... 59

5.2.2. Affordability .................................................................................................................... 66

5.2.3. Business Convenience .................................................................................................. 70

6. Conclusion: Global analysis and Suggestions .............................................................................. 75

6.1. Global analysis ...................................................................................................................... 75

6.2. Policy Suggestions ................................................................................................................ 77

6.3. Case Study Evaluation .......................................................................................................... 80

6.4. Concluding Remarks ............................................................................................................. 80

6.5. Further Research ................................................................................................................... 82

References ............................................................................................................................................ 83

Annex ..................................................................................................................................................... 89

Annex 1 - Public Holidays in the EU countries, the USA and Brazil ................................................. 89

Annex 2 - Airports to consider in the case study “European Union” ................................................. 91


VIII

Annex 3 – Airport Destinations in the EU ........................................................................................ 101

Annex 4 – Distance between European Capitals ............................................................................ 103

Annex 5 – GDP of each European Country .................................................................................... 105

Annex 6 – Airports to consider in the case study “Brazil” ................................................................ 107

Annex 7 – Airport Destinations in Brazil .......................................................................................... 111

Annex 8 – Distance between Brazilian states Capitals ................................................................... 113

Annex 9 – GDP of each Brazilian state ........................................................................................... 115

Annex 10 – Airports to consider in the case study “United States of America” ............................... 117

Annex 11 – Airport Destinations in the USA .................................................................................... 127

Annex 12 – Distance between the capitals of the states of the USA .............................................. 135

Annex 13 – GDP of each USA state ............................................................................................... 139

Annex 14 – Airport Websites ........................................................................................................... 141

Table Index

Table 1 - Some indicators of European Union, Brazil and United States of America ............................. 2

Table 2 - Principles of Equity, Fairness and Justice and Potential Transport Applications at the local

levels (Hay & Trinder, 1991) .................................................................................................................... 6

Table 3 - Equity and Transport (Banister) ............................................................................................... 7

Table 4 - Standardized scores to interpolate index scores: (Voorhees, 2009) ....................................... 9

Table 5 - Matrix to analyse Availability .................................................................................................. 21

Table 6 - Organisation of the information regarding the distance between capitals ............................. 21

Table 7 - Hubs chosen as representative of UE, USA and Brazil ......................................................... 22

Table 8 - Organisation of the information regarding the calculus of the Affordability indicator ............. 23

Table 9 - Organisation of information regarding the calculus of the Business Convenience indicator . 23

Table 10 – Indicators for the case study ............................................................................................... 24

Table 11 - Organisation of the top best and worst results for each indicator ........................................ 27

Table 12 – Order of the Classification of the Regions ........................................................................... 27

Table 13 - List of European Airports with at least 20% of the country’s passengers ............................ 29

Table 14 - Outcome of Availability Indicator for EU .............................................................................. 30

Table 15 – Top countries with more connectivity and less connectivity from their main airports to the

rest of European countries .................................................................................................................... 32

Table 16 - Descending order of the connectivity of the European Regions according to indicator (1) . 32

Table 17 - Significant Correlation Factors for Percentage of connections from the main airport to each

one of the other countries, for the EU ................................................................................................... 34

Table 18 – Top countries with more connectivity and less connectivity to each country main airport, in

the EU .................................................................................................................................................... 35

Table 19 - Descending order of the connectivity of the European Regions according to indicator (2) . 35

Table 20 - Significant Correlation Factors for the Percentage of connections to each country main

airport, for the EU .................................................................................................................................. 37

Table 21 - Outcome of Affordability Indicator for EU ............................................................................. 38


IX

Table 22 – Top countries with more and less affordability in the European Union ............................... 39

Table 23 - Descending order of the Affordability of the European Regions .......................................... 39

Table 24 - Outcome of the Affordability Indicator for EU taking just into account the minimum prices 40

Table 25 - Business relative cost outcome, for the EU ......................................................................... 41

Table 26 – Top countries with the best and the worst Business Convenience, in the EU .................... 42

Table 27 - Descending order of the Business Convenience of the European Regions ........................ 42

Table 28 - Significant Correlation Factors for the Business Convenience in the EU ............................ 44

Table 29 - List of Brazil airports with at least 20% of the state’s passengers ....................................... 45

Table 30 - Outcome of Availability Indicator for Brazil .......................................................................... 46

Table 31 – Top countries with more connectivity and less connectivity from their main airport to the

rest of the states, in Brazil ..................................................................................................................... 48

Table 32 - Descending order of the connectivity of the Brazil Regions according to indicator (1) ........ 48


one of the other states, for Brazil .......................................................................................................... 48

Table 34 - Descending order of the connectivity of Brazil Regions according to indicator (2) .............. 49

Table 35 – Top states with more connectivity and less connectivity to main airports of the rest of Brazil

states ..................................................................................................................................................... 50

Table 36 - Significant Correlation Factors for the Percentage of connections to each state main airport,

for Brazil................................................................................................................................................. 51

Table 37 - Outcome of Affordability Indicator for Brazil ......................................................................... 52

Table 38 – Top states with more and less Affordability in Brazil ........................................................... 52

Table 39 - Descending order of the Affordability of Brazil Regions ...................................................... 52

Table 40 - Significant Correlation Factors for the Affordability in Brazil ................................................ 53

Table 41 - Business relative cost outcome for Brazil ............................................................................ 54

Table 42 – Top states with best and worst Business Convenience, in Brazil ....................................... 55

Table 43 - Descending order of the Business Convenience of Brazil Regions ..................................... 55

Table 44 - Significant Correlation Factors for the Business Convenience in Brazil .............................. 56

Table 45 - List of USA airports with at least 20% of the state’s passengers ......................................... 57

Table 46 - Outcome of Availability Indicator for the USA ...................................................................... 59

Table 47 – Top countries with more connectivity and less connectivity from main airports to the rest of

the states, in the USA ............................................................................................................................ 62

Table 48 - Descending order of the connectivity of the USA Regions according to indicator (1) ......... 62


one of the other states, for the USA ...................................................................................................... 63

Table 50 – Top states with more connectivity and less connectivity to main airports of the rest of USA

states ..................................................................................................................................................... 64

Table 51 - Descending order of the connectivity of the USA Regions according to indicator (2) ......... 64

Table 52 - Significant Correlation Factors for the Percentage of connections from the main airport to

each one of the other states, for the USA ............................................................................................. 66

Table 53 - Outcome of Affordability Indicator for the USA .................................................................... 67


X

Table 54 – Top states with more and less Affordability in the USA ...................................................... 68

Table 55 - Descending order of the Affordability of the USA Regions .................................................. 68

Table 56 - Significant Correlation Factors for Affordability indicator ..................................................... 70

Table 57 - Business relative cost outcome for USA .............................................................................. 71

Table 58 – Top states with more and less business convenience, in the USA..................................... 72

Table 59 - Descending order of the business convenience, in the USA ............................................... 72

Table 60 - Significant Correlation Factors for the Business Convenience in the USA.......................... 73

Table 61 - Outcomes of the correlation study for the EU, the USA and BR ......................................... 75

Image Index

Figure 1 - Counties containing airports subsidised by Essential Air Service (excluding Alaska and

Hawaii) (SOURCE: Wikipedia) .............................................................................................................. 12

Figure 2 – Portugal continental distribution of public transportation (Adapted from the SOURCE:

IMT/SIGESCC 2012) ............................................................................................................................. 12

Figure 3 - Different possible results of the Pearson Correlation Coefficient. (Source:

StatisticsHowTo.com) ............................................................................................................................ 25

Figure 4 - Spearman correlation factor vs. Pearson correlation. (Source: StatisticsHowTo.com) ........ 26

Figure 5 - Outcome of Availability Indicator for the EU (Percentage of connections from the main

airport to each one of the other countries (1) on the right and Percentage of connections to each

country main airport (2) on the left). Green means better connections and Red worse connections. .. 30

Figure 6 - Geographical division of Europe according to the United Nations Statistic Division ............ 31

Figure 7 - Relation between the Percentage of connections from the main airport to each one of the

other countries and the countries percentage in EU GDP .................................................................... 33


other countries and the Resident population in each country, in the EU .............................................. 33


other countries and the balance of travel and tourism as a percentage of the country’s GDP, in the EU

............................................................................................................................................................... 33

Figure 10 - Relation between the Compensation of employees per capita and the Balance of travel

and tourism as a Percentage of GDP, in the EU ................................................................................... 34


other countries (1) and Percentage of connections to each country main airport (2) and the countries

percentage on EU GDP ......................................................................................................................... 36

Figure 12 -- Relation between the Percentage of connections from the main airport to each one of the

other countries (1) and Percentage of connections to each country main airport (2) and the Resident

Population, in the EU ............................................................................................................................. 36

Figure 13- Relation between the Percentage of connections to each country main airport (2) and the

foreign residents, in the EU ................................................................................................................... 36

file:///C:/Users/Frederico/Dropbox/Frederico%20Nunes%20-%20Dropbox/Tese/Tese.docx%23_Toc430300468




























XI

Figure 14 - Relation between the percentage of connections to each country main airport (2) and the

country’s area, in the EU ....................................................................................................................... 37

Figure 15 - Outcome of Affordability Indicator for the EU. Green means more Affordable (negative

result) than red (positive result). ............................................................................................................ 38

Figure 16 -Outcome of Affordability Indicator for the EU taking just into account the minimum prices.

Green means more Affordable (negative result) than red (positive result). .......................................... 40

Figure 17 - Outcome of the Business Convenience indicator for the EU. Green means better Business

cost (negative result) and red worse (positive result). ........................................................................... 41

Figure 18 - Relation between the Business Convenience and the contribution to the EU GDP ........... 43

Figure 19 - Relation between the Business Convenience and the GDP per capita, in the EU ............. 43

Figure 20 - Relation between the Business Convenience and the compensation of employees per

capita, in the EU .................................................................................................................................... 43

Figure 21 - Relation between the Business Convenience and the resident population, in the EU ....... 43

Figure 22 - Relation between the Business Convenience and the foreign resident population, in the EU

............................................................................................................................................................... 44

Figure 23 - Outcome of Avaiability Indicator for Brazil (Percentage of connections from the main

airport to each one of the other states (1) on the right and Percentage of connections to each state

main airport (2) on the left). Green means better connections and Red worse connections. ............... 46

Figure 24 - Geographical division of Brazil according to the law since 1969 (Source: Instituto Brasileiro

de Geografia e Estatística) .................................................................................................................... 47

Figure 25- Relation between the Percentage of connections from the main airport to each one of the

other states and the states percentage in Brazilian GDP ..................................................................... 49


other states and the Inequality of Income distribution, in Brazil ............................................................ 49


other states (1) and Percentage of connections to each state main airport (2) and the states

percentage on Brazilian GDP ................................................................................................................ 50

Figure 28 - Relation between the Percentage of connections to each state main airport (2) and the

states percentage on Brazilian GDP ..................................................................................................... 51

Figure 29 - Outcome of Affordability Indicator for Brazil. Green means more Affordable (negative

result) than red (positive result). ............................................................................................................ 51

Figure 30 – Relation between the Affordability and GDP per capita, in Brazil ...................................... 53

Figure 31 -Outcome of the Business Convenience indicator for Brazil. Green means better Business

cost (negative result) and red worse (positive result). ........................................................................... 54

Figure 32 - Relation between the Business Convenience and the contribution of the states to the

Brazilian GDP ........................................................................................................................................ 55

Figure 33 - Relation between the Business Convenience and the states GDP per capita, in Brazil .... 56

Figure 34 - Outcome of Avaiability Indicator for the USA (Percentage of connections from the main

airport to each one of the other states (1) on top and Percentage of connections to each state main

airport (2) down). Green means better connections and Red worse connections. ............................... 59










































XII

Figure 35 - Geographical division of the USA according to the United States Census Bureau (Source:

www.census.gov) .................................................................................................................................. 61


other states and the states percentage in USA GDP ........................................................................... 63


other countries and the Resident Population, in the USA ..................................................................... 63


other states and the foreign-born Population (%), in the USA .............................................................. 63


states (1) and Percentage of connections to each state main airport (2) and the states percentage on

USA GDP............................................................................................................................................... 65


other states (1) and Percentage of connections to each state main airport (2) and the states resident

population, in the USA ........................................................................................................................... 65


other states (1) and Percentage of connections to each state main airport (2) and the states foreign-

born residents (%), in the USA .............................................................................................................. 66

Figure 42 - Outcome of Affordability Indicator for the USA. Green means more Affordable (negative

result) than red (positive result) ............................................................................................................. 67

Figure 43 - Relation between the Affordability and the states contribution for the USA GDP .............. 69

Figure 44 - Relation between the Affordability and the compensation of employees per capita, in the

USA ....................................................................................................................................................... 69

Figure 45 - Relation between the Affordability and the Foreign-born Population, in the USA .............. 69

Figure 46 - Relation between the Affordability and the GDP per capita, in the USA ............................ 70

Figure 47 - Outcome of the Business Convenience indicator for the USA. Green means better

Business cost (negative result) and red worse (positive result). ........................................................... 71

Figure 48 - Relation between the Business Convenience and the contribution of each state to the USA

GDP ....................................................................................................................................................... 73

Figure 49 - Relation between the Business Convenience and the Inequality of income distribution, in

the USA ................................................................................................................................................. 73

Figure 50 - Relation between the Business Convenience and the Resident Population, in the USA... 73

Figure 51- Trans-European Transport Network (Source: European Comission) .................................. 78


































XIII


XIV

Exploring Connectivity in Air Transport as an Equity Factor| Frederico Valente Nunes | Introduction and Objectives

1

1. Introduction and Objectives

The European Union (EU) was established, as we know it nowadays, in 1992/3 with the

Maastricht Treaty. Besides giving the community its actual name, this treaty has defined the three pillars

in which Europe would be organised: the European Community, the Common Foreign and Security

Policy and the Justice and Home Affairs.

In what concerns the European Community pillar, the power resides in the European

Commission (EC), the European Parliament (EP) and the European Court of Justice. The power to make

and approve laws that are implemented in all member states is a responsibility of the EC and the EP.

Later, in 2007, the Lisbon Treaty was signed. In the Consolidated Version of The Treaty on the

Functioning of the EU, published in the Official Journal of the EU in 26.10.2012, it is written in the

Preamble that it is “Determined to lay the foundations of an even closer union among the people of

Europe, (…) intending to confirm the solidarity which binds Europe (…)” showing the importance of

solidarity among member states. It is also possible to see the importance of equity as we read the treaty.

In the context of increasing European cohesion, transportation has been one of the subjects in

which new policies have appeared due to the great impact of the decisions in this area to several of the

EU objectives. In the Consolidated Treaties Charter of Fundamental Rights, ten articles are dedicated

exclusively to this subject (in Title VI-Transport) and other four precisely in the Title XVIII-Economic,

Social and Territorial Cohesion.

It is in this context that this dissertation appears. Due to the fact that the economic core of the

EU is situated in Central Europe, linking London, Berlin, Paris and Milan some peripheral countries face

real challenges in competing against markets closer to this core. Therefore, the question that is

presented is “Is there a real equity, due to the geographical position of each country inside the European

Union?”.

The only way to bring peripheral countries closer to this core is by investing EU subsidies in

transport infrastructures and having common and integrated transport policies. Nevertheless, in spite of

existing a closer look and analysis to the situation of each country in Europe, the ideology in which

Europe is built is based on the free private initiative and therefore the action of the EU and of the Country

Governments is mostly based on incentives to increase the cohesion in Europe and not on taking the

place of private initiative.

The European Union is aware of the need to bring together the different countries and to

decrease the distance between them by investing on transportation. The greatest example is the Trans-

European Transport Network which supports the completion of 30 priority projects with high European

added values, as well as projects of common interest and traffic management systems which will play a

key role in facilitating the mobility of goods and passengers within the EU (Trans-European Transport

Network, 2015).

Bearing in mind this policy (but not exclusively), we can see that there is a major concern about

connecting countries by corridors, whether railways, roads, inland waterways or maritime waterways.


2

Keeping this in mind, it is easy to understand that the connections will improve between countries next

to each other, but what about countries that belong to the EU but are not close to each other? The

answer has to rely on air transportation if we want to achieve fast and safe connections.

When we look to the EC proposals for Air Transportation, four things pop-up: single market,

making easier cross-border investments; external aviation, providing a more coordinated aviation policy

with other countries outside the EU; Single European Sky, decreasing airspace congestion; and SESAR,

technology that makes the Single European Sky possible.

This way we can conclude that some important connection issues have been left out of the

discussion: the direct connection by air between distant countries in the EU. This dissertation appears

exactly at this point, willing to analyse whether there are reasons to take action also in this “battle front”.

Whereas this problem appears, several objectives of this work must be pointed-out: firstly there

is a need to analyse the offer of air passenger transportation between member states and evaluate if

the route offered accomplishes the desires of EU policies. Secondly, it is necessary to see if the ticket

prices are adequate to the cost of living of each country enabling a normal citizen to make these flights.

This analysis will be done by creating different indicators that will provide the necessary information to

evaluate the actual situation.

With these outcomes, by comparing and relating with other social, financial and economic

indicators, it will be possible to argue about the current EU policy in what concerns air passenger

transportation (supply, demand and costs), to compare with the policies carried out by other Federative

Nations and finally to find ways of action and improvement in EU policies to improve cohesion in this

field.

At this point it is important to explain the reason why the EU will be compared to Brazil and to

the USA. Table 1 shows information that will allow some comparison between these three case studies.

Table 1 - Some indicators of European Union, Brazil and United States of America

European Union Brazil

United States of

America

Countries/ States 28 273 513

Resident Population

(million)1 408 368 483 200 361 925 180 671 158

Area (km2) 4 494 515,2 8 358 140,0 9 147 420,0

GDP (PPS billion $)2 17 990,12 3 012,93 16 768,10

Air transport

(passengers)4 975 243 272 95 917 212 743 096 000

1 According to pordata.pt for 2013 (USA and EU); According to World Bank in 2013 (Brazil)

2 According to World Bank in 2013

3 States and Federal District

4 According to World Bank in 2013. Both national and international passengers from air carriers registered in the country


3

Although neither one of them is a total match, there are several similarities between the EU and

these two Federative Nations. Firstly, the EU and Brazil have almost the same amount of countries (in

the case of the EU) or states (in the case of Brazil). This means that the organisation of the territory is

similar if we compare it with the organisation and assumptions of this study. Secondly, the area of the

EU and Brazil is similar if we take into account that almost 61% of Brazil territory is Amazon Rainforest

(belonging to Amazonia Legal), which means that there is little necessity of regular high capacity air

flights in this area and therefore a similar demand-area comparing to the EU.

Then, the necessity of comparing the USA to the EU comes first because there is not a bigger

federalist nation in the developed world with such similar regime and policy. Also, the joint GDP of EU

is very similar to the USA, forming the two bigger economies in the world with about 45,98% of World’s

GDP in 2013. Likewise, EU and USA airlines together have 56,83% of the world’s passengers carried

by air in 2013, being the two prime sources of air passengers carried. Finally they are the third and

fourth biggest nations in what concerns population, only overcome by China and India, in first and

second place, respectively.

Therefore, with these two case-studies it will be possible to make some comparisons between

results, but also to find some ideas concerning policies already taken by these countries.

In order to accomplish this work firstly there will be a debate on the definition and types of Equity,

definition and approaches to Connectivity and the existing transport indicators (related with air

transportation or with international trips). Then, after defining the methodology that will guide the

analysis, the European Union case study will be approached, and conclusions will be reached. Finally,

following the same methodology, the situation in Brazil and the United States of America will be analysed

and the results will be compared with the EU. In conclusion, and based on the comparison made before,

between the EU, the USA and Brazil, some policies suggestions will be made, in order to increase equity

and improve the results of the created indicators.

In what concerns Methodology, first will be presented the construction of the indicators, the

sources of the input information and the explanation about the why and how they were created. Secondly

will be explained the way the information was gathered and organized and how the indicators were

calculated in order to allow future researchers to repeat this study for different countries and reasons.

Finally we will explain the way the information was analysed.


4

Exploring Connectivity in Air Transport as an Equity Factor| Frederico Valente Nunes | Literature Review

5

2. Literature Review

2.1. Equity

2.1.1. Introduction

Equity refers to the distribution of benefits and costs and whether that distribution is considered

fair and appropriate (Litman, 2014). Transport planning decisions have an important impact on citizen’s

life and therefore equity is a major aspect that always has to be taken into account in societies that tend

to be fair and try to decrease the gap between different groups while maintaining a capitalism system.

Nevertheless in transportation the idea behind equity is not just to make the distribution of

benefits and costs but also to give equal possibilities to everyone at the same time that we provide what

each community needs.

However, even if everyone agrees that equity is a necessary aspect in daily-life decisions, not

everyone agrees about the way equity should be seen and, therefore, three types of equity have been

defined, according to David Banister in “Equity and Acceptability Question in Internalising the Social

Costs of Transport” and according to Professor José Manuel Viegas and Professor Rosário Macário.

Horizontal Equity (also called fairness and equalitarianism) concerns the distribution of impacts

between individuals and groups considered equal in ability and need (Litman, 2014). From this point of

view equal individuals and groups should be given equal opportunities, which means that public policies

should avoid favouring one individual or group over the others. However, as Ellickson (1977) says,

“likeness” is a matter of degree, and to make policies according to this type of equity is always a

dangerous way.

Vertical Equity with Regard to Income and Social Class, is concerned with the distribution of

impacts between individuals and groups that differ in abilities and needs, in this case, by income or

social class, for example (Litman, 2014). This way, policies would be equitable if they tend to favour the

ones that society sees somehow as a disadvantage, as low income families or low social class groups.

This type of equity is used to support policies for investments in affordable modes, discounts and special

services trying this way to reduce the external costs for this groups.

Finally, Longitudinal Equity with Regard to Mobility Need and Ability, concerns the distribution

of impacts between individuals and groups that differ in mobility ability and need, and therefore the

degree to which the transportation system meets the needs of travellers with mobility impairments

(Litman, 2014). In what concerns the mobility ability, this work will not focus on this subject due to the

fact that there is already an European legislation that tries exactly to reduce the barriers to this kind of

people.

Hay and Trinder (1991) defined what they believed were the Ten Principles of Equity, Fairness

and Justice in Transport Policy, as we can see in Table 2. Although all ten principles should be


6

considered, it seems that three are crucial both to fulfil the requirements in terms of equalisation of

opportunity and outcomes, and in terms of public and political acceptability (Banister): Principle 2 –

Expectations, Principle 3 – Formal Equality and Principle 4 – Substantive Equality.

Bearing this in mind, it is possible to cross the principles with the types of equity in each the

indicators should be based on, as Banister did and they can be seen in Table 3.

Table 2 - Principles of Equity, Fairness and Justice and Potential Transport Applications at the local levels (Hay & Trinder, 1991)

Principle Definition Transport Application

1. Procedural Fairness Consistency, evenhandedness,

non-arbitrariness in procedures

Exclusion of certain interested

groups or individuals from the

policy process

2. Expectations

Maintenance of conditions

upon which reasonable

expectations have been formed

Sudden or major increase in

rail fares, unexpected siting of

a new road

3. Formal Equity

Equal treatment within a

reference group, like benefits

enjoyed by alike persons

All ratepayers to have access

to facilities through local

taxation

4. Substantive Equality Equality in final outcomes Provision to secure equal

access to facilities or equal use

5. Need as Demand A want backed by a willingness

to pay

Provision of unsubsidised

transport services

6. Basic Need Minimum requirements to fulfil

certain universal objectives

Provision of subsidised

transport services to rural

areas

7. Wider Need Wants Free public transport

8. Liberty Rights Rights of choice and the

corrective duties of forbearance

Rights to intervene in policy

process

9. Claim Rights Duty to provide something to

the rights-holder Right to concessionary fares

10. Desert/Credit

Distribution according to

individual desert/credit, merit or

contribution to the common

good

Uncertain possibly provision of

concessionary fares for the

aged


7

Table 3 - Equity and Transport (Banister)

Equalisation of Outcomes Equalisation of Opportunity

Horizontal Equity – Formal

Equality

Principle 5 – Service

Distribution according to

Demand

Principle 6 – Equality in Service

Distribution

Service Provided on

commercial criteria. Market-

based with no subsidy.

Service provided to all

communities at a similar level.

Standards or minimum levels of

service.

Vertical Equity – Substantive

Equality

Principles 7 and 9 – Service

Distribution according to Need

Principles 7, 9 and 10 - Positive

Discrimination for Particular

Disadvantaged Groups

Travel concessions for the

elderly, the young, the disabled

and other “need groups”

Special Services

The United States Department of Transportation synthetizes three fundamental environmental

justice principles relevant to transportation planning done by the agency:

1. To avoid, minimize, or mitigate disproportionately high and adverse human health and

environmental effects, including social and economic effects, on minority and low-income

populations;

2. To prevent the denial of, reduction in, or significant delay in the receipt of benefits by minority

and low-income populations;

3. Ensure full participation by all potentially affected communities.

2.1.2. Existing Equity Indicators in Transport

Countless works have already been done by many academics in the field of equity indicators in

transportation. Nevertheless, the majority of these works were done in the areas of accessibility (and

not mobility, as it is the aim of this work), related to urban areas (and not world-size) and associated

with urban means of transportation, as trains and cars (and not airplanes). Since no works were found

related with air flights between countries, existing equity indicators will be important to suggest some

variables and the way these indicators should be created.

Accessibility indicators for road transport planning

Morris, Dumble and Wigan, from the Australian Road Research Board made, in 1978, a

gathering of different indicators to help road transportation planning. However, their work was only

focused on the statistical analysis of the data, and not on the indicators that should be analysed.


8

There are two ideas that can be taken from their work:

1. There are two types of analysis of accessibility: Relative Accessibility, where only the distance

is taken into account, and Integral Accessibility, where cost, time and other indicators are also analysed;

2. How should the relation between two separate points be analysed: by taking only into account

the distance, bearing in mind that “the farther it is, the less demand there is” (decay factor), considering

that the demand varies with the capacity of the network, or a composed analysis between the distance

and the supply/demand?

The Chicago Region Expansion

Nathalie P. Voorhees (2009), from the University of Illinois in Chicago, did an equity analysis of

the expansion of the Red Line of Chicago Metro that connects the whole region. In her study three areas

were chosen for analysis: Transportation Equity, Environmental Social Justice and Livable Community

Potential. In what concerns Transportation Equity, five aspects were taken into account: Transit

dependence measured by disabled population, by households with no cars, by elderly population and

by high school students and inadequate access measured by excessive travelling time to work. In the

Livable Community Potential the economic health measured by unemployed population was taken into

account, business health measured by extensive business vacancy and the economic stability

measured by estimated high cost loans. Other criteria were also studied but there is no relation with this

work.

The Equity Index makes use of standardized scores as a means of comparing conditions across

a regional geography. Standardized scores allow for comparison across regions by looking at the range

or distribution of values, and then comparing individual values and their distance from significant values.

Standardized score values represent how many standard deviations from the significant value the value

is for a particular area, and is calculated as the z-score statistic for each geographic area unit (Voorhees,

2009).

The standard score is given by Equation 1:

𝑧 =𝑥 − 𝜇

𝜎 (1)

where:

𝑥 is a raw score to be standardized;

𝜇 s the value of the population;

𝜎 is the standard deviation of the population.

Then, it uses the standardized scores to interpolate index scores for each category (Table 4).

By adding the several index scores from each criteria (for each influence area of the three metro lines)

it is possible to analyse if the investment in the expansion of the red line is the most equitable.


9

Table 4 - Standardized scores to interpolate index scores (Voorhees, 2009)

Z-score Index Score

> 1,5 2

1,5 to 0,5 1

0,5 to -0,5 0

-0,5 to -1,5 -1

< -1,5 -2

2.3.1 Transport Equity in today’s society

In the last century governments from developed countries realized that one of the new century

challenges is not to ensure the strength of the borders but to improve the connection and cohesion

inside them. This way, all across the globe several countries have taken actions to improve this cohesion

by investing in transportation infrastructures or by improving legislation on transportation.

This reality is obviously greater in countries of large dimensions, like the United States of

America, for example, but also in small countries, like Portugal. The reasons for these transversal

concerns are due to different realities: if big countries or communities (like the EU) tend to invest to

promote the cohesion and to improve the public opinion on a determinate subject, other tend to invest

to decrease asymmetries between different parts of the country or to overcome natural barriers as the

sea, in the case of countries with islands, or as detached territories.

Transportation Equity Act for the 21st Century

One of the greatest examples in this field is the Transportation Equity Act for the 21st Century

(TEA-21), a United States public law enacted in June 9, 1998, which authorized federal surface

transportation programs for highways, highway safety and traffic between 1998 and 2003. This act

presents “new initiatives to meet the challenges of improving safety as traffic continues to increase at

record levels, protecting and enhancing communities and the natural environment while providing

transportation, and advancing America’s economic growth and competitiveness domestically and

internationally through efficient and flexible transportation.” (TEA-21 - Transportation Equity Act for the

21st Century, Moving Americans into the 21st Century, 2015).

According to this Act, seven planning factors are required to be included in regional

transportation plans for funds to be requested:

i. Support the economic vitality of the metropolitan planning area, especially by enabling global

competitiveness, productivity and efficiency;

ii. Increase the safety and security for the transportation system for motorized and non-motorized

users;

iii. Increase the accessibility and mobility options available for people and for freight;


10

iv. Protect and enhance the environment, promote energy conservation, improve quality of life, and

promote consistency between transportation improvement and state and local planned growth and

economic development patterns

v. Enhance the integration of connectivity of the transportation system, across and between modes,

for people and freight;

vi. Promote efficient system management and operation;

vii. Emphasize the efficient preservation of existing transportation system.

As it is possible to observe, the aim of this equity act covers a lot more areas than the study

proposed, but it shows without a doubt that several areas of equity are priorities in the transportation

future plans. Although air transportation is not taken under consideration in this law (it is only applicable

to surface transportation systems), the seven factors could also be applied to this mean of

transportation.

For this study, at least two of these factors are addressed: support the economic vitality of all

European Union countries, by improving competitiveness, productivity and efficiency and increase the

accessibility and mobility options available for people and for freight.

Public Service Obligations (PSO) in Air Transportation

Public Service Obligations in Air Transportation is the name given to the obligation given by a

Government to an airline to serve a specific route with specific rules with several possible objectives

determined by law.

In the EU, PSO are established according to European Regulation nº 1008/2008 of the

European Parliament and of the Council of 24 September 2008 on common rules for the operation of

air services in the Community taking “(11) into account the special characteristics and constraints of the

outermost regions, in particular their remoteness, insularity and small size, the need to properly link

them with the central regions of the Community”.

Under Chapter III – Access to Routes, Article 16 of the mentioned law, it is stated that “A Member

State, following consultations (…), may impose a public service obligation in respect of scheduled air

services between an airport in the Community and an airport serving a peripheral or development region

in its territory or on a thin route to any airport on its territory any such route being considered as vital for

the economic and social development of the region which the airport serves. That obligation shall be

imposed only to the extent necessary to ensure on that route the minimum provision of scheduled air

services satisfying fixed standards of continuity, regularity, pricing or minimum capacity, which air

carriers would not assume if they were solely considering their commercial interest.”. According to this,

“if no airline is willing to provide a service under the conditions imposed, the government may restrict

access to the route to a single carrier and award financial compensation to the carrier in return for

compliance with the PSO” (Williams, 2010).

Nevertheless this is not just a situation verified on the EU, having Canada and the USA, for

instance, similar laws. This need urges in countries where remote regions are separated from the


11

mainland due to natural barriers impassable by other fast means of transportation (like an island,

separated from the mainland by water) or due to high costs of construction of surface routes allied with

few users that make those routes unbearable to build. For instance, in “2006, rural Canada covered

99,8% of the nation’s territory and accounted for 24% of its population” (Metrass-Mendes, de Neufville,

& Costa, 2010).

Some cases in the EU and the USA will be adressed further in this work, but for now, a

framework of the actual situation is needed.

Regarding the authority to administer air transportation PSO, government departments are

responsible in Czech Republic, Finland, Greece, Ireland, Portugal and Sweden, while in France,

Germany, Italy and Spain, this authority is in the hands of regional responsibles. In the UK a mixed

situation can be found, where Scottish Government is responsible only for administering the routes

operated from Glasgow, regional authorities are responsible for services in Orkney, Shetland and

Western Isles, and Wish Assembly Government is the responsible in the case of Wales.

Concerning the number of PSO routes (in December 2014), Norway (although not in the EU, it

belongs to the European Economic Area) is the leader with 51 routes, followed by France with 42 and

Greece, Portugal, United Kingdom and Italy with between 30 and 20 routes each. The country with the

fewer PSO routes are Finland and Ireland with only 3. Nevertheless there are 18 countries in the Union

with no PSO routes. The share of domestic seats that are offered under the PSO regime is higher in

Portugal (40%) and Ireland (23%), followed by France and Norway with around 10% (Lian, 2010).

The average distance of the PSO routes varies from 600km in France and 200km in Norway

and the seating capacity average varies between 110-70 seats in Portugal and France, 50-35 seats in

Spain, Sweden and Germany and 15-10 seats in Scotland. In Norway, although the average consists

of 37 seats, there are aircrafts with a minimum of 15 seats.

Finally, concerning the average subsidy level per passenger, Germany is around 120 €, followed

by Norway, Sweden and Scotland with around 60 €, while France and Portugal are near 20 €.

The next two topics will show two different PSO, one in Portugal and the other in the USA.

Essential Air service and Alternative Essential Air Service

The United States government created a program in 1978 called Essential Air Service (EAS) as

a response to the Airline Deregulation Act, which gave US airlines almost total freedom to determine

which markets to serve domestically and what fares to charge for that service. To ensure that small

communities that did not have business interest for the airlines continue to have an air transportation

service, the Government created this program which subsidises the airlines that fly to selected counties

airports (Figure 1).

According to The New York Times, in 2014, the price for passenger (excluding Alaska

subsidised airports) was approximately 74 $ but some flights had subsidies as high as 801 $ per

passenger. In 2014 the budget for EAS was 241 million $, almost two times the money spent in 2011.


12

On the other hand, the Alternative Essential Air Service intends to subsidise not the airlines but

directly the municipality or the airport authority, what allows the community to recruit air service that is

not supported by EAS, as less-than-daily services, flights to differing destinations depending on the time

of the year or week, or air taxi service.

Strategic Plan for Transportation and Infrastructure 2014-2020

The Portuguese Strategic Plan for Transportation and

Infrastructure 2014-2020 brings two different contributions for this

subject: first it enhances the New Juridical Regime for Public

Transport Services where the Principle of equity in access to

transportation is addressed to and second it brings to discussion the

intercontinental air transportation.

The New Juridical Regime for Public Transportation Services

shows the political concerns about the distribution of the public

transportation in the different regions of Portugal. As it is possible to

see in Figure 2, the distribution of public transportation is not

homogeneous and there is the necessity of providing an efficient and

needed service to all citizens.

On the other hand, it also addresses the issue of the

intercontinental air transportation: in Portugal mainland there are few

frequent air-highway (Lisbon-Oporto, Lisbon-Faro and Oporto-Faro),

which does not ensure the cohesion of the territory. For this purpose

the Portuguese Government has launched an international tender in

the Official Journal of the European Union (European Comission, 2014) for the public service obligations

regarding scheduled air services between Bragança, Vila Real, Viseu, Cascais and Portimão. This

service, which is represented in Figure 2, is sponsored by the Government and entered into force of the

public service obligations on 1st July 2015.

Existing Air-highways

New Air-highway

Oporto

Bragança

Vila Real

Viseu

Lisbon

Faro Portimão

Cascais

Figure 2 – Portugal continental distribution of public transportation (Adapted from the SOURCE: IMT/SIGESCC 2012)

Figure 1 - Counties containing airports subsidised by Essential Air Service (excluding Alaska and Hawaii) (SOURCE: Wikipedia)


13

2.2. Connectivity

According to the Business Dictionary, Connectivity is the “measure of the extent to which the

components (nodes) of a network are connected to one another, and the ease (speed) with which they

can ‘converse’”. From this definition is easy to understand that connectivity has a direct connection with

the concept “network”.

2.2.1. Networks

Defining a network is not easy, mostly because there are many elements involved and even

more relations between them. In a simplified way we can say that a network is a series of points or

nodes (elements) interconnected by paths (relations).

Studies, like the one by R. Guimerà, S. Mossa, A. Turtschi and L. A. N. Amaral (2005) found

that the worldwide air transportation networks is a scale-free small-world network. A scale-free network

is characterized by a vertex connectivity distribution that decays as a power law. These emerge in the

context of a growing network in which new vertices connect preferentially to the more highly connected

vertices in the network. Nevertheless scale-free networks have to be small-world networks because (i)

they have clustering coefficients much larger than random networks and (ii) their diameter increases

logarithmically with the number of vertices n. (Amaral, Scala, Barthélémy, & Stanley, 2000).

The case of the worldwide air transportation network is consistent with a small-world network in

which the number of nonstop connections from a given city and the number of shortest paths going

through a given city have distributions that are scale-free. In the air transportation network, the average

shortest path length d is the average minimum number of flights that one needs to take to get from any

city to any other city in the world. Guimerà et all. found that “for the 719 cities in the Asia and Middle

East network, d=3,5 and that the average shortest path length between networks is only approximately

one step greater, d=4,4. Actually, most pairs of cities (56%) are connected by four steps or less” and

that “d grows logarithmically with the number S of cities in the network, d ≈ log S. This behaviour is

consistent with both random graphs and small-world networks but not with low-dimensional networks,

for which d grows more rapidly with S.” (Guimera, Mossa, Turtschi, & Amaral, 2005).

What is interesting to realize in almost all networks that exist in the world, from the worldwide

air transportation network, to the World Wide Web network (www.), even to networks in biology, is that

all them then to be ruled by the same mathematical principles. The study of some networks can, thereby

help to understand other networks and to organized human-networks in a proper way.

Albert-Laszlo Barabasi and Reka Albert reported in 1999 in Emergence of Scaling in Random

Networks that complex networks large-scale properties have a high degree of self-organization, showing

that (i) networks expand continuously by the addition of new vertices and (ii) new vertices attach

preferentially to sites that are already well connected. Besides this, they shown that “independently of


14

the system and the identity of its constituents, the probability P(k) that a vertex in the network interacts

with k other vertices decays as a power law, following 𝑃(𝑘)~𝑘−𝛾”.

2.2.2. Measuring connectivity in worldwide air transportation

The necessity of measuring connectivity is obvious: first of all this indicators can act as a

performance indicator to networks, airports and regions, what supports the creation of new policies;

secondly, the creation of such indicators can assess the impact of various measures to maintain or

enhance network performance; and finally together with ticket price it is an important variable in route

choice of passengers (Burghouwt & Redondi, Connectivity in air transport networks: models, measures

and applications, 2009).

In previous literature is difficult to find any comprehensive attempt to measure air transport

connectivity between countries using rigorous network analysis methods. The very first study about this

problematic was done by Jean-François Arvis and Ben Shepherd for The World Bank. Nevertheless,

we can find important contributions previous to this study.

First Pearce (2007) defined connectivity as summarizing the scope of access between an

individual airport or country and the global air transport network. Bearing this in mind the indicator that

he created combined information on the number of destinations served, the frequency of service, the

number of seats per flight, and the size of the destination airport. Using this indicator to measure the

connectivity of 47 countries he found a relation between connectivity and labour productivity and

competitiveness of the travel and tourism sector.

Focusing in transport, but not necessarily in air transport, UNCTAD – United Nations

Conference for Trade and Development - is developing a Linear Shipping Connectivity Index. They

define connectivity in terms of access to regular and frequent transport services, then use factor analysis

to bring together data on capacity and utilization in the liner shipping sector (UNCTAD, 2007).

According to Jean-François Arvis and Ben Shepherd (2011), essentially we can say that there

are four groups of connectivity measures applied so far to transport and economic problems:

Intuitive metrics, simply counting the number of connections by node, often referred as degree

centrality;

Concentration indicators, which makes use of more information from the matrix, and uses

concentration indices such as the Herfindhal or Theil indices of the floes to and from a node in the

network;

Clustering techniques, which is essentially a topological concept in which the clustering coefficient

of a node i is an intuitive measure of how well connected the nodes in the neighbourhood of i are;

Centrality indices (closeness centrality or PageRank), which measures the importance of a node in

relation to all the other nodes in the network (the more important a destination is for its neighbours,

the more central it is).


15

In the case of Jean-François Arvis and Ben Shepherd (2011) they were “interested in using

connectivity as a policy tool, rather than simply a mean of describing network properties, as in the applied

mathematics literature and they focus on the country as a the level of analysis. Therefore they created

a model to evaluate the connectivity of each country based on a generic bi-proportional gravity model:

𝑋𝑖𝑗 = 𝐴𝑖𝐵𝑗𝐾𝑖𝑗 (2)

where 𝐴𝑖 is the repulsive potential of node i, 𝐵𝑗 is the attractive potential of node j, 𝐾𝑖𝑗 is the bilateral

impedance which is exogenous and decreasing in the cost and distance and 𝑋𝑖𝑗 is “pushed” from i and

“pulled” to j (Arvis & Shepherd, 2011). Using this indicator they were able to rank countries according to

their connectivity: United States of America was on the 1st position with a connectivity of 22,78%,

followed by European Union (making the average of the countries in the Union and excluding them from

the ranking) in the 4th position with 21,07% of connectivity and Brazil with 2,67% of connectivity at the

96th place (excluding the European Countries from the ranking and adding the country “European

Union”). They also found that in 80% of times airports can be reached from any other node in three

steps or less and almost 100% of times can be reached in four steps or less.

Some other have intended to analyse the situation inside their own countries. For example

Ganesh Bagler (2008) analysed the domestic air transportation network in India, where we found that

from approximately 228 airports, only 7% of them were connected with direct flights and that 99% could

be reached in three or less steps.

Stefano Paleari, Renato Redondi and Paolo Maligheti (2010) made a comparative analysis of

the networks in China, Europa and United States of America. Together these networks account for

51,1% of all seats offered worldwide. They found that in Europe and China, connection distances are

concentrated around 1 000 – 1 200 km while in the USA they are concentrated between 1 000 – 1 200

km and around 4 500 km. The average shortest path ranges from 2,34 in China, to 2,80 in the European

Union and 3,38 in the United States of America (Paleari, Redondi, & Malighetti, 2010).

2.2.3. The Existing Air Transport Network

Before 1987 the aviation market in Europe was regulated, what means that the routes were the

result of Bilateral Air Service Agreements between countries where airlines (normally owned by national

governments) would fly according to strict rules. This meant that most part of routes were only operated

by one or two designated flag carriers who were constrained in terms of capacity and pricing (Lieshout,

Malighetti, Redondi, & Burghouwt, 2015).

After this period, European aviation market was gradually deregulated in three steeps, or

packages, in 1987,1990 and 1992 (Button, Haynes, & Stough, 1998). When the same happened in

United States of America in 1978, “airlines took advantage of the possibilities of the liberalised market

and reorganised their networks, most part by making their network point-to-point into hub-and-spoke

network. Direct flights from medium airports to other medium airports were increasingly replaced by

indirect flights via a central airport or hub” (Burghouwt & Wit, 2005). Hub-and-spoke networks allow the


16

hub-airline to maximize the number of connected city pairs given a certain number of flights by means

of spatial and temporal concentration of the network (Burghouwt G. , 2007). In this new organization of

networks that grown, spatial concentration and temporal concentration were the two main features

(Reynolds-Feighan, 2001). The spatial configuration can be defined as the level of concentration of an

airline network around one or a few central hub airports. Temporal configuration regards the organisation

of the airline flight schedule at an airline’s station resulting in a given number and quality of indirect

connections offered through that airline’s station.

Is important to say that for an accurate study, an indirect flight should have some defined

characteristics, like the time between flights, due to the fact that even if two flights can make a 2 steps

journey, if the time between them is too long, passengers will not have any interest in that connection.

Therefore airlines should organized their schedule in a wave-system structure. According to Bootsma

(1997) a connection wave is “a complex of incoming and outgoing flights, structured such that all

incoming flights connect to all outgoing flights”. A wave-system structure consists therefore in a number

of waves, the timing of the waves and the structure of the individual waves (Burghouwt & Wit, 2005).

This way, three elements determine the structure of a connection wave: (i) the minimum connecting time

for continental and intercontinental flights, (ii) the maximum connecting times and (iii) the maximum

number of flights that can be scheduled per time period.

As it was already said, the attractiveness of an indirect connection depends on several aspects,

as for example (Veldhuis, 1997):

Waiting time at the hub: atractivity declines when waiting time increases;

Routing factor: the in-flight time. Some indirect connections are not attractive for the average air

traveller because the detour factor is too large;

Perception: passengers perceive transfer time as being longer than in-flight time;

Fares: lower fares may compensate for longer transfer and in-flight times;

Flights of a certain airline may be attractive because the air traveller participates in the loyalty

programme of the airline and;

Amenities of the hub-airport involved in the transfer.

One of the consequences of the deregulation of Europe’s airline industry has been the need to

provide subsidies to ensure the continuation of air services to remote communities, previously cross-

subsidised by profits from busier routes of the same airline (Smyth, Christodoulou, Dennis, AL-Azzawi,

& Campbell, 2012). Public Service Obligations in Air Transportation, as it is called, was already

addressed in the previous charter.

But what is the importance of having a dynamic and more competitive air transportation market?

Various studies have shown a correlation between the provision of connectivity through transport

networks and economic growth. Interurban and international connections enable the development of

new production processes and, being able to national or international trade, regions and/or countries

will benefit from the increase specialisation in production of goods and services (Eddington, 2006).


17

Almost two-thirds of United Kingdom companies have reported that passenger services are

either vital or very important for sales and marketing, being air services particularly important for the

country’s trade with the fastest-growing regions of the world economy, Furthermore, 55% of the United

Kingdom’s exports by value of manufactured goods to countries outside the EU are transported by air

and more than 60% of imports of machinery, mechanical appliances and electric equipment from outside

the European Union are carried by air. The importance in the tourism sector is even more evident with

more than 75% of international visitors coming to the United Kingdom arriving by plane, a number that

grows to 87% in the Scottish case (Strategic Research Department, 2007). All these numbers lead to

tourism spending by visitors who came by air to reach 1,1% of the United Kingdom GDP and creating

170 000 jobs across all country. This example from UK can be extrapolated and shows the economic

and social importance of air transport market.

Austin Smyth et all. shown in Is air transport a necessity for social inclusion and economic

development? (2012) the importance of Public Service Obligations in the most various fields of public

life. Nevertheless, PSO are not the only way to improve connectivity (and equity as we already saw). It

were created several incentives to encourage the launch of new air routes however, public funding is

not the sole source of financial support, although regulation increasingly restricts the opportunity for

employing such supports. Other incentives may come in the shape of Route Development Fund, Tax

holidays and discounts, Revenue guarantees or Community Ticket Trusts.

One of the most used methods in United Kingdom is the Route Development Funds (RDF).

These are intended to benefit the overall economic development of the region but are not “intended to

replace the role of the airport and airlines in developing their business but rather to act as a catalyst for

promoting links either not under immediate consideration or ones thought to have marginal business

case in the short term” (Smyth, Christodoulou, Dennis, AL-Azzawi, & Campbell, 2012). These

mechanism intends to allocate funds to routs that are likely to become commercially viable after the first

three years giving this way a help in by creating demand by decreasing fairs. One of the benefits of this

mechanism is that contributes to raise profits of the airports and regions concerned, it encourages

airports to be more dynamic in their marketing and generally attract interest from airlines. Nevertheless,

there is no guarantee that routes which receive RDF funds will ultimately prove to be financially viable

in the longer term (UK Civil Aviation Authority, 2005). The Scottish Government has focussed on the

new services which have an average frequency of at least five return trips per week, operates on an all

year round basis and which would not go ahead without RDF investments.

By analysing the results over the years in Scotland Austin Smyth et all. found that the number

of passengers has grown from 286 000 in 2003/04 to 1,9 million in 2007/08. But what about the economic

impacts? The analysis shown that the RDF services returns a positive net present value and benefit-to-

cost ration greater than 1,0 suggesting the RDF programme has been successful in improving travel

conditions for business and leisure passengers. Of the 52 services with RDF funds, only 2 services

produced negative returns. In the case of financial impact, only taking in account revenues of tourism

sector, this funds produced a profit of more £19,9 million in 2008 when compared with before the

implementation of the RDF funds. Concerning business expenditure the study found a positive impact


18

of more than £7,8 million in 2008. Thirdly, the social impacts were also massive: with the allocation of

this founds the journey times decreases on average 62% when compared with surface journeys and

also 24% shorter in distance travelled what leads to the reduction of the perception of the remoteness

of parts of Scotland. Finally, it is true that the allocation of this founds created more environmental

impacts by increasing the level of air pollution and emissions. As studies shown that the CO2 emissions

from RDF services were just under 4,02m tonnes over a 10-year appraisal period what gives to the UK

Government costs of carbon to these emissions values at £69m (2002 prices). However, is important to

remember that while there is an increase of emissions because of the increase in air flights, there is also

a reduction due to the decrease in other transports modes. After considering all these aspects, Austin

Smyth et all. considered that the application of funds in the way of RDF brings long term positive impacts

to the economic, financial, social and environmental life in Scotland.

The results presented by Austin Smyth et all. show what Jean-François Arvis and Ben Shepherd

(2011) found: that the connectivity of a country (according to their indicator) is “strongly correlated with

the degree of liberalization in air services markets, which suggests that policy can play an important role

in shaping connectivity”. They also found that “better connected countries tend to be more specialized

in trade in machinery parts and components, which is consistent with their being more deeply integrated

into international production networks, which rely heavily on air transport”.

Exploring Connectivity in Air Transport as an Equity Factor| Frederico Valente Nunes | Methodology

19

3. Methodology

3.1. Construction of Indicators for Air Transportation

Equity in the air transportation, as it is going to be approached in this work, is related to two

major aspects in what concerns the supply: availability, if there is on the market a flight from the point

of origin to the point of destiny; and affordability, if the price of the flight is affordable bearing in mind the

available resources. On the other hand, in the demand one of the indicators can be the cost of business

travels, or as it is going to be called, business convenience.

Availability

The existence of availability can be analysed taking into account the percentage of connections

to the other countries or states as represented in Equation 3. This type of measurement of connectivity

is known as Intuitve Metrics (Arvis & Shepherd, 2011).

%𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 = 𝐸𝑥𝑖𝑠𝑡𝑖𝑛𝑔 𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑖𝑜𝑛𝑠

𝑛º 𝑜𝑓 𝑠𝑡𝑎𝑡𝑒𝑠/𝑐𝑜𝑢𝑛𝑡𝑟𝑖𝑒𝑠× 100 (3)

Affordability

In the case of this indicator the analysis is more complex because affordability depends on the

amount of resources (money) available from one individual. Therefore, this indicator has to be related

not only to the price of the trip, but also to the purchasing power of each country’s citizens.

Each country will be connected to another by an average price per kilometre (€/km). This

average will be called €/𝑘𝑚̅̅ ̅̅ ̅̅ ̅̅𝐴−𝐵, where A-B represent the pair of connected countries.

Therefore, the relative price of a trip for those citizens is given by Equation 4.

𝑃𝑟𝑖𝑐𝑒𝑟𝑒𝑙𝑎𝑡𝑖𝑣𝑒,𝐴−𝐵 =€/𝑘𝑚̅̅ ̅̅ ̅̅ ̅̅

𝐺𝐷𝑃𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎,𝐴

(4)

Using the outcome of Equation 4, this relative price has to be compared with the other

connection pairs as represented by Equation 5 (where 𝑃𝑟𝑖𝑐𝑒̅̅ ̅̅ ̅̅ ̅𝑟𝑒𝑙𝑎𝑡𝑖𝑣𝑒 is the average of 𝑃𝑟𝑖𝑐𝑒𝑟𝑒𝑙𝑎𝑡𝑖𝑣𝑒,𝑖−𝐵):

%𝐴𝑓𝑓𝑜𝑟𝑑𝑎𝑏𝑖𝑙𝑖𝑡𝑦,𝐴−𝐵 =𝑃𝑟𝑖𝑐𝑒𝑟𝑒𝑙𝑎𝑡𝑖𝑣𝑒,𝐴−𝐵 − 𝑃𝑟𝑖𝑐𝑒̅̅ ̅̅ ̅̅ ̅

𝑟𝑒𝑙𝑎𝑡𝑖𝑣𝑒

𝑃𝑟𝑖𝑐𝑒̅̅ ̅̅ ̅̅ ̅𝑟𝑒𝑙𝑎𝑡𝑖𝑣𝑒

(5)

As the indicator is related to each country and not to each connection, Equation 6 is applied:

%𝐴𝑓𝑓𝑜𝑟𝑑𝑎𝑏𝑖𝑙𝑖𝑡𝑦 =∑ %𝐴𝑓𝑓𝑜𝑟𝑑𝑎𝑏𝑖𝑙𝑖𝑡𝑦,𝐴−𝑛𝑛

𝑛 (6)


20

Business Convenience

In a single market like the one in the European Union it is common for business men to travel

around the countries to trade. Therefore, each country has a cost to make business in the European

Union that depends of the attractiveness of the other countries, of the distance to that country and of

the frequency of flights (less frequency means more wasted time and larger costs). Due to the fact that

was not possible to get access to a database with the information regarding the frequency of the flights,

it was used as measure the number of airlines that operate in each route (information from

openflights.org), what is not the best variable, but gives an idea of each route frequency, as represented

by Equation 7.

𝐵𝑢𝑠𝑖𝑛𝑒𝑠𝑠𝑟𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝑐𝑜𝑠𝑡,𝑖 = ∑𝐺𝐷𝑃𝑖

∑ 𝐺𝐷𝑃× 𝐷𝑖−𝑛 ×

1

𝐴𝑖−𝑛

(7)

where,

𝑖 is the country under analysis,

𝐺𝐷𝑃𝑖 is the GDP of the country i using PPP rates,

∑ 𝐺𝐷𝑃 is the total GDP of all European Union also using PPP rates,

𝐷𝑖−𝑛 is the distance of the country i to the country n,

𝐴𝑖−𝑛 number of airlines operating in the route from i to n.

For the 𝐴𝑖−𝑛 variable, it was taken as sample the airlines that operate between two

representative airports from different countries/states. In the case of a country or state having more than

one representative airport, it was considerer the airport with more airlines operating in that route. In the

case of two countries/states not having a diret route connecting them, 𝐴𝑖−𝑛 becomes 0,5 instead of 0,

what means that is considered that the relative cost is two times higher than having at least one airline

operating in that route.

3.2. Study restrictions and data organisation

In order to make this work humanly possible, it was necessary to reduce the sample of airports

and airlines in this study.

The Federal Aviation Administration (FAA) of the United States classify as a Large Hub all

airports that have more than 1% of the country passengers. Nevertheless, in Europe the number of

Airports is lower and the definition from the FAA would not be a benefit. This way, in this work it is going

to be considered as representative of a country’s connections the sample of all airports that have at

least 20% of the country’s passengers. This rate is also going to be used in the USA case study, even

with the existing FAA rates. In the case of Brazil airports, once there is no correct information about the

airports which are under Municipal authority, the sample will only take into account airports belonging

to Infraero, DAESP (Airports Department of the State of São Paulo) and private ones.

Having these airports as a representative sample (Annex 2, 6 and 10) of the country’s

connections, this is the moment to proceed to the evaluation of the indicators.


21

Availability

For each airport of the sample the destinations with departure in that airport were collected.

Those destinations were aggregated into countries/states, and therefore each airport had the number

of countries/states connected.

This data was analysed in a matrix (example in Table 5), so that two different analysis could be

made: (1) for each country/state, the percentage of connections from the main airport to each one of the

other countries/states; (2) for each country/state, the percentage of connections to each country main

airport.

Table 5 - Matrix to analyse Availability

Country/State 1 …

Airport 1 (1) Percentage of connections from

the main airport to each one of

the other countries/states …

(2) Percentage of connections to

each country main airport

Affordability

The distance between state capitals was calculated by Equation 8 having the states capitals

coordinates:

𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 (𝑘𝑚) = arccos (𝑠𝑒𝑛(𝐿𝑎𝑡𝑆𝑡𝑎𝑡𝑒1) × 𝑠𝑒𝑛(𝐿𝑎𝑡𝑆𝑡𝑎𝑡𝑒2)

+ cos(𝐿𝑎𝑡𝑆𝑡𝑎𝑡𝑒1) × cos (𝐿𝑎𝑡𝑆𝑡𝑎𝑡𝑒2) × cos (𝐿𝑜𝑛𝑆𝑡𝑎𝑡𝑒2 − 𝐿𝑜𝑛𝑆𝑡𝑎𝑡𝑒1))

×180 ∗ 60 ∗ 1,852

𝜋

(8)

where,

𝐿𝑎𝑡 is the latitude of the state in radians;

𝐿𝑜𝑛 is the longitude of the state in radians.

Then the information is going to be organised in a symmetric matrix, as Table 6 shows.

Table 6 - Organisation of the information regarding the distance between capitals

Capital 1 …

Capital 1 0

… 0

Due to the fact that it was humanly impossible to make this analysis having in mind all different

pairs of airports in analysis, only the connections between the countries/states capitals and the main

Hubs of EU, USA and Brazil (Table 7) are going to be chosen. For this ranking, in the case of the EU

Hubs we chose the European airports that have won a place on the Preliminary Ranking of 20th World’s

Busiest Airport (taking into account the Total International Passenger Traffic in 2014) according to the

Airports Council International (when a country has more than one airport in the ranking, only the first is


22

taken into account). In the case of USA Hubs, we chose the six busiest airports according to the

Preliminary FAA data for 2013 (taking into account the total number of passengers) and for Brazil the

six busiest airports according to Infraero (once again, when a state has more than one airport in the

ranking, just the first is taken into account).

Table 7 - Hubs chosen as representative of UE, USA and Brazil

European Union United States of America Brazil

London Heathrow Airport UK Hartsfield-Jackson Atlanta

International Airport GA Guarulhos International

Airport SP

Charles de Gaulle Airport FR Los Angeles International Airport

CA Brasília International Airport

DF

Amsterdam Airport Schiphol

NL Chicago O'Hare International Airport

IL Galeão International Airport

RJ

Frankfurt Airport DE Dallas/Fort Worth International Airport

TX Tancredo Neves International Airport

MG

Adolfo Suarez Madrid - Barajas Airport

ES Denver International Airport

CO Dep. Luís Eduardo Magalhães International Airport

BA

Leonardo da Vinci - Fiumicino Airport

IT John F. Kennedy International Airport

NY Salgado Filho International Airport

RS

The problem begins when trying to establish a medium price per kilometre between State

capitals and the selected Hubs. In fact the prices vary according to different things, such as:

the distance between the date of the flight and the date of purchase (the prices get expensive as

the date comes closer);

the week day of the flight (weekdays are cheaper than weekend days and Friday is more expensive

than the other days of the week);

the proximity to national holidays (prices are more expensive on national holidays or next to them);

the time of the year of the flight (prices are more expensive in the summer);

etc.;

Due to the fact that there is not a statistic data base to provide this type of data, certain

parameters had to be established for getting this information. This information will be searched taking

advantage of a single travelling research engine (skyscanner.pt) to be sure that the parameters of the

price do not change according to the website.

First of all, as it was said, the prices vary according to the temporal distance between the date

of purchasing and the date of the flight. Consequently, a date has to be chosen that is distant enough

from today, so that the demand may be so low that the prices had not been compromised yet.

Secondly, we chose three days of the week for the analysis, Monday, Wednesday and Friday,

so that it is possible to see the cheapest and the most expensive flight for each route.

To make sure that the data had not been compromised we also chose these three days of the

week bearing in mind that they could not be public holidays in neither one of the countries in analysis


23

(EU countries, USA and Brazil) or that the days between them or immediately before and after were not

public holidays either (Annex 1 shows the calendar of public holidays in the countries in analysis).

Finally, we chose a date in October 2015 because it is not compromised by summer or winter

holidays.

Bearing in mind the restrictions already mentioned, the dates chosen as a sample for this

analysis were the 16th, 19th and 21st October 2015, respectively, Friday, Monday and Wednesday.

With this information and applying the formulas already mentioned, the information was

organised as Table 8 shows.

Co

un

try/

Sta

te

Cap

ital

Sta

tes G

DP

Dis

tan

ce

to

Air

po

rt 1

Dis

tan

ce

to

… Airport 1

Avera

ge P

rice

Pri

ce/k

m

Pri

ce R

ela

tive

%A

ffo

rda

bil

ity

, A

-B

…

Avera

ge

%A

ffo

rda

bil

ity

16th 19th 21st

Country

1

Min

Max

… …

Table 8 - Organisation of the information regarding the calculus of the Affordability indicator

It is important to call the attention for some details:

The prices are always from the Country/State to the Selected Hubs (one way adult tickets);

All prices (for EU, USA and Brazil) are in Euro and the information was gathered between 10th and

17th June, disregarding the fluctuation of the exchange rate;

In the case of the EU and Brazil we always selected the airports in the states capitals as

representative of the country/state. In the case of the USA we selected the airports from the states’

most populated cities (in the USA, in 35 states the capital is not the most populated city).


With the information regarding the distance between capitals, already used in the previous

indicator, as well as the countries responsibility on the EU GDP applying PPP rates and the number of

airlines per route, the indicator will be calculated applying the formula already mentioned, as the Table

9 shows.

Table 9 - Organisation of information regarding the calculus of the Business Convenience indicator

Country 1 … Distance

weighted by

GDP

Difference

with EU’s

average

% in EU’s

GDP

Distance to

countries capitals

Nº of airlines

per route …

Country 1 0

…

Country n


24

3.3. Study of the outcomes

After calculating the outcome of the created indicators several analysis will be needed. Two

types of analysis can be made: one that compares and tries to find links between the created indicators

and several statistical indicators; another that looks case-by-case and tries to exclude some

geographical, political or other kind of aspects that may have influenced the results.

For the first type of analysis, the created indicators will be compared by correlation methods

with several other indicators. Although the best options were to compare the same indicators for the EU,

the USA and Brazil, this is not possible due to the fact that each union/federation has different indicators.

Therefore, it will be compared to similar indicators but not the same. Table 10 shows the indicators used

for each federation and the source of the information.

Table 10 – Indicators for the case study

European Union United States of America Brazil

Contribution of each country to the

EU GDP, applying PPP rates (%)

Contribution of each state to the

USA GDP (%)

Contribution of each state to the

Brazilian GDP (%)

SOURCE: www.pordata.pt (2012) SOURCE: U.S. Department of Commerce

(2013)

SOURCE: Instituto Brasileiro de Geografia e

Estatística (2012)

GDP per capita, applying PPP rates

(€) GDP per capita (chained 2009 $) GDP per capita (R$)

SOURCE: www.pordata.pt (2012) SOURCE: U.S. Department of Commerce

(2013)


Estatística (2012)

Unemployment Rate (%) Unemployment Rate (%) Unemployment Rate (%)

SOURCE: www.pordata.pt (2014) SOURCE: Bureau of Labour Statistics (Feb

2015, with seasonality correction)

SOURCE: Instituto de Pesquisa Económica

Aplicada (2013)

Compensation of the employees

per capita, applying PPP rates (€)


per capita in one year ($)


per capita (R$)

SOURCE: www.pordata.pt (2013) SOURCE: Bureau of Labour Statistics (May

2014)


Aplicada (2013)

Inequality of income distribution

(according to S80/S20 ratio)


(according to Gini Coefficient)


(according to Gini Coefficient)

SOURCE: www.pordata.pt (2013) SOURCE: U.S. Census Bureau (2010) SOURCE: Instituto de Pesquisa Económica

Aplicada (2013)

Resident Population (pax) Resident Population (pax) Resident Population (pax)

SOURCE: www.pordata.pt (2014) SOURCE: U.S. Census Bureau (estimation

1 July 2014)


Aplicada (2010)

Foreign Resident Population (pax) Foreign-USA Resident Population

(pax)

Foreign-state Resident Population

(pax)

SOURCE: www.pordata.pt (2014) SOURCE: U.S. Census Bureau (average

2006-2010)


Estatística (2010)

Country Area (km2) State Area (km2) State Area (km2)

SOURCE: www.pordata.pt (2014) SOURCE: U.S. Census Bureau (average

2014)


Aplicada (2010)


25

Population aged 25 to 64 having

completed at least upper secondary

education (%)

Population 24-over having

completed at least upper secondary

education (%)

Population aged 25-over having

completed at least 11 study years

(%)

SOURCE: www.pordata.pt (2013) SOURCE: U.S. Census Bureau (2010) SOURCE: Instituto de Pesquisa Económica

Aplicada (2000)

Outbound tourist trips by Airplane

(%) NO SIMILAR DATA FOUND

People arriving to the state by

airplane (%)

SOURCE: www.pordata.pt (2013) SOURCE: Secretaria Nacional de Políticas

de Turismo (2013)

Contribution of Tourism to the

country’s GDP (%)

Coefficient of tourism spending with

the states GDP (%)

Distribution of the country

spendings/earnings from Domestic

Tourism (%)

SOURCE: www.pordata.pt (2013)

SOURCE: GDP - U.S. Department of

Commerce (2013); Tourism Spendings –

U.S. Travel Association (2013)

SOURCE: Fundação Instituto de Pesquisas

Económicas (2010/2011)

For the purpose of this work two different correlation methods will be applied, the Pearson

product-moment correlation coefficient and the Spearman’s rank correlation coefficient. Although we do

not wish to enter exhaustively in the subject, some considerations have to be made about these two

correlation coefficients and the way they were calculated.

Pearson Product-Moment Correlation Coefficient

This correlation coefficient is used in statistics to measure a linear correlation or dependency

between two different variables, having, as an outcome, a result between -1 and 1 where -1 or 1 means

total correlation (negative or positive, respectively) and 0 means no correlation at all. Figure 3 shows

different possible results for this correlation coefficient.

Figure 3 - Different possible results of the Pearson Correlation Coefficient. (Source: StatisticsHowTo.com)

In this work, we are going to take advantage of the already existing Pearson Correlation formula

in Excel, by applying the =CORREL(matrix1;matrix2) formula.

Regarding the analysis of the coefficient, the following rule will be applied: −1 < 𝑥 <

−0,7 𝑜𝑟 0,7 < 𝑥 < 1 means a high correlation; −0,7 < 𝑥 < −0,5 𝑜𝑟 0,5 < 𝑥 < 0,7 means a low

correlation; and −0,5 < 𝑥 < 0,5 means no correlation.


26

Spearman’s Rank Correlation Coefficient

This correlation coefficient is less used than the Pearson’s but it allows to find other types of

correlations that are not linear. It is a nonparametric measure of statistical dependence and assesses

how well the relation is between two different variables that describe a monotonic function. Figure 4

shows the function for a total correlation according to Spearman and the expected result using Pearson’s

instead.

In this case there is not a template formula in Excel, so it had to be programmed. Statistically

the Spearman’s correlation coefficient can be calculated by Equation 9:

𝜌 = 𝑟𝑠 =∑ (𝑥𝑖 − �̅�𝑛

𝑖=1 )(𝑦𝑖 − �̅�)

√∑ (𝑥𝑖 − �̅�)2𝑛𝑖=1 ∑ (𝑦𝑖 − �̅�)2𝑛

𝑖=1

(9)

To programme in Excel, a simpler formula, Equation 10, can be used:

𝜌 = 𝑟𝑠 = 1 −6 ∑ [𝑅𝑎𝑛𝑘(𝑋𝑖) − 𝑅𝑎𝑛𝑘(𝑌𝑖)]2𝑛

𝑖=1

𝑛(𝑛2 − 1) (10)

Nevertheless, this result is not conclusive and has to be compared with the critical r, this means

that for a certain likelihood of the correlation occurring by chance (called Significance Level and

represented by α and for a certain number of degrees of freedom (df, in this case just two because there

are only two variables), there is a correlation if the rs (as an absolute value) is bigger than the rcritical

otherwise, there is no correlation. Therefore, in Excel, the critical r can be programmed by Equation 11:

𝑟𝑐𝑟𝑖𝑡𝑖𝑐𝑎𝑙(∝, 𝑑𝑓) =𝑇. 𝐼𝑁𝑉(1−∝/2, 𝑑𝑓)

√𝑇. 𝐼𝑁𝑉(1−∝/2, 𝑑𝑓))2 + 𝑑𝑓 (11)

For the second type of analysis, the one that does not take into account the correlation with

other indicators, several analysis can be made as, for instance, political or geographical. In what

concerns the geographical analysis, countries or states will be firstly ordered and the first and last five

positions of each indicator will be presented (Table 11). Also, countries or states will be organised into

Figure 4 - Spearman correlation factor vs. Pearson correlation. (Source: StatisticsHowTo.com)


27

groups taking into account their geographical position and the order of their positions will be presented

for each indicator (Table 12).

Table 11 - Organisation of the top best and worst results for each indicator

Best results for indicator n Worst results for indicator n

Country/State Indicator Result Country/State Indicator Result

… … … …

Table 12 – Order of the Classification of the Regions

Descending order for indicator n

Region Classification

… …

For this classification, the countries will be ordered ascending for each indicator and a number

will be given according to the position on that list. Then for each region the number assigned to each

country will be added and the result divided by the number of countries of that region, resulting in a

classification for that indicator. Every time two or more countries have the same result for the indicator

a medium result will be given.

Finally it is important to make just a small note. In this study a composite indicator will not be

created, once it is thought that it would not contribute to the outcomes of the study. A composite indicator

is an indicator that gathers the information from several other indicators and gives a different weight to

each one of those. In this case we are not looking to create an indicator to statistically measure the

Equity in Transportation and to follow the improvement of the situation in the following years. The idea

is to create indicators to evaluate the current situation with different approaches (regarding tourism and

business) and with the outcomes of those indicators to find new ideas to contribute to the EU policies.


28

Exploring Connectivity in Air Transport as an Equity Factor| Frederico Valente Nunes | Case of Study – European Union

29

4. Case of Study – European Union

Applying the principle that is representative for the study of the airports that have at least 20%

of the countries’ air passengers, we can see in Table 13 the airports that will be used for the study. In

Annex 2, all Europe’s International Airports and their share of the country’s passengers are represented,

which served as basis for the production of the next table.

Table 13 - List of European Airports with at least 20% of the country’s passengers

Country City Airport Passengers Country

Passengers %

AT Vienna Vienna International Airport 21 999 820 26 334 634 83,54%

BE Brussels Brussels Airport 19 133 222

26 861 760 71,23%

Brussels Brussels South Charleroi Airport 6 786 979 25,27%

BG Sofia Sofia Airport 3 504 326

7 357 179 47,63%

Burgas Burgas Airport 2 461 648 33,46%

HR

Dubrovnik Dubrovnik Airport 1 502 165

6 304 089

23,83%

Split Split Airport 1 558 812 24,73%

Zagreb Zagreb Airport 2 285 992 36,26%

CY Larnaca Larnaca International Airport 5 636 426

7 423 373 75,93%

Paphos Paphos International Airport 1 786 947 24,07%

CZ Prague Václav Havel Airport Prague 10 974 196 11 955 089 91,80%

DK Copenhagen Copenhagen Airport 12 016 000 14 371 000 83,61%

EE Tallin Tallinn Airport 1 958 801 1 972 491 99,31%

FI Helsinki Helsinki Airport 15 279 043 18 373 279 83,16%

FR Paris Charles de Gaulle Airport 62 052 917 170 210 331 36,46%

DE Frankfurt Frankfurt Airport 58 036 948 208 778 086 27,80%

EL Athens Athens International Airport 12 459 801 38 604 975 32,28%

HU Budapest Budapest Ferenc Liszt International Airport 8 520 880 8 726 209 97,65%

IE Dublin Dublin Airport 18 431 625 23 960 055 76,93%

IT Rome Leonardo da Vinci - Fiumicino Airport 36 166 345 146 500 760 24,69%

LV Riga Riga International Airport 4 814 073 4 814 073 100,00%

LT Kaunas Kaunas Airport 830 268

3 621 490 22,93%

Vilnius Vilnius Airport 2 661 869 73,50%

LU Luxembourg Luxembourg Findel Airport 2 197 331 2 197 331 100,00%

MT Valletta Malta International Airport 4 052 000 4 052 000 100,00%

NL Amsterdam Amsterdam Airport Schiphol 52 527 699 58 079 961 90,44%

PL Warsaw Warsaw Chopin Airport 10 683 000 25 171 000 42,44%

PT Lisbon Lisbon Portela Airport 16 024 955 32 053 949 49,99%

RO Bucharest Henri Coanda International Airport 7 643 467 10 781 863 70,89%

SK Bratislava Bratislava Airport 1 373 078 1 635 058 83,98%

SI Ljubljana Ljubljana Joze Pucnik Airport 1 321 100 1 357 363 97,33%

ES Madrid Adolfo Suarez Madrid - Barajas Airport 39 729 027 186 688 195 21,28%

SE Stockholm Stockholm Arlanda Airport 20 681 554 34 519 764 59,91%

UK London London Heathrow Airport 72 367 054 231 469 055 31,26%

Sources in Annex 1.


30

4.1. Availability

After processing the data found, it was possible to produce Annex 3, where all country

destinations for each one of the airports sample are shown. In Figure 5 and Table 14 the final numbers

are shown.

Table 14 - Outcome of Availability Indicator for EU

Co

un

try

Percentage of connections to each country main airport

(2)

Airports

Percentage of connections from the

main airport to each one of the other countries (1)

AT 96,30 Vienna International Airport 85,19 BE 96,30 Brussels and Brussels South Charleroi Airports 92,59 BG 62,96 Sofia Airport and Burgas Airport 74,07 HR 59,26 Dubrovnik, Split and Zagreb Airports 66,67 CY 55,56 Larnaca and Paphos International Airports 59,26 CZ 92,59 Václav Havel Airport Prague 96,30 DK 85,19 Copenhagen Airport 81,48 EE 51,85 Tallinn Airport 44,44 FI 81,48 Helsinki Airport 62,96 FR 100,00 Charles de Gaulle Airport 92,59 DE 96,30 Frankfurt Airport 96,30 EL 81,48 Athens International Airport 62,96 HU 85,19 Budapest Ferenc Liszt International Airport 77,78 IE 81,48 Dublin Airport 100,00 IT 96,30 Leonardo da Vinci - Fiumicino Airport 92,59 LV 70,37 Riga International Airport 70,37

Figure 5 - Outcome of Availability Indicator for the EU (Percentage of connections from the main airport to each one of the other countries (1) on the right and Percentage of connections to each country main airport (2) on the left). Green means better connections and Red worse connections.


31

LT 55,56 Kaunas and Vilnius Airports 70,37 LU 48,15 Luxembourg Findel Airport 70,37 MT 51,85 Malta International Airport 88,89 NL 96,30 Amsterdam Airport Schiphol 96,30 PL 92,59 Warsaw Chopin Airport 88,89 PT 66,67 Lisbon Portela Airport 70,37 RO 77,78 Henri Coanda International Airport 70,37 SK 33,33 Bratislava Airport 37,04 SI 33,33 Ljubljana Joze Pucnik Airport 29,63 ES 88,89 Adolfo Suarez Madrid - Barajas Airport 77,78 SE 88,89 Stockholm Arlanda Airport 88,89 UK 100,00 London Heathrow Airport 81,48

Analysis of the outcomes

If we start by analysing the indicator “Percentage of connections from the main airport to each

one of the other countries (1)” it is possible to see the top five groups of countries with more and less

connectivity to other European countries from their main airport (Table 15) and to see the descending

order of the connectivity of the European regions (Table 16). The EU regions will be analysed as the

regions suggested by the United Nations Statistic Division (UNSD, Figure 6).

According to this division four regions are considered:

Northern Europe: United Kingdom, Ireland, Denmark, Finland, Estonia, Lithuania, Latvia and

Sweden;

Western Europe: France, Luxemburg, Belgium, Germany, Austria and the Netherlands;

Southern Europe: Portugal, Spain, Italy, Slovenia, Croatia, Malta, Cyprus and Greece;

Eastern Europe: Poland, Czech Republic, Slovakia, Bulgaria, Hungary and Romania.

Figure 6 - Geographical division of Europe according to the United Nations Statistic Division


32

Table 15 – Top countries with more connectivity and less connectivity from their main airports to the rest of European countries

More Connectivity on indicator (1) Less Connectivity on indicator (1)

Country Indicator (1) result Country Indicator (1) result

Ireland 100% Slovenia 29,63%

Netherlands

Germany

Czech Republic

96,30% Slovakia 37,04%

Italy

France

Belgium

92,59% Estonia 44,44%

Sweden

Poland

Malta

88,89% Cyprus 59,26%

Austria 85,19% Greece

Finland 62,96%

Table 16 - Descending order of the connectivity of the European Regions according to indicator (1)

Descending order of the connectivity according to indicator (1)

Regions Classification

Western Europe 21

Northern Europe

Eastern Europe 14

Southern Europe 11

* A higher classification means more connectivity and therefore it is more benefitial

Looking to Table 15 it is possible to see that the majority of countries with more connectivity are

Western Europe countries (five), followed by Northern Europe (four). On the other hand the majority of

the countries with less connectivity are in Southern Europe (four). This is again the outcome from Table

16 when it is seen that the southern countries are less connected to the EU countries than the western

countries. Looking to Figure 5 it is also easy to see that Western European countries clearly have more

connections than the rest of Europe, and that these connections tend to decrease when it is a peripheral

country. It is also possible to see that Slovakia has always a low number of connections: this is the result

of the proximity of the capital, Bratislava, from the Austrian capital, Wien, what makes most citizens

travel from Wien instead of Bratislava. The same happens in Slovenia, where Zagreb, the Croatian

capital, is too near the border and therefore Slovenian citizens prefer to travel from Zagreb than from

Ljubljana.


33

Also we can find correlations of this indicator with the countries percentage in the European

GDP, with the Total Resident Population and with the Balance of travel and tourism as a percentage of

GDP. Figure 7 to 9 and Table 17 show the outcomes of the study.

0%

20%

40%

60%

80%

100%

0,01% 0,10% 1,00% 10,00% 100,00%

Per

cen

tage

of

con

nec

tio

ns

fro

m t

he

mai

n a

irp

ort

to

eac

h

on

e o

f th

e o

ther

co

un

trie

s

% EU GDP

Figure 7 - Relation between the Percentage of connections from the main airport to each one of the other countries and the countries percentage in EU GDP

0%

20%

40%

60%

80%

100%

0 40 80

Per

cen

tage

of

con

nec

tio

ns

fro

m t

he

mai

n a

irp

ort

to

eac

h

on

e o

f th

e o

ther

co

un

trie

s

Resident Population (million)

Figure 8 - Relation between the Percentage of connections from the main airport to each one of the other countries and the Resident population in each country, in the EU

Croatia

0%

20%

40%

60%

80%

100%

-5 0 5 10 15

Per

cen

tage

of

con

nec

tio

ns

fro

m t

he

mai

n a

irp

ort

to

eac

h

on

e o

f th

e o

ther

co

un

trie

s

Balance of travel and tourism as a % of GDP

Figure 9 - Relation between the Percentage of connections from the main airport to each one of the other countries and the balance of travel and tourism as a percentage of the country’s GDP, in the EU


34

Table 17 - Significant Correlation Factors for Percentage of connections from the main airport to each one of the other countries, for the EU

Percentage of connections from the main airport to each

one of the other countries (1)

% EU GDP Resident

Population

Balance of travel and

tourism as a % of GDP

Pearson Correlation ρ Not significant Not significant Not significant

Spearman’s

Correlation

rs 0,618 0,537 -0,676

rcritical 0,515 0,515 0,534

In what concerns the relation between the first indicator and the percentage of the countries

contribution to the EU GDP, it is possible to see clearly that there is a relation between the two variables.

Although this relation can only be seen when the horizontal axis is on logarithmic scale, it is possible to

see that normally a low contribution to EU GDP is correlated with a low percentage of connection to the

major European Airports of each country. Nevertheless this relation is only valid for countries with a

contribution of less than 1% to EU GDP, which means 10 countries.

Also, high levels of resident population tend to correspond to high levels of connectivity, with

countries with more than 20 million inhabitants with at least 77% of connectivity. Nevertheless, the

countries in this case are only six (France, Germany, Italy, Poland, Spain and the United Kingdom)

which does not permit to conclude that there is such a strong correlation (the Spearman’s coefficient is

too close to the critical value).

Finally we can see that there is a really strong correlation between this type of connectivity and

the balance of travel in each country GDP. We can see that countries where tourism has a positive

contribution (foreign people spend more money in the country than the resident people overseas) have

less connectivity. Nevertheless, this result can be explained, as the Figure shows, by the relation of the

Compensation of employees and the Balance of travelling: citizens of countries where the compensation

is higher tend to spend more money aboard, making a deficit in the country’s balance (the Spearman’s

correlation coefficient for this case is -0,76 and the rcritical is 0,53).

Croatia

Luxemburg

0

5

10

15

20

25

30

35

40

-5 0 5 10 15Co

mp

ensa

tio

n o

f em

plo

yees

p

er c

apit

a (t

ho

usa

nd

PP

S)

Balance of travel and tourism as a % of GDP

Figure 10 - Relation between the Compensation of employees per capita and the Balance of travel and tourism as a Percentage of GDP, in the EU


35

Secondly the analysis of the indicator “Percentage of connections to each country main airport

(2)” has the following Top 5 more and less connected groups (Table 18) and the following order of

regions (Table 19):

Table 18 – Top countries with more connectivity and less connectivity to each country main airport, in the EU


Country Indicator (2) result Country Indicator (2) result

France

United Kingdom 100%

Slovakia

Slovenia 33,33%

Austria

Belgium

Germany

Italy

Netherlands

96,30% Luxemburg 48,15%

Czech Republic

Poland 92,59% Malta 51,85%

Spain

Sweden 88,89%

Latvia

Cyprus 55,56%

Denmark

Hungary 85,19% Croatia 59,26

Table 19 - Descending order of the connectivity of the European Regions according to indicator (2)



Western Europe 21

Northern Europe 14

Eastern Europe 13

Southern Europe 11

* A higher classification means more connectivity and therefore it is more beneficial

Once again it is possible to see that Northern Europe leads the top 5 of the best connected

countries (five out of thirteen) and Southern Europe has the worst place with four out of seven, of the

least connected countries. The same conclusion can be taken from Table 19, where the connectivity of

Western Europe is almost 100% higher than the Southern Europe.

Also for this indicator it is possible to find correlations between this indicator and the countries

percentage in the European GDP, with the Total Resident Population, with the Total Foreign Residents

and with the Countries areas. The Figures 11 to 14 show these results as well as Table 20. In Figures

11 and 12 the results of the first indicator, already analysed, will also be shown.


36

Germany

0%

20%

40%

60%

80%

100%

-100% -50% 0% 50% 100% 150%

%

% EU GDP

Percentage of connections from the main airport to each oneof the other countries (1)

Percentage of connections to each country main airport (2)

Linear (Percentage of connections to each country mainairport (2))

Figure 11 - Relation between the Percentage of connections from the main airport to each one of the other countries (1) and Percentage of connections to each country main airport (2) and the countries percentage on EU GDP

Germany

20%

40%

60%

80%

100%

3 4 5 6 7 8

%


Percentage of connections from the main airport to each oneof the other countries (1)

Percentage of connections to each country main airport (2)

Linear (Percentage of connections to each country mainairport (2))

Figure 12 -- Relation between the Percentage of connections from the main airport to each one of the other countries (1) and Percentage of connections to each country main airport (2) and the Resident Population, in the EU

Germany

20%

40%

60%

80%

100%

0 2 4 6 8

Per

cen

tage

of

con

nec

tio

ns

to

each

co

un

try

mai

n a

irp

ort

Foreign Residents (million)

Figure 13- Relation between the Percentage of connections to each country main airport (2) and the foreign residents, in the EU


37

Table 20 - Significant Correlation Factors for the Percentage of connections to each country main airport, for the EU

Percentage of connections to each country main airport

(2)

% EU

GDP

Resident

Population

Foreign

Residents Area

Pearson Correlation ρ 0,596 0,609 0,531 0,552

Spearman’s

Correlation

rs 0,894 0,836 0,749 0,564

rcritical 0,515 0,515 0,515 0,515

In what concerns the percentage of the countries’ contribution to the EU GDP and the Resident

Population, the relation in this indicator is really similar to the first one. Nevertheless in this case the

Spearman’s correlation factor is even stronger.

On the other hand two new correlations appear in this indicator: the foreign resident’s indicator

shows that the connectivity measured by this indicator grows with the foreign population and that

countries with a bigger area have a higher connectivity.

Regarding the Foreign Residents an interesting conclusion can be reached: countries with larger

foreign communities have more connections to the other countries by secondary airports than countries

with small communities. One possibility for this evidence is that foreign residents tend to find the

cheapest flights to visit their home country which, most of the times, leave from secondary airports where

airport charges are lower.

In the case of the correlation with the countries area, it is also easy to understand that big

countries tend to have more airports and therefore their connectivity may be higher. Nevertheless, this

reason does not justify the correlation with this second indicator but not with the first one.

20%

40%

60%

80%

100%

0 100 200 300 400 500 600 700

Per

cen

tage

of

con

nec

tio

ns

to

each

co

un

try

mai

n a

irp

ort

Area (thousand km2)

Figure 14 - Relation between the percentage of connections to each country main airport (2) and the country’s area, in the EU


38

4.2. Affordability

After gathering all the information needed, it was possible to produce Figure 15 and Table 21.

It is once again important to remember that this indicator was built according to the EU average and

therefore a negative affordability means less effort when compared with the average countries and a

positive affordability means a higher effort when compared with the average countries.

Table 21 - Outcome of Affordability Indicator for EU

%Affordability

%Affordability

AT 30,45 IT 9,87

BE 82,75 LV -11,88

BG -6,32 LT -32,31

HR 43,57 LU -28,05

CY -74,03 MT -58,07

CZ 55,97 NL 6,80

DK -11,04 PL -4,73

EE 13,82 PT 57,69

FI -33,49 RO -24,38

FR 62,92 SK -64,39

DE -14,32 SI -2,71

EL -42,79 ES -33,76

HU 10,55 SE -53,03

IE -45,47 UK 104,51


In the case of the indicator “Affordability”, the Top 5 of the best and worst classified countries is

presented on Table 22 as well as the order of the regions on Table 23.

Figure 15 - Outcome of Affordability Indicator for the EU. Green means more Affordable (negative result) than red (positive result).


39

Table 22 – Top countries with more and less affordability in the European Union

Higher Affordability Lower Affordability

Country Indicator result Country Indicator result

Cyprus -74,03% United Kingdom 104,51%

Slovakia -64,39% Belgium 82,75%

Malta -58,07% France 62,92%

Sweden -53,03% Portugal 57,69%

Ireland -45,47% Czech Republic 55,97%

Table 23 - Descending order of the Affordability of the European Regions

Descending order of the Affordability


Northern Europe

South Europe 13

Eastern Europe 15

Western Europe 19

* A lower classification means smaller effort to the citizens therefore it is more beneficial

It is also important to remember that this outcome takes into account the maximum and minimum

prices for each connections (we will see in a while the consequence of this type of analysis). The analysis

of this table shows some interesting results. First of all it is interesting to see that Western Europe is the

region where more effort is put on citizens to travel, according to money available. This is the result of

two different things: first of all the distances between capitals in this part of Europe are quite smaller

than in the rest of Europe what reduces the variable costs but maintains the fixed costs, making the cost

per kilometer higher than in the other regions; secondly, because of the smaller distances in this region,

the train is fairly competitive in time and cost when compared with the plane, which means that a higher

effort does not mean less possibility to travel in this region, just the opposite.

It is also interesting to see that there is no correlation between any of the indicators chosen and

the results of this indicator. This shows that the prices are made according to different criteria that

depend of many variables. Nevertheless, it also shows that the prices are not made having in mind the

affordability of the citizens.

Another thing that was possible to see while gathering the data was that the countries with the

biggest hubs in Europe also had higher maximum prices than the other countries. The reason was easy

to find out: there are many airlines coming from faraway Europe that make two stops along the way (for

example Tokyo-Amsterdam-London); this makes the trip Amsterdam-London more expensive than in a

single flight from Amsterdam-London. Therefore it is interesting to see the results if we only take into

account the minimum average price, as Figure 16 and Table 24 shows.


40

Table 24 - Outcome of the Affordability Indicator for EU taking just into account the minimum prices

%Affordability %Affordability

Min+Max € Min € Variation Min+Max € Min € Variation

AT 30,45 77,51 47,06 IT 9,87 -36,98 -46,85

BE 82,75 121,06 38,31 LV -11,88 -9,08 2,80

BG -6,32 64,64 70,96 LT -32,31 7,49 39,80

HR 43,57 173,85 130,28 LU -28,05 16,47 44,52

CY -74,03 -73,19 0,84 MT -58,07 -52,10 5,97

CZ 55,97 -0,66 -56,63 NL 6,80 27,81 21,01

DK -11,04 -59,26 -48,22 PL -4,73 5,03 9,76

EE 13,82 28,31 14,49 PT 57,69 -41,95 -99,64

FI -33,49 -39,52 -6,03 RO -24,38 9,97 34,35

FR 62,92 -27,01 -89,93 SK -64,39 -19,42 44,97

DE -14,32 -49,93 -35,61 SI -2,71 17,05 19,76

EL -42,79 -42,53 0,26 ES -33,76 -22,48 11,28

HU 10,55 60,39 49,84 SE -53,03 -74,15 -21,12

IE -45,47 -61,55 -16,08 UK 104,51 -20,73 -125,24

The difference between the two Figures shows the importance and the impact of low cost

airlines in Europe. In the west part of Europe when we only take just into account the minimum prices

(where low cost airlines are leaders) we see a great improvement in citizens Affordability, but in the east

part of Europe there is almost no difference or, if there is, it is a difference for the worst. Therefore we

may conclude that there is a lack of low cost destinations in this part of Europe which is a consequence

of the low purchasing power from the citizens of those countries (in most cases). It is also important to

see that most of low cost airlines in Europe have their hubs in the Western European countries which

makes travelling to the east part of Europe more expensive and makes the variable cost increase (and

low cost airlines tend to get low prices by decreasing the fixed costs). Nevertheless there are some new

low cost airlines in this part of Europe, as Wizz Air in Hungary, what gives us the expectation that in a

near future this situation may change.

Figure 16 -Outcome of Affordability Indicator for the EU taking just into account the minimum prices. Green means more Affordable (negative result) than red (positive result).


41

4.3. Business Convenience

The distance between European capitals was calculated with the coordinates and applying

Equation 7 (results in Annex 4), the information about the GDP of each country in 2012 was taken from

official records on PortData.pt (Annex 5) and the information regarding the number of airlines from

openflights.org.

Applying the formula for this indicator, Table 25 and Figure 17 show the outcomes for this

indicator.

Table 25 - Business relative cost outcome, for the EU

Business

relative cost Difference with the EU average

Business

relative cost Difference with the EU average

AT 345 -72,56% IT 772 -38,65%

BE 465 -63,06% LV 1631 29,57%

BG 1358 7,90% LT 1961 55,80%

HR 1186 -5,79% LU 925 -26,52%

CY 3191 153,51% MT 1744 38,56%

CZ 537 -57,33% NL 453 -63,98%

DK 619 -50,86% PL 1024 -18,64%

EE 2838 125,40% PT 1320 4,84%

FI 1276 1,33% RO 1292 2,66%

FR 480 -61,90% SK 1902 51,09%

DE 521 -58,64% SI 1287 2,21%

EL 3706 194,39% ES 949 -24,60%

HU 922 -26,76% SE 1111 -11,73%

IE 1038 -17,52% UK 394 -68,74%

Average 1259

Figure 17 - Outcome of the Business Convenience indicator for the EU. Green means better Business cost (negative result) and red worse (positive result).


42


In the case of the indicator “Business Convenience”, the Top 5 of the best and worst classified

countries is presented on Table 26 as well as the order of the regions on Table 27.

Table 26 – Top countries with the best and the worst Business Convenience, in the EU

Best Business Convenience Worst Business Convenience

Country Indicator result Country Indicator result

Austria -72,56% Greece 194,39%

United Kingdom -68,74% Cyprus 153,51%

Netherlands -63,98% Estonia 125,40%

Belgium -63,06% Lithuania 55,80%

France -61,90% Slovakia 51,09%

Table 27 - Descending order of the Business Convenience of the European Regions

Descending order of the Business Convenience


Western Europe 5

Northern Europe

Eastern Europe 16

Southern Europe 19

* A lower classification means a smaller distance to make business and therefore it is more beneficial

The analysis of this table shows results that were probably already expected: all the countries

with the best business convenience are Western Europe countries (except UK) and the five worst

business convenience countries are in Southern or Eastern Europe. These results would be already

expected due to the fact that the countries with a bigger GDP and the European larger hubs are in

Western Europe. What is interesting to see is that due to the fact that in Eastern Europe the distance

between countries is smaller the business convenience would be reasonably decent if the number of

airlines were not taken into account. Nevertheless the most important thing to take from this indicator is

that Southern Europe has almost four times worst business convenience than Western Europe.

In the case of Portugal, Spain, Malta and Cyprus the situation is even more worrying because

the countries in the eastern part of Europe have the possibility of making deals with other countries

which despite not being in the European Union, they are near. In the case of Malta and Cyprus, even if

they have close neighbours, being an island gives them higher transportation costs, and in the case of

Portugal and south and western Spain the problem is more noticeable because there are not too close

neighbours or if there are, there is the Mediterranean Sea to cross.

Also the analysis of this indicator shows correlations with three indicators: GDP per capita,

contribution of the countries to the EU GDP, compensation of the employees per capita (taking into

account PPP rates), resident population and foreign residents, as the Figures 18 to 22 and Table 28

show.


43

Figure 20 - Relation between the Business Convenience and the compensation of employees per capita, in the EU

Figure 18 - Relation between the Business Convenience and the contribution to the EU GDP

Austria

-100%

-50%

0%

50%

100%

150%

200%

0% 0% 1% 10% 100%

Bu

sin

ess

Co

nvi

nie

nce

(d

iffe

ren

ce w

ith

EU

ave

rage

)

% EU GDP

Luxembourg

-100%

-50%

0%

50%

100%

150%

200%

0 20 40 60 80

Bu

sin

ess

Co

nvi

nie

nce

(d

iffe

ren

ce w

ith

EU

ave

rage

)

GDP per capita (thousand PPS)

Figure 19 - Relation between the Business Convenience and the GDP per capita, in the EU

Luxembourg

-100%

-50%

0%

50%

100%

150%

200%

0 10 20 30 40

Bu

sin

ess

Co

nvi

nie

nce

(d

iffe

ren

ce w

ith

EU

ave

rage

)

Compensation of employees per capita (thousand PPS)

-100%

-50%

0%

50%

100%

150%

200%

0 40 80

Bu

sin

ess

Co

nvi

nie

nce

(d

iffe

ren

ce w

ith

EU

ave

rage

)

Resident Population (million pax)

Figure 21 - Relation between the Business Convenience and the resident population, in the EU


44

Table 28 - Significant Correlation Factors for the Business Convenience in the EU


Compensation of

the employees

(PPS)

GDP per

capita

% EU

GDP

Resident

Population

Forein

Resident

Population

Pearson Correlation ρ Not Significant Not

Significant

Not

Significant

Not

Significant

Not

Significant

Spearman’s Correlation rs -0,655 -0,589 -0,714 -0,596 -0,623

rcritical 0,515 0,515 0,515 0,515 0,515

In the case of the Business Convenience, we can see that there is correlations with several

economic and finantial indicators. This occurs due to the fact that the most rich countries are benefitted

from several variables of this indicator: they have a big impact in the EU GDP, they are in Western

Europe where countries are smaller and where traveling distances are smaller, and they have the largest

hugs in Europe and therefore a lot of different airlines operating different routes. Is interesting to notice

that there is a correlation both with the resident population and the foreign resident population. Acttualy

when we look to Table 26 we see that the countries with the Best Business Convenience do not have

necessarily a large population, but the countries with Worst Business Convenience are all small size

countries. Looking to all this factors we can say that a small size allied with a low economic importance

brings us to bad results in the business convenience indicator.

-100%

-50%

0%

50%

100%

150%

200%

0 2 4 6 8B

usi

nes

s C

on

vin

ien

ce

(dif

fere

nce

wit

h E

U a

vera

ge)

Foreign Residents (million pax)

Figure 22 - Relation between the Business Convenience and the foreign resident population, in the EU

Exploring Connectivity in Air Transport as an Equity Factor| Frederico Valente Nunes | Brazil and USA – Comparative Analysis

45

5. Brazil and USA – Comparative Analysis

5.1. Brazil

Applying the principles that are representative for the study of the airports that have at least

20% of the countries air passengers, as it was done in the EU case, we can see in Table 29 the airports

that will be used for the study. In Annex 6, we give all Brazilian Airports and their share of the country’s

passengers, which served as basis for the production of the next table.

Table 29 - List of Brazil airports with at least 20% of the state’s passengers

State City Airport Passengers State

Passengers %

AC Rio Branco Plácido de Castro - Rio Branco International Airport

378 130 448 346 84,34%

AL Maceió Zumbi dos Palmares International Airport 1 893 488 1 893 488 100,00%

AP Macapá Macapá International Airport 624 716 624 716 100,00%

AM Manaus Eduardo Gomes International Airport 3 077 077 3 204 892 96,01%

BA Salvador Dep. Luís Eduardo Magalhães International Airport

8 589 663 9 092 948 94,47%

CE Fortaleza Pinto Martins International Airport 5 952 535 6 340 525 93,88%

DF Brasília Brasília International Airport 16 610 000 16 610 000 100,00%

ES Vitória Eurico de Aguiar Sallles Airport 3 450 695 3 450 695 100,00%

GO Goiânia Saint Genoveva Airport 3 000 592 3 000 592 100,00%

MA São Luis Marechal Cunha Machado International Airport

1 740 656 2 078 939 83,73%

MT Cuiabá Marechal Rondon International Airport 2 995 676 2 995 676 100,00%

MS Campo Grande Campo Grande International Airport 1 496 288 1 591 157 94,04%

MG Confins Tancredo Neves International Airport 10 002 477 12 707 484 78,71%

PA Belém Val de Cans International Airport 3 473 945 4 791 817 72,50%

PB João Pessoa Presidente Castro Pinto International Airport 1 210 870 1 354 636 89,39%

PR São José dos Pinhais

Afonso Pena International Airport 6 740 024 10 264 276 65,66%

PE Recife Guararapes International Airport 6 817 790 7 397 924 92,16%

PI Teresinha Senador Petrônio Portella Airport 1 091 173 1 092 254 99,90%

RJ

Rio de Janeiro Galeão International Airport 17 109 590 26 922 394

63,55%

Rio de Janeiro Santos Dumont Airport 9 102 187 33,81%

RN Natal Augusto Severo International Airport 2 375 771 2 375 771 100,00%

RS Porto Alegre Salgado Filho International Airport 7 993 164 8 033 158 99,50%

RO Porto Velho Governador Jorge Teixeira International Airport

905 103 905 103 100,00%

RR Boa Vista Boa Vista International Airport 350 195 350 195 100,00%

SC Florianópolis Hercílio Luz International Airport 3 872 637

5 657 828 68,45%

Navegantes Navegantes International Airport 1 202 625 21,26%

SP

São Paulo Congonhas Airport 17 119 530 66 200 985

25,86%

São Paulo Guarulhos International Airport 36 678 452 55,40%

SE Aracaju Santa Maria International Airport 1 343 899 1 343 899 100,00%

TO Palmas Palmas Airport 778 245 778 245 100,00%


46

5.1.1. Availability

After processing the data found, it was possible to produce Annex 7, where all state destination

for each of the airport sample are shown. In Table 30 and Figure 23 the final numbers are shown.

Table 30 - Outcome of Availability Indicator for Brazil

Sta

te Percentage of

connections to each state main airport (2)

Airports


main airport to each one of the other states (1)

AC 19,23 Plácido de Castro - Rio Branco International Airport

30,77

AL 42,31 Zumbi dos Palmares International Airport 42,31

AP 23,08 Macapá International Airport 38,46

AM 69,23 Eduardo Gomes International Airport 69,23

BA 69,23 Dep. Luís Eduardo Magalhães International Airport

69,23

CE 76,92 Pinto Martins International Airport 73,08

DF 100,00 Brasília International Airport 100,00

ES 26,92 Eurico de Aguiar Sallles Airport 26,92

GO 57,69 Saint Genoveva Airport 46,15

MA 46,15 Marechal Cunha Machado International Airport 46,15

MT 42,31 Marechal Rondon International Airport 34,62

MS 38,46 Campo Grande International Airport 34,62

MG 80,77 Tancredo Neves International Airport 69,23

PA 61,54 Val de Cans International Airport 65,38

PB 26,92 Presidente Castro Pinto International Airport 19,23

Figure 23 - Outcome of Avaiability Indicator for Brazil (Percentage of connections from the main airport to each one of the other states (1) on the right and Percentage of connections to each state main airport (2) on the left). Green means better connections and Red worse connections.


47

PR 61,54 Afonso Pena International Airport 65,38

PE 65,38 Guararapes International Airport 69,23

PI 23,08 Senador Petrônio Portella Airport 23,08

RJ 88,46 Galeão and Santos Dumont Airports 84,62

RN 50,00 Augusto Severo International Airport 50,00

RS 61,54 Salgado Filho International Airport 50,00

RO 38,46 Governador Jorge Teixeira International Airport

46,15

RR 7,69 Boa Vista International Airport 19,23

SC 30,77 Hercílio Luz and Navegantes International Airports

30,77

SP 100,00 Congonhas and Guarulhos Airports 88,46

SE 34,62 Santa Maria International Airport 42,31

TO 30,77 Palmas Airport 38,46



one of the other states (1)” it is possible to see the top five groups of states with more and less

connections from their main airport to each one of the other states (Table 31) and to see the descending

order of the connectivity of the Brazil regions (Table 32). Brazil regions will be analysed as legally

established since 1969 (Figure 24).

According to this division five regions are considered:

North Region: Tocantins, Pará, Amapá, Roraima, Amazonas, Rondônia and Acre;

Northeast Region: Maranhão, Piauí, Bahia, Sergipe, Alagoas, Pernambuco, Paraíba, Ceará and

Rio Grande do Norte;

Central-West Region: Mato Grosso, Goiás, Distrito Federal and Mato Grosso do Sul;

Southeast Region: São Paulo, Rio de Janeiro, Espírito Santo and Minas Gerais;

South Region: Rio Grande do Sul, Santa Catarina and Paraná.

Figure 24 - Geographical division of Brazil according to the law since 1969 (Source: Instituto Brasileiro de Geografia e Estatística)

North Region

Northeast Region

Central-West Region

Southeast Region

South Region


48

Table 31 – Top countries with more connectivity and less connectivity from their main airport to the rest of the states, in Brazil


State Indicator (1) result State Indicator (1) result

Distrito Federal 100% Roraima

Paraíba 19,23%

São Paulo 88,46% Piauí 23,08%

Rio de Janeiro 84,62% Espírito Santo 26,92%

Ceará 73,08% Acre

Santa Catarina 30,77%

Amapá

Bahia

Minas Gerais

Pernambuco

69,23% Mato Grosso

Mato Grosso do Sul 34,62%

Table 32 - Descending order of the connectivity of the Brazil Regions according to indicator (1)



Southeast Region 23

South Region 17

Northeast Region 14

Central-West Region 11

North Region 10


Looking at Table 31 and 32, it is interesting to see that four out of the five regions have a place

on the Top 5 best connected and all five regions have a place on the worst connected. This reality shows

that in every region there is a state with more importance and therefore with better connections with the

rest of the states and, looking closer to the data, it appears to be a hub-and-spoke model due to the fact

that the airports from a same region seem to be better connected with each other than with the others.

Also we can find correlations of this indicator with the state percentage in the Brazilian GDP and

with the Inequality of income distribution. Figures 25 and 26 and Table 33 show the outcomes of the

study.

Table 33 - Significant Correlation Factors for Percentage of connections from the main airport to each one of the other states, for Brazil

Percentage of connections from the main airport

to each one of the other states (1)

% Brazil GDP Inequality of Income Distribution

Pearson Correlation ρ 0,548 0,548

Spearman’s Correlation rs 0,663 Not significant

rcritical 0,524 Not significant


49

Figure 25 clearly shows that states with higher contributions to the country’s GDP have larger

connections to the other states. Surprisingly, Figure 26 shows that the bigger the inequality of the income

distribution is, the bigger the connection to the other states and, even more unsuspected it is to see that

there is no correlation between the state’s contribution to the country’s GDP and the Inequality of Income

distribution (it is not in the richer states that the inequality is bigger).

In second place the analysis of the indicator “Percentage of connections to each state main

airport (2)” has the following Top 5 more and less connected groups (Table 35) and the following order

of regions (Table 34):

Table 34 - Descending order of the connectivity of Brazil Regions according to indicator (2)



Southeast Region 25

South Region 18

Northeast Region

Central-west Region 14

North Region 8


Distrito Federal

0%

20%

40%

60%

80%

100%

0% 1% 10% 100%

Per

cen

tage

of

con

nec

tio

ns

fro

m t

he

mai

n a

irp

ort

to

eac

h

on

e o

f th

e o

ther

sta

tes

% Brazilian GDP

Figure 25- Relation between the Percentage of connections from the main airport to each one of the other states and the states percentage in Brazilian GDP

Distrito Federal

0%

20%

40%

60%

80%

100%

0,40 0,45 0,50 0,55 0,60

Per

cen

tage

of

con

nec

tio

ns

fro

m t

he

mai

n a

irp

ort

to

eac

h

on

e o

f th

e o

ther

co

un

trie

s


Figure 26 - Relation between the Percentage of connections from the main airport to each one of the other states and the Inequality of Income distribution, in Brazil


50

Table 35 – Top states with more connectivity and less connectivity to main airports of the rest of Brazil states



Distrito Federal

São Paulo 100% Roraima 7,69%

Rio de Janeiro 88,46% Acre 19,23%

Minas Gerais 80,77% Piauí

Amapá 23,08%

Ceará 76,92% Paraíba

Espírito Santo 26,92%

Amazonas

Bahia 69,23%

Tocantins

Santa Catarina 30,77%

Once again, looking at Table 34 and 35 it is possible to see that four out of five regions appear

in the Top 5 best and worst connected regions. It is interesting to notice that even if the South Region

does not have any state in the Top 5 best connected states, it lies in the second place in the overall

classification. The only thing to point out is that the North Region is in fact the worst connected region

not having a place in the best Top 5 and having four places on the worst Top 5.

Also in this case it is possible to find correlations of this indicator with the state percentage in

the Brazil GDP and with the state GDP per capita. Figures 27 and 28 and Table 36 show the outcomes

of the study.

0%

20%

40%

60%

80%

100%

0% 1% 10% 100%

%

% Brazil GDP

Percentage of connections from the main airport to each one ofthe other states (1)

Percentage of connections to each state main airport (2)

Linear (Percentage of connections from the main airport to eachone of the other states (1))

Linear (Percentage of connections to each state main airport (2))

Figure 27 - Relation between the Percentage of connections from the main airport to each one of the other states (1) and Percentage of connections to each state main airport (2) and the states percentage on Brazilian GDP


51

Table 36 - Significant Correlation Factors for the Percentage of connections to each state main airport, for Brazil

Percentage of connections to each state

main airport (2)

% Brazil GDP GDP per capita

Pearson Correlation ρ 0,626 0,506

Spearman’s Correlation rs 0,789 Not significant

rcritical 0,524 Not significant

Regarding the relation with the state’s contribution to the country’s GDP, the correlation is similar

to the first indicator and shows that the bigger the importance of the state, the greater the connection to

the other states. In the case of the GDP per capita, the correlation is being pulled out by the result of

the Distrito Federal: if we look to the figure we will see that there is no special correlation between the

data.

5.1.2. Affordability

After gathering all the information

needed, it was possible to produce Figure

29 and Table 37. It is once again important

to remember that this indicator was built

according to the Brazil average and

therefore a negative affordability means less

effort when compared with the average

states and a positive affordability means a

greater effort when compared with the

average states.

Distrito Federal

0%

20%

40%

60%

80%

100%

0 10 20 30 40 50 60 70

Per

cen

tage

of

con

nec

tio

ns

to

each

sta

te m

ain

air

po

rt (

2)

GDP per capita (x103 R$)

Figure 28 - Relation between the Percentage of connections to each state main airport (2) and the states percentage on Brazilian GDP

Figure 29 - Outcome of Affordability Indicator for Brazil. Green means more Affordable (negative result) than red (positive result).


52

Table 37 - Outcome of Affordability Indicator for Brazil

%Affordability

%Affordability

AC -8,62 PB 73,46 AL 39,95 PR -12,04 AP 45,31 PE -0,34 AM -50,32 PI 77,70 BA 17,77 RJ -34,32 CE 21,11 RN 4,00 DF -74,33 RS -54,75 ES -42,12 RO -36,53 GO 33,45 RR -30,10 MA 22,21 SC 32,04 MT -56,47 SP -24,18 MS -31,75 SE 34,70 MG 0,24 TO 50,63 PA 43,66


In the case of the indicator “Affordability”, the Top 5 of the best and worst classified states are


Table 38 – Top states with more and less Affordability in Brazil


State Indicator result State Indicator result

Distrito Federal -74,33% Piaui 77,70%

Mato Grosso -56,47% Paraíba 73,46%

Rio Grande do Sul -54,75% Tocantins 50,62%

Amazonas -50,32% Amapá 45,31%

Espírito Santo -42,12% Pará 43,66%

Table 39 - Descending order of the Affordability of Brazil Regions



Central-West Region 5

Southeast Region 9

South Region 11

North Region 12

Northeast Region 18



53

The analysis of these two tables shows some interesting results that differ from the results of

the European Union situation. In this case most of the states with less effort to travel are also states

where the selected Hubs are. Nevertheless, we can see in the overall analysis that the results are a

combination of different variables that have impact on this indicator:

- First of all we have again the impact of the fixed costs that make the Southeast Region, where are

three of the six Hubs, not to be in the first position of the ranking;

- Secondly it is important to notice that the Central-West Region is geographically in the middle of

the country and it is the region where the Federal District (Distrito Federal) is. The Federal District

is becoming one of the main Hubs of domestic flights what makes the trips to this destination

cheaper because most of the times it is not the final destination;

- Thirdly, we can see the impact of the purchasing power of the state’s citizens in this ranking: the

Northeast Region has the poorest states of Brazil, what makes the flights for these states more

interesting to the tourists than to the citizens themselves. Therefore the prices are higher and the

GDP per capita lower, which results in the worst place in the ranking.

Regarding what was said, a correlation is visible between the results and the GDP per capita.

Figure 30 and Table 40 show the outcome of the study.

Table 40 - Significant Correlation Factors for the Affordability in Brazil

Affordability

GDP per capita

Pearson Correlation ρ -0,663

Spearman’s Correlation rs -0,703

rcritical -0,524

As it is possible to see the correlation is moderately strong between the results of the indicator

and the GDP per capita, especially according to Spearman Correlation. In fact looking to the Figure, we

can see that the states with less GDP have a higher effort to travel than the states with higher GDP.

Distrito Federal

-100%

-60%

-20%

20%

60%

100%

0 10 20 30 40 50 60 70

Aff

ord

abili

ty (

wh

en c

om

par

ed w

ith

th

e av

erag

e o

f th

e st

ates

)

GDP per capita (thousand R$)

Figure 30 – Relation between the Affordability and GDP per capita, in Brazil


54

5.1.3. Business Convenience

The distance between states capitals was calculated by the states capitals coordinates using

Equation 7 (results in Annex 8), the information about the GDP of each state in 2013 that was taken

from official records of Instituto Brasileiro de Geografia e Estatística (Annex 9) and the information

regarding the number of airlines from openflights.org.

Applying the formula for this indicator, in Table 41 and Figure 31 the outcomes for this indicator

are shown.

Table 41 - Business relative cost outcome for Brazil

Business

relative cost Difference with Brazil’s average

Business

relative cost Difference with Brazil’s average

AC 5610 156,46% PB 2419 10,57%

AL 2034 -7,00% PR 1160 -46,96%

AP 4791 119,05% PE 1840 -15,88%

AM 2632 20,35% PI 2572 17,57%

BA 1259 -42,42% RJ 411 -81,20%

CE 2002 -8,47% RN 2181 -0,28%

DF 382 -82,53% RS 1621 -25,88%

ES 1362 -37,72% RO 4714 115,50%

GO 1318 -39,74% RR 5210 138,20%

MA 2322 6,16% SC 1488 -31,99%

MT 1749 -20,05% SP 304 -86,08%

MS 1672 -23,55% SE 2074 -5,18%

MG 712 -67,43% TO 2787 27,42%

PA 2429 11,07% Average 2187

Figure 31 -Outcome of the Business Convenience indicator for Brazil. Green means better Business cost (negative result) and red worse (positive result).


55



states is presented on Table 42 as well as the order of the regions on Table 43.

Table 42 – Top states with best and worst Business Convenience, in Brazil


States Indicator result States Indicator result

São Paulo -86,08% Acre 156,46%

Distrito Federal -82,53% Roraina 138,20%

Rio de Janeiro -81,20% Amapá 119,05%

Minas Gerais -67,43% Rondônia 115,50%

Paraná -46,96% Tocantins 27,42%

Table 43 - Descending order of the Business Convenience of Brazil Regions



Central-west Region 5

Southeast Region 9

South Region 11

North Region 12

Northeast Region 18


Looking to both tables is interesting to see that Southeast Region has three out of five of the

best business conveniente places on the Top 5, but is Central-west Region the one with the best

business convenience in Brazil. In the other side, all states in the Top 5 of the Wost Business

Convenience are of the North Region what explains the four times difference between the best rated

and worst rated regions.

For this indicator it is possible to find correlations with the percentage of contribution to the

Brazilian GDP and with the GDP per capita. Figures 32 and 33 and Table 44 show the outcomes of the

study.

São Paulo

-90%

-40%

10%

60%

110%

160%

0% 5% 10% 15% 20% 25% 30% 35%

Bu

sin

ess

Co

nve

nie

nce

(d

iffe

ren

ce w

ith

Bra

zil a

vera

ge)

% Brazil GDP

Figure 32 - Relation between the Business Convenience and the contribution of the states to the Brazilian GDP


56

Table 44 - Significant Correlation Factors for the Business Convenience in Brazil


% Brazil GDP GDP per capita

Pearson Correlation ρ -0,555 Not significant

Spearman’s

Correlation

rs -0,898 -0,604

rcritical 0,524 0,524

The results from this indicator show that the higher the percentage of the state’s contribution to

the country’s GDP and the higher the GDP per capita, the better the Business Convenience is. The

reason for this result is that, in a country with such different contributions to the GDP (it spans from

0,17% in Roraima to 32,08% in São Paulo), distance tends not to have so much effect on the indicator

and the contribution makes the biggest difference. Also with more money, cames more business trips

what in Brazil has a big impact in domestic flights, increasing connections in the most rich states.

Distrito Federal-90%

-40%

10%

60%

110%

160%

0 10 20 30 40 50 60 70

Bu

sin

ess

Co

nve

nie

nce

(d

iffe

ren

ce w

ith

Bra

zil a

vera

ge)

GDP per capita (thousand R$)

Figure 33 - Relation between the Business Convenience and the states GDP per capita, in Brazil


57

5.2. USA

Applying the principle that a representative airport is the ones with at least 20% of the countries

air passengers, we can see in Table 45 the airports that will be used for the study. In Annex 10, all

American International Airports are given and their share of the country’s passengers, which served as

basis for the production of the next table.

Table 45 - List of USA airports with at least 20% of the state’s passengers

State City Airport Passengers State

Passengers %

AL Birmingham

Birmingham–Shuttlesworth International Airport

1 335 215 2 334 798

57,19%

Huntsville Huntsville International Airport 505 541 21,65%

AK Anchorage Ted Stevens Anchorage International Airport 2 325 030 3 967 193 58,61%

AZ Phoenix Phoenix Sky Harbor International Airport 19 525 829 22 256 307 87,73%

AR Fayetteville Northwest Arkansas Regional Airport 558 218

1 729 450 32,28%

Little Rock Bill and Hillary Clinton National Airport 1 055 608 61,04%

CA Los Angeles Los Angeles International Airport 32 427 115

89 040 919 36,42%

San Francisco San Francisco International Airport 21 706 567 24,38%

CO Denver Denver International Airport 25 497 348 26 926 648 94,69%

CT Hartford Bradley International Airport 2 681 718 2 719 152 98,62%

DE Wilmington Wilmington-Philadelphia Regional Airport 52 456 52 475 99,96%

DC NO AIRPORTS 0,00%

FL Miami Miami International Airport 16 194 277

76 313 877 21,22%

Orlando Orlando International Airport 17 614 745 23,08%

GA Atlanta Hartsfield-Jackson Atlanta International Airport

45 308 685 45 780 671 98,97%

HI Honolulu Honolulu International Airport 9 466 995 15 865 694 59,67%

ID Boise Boise Airport 1 313 741 1 627 792 80,71%

IL Chicago Chicago O'Hare International Airport 32 278 906

43 095 921 74,90%

Chicago Chicago Midway International Airport 9 919 985 23,02%

IN Indianapolis Indianapolis International Airport 3 535 579 4 321 229 81,82%

IA Des Moines Des Moines International Airport 1 079 189 1 210 987 89,12%

KS Manhattan Manhattan Regional Airport 65 683

163 761 40,11%

Wichita Wichita Dwight D. Eisenhower National Airport

73 622 44,96%

KY Covington

Cincinnati/Northern Kentucky International Airport

2 776 377 5 092 212

54,52%

Louisville Louisville International Airport 1 669 470 32,78%

LA New Orleans Louis Armstrong New Orleans International Airport

4 577 498 5 856 865 78,16%

ME Bangor Bangor International Airport 265 245

1 140 417 23,26%

Portland Portland International Jetport 837 335 73,42%

MD Baltimore Baltimore/Washington International Thurgood Marshall Airport

11 134 130 11 153 338 99,83%

MA Boston Gen. Edward Lawrence Logan International Airport

14 721 693 15 073 021 97,67%

MI Detroit Detroit Metropolitan Wayne County Airport 15 683 787 18 025 847 87,01%

MN Minneapolis Minneapolis–St. Paul International Airport 16 282 038 16 629 377 97,91%


58

MS Gulfport/Biloxi Gulfport-Biloxi International Airport 369 597

980 389 37,70%

Jackson Jackson-Evers International Airport 596 045 60,80%

MO Kansas City Kansas City International Airport 4 836 221

11 487 988 42,10%

St. Louis Lambert-St. Louis International Airport 6 213 972 54,09%

MT

Billings Billings Logan International Airport 387 368

1 359 029

28,50%

Bozeman Bozeman Yellowstone International Airport 442 788 32,58%

Missoula Missoula International Airport 298 253 21,95%

NE Omaha Eppley Airfield 1 977 480 2 196 683 90,02%

NV Las Vegas McCarran International Airport 19 923 594 21 701 656 91,81%

NH Manchester Manchester–Boston Regional Airport 1 190 082 1 201 035 99,09%

NJ Newark Newark Liberty International Airport 17 514 139 18 196 599 96,25%

NM Albuquerque Albuquerque International Sunport 2 477 960 2 607 930 95,02%

NY New York John F. Kennedy International Airport 25 036 855

45 771 671 54,70%

New York LaGuardia Airport 13 353 365 29,17%

NC Charlotte Charlotte/Douglas International Airport 21 347 428 27 970 347 76,32%

ND Bismarck Bismarck Municipal Airport 238 929

1 143 222 20,90%

Fargo Hector International Airport 403 786 35,32%

OH Cleveland Cleveland-Hopkins International Airport 4 375 822

9 690 068 45,16%

Columbus Port Columbus International Airport 3 065 569 31,64%

OK Oklahoma City Will Rogers World Airport 1 790 407

3 169 876 56,48%

Tulsa Tulsa International Airport 1 323 943 41,77%

OR Portland Portland International Airport 7 453 098 8 184 438 91,06%

PA Philadelphia Philadelphia International Airport 14 705 014 19 162 440 76,74%

RI Providence/ Warwick

Theodore Francis Green State Airport 1 951 566 1 962 968 99,42%

SC Charleston Charleston International Airport / Charleston AFB

2 593 063 4 873 497 53,21%

SD Rapid City Rapid City Regional Airport 284 126

674 840 42,10%

Sioux Falls Sioux Falls Regional Airport 355 939 52,74%

TN Memphis Memphis International Airport 4 930 935

10 399 776 47,41%

Nashville Nashville International Airport 4 432 527 42,62%

TX

Dallas-Fort Worth

Dallas/Fort Worth International Airport 27 100 656 67 544 782

40,12%

Houston George Bush Intercontinental Airport 19 528 631 28,91%

UT Salt Lake City Salt Lake City International Airport 9 910 493 9 999 947 99,11%

VT Burlington Burlington International Airport 64 079 64 079 100,00%

VA

Washington, D.C.

Ronald Reagan Washington National Airport 8 736 804 24 466 050

35,71%

Washington, D.C.

Washington Dulles International Airport 11 276 481 46,09%

WA Seattle/ Tacoma

Seattle–Tacoma International Airport 15 406 243 17 884 275 86,14%

WV Charleston Yeager Airport 264 818

414 317 63,92%

Huntington Tri-State Airport 115 263 27,82%

WI Milwaukee General Mitchell International Airport 3 861 333 5 336 419 72,36%

WY Jackson Jackson Hole Airport 305 566 505 243 60,48%

Source: FAA Airports, Calendar Year 2011 Enplanements for U.S. Airports, by State


59

5.2.1. Availability

After processing the data found, it was possible to produce Annex 11, where all state destination

for each of the airport sample are shown. In Table 46 and Figure 34 the final numbers are shown.

Table 46 - Outcome of Availability Indicator for the USA

Sta

te Percentage of

connections to each state main airport (2)

Airports


main airport to each one of the other states (1)

AL 28,57 Birmingham-Shuttlesworh and Huntsville International Airports

22,45

AK 24,49 Ted Stevens Anchorage International Airport 24,49

AZ 77,55 Phoenix Sky Harbor International Airport 65,31

AR 28,57 Northwest Arkansas and Bill and Hillary Clinton Airports

30,61

CA 77,55 Los Angeles and San Francisco International Airports

79,59

CO 83,67 Denver International Airport 81,63

CT 30,61 Bradley International Airport 26,53

DE 0,00 Wilmington-Philadelphia Regional Airport 10,20

DC 0,00 NO AIRPORT 0,00

FL 81,63 Miami and Orlando International Airports 63,27

Figure 34 - Outcome of Avaiability Indicator for the USA (Percentage of connections from the main airport to each one of the other states (1) on top and Percentage of connections to each state main airport (2) down). Green means better connections and Red worse connections.

Alaska

Hawaii

Alaska

Hawaii


60

GA 91,84 Hartsfield-Jackson Atlanta International Airport 83,67

HI 28,57 Honolulu International Airport 28,57

ID 18,37 Boise Airport (Boise Air Terminal) (Gowen Field)

18,37

IL 95,92 Chicago O'Hare and Chicago Midway International Airports

95,92

IN 46,94 Indianapolis International Airport 36,73

IA 28,57 Des Moines International Airport 28,57

KS 20,41 Manhattan and Wichita Dwight Airports 18,37

KY 51,02 Cincinnati/Northern Kentucky and Louisville International Airports

51,02

LA 44,90 Louis Armstrong New Orleans International Airport

48,98

ME 18,37 Bangor and Portland International Airports 22,45

MD 73,47 Baltimore/Washington International Thurgood Marshall Airport

69,39

MA 51,02 Gen. Edward Lawrence Logan International Airport

61,22

MI 79,59 Detroit Metropolitan Wayne County Airport 87,76

MN 83,67 Minneapolis–St. Paul International Airport (Wold–Chamberlain Field)

83,67

MS 14,29 Gulfport-Biloxi and Jackson-Evers International Airports

16,33

MO 57,14 Kansas City and Lambert-St. Louis International Airports

65,31

MT 18,37 Billings Logan, Bozeman Yellowstone and Missoula International Airports

26,53

NE 32,65 Eppley Airfield 32,65

NV 81,63 McCarran International Airport 85,71

NH 26,53 Manchester–Boston Regional Airport 26,53

NJ 69,39 Newark Liberty International Airport 69,39

NM 30,61 Albuquerque International Sunport 30,61

NY 79,59 John F. Kennedy and La Guardia Airports 73,47

NC 75,51 Charlotte/Douglas International Airport 69,39

ND 12,24 Bismarck and Hector Airports 12,24

OH 55,10 Cleveland-Hopkins and Port Columbus International Airports

60,42

OK 33,33 Will Rogers World and Tulsa Airports 32,65

OR 48,98 Portland International Airport 44,90

PA 67,35 Philadelphia International Airport 69,39

RI 26,53 Theodore Francis Green State Airport 32,65

SC 32,65 Charleston International Airport / Charleston AFB

22,45

SD 16,33 Rapid City and Sioux Falls Airports 18,37

TN 55,10 Memphis and Nashville Airports 69,39

TX 85,71 Dallas/Fort Worth and George Bush International Airports

81,63

UT 67,35 Salt Lake City International Airport 67,35

VT 16,33 Burlington International Airport 18,37

VA 77,55 Ronald Reagan Washington and Washington Dulles International Airports

77,55


61

WA 63,27 Seattle–Tacoma International Airport 57,14

WV 12,24 Yeager and Tri-State Airports 20,41

WI 46,94 General Mitchell International Airport 51,02

WY 14,29 Jackson Hole Airport 12,24



one of the other states (1)” it is possible to see the top five groups of states with more connections from

this main airport to each one of the other states (Table 47) and to see the descending order of the

connectivity of the American regions (Table 48). The USA regions will be analysed as suggested by the

United States Census Bureau (Figure 35).

According to this division nine regions are considered:

New England: Connecticut, Maine, Massachussets, New Hampshire, Rhode Island and Vermont;

Mid-Atlantic: New Jersey, New York and Pennsylvania

East North Central: Illinois, Indiana, Michigan, Ohio and Wisconsin;

West North Central: Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota and South

Dakota;

South Atlantic: Delaware, Florida, Georgia, Maryland, North Carolina, South Carolina, Virginia,

Washington D.C. and West Virginia;

East South Central: Alabama, Kentucky, Mississippi and Tennessee;

West South Central: Arkansas, Louisiana, Oklahoma and Texas;

Mountain: Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah and Wyoming;

Pacific: Alaska, California, Hawaii, Oregon and Washington.

Figure 35 - Geographical division of the USA according to the United States Census Bureau (Source: www.census.gov)


62

Table 47 – Top countries with more connectivity and less connectivity from main airports to the rest of the states, in the USA



Illinois 95,92% Delaware 10,20%

Michigan 87,76% North Dakota

Wyoming 12,24%

Nevada 85,71% Mississippi 16,33%

Georgia

Minnesota 83,67%

Idaho

Kansas

South Dakota

Vermont

18,37%

Colorado

Texas 81,63% West Virginia 20,41%

Table 48 - Descending order of the connectivity of the USA Regions according to indicator (1)



Mid-Atlantic 39

South Atlantic 36

East North Central 31

Mountain 28

Pacific 26

West South Central 25

East South Central 20

West North Central 19

New England 16


Looking at Table 47 it is possible to see that the majority of the states with more connectivity

are in the East North Central and Mountain regions (both with two positions). On the other hand the

majority of states with less connectivity are in the West North Central and in the South Atlantic regions

with three and two positions, respectively. Nevertheless these results are not that important when

compared with Table 48 results due to the fact that the regions with more connectivity do not have the

more connected states.

Also we can find correlations of this indicator with the state percentage in the USA GDP, with

the Total Resident Population and with the Foreign-born population. Figures 36 to 38 and Table 49 show

the outcomes of the study.


63

Table 49 - Significant Correlation Factors for Percentage of connections from the main airport to each one of the other states, for the USA


one of the other states (1)

% USA GDP Resident

Population

Foreign-born

Population (%)

Pearson Correlation ρ 0,598 0,621 Not significant

Spearman’s

Correlation

rs 0,793 0,801 0,529

rcritical 0,387 0,387 0,387

As it is possible to see in Figure 36, there is in fact a strong correlation between the indicator

(1) and the percentage of each state in the US GDP. Although this correlation is better seen in a

0%

20%

40%

60%

80%

100%

0% 1% 10% 100%

Per

cen

tage

of

con

nec

tio

ns

fro

m t

he

mai

n a

irp

ort

to

eac

h

on

e o

f th

e o

ther

sta

tes

% USA GDP

Figure 36 - Relation between the Percentage of connections from the main airport to each one of the other states and the states percentage in USA GDP

California

0%

20%

40%

60%

80%

100%

0 10 20 30 40 50

Per

cen

tage

of

con

nec

tio

ns

fro

m t

he

mai

n a

irp

ort

to

eac

h

on

e o

f th

e o

ther

sta

tes


Figure 37 - Relation between the Percentage of connections from the main airport to each one of the other countries and the Resident Population, in the USA

California

0%

20%

40%

60%

80%

100%

0% 5% 10% 15% 20% 25% 30%

Per

cen

tage

of

con

nec

tio

ns

fro

m t

he

mai

n a

irp

ort

to

eac

h

on

e o

f th

e o

ther

co

un

trie

s

Foreign-born Population (%)

Figure 38 - Relation between the Percentage of connections from the main airport to each one of the other states and the foreign-born Population (%), in the USA


64

logarithmic scale, it is possible to see the Pearson correlation represented by the tendency line. As it is

clear, a low contribution for the US GDP is normally related with less connectivity according to this

indicator and, on the other hand, high contributions to the US GDP are related with high levels of

connectivity.

Secondly, the connectivity is also related to the Resident Population and, although this is visible

in the Pearson Correlation, the relation is also very clear in the Spearman’s correlation with high

numbers in the resident population corresponding to high levels of connectivity. Finally, although there

is a small correlation between Foreign-born population and the connectivity according to this indicator,

this correlation is not as strong as the two before and it is not obvious in Figure 38.

In second place the analysis of the indicator “Percentage of connections to each state main

airport (2)” has the following Top 5 more and less connected groups (Table 50) and the following order

of regions (Table 51).

Table 50 – Top states with more connectivity and less connectivity to main airports of the rest of USA states



Illinois 95,92% Delaware 0,00%

Georgia 91,84% North Dakota

West Virginia 12,24%

Texas 85,71 Mississippi

Wyoming 14,29%

Colorado

Minnesota 83,67%

South Dakota

Vermont 16,33%

Florida

Nevada 81,63%

Idaho

Maine

Montana

18,37%

Table 51 - Descending order of the connectivity of the USA Regions according to indicator (2)



South Atlantic 39

Mid-Atlantic 37


Mountain

Pacific 28




New England 15



65

While looking at Table 50 and 51 it is possible to see that there are two regions with states in

opposite situations: the South Atlantic and the Mountain regions both have two positions on the Top 5

of the best connected but also have two and three, respectively, on the Top 5 of the less connected.

Also it is possible to see that the New England region is worse connected than the other regions.

Also in this case it is possible to find correlations of this indicator with the state percentage in

the USA GDP, with the Total Resident Population and with the Foreign-born population. Figures 39 to

41 and Table 52 show the outcomes of the study.

0%

20%

40%

60%

80%

100%

10% 100%

%

% USA GDP

Percentage of connections from the main airport to each one of the otherstates (1)


Linear (Percentage of connections from the main airport to each one ofthe other states (1))


Figure 39 - Relation between the Percentage of connections from the main airport to each one of the states (1) and Percentage of connections to each state main airport (2) and the states percentage on USA GDP

Figure 40 - Relation between the Percentage of connections from the main airport to each one of the other states (1) and Percentage of connections to each state main airport (2) and the states resident population, in the USA

0,00%

20,00%

40,00%

60,00%

80,00%

100,00%

120,00%

140,00%

0 10 20 30 40 50

%




Linear (Percentage of connections from the main airport to each one ofthe other states (1))



66

Table 52 - Significant Correlation Factors for the Percentage of connections from the main airport to each one of the other states, for the USA

Percentage of connections to each state main airport

(2)

% US GDP Resident

Population

Foreign-born

Residents

Pearson Correlation ρ 0,619 0,647 0,528

Spearman’s Correlation rs 0,850 0,848 0,578

rcritical 0,387 0,387 0,387

As it is possible to see, the distribution of the data for this indicator is really similar to the first

one, and no more analysis is needed. As a result we can assume that this indicator is not as determinant

for the USA case as it was for the EU, as it was already seen.

5.2.2. Affordability

After gathering all the information needed, it was possible to produce Table 53 and Figure 42.

It is once again important to remember that this indicator was built according to the USA average and

therefore a negative affordability means less effort when compared with the average states and a

positive affordability means a bigger effort when compared with the average states.

0%

20%

40%

60%

80%

100%

120%

0% 5% 10% 15% 20% 25% 30%

%

Foreign Residents




Figure 41 - Relation between the Percentage of connections from the main airport to each one of the other states (1) and Percentage of connections to each state main airport (2) and the states foreign-born residents (%), in the USA


67

Table 53 - Outcome of Affordability Indicator for the USA

%Affordability

%Affordability

AL 96,21 MT 53,69 AK -57,06 NE -6,30 AZ -48,84 NV -18,98 AR 46,36 NH -45,03 CA -74,04 NJ -44,26 CO -51,85 NM 16,84 CT -51,41 NY -44,32 DE No Flights from

the selected Hubs

NC 10,27 DC ND -4,69 FL -26,62 OH -3,43 GA -26,56 OK -17,21 HI -34,84 OR -57,46 ID 60,26 PA -5,70 IL -40,13 RI -41,19 IN 44,48 SC 87,10 IA 46,71 SD 45,84 KS 45,27 TN 54,44 KY 75,26 TX -51,38 LA -26,09 UT -26,87 ME 25,38 VT 46,00 MD -36,48 VA -24,51 MA -18,14 WA -62,63 MI 12,85 WV 197,40 MN -36,08 WI -4,95 MS 124,52 WY 952,56 MO -0,32

Figure 42 - Outcome of Affordability Indicator for the USA. Green means more Affordable (negative result) than red (positive result)

Alaska

Hawaii


68


In the case of the indicator “Affordability”, the Top 5 of the best and worst classified states is


Table 54 – Top states with more and less Affordability in the USA


State Indicator result State Indicator result

California -74,04% Wyoming 952,56%

Washington -62,63% West Virginia 197,40%

Oregon -57,46% Mississippi 124,52%

Alaska -57,06% Alabama 96,21%

Colorado -51,85% South Carolina 87,10%

Table 55 - Descending order of the Affordability of the USA Regions



Pacific 3

Mid-Atlantic 11

New England 18

South Atlantic 21


East North Central

Mountain 29




First of all it is important to underline two aspects: firstly District Columbia and Delaware do not

have results for this analysis – the District of Columbia does not have airports and the Delaware main

Airport does not have flights to the selected Hubs; secondly, Wyoming only has connections to one of

the selected Hubs (Denver International Airport, Colorado) which is only at eight-hour-drive distance

from the Wyoming main airport, therefore, the result is not representative for the analysis.

Regarding Table 54 and 55, we can notice that the regions with less effort are the ones next to

the Pacific and the Atlantic, having the first four places in the Regions Top. It is also possible to see that

(excluding Wyoming) all bottom five states are in the East South Central and South Atlantic Regions.

The reason why the South Atlantic Region is not in the bottom of the regions ranking is because it has

other States that even if they are not on the Top Higher Affordability they have fairly good affordability.


69

When we look to the results of the correlation with the selected indicators, a relation is visible

between the results and the contribution to the USA GDP, with the compensation of employees, with

the Foreign Born Population and with the GDP per capita. Figures 43 to 46 and Table 56 show the

outcome of the study.

California

West Virginia

-100%

-50%

0%

50%

100%

150%

200%

0% 2% 4% 6% 8% 10% 12% 14%

Aff

ord

abili

ty (

com

par

ed w

ith

th

e U

SA

Ave

rage

)

% USA GDP

District of Columbia

West Virginia

-100%

-50%

0%

50%

100%

150%

200%

30 40 50 60 70 80

Aff

ord

abili

ty (

com

par

ed w

ith

th

e U

SA

Ave

rage

)

Compensation of employees per capita (thousand PPS)

California

West Virginia

-100%

-50%

0%

50%

100%

150%

200%

0% 5% 10% 15% 20% 25% 30%

Aff

ord

abili

ty (

com

par

ed w

ith

th

e U

SA

Ave

rage

)

Foreign-born Population (%)

Figure 43 - Relation between the Affordability and the states contribution for the USA GDP

Figure 44 - Relation between the Affordability and the compensation of employees per capita, in the USA

Figure 45 - Relation between the Affordability and the Foreign-born Population, in the USA


70

Table 56 - Significant Correlation Factors for Affordability indicator

Affordability

% USA

GDP

Compensation of

the employees per

capita

Foreign-born

Population (%)

GDP per

capita

Pearson

Correlation ρ

Not

significant Not significant Not significant Not significant

Spearman’s

Correlation

rs -0,449 -0,732 0,681 -0,579

rcritical 0,387 0,387 0,387 0,387

As it is possible to see on Figure 43 and Table 56, although there is a correlation with the state’s

contribution to the USA GDP, it is not significant or even noticeable. On the other hand we can see a

strong correlation with the other indicators, where states with a higher compensation of the employees

per capita, higher foreign born population and higher GDP per capita also have a higher affordability.

During this analysis it is possible to see that all the results tend to benefit people who live near

the east and west coast, regarding the location of the selected Hubs and the higher affordabilities. This

is also compatible with the states where more people live and where the GDP per capita is higher. In

other words, the USA live a situation where most part of the flights tend to go from one cost to the other,

what means higher number of passengers and also longer distances: these two points make the prices

go down because of competition between flight companies and also because of the reduced impact of

fixed costs when compared with variable costs. On the other hand, the states inland tend to have lower

affordability because of the balance of the ticket prices (that are higher because of lower competition

and bigger impacts of the fixed costs) and the state’s GDP (that is lower than in the east and west coast

states).

5.2.3. Business Convenience

The distance between states capitals was calculated by the states capitals coordinates using

Equation 7 (results in Annex 12, the information about the GDP of each state in 2013 which was taken

District of Columbia

West Virginia

-100%

-50%

0%

50%

100%

150%

200%

0 50 100 150 200

Aff

ord

abili

ty (

com

par

ed w

ith

th

e U

SA

Ave

rage

)

GDP per capita (thousand $)

Figure 46 - Relation between the Affordability and the GDP per capita, in the USA


71

from official records of the U.S. Department of Commerce (Annex 13) and the information regarding the

number of airlines from openflights.org. Applying the formula for this indicator, on Table 57 and Figure

47 the outcomes for this indicator are shown.

Table 57 - Business relative cost outcome for USA

Business

relative cost Difference with

the USA average

Business

relative cost Difference with

the USA average

AL 2400 15,28% MT 4180 100,79%

AK 6473 210,98% NE 2053 -1,38%

AZ 1123 -46,03% NV 1390 -33,24%

AR 1609 -22,69% NH 3436 65,05%

CA 1065 -48,82% NJ 1101 -47,11%

CO 962 -66,75% NM 2536 21,84%

CT 1887 -9,37% NY 723 -65,28%

DE 3170 52,30% NC 915 -56,05%

DC No Airports ND 3145 51,10%

FL 841 -59,59% OH 1068 -48,67%

GA 305 -85,37% OK 1929 -7,33%

HI 8649 315,50% OR 2719 30,60%

ID 4268 105,03% PA 819 -60,65%

IL 377 -81,87% RI 3351 60,97%

IN 1074 -48,40% SC 2448 17,58%

IA 2143 2,97% SD 2971 42,73%

KS 2303 10,66% TN 1031 -50,47%

KY 1371 -34,12% TX 636 -69,46%

LA 1120 -46,18% UT 1684 -19,09%

ME 4052 94,66% VT 3595 72,70%

MD 1022 -50,91% VA 803 -61,40%

MA 1063 -48,91% WA 2100 0,87%

MI 784 -62,32% WV 2818 35,36%

MN 751 -63,94% WI 1099 -47,18%

MS 2745 31,88% WY 3498 68,03%

MO 743 -64,32% Average 2082

Figure 47 - Outcome of the Business Convenience indicator for the USA. Green means better Business cost (negative result) and red worse (positive result).

Alaska

Hawaii


72



states is presented on Table 58 as well as the order of the regions on Table 59.

Table 58 – Top states with more and less business convenience, in the USA


States Indicator result States Indicator result

Georgia -85,37% Hawaii 315,50%

Illinois -81,87% Alaska 210,98%

Texas -69,46% Idaho 105,03%

Colorado -66,75% Montana 100,79%

New York -65,28% Maine 94,66%

Table 59 - Descending order of the business convenience, in the USA



Mid-Atlantic 10

South Atlantic 13




Mountain 31


Pacific 37

New England 44


As it is possible to see on Table 58 and corroborated by Table 59, the states with higher

business cost are in the New England and in the Pacific Region and the ones with lower business cost

are in the Mid-Atlantic and South Atlantic regions. In fact, the business cost for the New England region

is more than four times higher than the Mid-Atlantic region, and even if we take into account that the

New England is a small region and that Pacific region has Alaska and Hawaii that do not share their

borders with other states, the West North Central region also has more than three times the business

cost than the Mid Atlantic region. Nevertheless is important to say that these differences are smaller

than the one analysed in EU and Brazil cases.

For this indicator it is possible to find correlations with the contribution to the USA GDP, with the

Inequality of income distribution and with the resident population. Figures 48 to 50 and Table 60 show

the outcomes of the study.


73

Table 60 - Significant Correlation Factors for the Business Convenience in the USA


% USA GDP Inequality of

Income Distribution

Resident

Population

Pearson Correlation ρ Not Significant Not Significant Not Significant

Spearman’s

Correlation

rs -0,881 -0,498 -0,860

rcritical 0,387 0,387 0,387

Figure 48 - Relation between the Business Convenience and the contribution of each state to the USA GDP

-100%

-50%

0%

50%

100%

150%

200%

250%

300%

0% 1% 10% 100%Bu

sin

ess

Co

nve

nie

nce

(d

iffe

ren

ce w

ith

USA

ave

rage

)

% USA GDP

-100%

-50%

0%

50%

100%

150%

200%

250%

300%

0,40 0,42 0,44 0,46 0,48 0,50 0,52 0,54Bu

sin

ess

Co

nve

nie

nce

(d

iffe

ren

ce w

ith

USA

ave

rage

)


Figure 49 - Relation between the Business Convenience and the Inequality of income distribution, in the USA

-100%

-50%

0%

50%

100%

150%

200%

250%

300%

0 10 20 30 40Bu

sin

ess

Co

nve

nie

nce

(d

iffe

ren

ce w

ith

USA

ave

rage

)

Resident Population (million pax)

Figure 50 - Relation between the Business Convenience and the Resident Population, in the USA


74

The analysis of Table 60, shows a clear correlation between this indicator and the contribution

to the USA GDP and the resident population. Nevertheless we can see that the states with higher

contribution to the USA GDP are also the ones with the larger population (correlation of 0,975). Once

again we can see the importance of the economic factors in this indicator.

What is strange to see is that the business convenience improves for states with more inequality

in income distribution. One possible explanation is that in states with higher contributions to the USA

GDP there is a large upper-high class society what makes the gap between poor and rich to get bigger

and therefore this correlation with the inequality in income distribution. Nevertheless it would be

expected this to happen in Brazil and not in the USA, and therefore we could not find a reasonably

explanation for this correlation.

Exploring Connectivity in Air Transport as an Equity Factor| Frederico Valente Nunes | Conclusion: Global analysis and Suggestions

75

6. Conclusion: Global analysis and Suggestions

After the hard analysis of the results of the study, it is important to consolidate the outcomes

and to face the real challenges that these outcomes present. Therefore, a Global Analysis will be made

and some Policy Suggestions will be shared.

6.1. Global analysis

Table 61 shows the outcomes of the correlation study for the EU, the USA and Brazil.

As it was stated in the beginning of this work, the idea was to compare the situation in the EU,

the USA and Brazil in order to find similarities and differences and to search for policy approaches that

can be applied in the EU in order to increase the cohesion of the European territory. Therefore, it is

important to analyse these similarities and results for each one of the indicators.

Availability

When we look to the first indicator that gives us the percentage of connections from the main

airport to each one of the other states/countries it is clear to see that this percentage gets higher when

the country has a higher GDP and a large population. Also, we can see that this percentage is not really

positively influenced by tourism, which (in the case of the EU) means that countries from Northern and

Western Europe which have a lower impact of Tourism on the GDP have higher connectivity.

• EU, USA, BR: ↑ % GDP

• EU, USA: ↑Resident Population

• EU: ↓ % Tourism GDP

• BR: ↑ Gini Coeficient

Improvement in Percentage of connections from the main airport

to each one of the other countries/states(1)


• EU, USA: ↑Resident Population

• EU, USA: ↑Foreign-born Population

• EU: ↑ Area

• BR: ↑ GDP per capita

Improvement in Percentage of connections to each country/state

main airport (2)

•BR, USA: ↑ GDP per capita

•USA: ↑ % GDP

•USA: ↑ Compensation of employees

•USA: ↑ Foreign-born Population

Improvement in Affordability


• EU, BR: ↑ GDP per capita

• EU, USA: ↑ Resident Population

• EU: ↑ Compensation of the employees

• USA: ↑ Inequality in income distribution

• EU: ↑ Foreign Resident Population

Improvement in Business Convenience

Table 61 - Outcomes of the correlation study for the EU, the USA and BR


76

We also see that in Brazil the connectivity tends to increase with the inequality of income

distribution. This is not something we can compare with the developed countries situation due to the

fact that Brazil has a large low social class that can not afford to travel by airplane (it uses the bus), and

therefore the airplane passengers (and the connectivity) increase with the increase of a powerful upper

class.

From these outcomes we can say that:

Population and GDP have a positive impact on the connectivity;

Tourism is not necessarily a way to increase connectivity; business and wealth have a greater

impact on it.

When we look to the second part of the indicator that shows the connections to each

country/state main airport, two different correlations appear and we can see that this indicator increases

with a larger foreign born population and larger countries/states areas. This correlation is even stronger

in Europe where we can see that the difference between the minimum and maximum flight prices vary

a lot more than in the USA, for example. Therefore we can assume two things: that in Europe the flight

prices tend to vary more due to low cost airlines (in the EU the cost per kilometre decreases 52% if we

take only into account the minimum prices, while in Brazil decreases 30% and in the USA decreases

19%); and low cost airlines tend to use secondary airports what increases the country connectivity

globally, instead of the connectivity through the main airports.

From these outcomes we can say that:

Low cost airlines have a great importance in the connectivity of a country;

When the country/state area increases, there are more secondary airports and therefore more

possibilities of connections with reduced prices to other countries.

Affordability

First of all it is interesting to see that there is no correlation between the flight prices and the

GDP or the GDP per capita in Europe, which does not happen in Brazil and the USA. This obviously

means that there is not the concern from the airlines to make prices affordable for citizens who pretend

to travel or it can mean that the airlines already make the prices as low as possible even if we are talking

about a rich or a poor country.

Nevertheless, due to the fact that airlines have a profit margin, they could make some

adjustments bearing in mind that they should reduce it (and consequently reduce the prices) creating

some relation with the countries purchasing power.

That way, it is possible to say that:

Higher GDP, GDP per capita and Compensation of employees make it more affordable for citizens

to travel and therefore, prices should be established according to people’s purchasing power.


77


The result of the Top 5 regions shows two completely different situations. In the EU the

geographical centre of the union has lower business costs because the most important markets are

located there. In the USA there is not a single economic center, because in both coasts there is important

markets; therefore the geographical center of the country should be also the one with the best business

convenience due to the proximity to both coasts. Nevertheless we see the importance of the number of

airlines operating in that states what brings a mix of colors in the USA map of Business Convenience.

This brings a new look to what really means pheripherality and what the consequences of it are.

In the case of this indicator the correlations were really different between the studied cases, but

in all three or at least two out of three we can see important correlations with economic indicators

(contribution to the countries/union GDP and GDP per capita) and with the resident population. Once

again we can say that economic factors have special importance in this matters.

Looking to the outcomes of this indicator it is possible to say that:

The business cost of travelling is greatly influenced by many indicators but the most important is to

see the relation between these indicators and GDP per capita and GDP. Therefore the only way to

make business travel more affordable is through a more equitable economic and financial

distribution between all countries.

6.2. Policy Suggestions

First of all, as we have already seen, most of the asymmetries that we face can and would be

solved if the territory was homogenous, this means, if the impact of each territory in the economy of the

European Union was the same, if each country had the same population, and so on. Nevertheless we

know that if in some cases nothing can be done (it is impossible to fit the population of Germany in the

island of Malta), other things can be improved with other types of policies that can make a difference in

a far future. Anyway, this future is too far away or probably will never be reached which means that in

both cases policies must be addressed to improve citizens “travelling life”.

Even if some myths were destroyed, as Portugal being “on the edge” of Europe it could be a

reason for remoteness per se (just by comparing the situation in Europe to the situation in the USA or

even Brazil, where Florida, Washington and Porto Alegre, for example, are not, in terms of air

transportion, considered remote destinations), some other “myths” are real, as being far from mainland

to be a disadvantage, as we saw with Hawaii, Alaska, Malta or Cyprus.

At this point it is important to remember that this study intends to give some ideas as to how the

European Union authorities should deal with this situation and improve the cohesion inside Europe. In

some way the idea is to extrapolate the Public Service Obligations in Air Transportation from countries

to the Union, and to give that responsibility to the European authorities as PSO is a responsibility of the

national governments. In fact this is not a new idea, as we have already seen, as the USA have already

done the Essential Air Service and the Alternative Essential Air Service. Nevertheless, it is important to


78

reinforce that the main idea is to evaluate the situation in Europe, as we have done so far, and to shed

some light on some policies that can be addressed.

Another thing that is important to remember is that the European Authorities have limited power

inside the EU to establish new strategies to fix the problems that were found. Therefore some of the

suggestions could be halted until the powers of the European Parliament or the European Commission

are reinforced. As an example, we can say that tax benefits or airport tax benefits could be an idea, but

it would have a difficult application due to the fact that the European authorities can not collect taxes.

Trans-European Transport Network + Air Transportation

The current construction of the Trans-European Transport Network (Figure 51) assures the

connection of main cities, ports and logistics and economic points of interest by train, road and sea

transportation. This idea is accurate if we take into account only the connection between neighbouring

member states or between regions of Europe where countries are small enough to bring several of them

together by this means of transportation.

Nevertheless, it is equally important to ensure that the connection of the most peripheral

countries (such as Portugal, Spain, Italy, Malta, Greece, Cyprus, Bulgaria, Romania, Estonia, Latvia,

Lithuania, Finland, Sweden, the United Kingdom and Ireland) is made without the need of travelling to

central Europe. Therefore some political decisions should be made to ensure that these connections

are taken into account, in particular, through air transportation.

Geographical parameters in the distribution of Community Assistance

Figure 51- Trans-European Transport Network (Source: European Comission)


79

The distribution of community money support should take into account that the geographical

position of each European country has an impact on their economy. This impact can be measured by

two different ways:

the proximity to the economic core of Europe;

and the position as an intermediary country in the economic route of peripheral countries.

The first point is somehow already taken into account in the contributions to the EU budget. For

those who do not know, the EU budget receives contributions from Traditional Own Resources (which

consists of duties that are charged on imports of products coming from a non-EU state – 12%), resources

based on Value Added Tax (a uniform percentage rate that is applied to each member state’s

harmonized VAT taxes – 11%) and resources based on Gross National Income (as a uniform rate

applied to the GNI of each Member state – 76%). This way “revenue flows into the budget in a way

which is roughly proportionate to the economic prosperity of the Member States” (European Comission,

2010).

For the second point however, some member states are being favoured and there is no

redistribution procedures for this situation. For example, when a truck departures from Lisbon to

Germany to make some business, there are some costs associated with the payment of roadways in

countries like Spain and France. Therefore these two countries are being beneficiated with their

geographical position at the expense of a peripheral country, Portugal. The same can be applied in the

opposite way, when Spain brings cargo to go overseas in Sines Port, in Portugal. Similar examples

could be applied in air transportation.

Therefore, it would be interesting to think of a mechanism that would improve equality, by

decreasing the benefits and losses of the member states because of their geographical position.

Public Service Obligation in Air Transportation by the EU and/or Route Development Funds

The same way as countries have public service obligations to increase cohesion inside their

territories, due to different possible reasons, the EU should have the same.

Normally, a Public Service Obligation appears when there is no interest from private companies

to provide a service because there is no interesting revenue from this service. Nevertheless, this

ideology should be improved and changed from “no service” to “no quality service” (being quality the

price, the quality of the service or the frequency of the service).

In Portugal, the national government has created rules that established the maximum value a

flight from Portugal mainland to Madeira or Azores can cost (134 € and 119 €, respectively). This way,

Portugal ensured that national citizens from these islands can connect with the mainland ensuring

national cohesion. A similar strategy could be created in the EU, with EC establising a maximum price

per route that nationals from each country could pay to travel. This would be a great way not only to

improve cohesion in Europe but also to adress some of the questions that were analysed in this study:


80

The Affordability indicator would improve due to the fact that ticket prices would decrease for some

selected countries with less GDP and far from inland;

By establishing the necessary routes to improve the availability of flights and by subsidising these

routes, the availability indicator would improve;

Business Convenience, would improve due to the fact that the number of routes and airlines

operating on them would improve and more flights and passengers would mean also improvements

on the GDP.

Nevertheless is important to say that is not indented to transfer the air transportation business

risk from the hands of the private airlines and putted in the hands of the European Authorities. What is

suggested is a careful analysis to find possible situations where the creation of Route Development

Funds would be necessarily to create demand, and therefore in a short period of time give the business

risk back to private airlines, and in possible situations where the demand will never be enough and there

is actually the need of increasing cohesion and connectivity between EU territories by subsidising these

routes

6.3. Case Study Evaluation

After finalising this case study it is important to evaluate and find possibilities of improvement.

During the elaboration of this study, the gathering of information was a difficult task, due to the fact that

most of the time the information was not easily available or the source was not official. Therefore this

work could be improved by the use of more accurate information if it was available.

Also, the information used is always changing and there is the possibility that some of the

information is not accurate during the time of the reading of this work. Therefore in this work we tried to

give all the information needed to repeat the analysis in the future if someone desires to see the evolution

of the situation. Nevertheless it is important to ensure that everything possible was done to guarantee

the trust in the data and in the results of the study.

In the case of the Affordability indicator, it would be interesting to analyse the results for all pairs

of countries in the EU because the price connections between smaller countries would increase due to

the decrease of the low cost flights between these destinations and consequently the affordability

indicator would become worse. Nevertheless it was impossible to make this kind of analysis due to the

huge amount of information that would be needed and, once again, cannot be found easily.

Probably it would be possible to create other indicators to analyse the same as it was proposed

in this work, but the reality is that the information needed to evaluate each one of the suggested

indicators is massive and to create and evaluate more indicators would make this project endless.

Nevertheless, we encourage other people to create and evaluate new indicators and try to find new

correlations of the current situation with other economical, financial and social indicators.

6.4. Concluding Remarks


81

Equity is often related to solidarity being therefore a major aspect of European Union ideology.

Nevertheless we face a situation nowadays where equity is not addressed when analysing and

considering different projects. In fact only Germany considers equity between regions in their projects

but not in every aspect of the word or even in all fields of application.

The European Comission has created a work team to address this problem and to increase the

awareness of the subject, hoping with this to create new studies that may in the future become the new

European rules that will try to improve the equity between member states.

Nevertheless, as we saw, if equity in transportation is a non-subject most of the times in the EU,

equity in air transportation is even more neglected. When the EC presents the Trans-European

Transport Network, almost no words are related with Air transportation or in increasing connectivity

between countries far from each other and far from Western Europe. Therefore I would say that there is

great opportunity for future works in this area.

As we have seen many of the problems that we face nowadays in the European Union in the

field of equity in air transportation (as it is addressed in this study) can and would be majorly corrected

just by decreasing the asymmetries between European countries in what concerns economical matters.

Nevertheless other problems would not be solved, as the importance of the population for this subject.

Several outcomes were taken from this study and it is based on them that the policy suggestions

were made. First of all, it is important to understand that being on the periphery of Europe is not per se

a disadvantage in what concerns connectivity. As it was seen in Brazil and the United States of America,

if a peripheral country/state has economical importance its connection will be assured.

Secondly it is of the utter importance to underline the significance of low cost airlines in the EU.

They ensure a better, more frequent and more affordable connection between European countries

consequently ensuring the cohesion between member states.

Thirdly, although tourism can improve the economy of a country, it is not that important in what

concerns improving their equity in transportation. Tourism in Southern European countries tend to be

majorly seasonal which does not contribute to a long standing equity in air transportation.

Finally, we see that nowadays there are already some programmes that try to correct these

assymetries between states. In fact an implementation in the EU of a service similar to PSO would

improve several of the indicators of this study. These Public Service Obligations should be presented in

two ways: one which ensures the connection between member states that nowadays are not connected

and in which the connections would greatly improve the cohesion in the EU territory and also expand

national economies; and another where it establishes maximum prices that would be subsidised by the

European Union in order to increase cohesion, namely with island-member-states (Cyprus and Malta,

for example) and with more peripheral countries (Portugal, Greece, Estonia, Latvia and others).


82

As it is obvious, this study does not intend to make any extrapolation between the relation of

each country with the EU and the relation of each country with the rest of the world. Each country should

take advantage of their geographical, cultural, political and economical situation to improve their

connection with all the other countries of the World, not forgetting that these connections with other parts

of the world can, and will, improve their connection inside the EU.

6.5. Further Research

In the end of this study it is easy to say that a lot more work can be done on this subject. First

of all because, as we saw, the question of equity in transportation is not well studied yet and when

applied to air transportation between countries was in fact never addressed.

Projects submitted to the European Commission for approval and allocation of EU funds, are

submitted to a series of evaluation according to certain parameters. Nevertheless, improvements in the

equity between member states or between regions are not, for now, subject to evaluation. Nevertheless

the EU has given the first steps by creating a work team (TEACOST) to get awareness on this subject

with the objective of increasing the number of studies in the area and, in the future, creating parameters

that may give Equity the right importance. Therefore, the creation of new indicators and the analysis of

the impact of projects in decreasing asymmetries in the field of transportation is a new and unexplored

field for studies.

Also, in the author’s opinion, there is a lot more space for analysis for an indicator like

Affordability. First, because in this study we did not study all the pairs of connections inside the European

Union and also because by collecting information about the prices it would be possible to understand a

lot about their dynamic. Also, by changing the construction of the indicator, it may be possible to get

new approaches to the problem and therefore to find new possibilities of improvement.

Besides giving importance to equity in the evaluation of new projects, it is also important to think

where the EU authorities may have to intervene to create good conditions for the private initiatives to

exist and to increase the connection between European countries. If it is true that National Governments

have the right to create Public Service Obligations, there is no reason for the EU not to have the right of

using the same strategy in a trans-national situation.

Consequently, there is a lot of space for improvement of public strategies in this field and the

idea of European Public Service Obligation should be considered. A study like this may identify possible

routes and possible criteria to the application of such services. A study like this could lead to a change

in some policies in the EU and hopefully a more connected Europe.

Exploring Connectivity in Air Transport as an Equity Factor| Frederico Valente Nunes | References

83

References

(2014, January 10). Retrieved from International Civil Aviation Organization: http://www.icao.int/

(2015, January 5). Retrieved from AENA: http://www.aena.es/csee/Satellite/HomeAena

(2015, January 5). Retrieved from Assaeroporti, Associazione Italiana Gestori Aeroporti:

http://www.assaeroporti.com/

(2015, January 5). Retrieved from AZWorldAirports:

http://www.azworldairports.com/cfm/homepage.cfm

(2015, January 5). Retrieved from Central Statistics Office, Ireland: http://www.cso.ie/en/index.html

(2015, January 5). Retrieved from Danmarks Statistik: http://www.dst.dk/en.aspx

(2015, January 5). Retrieved from EUROCONTROL: https://www.eurocontrol.int/

(2015, January 5). Retrieved from Hungarian Central Statistical Office: https://www.ksh.hu/?lang=en

(2015, January 5). Retrieved from Swedavia, Swedish Airports: http://www.swedavia.com/

(2015, March 20). Retrieved from United States Census Bureau: www.census.gov

(2015, January 5). Retrieved from Urzad Lotnictwa Cywilnego: http://www.ulc.gov.pl/en

(2015, January 2). Retrieved from US Department of Transportation Environmental Justice:

http://www.dotcr.ost.dot.gov/asp/ej.asp

Air Transport. (2015, January 5). Retrieved from Department of Civil Aviation, Republic of Cyprus:

http://www.mcw.gov.cy/mcw/dca/dca.nsf/DMLindex_en/DMLindex_en?OpenDocument

Air Transport Statistics. (2015, January 5). Retrieved from DLR, Intitute of Air Transport and Airport

Research: http://www.dlr.de/fw/en/

Airports Council International. (2015). Preliminary World Airport Traffic and Rankings for 2014.

Amaral, L., Scala, A., Barthélémy, M., & Stanley, H. (2000, July 13). Classes of small-world networks.

PNAS, 97, 11149-11152.

Arvis, J.-F., & Shepherd, B. (2011). The Air Connectivity Index - Measuring Integration in the Global Air

Transport Network. The World Bank, International Trade Department. The World Bank.

Aviation. (2015, 1 5). Retrieved from Statistics Netherlands: http://www.cbs.nl/en-

GB/menu/home/default.htm

Aviation. (2015, January 5). Retrieved from Statistics Austria, The Information Manager:

http://www.statistik.at/web_en/statistics/index.html

Aviation Statistics. (2015, January 5). Retrieved from Croatian Bureau of Statistics:

http://www.dzs.hr/default_e.htm


84

Bagler, G. (2008, January 2008). Analysis of the airport network of India as a complex weighted

network. ELSEVIER, pp. 2972-2980.

Banister, D. (n.d.). Internalising the Social Costs of Transport, Chapter 6 - Equity and Acceptability

Question in Internalising the Social Costs of Transport.

Barabási, A.-L., & Albert, R. (1999, October 15). Emergence of Scaling in Random Networks. 286, 509-

512. Retrieved from www.sciencemag.org

Barnier, M. (2011). As Regiões Ultraperiféricas Europeias no Mercado Único: A projecção da UE no

mundo. Comissão Europeia.

Bootsma, P. (1997). Airline Flight Schedule Development; Analysis and Design Tools for European

Hinterland Hubs. Utrecht.

Braathen, S. (2011). Air Transport Services in Remote Regions. Norway: OECD - International

Transport Forum.

Burghouwt, G. (2007). Airline network development in Europe and its implications for airport

planning. Ashgate.

Burghouwt, G., & Redondi, R. (2009). Connectivity in air transport networks: models, measures and

applications. Bergamo: Universita degli studi di Bergamo.

Burghouwt, G., & Wit, J. (2005). Temporal configurations of European airline networks. (ELSEVIER,

Ed.) Journal of Air Transport Management, 11, pp. 185-198.

Business Dictionary. (2015, August 26). Retrieved from Business Dictionary:

http://www.businessdictionary.com/definition/connectivity.html

Button, K., Haynes, K., & Stough, R. (1998). Flying into the Future. Air Transport Policy in the European

Union.

Code Plex. (2015, June 18). Map Gallery Code Plex - Europe Map. Retrieved from

http://mapgallery.codeplex.com/

Coelho, A. A. (2012). Um Estudo Geográfico do Transporte Aéreo no Brasil. Universidade Federal de

Santa Catarina, Centro de Filosofia e Ciências Humanas, Florianópolis.

Consolidated Treaties Charter of Fundamental Roghts. (2010, 3).

Consolidated Version of The Treaty on the functioning of the EU. (2012, 10 26). Official Jornal of the

European Union.

Departamento Aéreo do Estado de São Paulo. (2015, March 3). Retrieved from

http://www.daesp.sp.gov.br/estatistica-consulta/

DIEESE - Departamento Intersindical de Estatística e Estudos Socioeconómicos. (2015). Preço da

Cesta Básicas.


85

Eddington, R. (2006). The Eddington Transport Study: the case for action: Sir Rod Eddington's advice

to Government. London: Transport for London, Her Majesty's Stationery Office.

Ellickson, R. C. (1977). Suburban Growth Controls: An Economic and Legal Analysis. The Yale Law

Jornal 86(2), 385-511.

Estatística, I. B. (2013). Contas Regionais do Brasil 2012 - PIB per capita.

European Comission. (2010). The European Union budget at a glance. Brussels.

European Comission. (2014, December 24). Commission communication pursuant to Article 16(4) of

Regulation (EC) No 1008/2008 of the European Parliament and of the Council on common

rules for the operation of air services in the Community. Official Jornal of the European

Union, p. C465/27.

European Union. (2014, May 10). Retrieved from http://europa.eu/

European Union Law. (2014, May 10). Retrieved from http://eur-lex.europa.eu/

FAA Airports. (2012). Calendar Year 2011 Enplanements for U.S. Airports, by State.

Finance Twitter. (2015, June 2). Retrieved from http://www.financetwitter.com/

Français, U. d. (2013). Résultats d'activité des aéroports Français, Statistiques de trafic. Paris.

Guimera, R., Mossa, S., Turtschi, A., & Amaral, L. (2005, April 5). The worldwide air transportation

network: Anomalous centrality, community structure, and cities' global roles. (K. W.

Wachter, Ed.) PNAS, 102, 7794-7799.

Hay, A., & Trinder, E. (1991). Concepts of Equity, Fairness and Justice Expressed by Local Transport

Policy Makers. In Environment and Planning (pp. 453-465).

INFRAERO. (2013). Anuário Estatístico Operacional. Brasília.

Instituto Brasileiro de Geografia e Estatística. (2013). Contas Regionais do Brasil 2002-2012 - PIB.

Kneib, E. C. (n.d.). As diferentes Escakas de Análise Espacial de um Índice de Acessibilidade.

Lian, J. (2010). The economic impact of air transport in remote regions, In Williams G and S. Brathen

(Eds): Air Transport Provision in Remote Regions. UK: Ashgate Publishing.

Lieshout, R., Malighetti, P., Redondi, R., & Burghouwt, G. (2015). The competitive landscape of Air

Transport in Europe. Journal of Transport Geography.

Litman, T. (2014). Evaluating Transportation Equity - Guidance For Incorporating Distributional

Impacts in Transport Planning. Victoria Transport Policy Institute. Retrieved 11 14, 2014,

from http://www.vtpi.org/equity.pdf

Low Cost Airlines Graveyard - about fake or sponsored low cost airlines. (2015, March 7). Retrieved

from European Travel Network: http://www.etn.nl


86

Metrass-Mendes, A., de Neufville, R., & Costa, A. (2010). Air Transport Policy for small communities:

Lessons from the U.S. experience. Proceedings from the 14th ATRS Conferance. Oporto.

Ministério da Economia. (2014). Plano Estratégico de Transportes e Infraestruturas 2014-2020.

Secretaria de Estado dos Transportes e Comunicações.

Ministérios das Finanças e da Economia. (2015, March 27). Portaria 95-A/2015, de 27 de Março.

Diário da República.

Ministry of Infrastructure, Transport and Networks. (2015, January 5). Retrieved from Greek Travel

Pages: http://www.gtp.gr/TDirectoryDetails.asp?ID=12330

Morris, J. M., Dumble, P. L., & Wigan, M. R. (1979, November 6). Accessibility Indicators For

Transport Planning. («, Ed.) Transpn Res., 13A, pp. 91-109.

Open Flights. (2015, September 1). Route Database. Retrieved from Open Flights: openflights.org

Paleari, S., Redondi, R., & Malighetti, P. (2010). A comparative study of airport connectivity in China,

Europe and US: Which network provides the best service to passengers? Transportation

Research Part E, 198-210.

Pearce, B. (2007). Investing in Air Transport Connectivity to Boost National Productivity and Economic

Growth. Davos: World Economic Forum: The Travel and Toursim Competitiveness Report

2007: Furthering the Process of Economic Development.

Portugal, A. A. (2013). Relatório de Gestão e Contas. Lisboa.

Regulation nº 1008/2008 of the European Parliament and of the Council of 24 September 2008 on

common rules for the operation of air services in the Community. (2008). Official Journal of

the European Union.

República Portuguesa. (2015, March 24). Decreto-lei 41/2015, de 24 de Março. Diário da República.

Reynolds-Feighan, A. (2001). Traffic distribution in low-cost and full-service carrier networks. Journal

of Air Transport Mnagement 7, 265-275.

Rodrigue, J.-P., Comtois, C., & Slack, B. (n.d.). The Geography of Transport Systems. Retrieved 11 14,

2014, from People.Hofstra Edu: http://www.people.hofstra.edu/geotrans/index.html

Smyth, A., Christodoulou, G., Dennis, N., AL-Azzawi, M., & Campbell, J. (2012). Is air transport a

necessity for social inlusion and economic development? Journal of Air Transport

Management 22, pp. 53-59.

Strategic Research Department. (2007). Tourim in Scotland. Edinburgh: VisitScotland.

TEA-21 - Transportation Equity Act for the 21st Century, Moving Americans into the 21st Century.

(2015, April 2). Retrieved from http://fhwa.dot.gov/tea21/sumover.htm

Trans-European Transport Network. (2015, February 25). Retrieved from European Comission:

Mobility and Transport: http://ec.europa.eu/transport/infrastructure/tentec/tentec-

portal/site/index_en.htm


87

U.S. Department of Commerce. (2015, March 12). Retrieved from Bureau of Economic Analysis:

www.bea.goz

UK Airport Statistics: 2013 - anual. (2015, January 5). Retrieved from Civil Aviation Authority:

http://www.caa.co.uk/homepage.aspx

UK Civil Aviation Authority. (2005). CAP 754 UK Regional Air Services. London: CAA.

UNCTAD. (2007). Transport Newsletter No. 36. Geneva: UNCTAD.

Union, P. O. (2011). Interinstitutional Style Guide. Brussels. Retrieved from

http://publications.europa.eu/code/en/en-000100.htm

USP - Universidade de São Paulo. (2015, June 19). NEREUS. Retrieved from

http://www.usp.br/nereus/?dados=brasil

Vasigh, B., Fleming, K., & Tacker, T. (2008). Introduction to Air Transport Economis: From Theory to

Applications. Ashgate.

Veldhuis, J. (1997). The competitive position of airline networks. Journal of Air Transport

Management, 181-188.

Voorhees, N. P. (2009). Transit Equity Maters: An Equity Index and Regional Analysis of the Red Line

and Two Other Proposed CTA Transit Extensions. Coolege of Urban Planning and Public

Affairs, Center for the Neighborhood and Community Improvement. United States of

America: University of Illinois and Chicago.

Williams, G. (2010). European Experience of Public Service Obligations. In Williams G. and S. Brathen

(Eds): Air Transport Provision in Remote Regions. UK: Ashgate Publishing.


88

Exploring Connectivity in Air Transport as an Equity Factor| Frederico Valente Nunes | Annex

89

Annex

Annex 1 - Public Holidays in the EU countries, the USA and Brazil

October 2015

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

AT BE BG HR CY CZ DK EE FI FR DE EL HU IE IT LV LT LU MT NL PL PT RO SK SI ES SE UK

USA BR

Weekend Public Holidays


90


91

Annex 2 - Airports to consider in the case study “European Union”

Country City Airport Passengers Country

Passengers %

Austria[1]

Vienna Vienna International Airport 21 999 820

26 334 634

83,54%

Graz Graz Airport 881 565 3,35%

Klagenfurt Klagenfurt Airport 259 336 0,98%

Innsbruck Innsbruck Airport 981 118 3,73%

Salzburg Salzburg Airport 1 662 834 6,31%

Linz Linz Airport 549 961 2,09%

Belgium[2]

Brussels Brussels Airport 19 133 222

26 861 760

71,23%

Antwerp Antwerp Airport 137 015 0,51%

West Flanders Ostend-Bruges International Airport 247 669 0,92%

Liège Liège Airport 309 206 1,15%

Ostend-Bruges Ostend-Bruges International Airport 247 669 0,92%

Brussels Brussels South Charleroi Airport 6 786 979 25,27%

Bulgaria[3]

Sofia Sofia Airport 3 504 326

7 357 179

47,63%

Burgas Burgas Airport 2 461 648 33,46%

Plovivo Plovdiv Airport 87 526 1,19%

Varna Varna Airport 1 303 679 17,72%

Croatia[4]

Dubrovnik Dubrovnik Airport 1 502 165

6 304 089

23,83%

Brac Bol Airport 9 433 0,15%

Osijek Osijek Airport 3 404 0,05%

Pula Pula Airport 351 196 5,57%

Rijeka Rijeka Airport 139 296 2,21%

Split Split Airport 1 558 812 24,73%

Zadar Zadar Airport 453 791 7,20%

Zagreb Zagreb Airport 2 285 992 36,26%

Cyprus[5] Larnaca Larnaca International Airport 5 636 426

7 423 373 75,93%

Paphos Paphos International Airport 1 786 947 24,07%

Czech Republic[6]

Brno Brno - Turany Airport 463 023

11 955 089

3,87%

Karlovy Vary Karlovy Vary Airport 104 469 0,87%

Ostrava Leos Janacek Ostrava Airport 288 393 2,41%

Pardubice Pardubice Airport 125 008 1,05%

Prague Václav Havel Airport Prague 10 974 196 91,80%

Denmark[7]

Aalborg Aalborg Airport 708 000

14 371 000

4,93%

Aarhus Aarhus Airport 231 000 1,61%

Billund Billund Airport 1 416 000 9,85%

Copenhagen Copenhagen Airport 12 016 000 83,61%

Estonia[8] Tartu Tartu Airport 13 690

1 972 491 0,69%

Tallin Tallinn Airport 1 958 801 99,31%

Finland[9]

Kittila Kittila Airport 214 493

18 373 279

1,17%

Kuopio Kuopio Airport 253 612 1,38%

Kuusamo Kuusamo Airport 82 497 0,45%


92

Lappeeranta Lappeenranta Airport 98 300 0,54%

Mariehamn Mariehamn Airport 48 672 0,26%

Oulu Oulu Airport 877 080 4,77%

Rovaniemi Rovaniemi Airport 309 821 1,69%

Tampere Tampere-Pirkkala Airport 466 671 2,54%

Turku Turku Airport 454 948 2,48%

Vaasa Vaasa Airport 288 142 1,57%

Helsinki Helsinki Airport 15 279 043 83,16%

France[10]

Paris Orly Airport 28 274 154

170 210 331

16,61%

Paris Charles de Gaulle Airport 62 052 917 36,46%

Agen Agen Airport 36 716 0,02%

Ajaccio Campo dell'Oro Airport 1 350 431 0,79%

Aurillac Aurillac Airport 23 958 0,01%

Bastia Poretta Airport 1 126 096 0,66%

Beauvais Tillé Airport 3 952 908 2,32%

Bergerac Bergerac-Roumanière Airport 286 226 0,17%

Béziers Béziers Cap d'Agde Airport 228 024 0,13%

Biarritz Biarritz Airport 1 098 079 0,65%

Bordeaux Bordeaux-Mérignac Airport 4 617 608 2,71%

Bora Bora Bora Bora Airport 264 103 0,16%

Brest Brest Bretagne Airport 1 003 836 0,59%

Brive Brive Airport 63 877 0,04%

Caen Caen Airport 105 022 0,06%

Calvi Calvi Airport 302 672 0,18%

Cayenne Cayenne Airport 436 991 0,26%

Castres Castres Airport 42 278 0,02%

Carcassonne Salvaza Airport 432 712 0,25%

Clermont-Ferrand

Clermont-Ferrand Airport 425 896 0,25%

Paris Vatrt Châlons Vatry Airport 100 857 0,06%

Chambéry Chambéry-Savoie Airport 218 120 0,13%

Deauville Deauville Airport 138 554 0,08%

Dijon Dijon Airport 25 551 0,02%

Dinard Pleurtuit Airport 130 771 0,08%

Dole Dole Airport 80 028 0,05%

Figari Figari Sud-Corse Airport 451 446 0,27%

Fakarava Fakarava Airport 25 686 0,02%

Fort-de-France Fort-de-France Airport 1 685 108 0,99%

Grenoble Grenoble-Isère Airport 337 603 0,20%

Hiva Hoa Hiva Hoa Airport 26 849 0,02%

Huahine Huahine Airport 121 431 0,07%

Ile des Pins Ile des Pins Airport 85 109 0,05%

La Rochelle Île de Ré Airport 216 221 0,13%

Lannion Lannion Airport 35 119 0,02%

Lifou Lifou Airport 162 836 0,10%

Lille Lille Lesquin Airport 166 141 0,10%


93

Limoges Bellegarde Airport 299 654 0,18%

Lorient Lorient Airport 166 034 0,10%

Lyon Saint-Exupéry Airport 8 562 298 5,03%

Mare Mare Airport 71 601 0,04%

Marseille Provence Airport 8 260 619 4,85%

Maupiti Maupiti Airport 21 783 0,01%

Mayotte Mayotte Airport 325 670 0,19%

Metz Nancy Metz Airport 242 995 0,14%

Montpellier Méditerranée Airport 1 422 793 0,84%

Moorea Moorea Airport 85 542 0,05%

Mulhouse Basel-Mukhouse-Freiburg Airport 5 876 042 3,45%

Nantes Nantes Atlantique Airport 3 930 849 2,31%

Nice Côte d'Azur Airport 11 554 251 6,79%

Nîmes Gorns Airport 195 319 0,11%

Nouméa Nouméa La Toutouta Airport 476 174 0,28%

Nouméa Nouméa Magenta 392 317 0,23%

Nuku Hiva Nuku Hiva Airport 38 755 0,02%

Ouvea Ouvea Airport 75 952 0,04%

Perpignan Llabanère Airport 366 551 0,22%

Pinte-à-Pitre Point-à-Pitre Airport 2 033 763 1,19%

Poitiers Biard Airport 107 964 0,06%

Quimper Quimper Airport 113 419 0,07%

Raiatea Raiatea Airport 207 065 0,12%

Rangiroa Rangiroa Airport 75 486 0,04%

Rennes Rennes Airport 480 237 0,28%

Rodez Marcillac Airport 143 392 0,08%

Rurutu Rurutu Airport 20 671 0,01%

Réunion Reúnion Roland Garros Airport 2 001 001 1,18%

Gustavia Saint Barthelemy Airport 162 641 0,10%

Grand Case Saint Martin Airport 198 603 0,12%

Saint Nazaire Saint Nazaire Airport 24 793 0,01%

Saint Pierre Saint Pierre Airport 82 748 0,05%

Saint Étienne Saint Étienne Airport 133 807 0,08%

Bouthéon Bouthéon Airport 133 807 0,08%

Strasbourg Strasbourg Airport 1 181 149 0,69%

Tahiti Tahiti Faa'a Airport 1 150 610 0,68%

Tarbes Tarbes-Lourdes-Pyrénées Airport 382 186 0,22%

Tikehau Tikehau Airport 37 490 0,02%

Toulon Hyères Le Palyvestre Airport 582 132 0,34%

Toulouse Blagnac Airport 7 567 634 4,45%

Tours Tours Loire Valley Airport 181 769 0,11%

Tubuai Tubuai Mataura Airport 20 573 0,01%

Mata-Utu Wallis Hihifo Airport 44 681 0,03%

Pau Uzein Airport 645 577 0,38%

Germany[11] Berlin Berlin Tegel Airport 19 591 838 208 778 086 9,38%


94

Belin Berlin Schonefeld Airport 6 727 306 3,22%

Baden Baden Airpark 1 059 227 0,51%

Freiburg Basel-Mukhouse-Freiburg Airport 5 876 042 2,81%

Bremen Bremen Airport 2 612 627 1,25%

Cologne Cologne/Bonn Airport 9 077 346 4,35%

Dresden Dresden Airport 1 754 139 0,84%

Dortmund Dortmund Airport 1 924 386 0,92%

Dusseldorf Dusseldorf Airport 21 228 226 10,17%

Erfurt Erfurt Airport 214 948 0,10%

Frankfurt Frankfurt Airport 58 036 948 27,80%

Hahn Frankfurt-Hahn Airport 2 667 402 1,28%

Friedrichshafen Friedrichshafen Airport 536 029 0,26%

Hamburg Hamburg Airport 13 502 553 6,47%

Hanover Langenhagen Airport 5 234 909 2,51%

Leipzig Leipzig/Halle Airport 2 234 231 1,07%

Munich Munich Airport 38 672 644 18,52%

Munster Munster Osnabruck Airport 853 904 0,41%

Nuremberg Nuremberg Airport 3 309 629 1,59%

Paderborn Paderborn Airport 794 889 0,38%

Rostock Rostock Airport 177 464 0,09%

Saarbrucken Saarbrucken Airport 405 265 0,19%

Stuttgart Stuttgart Airport 9 577 551 4,59%

Zweibrucken Zweibrucken Airport 220 740 0,11%

Weeze Weeze Airport 2 487 843 1,19%

Greece[12]

Athens Athens International Airport 12 459 801

38 604 975

32,28%

Araxos Araxos Airport 139 689 0,36%

Astypalaia Astypalaia Airport 11 940 0,03%

Alexandroupolis Alexandroupolis Airport 316 365 0,82%

Chania Chania International Airport 2 078 857 5,38%

Chios Chios International Airport 173 540 0,45%

Corfu Ioannis Kapodistrias Airport 2 106 827 5,46%

Cephalonia Cephalonia Airport 430 362 1,11%

Ikaria Ikaria Airport 36 162 0,09%

Heraklion Heraklion International Airport 5 778 764 14,97%

Kalamata Kalamata International Airport 136 992 0,35%

Karpathos Karpathos Island National Airport 168 190 0,44%

Kithira Kithira Airport 33 183 0,09%

Kalymnos Kalymnos Airport 20 677 0,05%

Kavala Kavala International Airport 209 400 0,54%

Kastellorizo Kastellorizo Airport 7 946 0,02%

Kozani Kozani Airport 3 504 0,01%

Kasos Kasos Airport 3 265 0,01%

Kastoria Kastoria Airport 5 115 0,01%

Kos Kos Island International Airport 2 028 618 5,25%

Milos Milos Airport 30 744 0,08%


95

Mykonos Mykonos Island National Airport 584 559 1,51%

Mytilene Mytilene International Airport 400 911 1,04%

Naxos Naxos Airport 23 442 0,06%

Lemnos Lemnos Airport 81 201 0,21%

Leros Leros Airport 25 680 0,07%

Ionnina Ioannina Airport 64 489 0,17%

Paros Paros Airport 36 429 0,09%

Preveza Aktion National Airport 316 435 0,82%

Rhodes Rhodes International Airport 4 200 059 10,88%

Sitia Sitia Airport 35 962 0,09%

Samos Samos International Airport 343 717 0,89%

Santorini Santorini National Airport 898 153 2,33%

Skiathos Skiathos Island National Airport 265 773 0,69%

Syros Syros Airport 13 715 0,04%

Skyros Skyros Island National Airport 20 368 0,05%

Thessaloniki Thessaloniki International Airport 4 039 576 10,46%

Volos Nea Anchialos National Airport 70 079 0,18%

Zakynthos Zakynthos International Airport 1 004 486 2,60%

Hungary[13]

Debrecen Debrecen International Airport 129 231

8 726 209

1,48%

Gyor-Per Gyor Pér Airport 31 274 0,36%

Héviz-Balaton Héviz-Balaton Airport 25 015 0,29%

Nyíregyháza Nyíregyháza Airport 15 863 0,18%

Pécs-pogány Pécs-Pogány Airport 3 946 0,05%

Budapest Budapest Ferenc Liszt International Airport

8 520 880 97,65%

Ireland[14]

Cork Cork Airport 2 425 131

23 960 055

10,12%

Galway Galway Airport 160 000 0,67%

Donegal Donegal Airport 48 000 0,20%

Kerry Kerry Airport 424 599 1,77%

Knock Ireland West Airport Knock 589 193 2,46%

Sligo Sligo Airport 21 500 0,09%

Waterford Waterford Airport 104 000 0,43%

Shannon Shannon Airport 1 756 007 7,33%

Dublin Dublin Airport 18 431 625 76,93%

Italy[15]

Rome Leonardo da Vinci - Fiumicino Airport 36 166 345

146 500 760

24,69%

Rome Ciampino - G. B. Pastine International Airport

4 749 251 3,24%

Alghero Ferilia Airport 1 518 870 1,04%

Ancona Ancona Airport 564 576 0,39%

Palese Palese Airport 3 599 910 2,46%

Bergamo Orio al Serio Airport 8 890 720 6,07%

Bologna Bologna Airport 6 193 783 4,23%

Brescia Brescia Airport 22 669 0,02%

Brindisi Brindisi Airport 2 101 045 1,43%

Cagliari Cagliari Airport 3 592 020 2,45%

Elmas Elmas Airport 3 592 020 2,45%


96

Catania Fontanarossa Airport 6 400 127 4,37%

Cuneo Cueno Levaldigi Airport 236 113 0,16%

Florence Peretola Airport 1 983 268 1,35%

Genoa Cristoforo Colombo Airport 1 303 571 0,89%

Lamezia Lamezia Terme Airport 2 184 102 1,49%

Milan Malpensa Airport 18 537 301 12,65%

Milan Linate Airport 9 229 890 6,30%

Naples Capodichino Airport 5 801 836 3,96%

Olbia Costa Smeralda Airport 1 887 640 1,29%

Palermo Palermo Airport 4 349 672 2,97%

Parma Parma Airport 177 807 0,12%

Perugia San Egidio Airport 201 926 0,14%

Pescara Abruzzo Airport 548 217 0,37%

Pisa Galileo Galilei Airport 4 494 915 3,07%

Rimini Federico Fellini Airport 795 872 0,54%

Trapani Vicenzo Florio Airport 1 878 557 1,28%

Trieste Friuli Venezia Giulia Airport 853 285 0,58%

Turin Caselle Airport 3 521 847 2,40%

Venice Venice Marco Polo Airport 8 403 790 5,74%

Verona Verona Airport 2 719 815 1,86%

Latvia[16] Riga Riga International Airport 4 814 073 4 814 073 100,00%

Lithuania[17]

Kaunas Kaunas Airport 830 268

3 621 490

22,93%

Palanga Palanga International Airport 128 169 3,54%

Siauliai Siauliai International Airport 1 184 0,03%

Vilnius Vilnius Airport 2 661 869 73,50%

Luxembourg[18] Luxembourg Luxembourg Findel Airport 2 197 331 2 197 331 100,00%

Malta[19] Valletta Malta International Airport 4 052 000 4 052 000 100,00%

Netherlands [20]

Eindhoven Eindhoven Airport 3 425 485

58 079 961

5,90%

Groningen Groningen Airport Eeelde 208 660 0,36%

Maastricht Maastricht-Aachen Airport 429 545 0,74%

Rotterdam Rotterdam The Hague Airport 1 488 572 2,56%

Amsterdam Amsterdam Airport Schiphol 52 527 699 90,44%

Poland[21]

Warsaw Warsaw Chopin Airport 10 683 000

25 171 000

42,44%

Warsaw Warsaw - Modlin Mazovia Airport 344 000 1,37%

Warsaw Wroclaw - Copernicus Airport 1 920 000 7,63%

Bydgoszcz Bydgoszcz Ignacy Jan Paderewski Airport 343 000 1,36%

Gdansk Gdansk Lech Walesa Airport 2 842 000 11,29%

Katowice Katowice International Airport 2 544 000 10,11%

Krakow John Paul II Internation Airport Krakow-Balice

3 647 000 14,49%

Lodz Lodz Wladyslaw Reymont Airport 353 000 1,40%

Poznan Poznan Lawica Airport 1 355 000 5,38%

Lublin Lublin Airport 189 000 0,75%

Zielona Góra Zielona Góra-Babimost Airport 13 000 0,05%

Rzeszow Rzeszow-Jasionka Airport 589 000 2,34%

Szczecin Szczecin-Goleniow Solidarnosc Airport 349 000 1,39%


97

Portugal[22]

Faro Faro Airport 5 978 685

32 053 949

18,65%

Funchal Madeira Airport 2 311 380 7,21%

Porto Santo Porto Santo Airport 106 592 0,33%

Porto Francisco Sá Carneiro Airport 6 370 749 19,88%

Ponta Delgada João Paulo II Airport 928 812 2,90%

Santa Maria Santa Maria Airport 93 448 0,29%

Flores Flores Airport 45 122 0,14%

Beja Beja International Airport 2 237 0,01%

Horta Horta Airport 191 969 0,60%

Lisbon Lisbon Portela Airport 16 024 955 49,99%

Romania[23]

Cluj-Napoca Cluj-Napoca International Airport 1 035 438

10 781 863

9,60%

Constanta Mihail Kogalniceanu International Airport 73 301 0,68%

Bacau George Enescu International Airport 307 488 2,85%

Iasi Iasi International Airport 231 933 2,15%

Sibiu Sibiu International Airport 189 152 1,75%

Targu Mures Transilvania Airport 363 389 3,37%

Craiova Craiova International Airport 40 291 0,37%

Arad Arad International Airport 39 901 0,37%

Oradea Oradea Airport 39 440 0,37%

Suceava Stefan cel Mare Airport 20 054 0,19%

Baia Mare Baia Mare Airport 16 798 0,16%

Satu Mare Satu Mare International Airport 16 192 0,15%

Bucharest Aurel Vlaicu International Airport 6 036 0,06%

Tulcea Danube Delta Tulcea Airport 1 887 0,02%

Timisoara Traian Vuia International Airport 757 096 7,02%

Bucharest Henri Coanda International Airport 7 643 467 70,89%

Slovakia[24]

Kosice Kosice International Airport 237 165

1 635 058

14,50%

Poprad Poprad-Tatry Airport 24 815 1,52%

Bratislava Bratislava Airport 1 373 078 83,98%

Slovenia[25]

Portoroz Portoroz Airport 21 263

1 357 363

1,57%

Maribor Maribor Edvard Rusjan Airport 15 000 1,11%

Ljubljana Ljubljana Joze Pucnik Airport 1 321 100 97,33%

Spain[26]

A Coruna Alvedro Airport 839 837

186 688 195

0,45%

Alicante Alicante Airport 9 638 860 5,16%

Almeria Almeria International Airport 705 552 0,38%

Asturias Asturias Airport 1 039 409 0,56%

Barcelona Barcelona Airport 35 210 735 18,86%

Bilbao Bilbao Airport 3 800 789 2,04%

Fuerteventura Fuerteventura Airport 4 259 341 2,28%

Girona Girona-Costa Brava Airport 2 736 867 1,47%

Gran Canaria Gran Canaria Airport 9 770 253 5,23%

Granada Granada Airport 636 289 0,34%

Ibiza Ibiza Airport 5 726 581 3,07%

Jerez de la Frontera

Jerez Airport 811 504 0,43%


98

Santa Cruz de la Palma

La Palma Airport 809 521 0,43%

Lanzarote Lanzarote Airport 5 334 598 2,86%

Lleida Alguaire Airport 61 679 0,03%

Melilla Melilla Airport 260 271 0,14%

Málaga Málaga Airport 12 922 403 6,92%

Minorca Menorca Airport 2 565 466 1,37%

Murcia Murcia-San Javier Airport 1 140 447 0,61%

Palma de Mallorca

Palma de Mallorca Airport 22 768 082 12,20%

Reus Reus Airport 971 166 0,52%

Santander Santander Airport 974 043 0,52%

Santiago de Compostela

Santiago de Compostela Airport 2 073 055 1,11%

Seville Seville Airport 3 687 727 1,98%

Tenerife Tenerfe North Airport 3 516 445 1,88%

Tenerife Tenerife South Airport 8 701 983 4,66%

Valencia Valencia Airport 4 599 990 2,46%

Vigo Vigo Airport 678 720 0,36%

Valladolid Valladolid Airport 260 271 0,14%

Zaragoza Zaragoza Airport 457 284 0,24%

Madrid Adolfo Suarez Madrid - Barajas Airport 39 729 027 21,28%

Sweden[27]

Stockholm Stockholm Arlanda Airport 20 681 554

34 519 764

59,91%

Stockholm Stockholm Skavsta Airport 2 279 501 6,60%

Stockholm Stockholm Vasteras Airport 174 496 0,51%

Are Are Ostersund Airport 408 700 1,18%

Lulea Lulea Airport 1 106 638 3,21%

Gothenburg Gothenburg-Landvetter Airport 5 004 093 14,50%

Gothenburg Gothenburg City Airport 807 763 2,34%

Kiruna Kiruna Airport 226 282 0,66%

Ronneby Ronneby Airport 213 418 0,62%

Umea Umea Airport 989 094 2,87%

Vaxjo Vaxjo Airport 148 442 0,43%

Malmo Malmo Airport 2 127 586 6,16%

Visby Visby Airport 352 197 1,02%

United Kingdom[28]

London Lodon City Airport 3 379 753

231 469 055

1,46%

London Gatwick Airport 35 444 206 15,31%

London London Heathrow Airport 72 367 054 31,26%

London Luton Airport 9 697 944 4,19%

London London Southend Airport 969 912 0,42%

London London Stansted Airport 17 852 393 7,71%

Aberdeen Aberdeen Airport 3 440 765 1,49%

Alderney Alderney Airport 62 855 0,03%

Belfast Belfast International Airport 4 023 336 1,74%

Belfast George Best Belfast City Airport 2 541 759 1,10%

Birmingham Birmingham International Airport 9 120 201 3,94%

Blackpool Blackpool Airport 262 630 0,11%


99

Bournemouth Bournemouth Airport 660 272 0,29%

Bristol Bristol Airport 6 131 896 2,65%

Cardiff Cardiff Airport 1 072 062 0,46%

City of Derry City of Derry Airport 384 973 0,17%

Doncaster Robun Hood Airport Doncaster Sheffield 690 351 0,30%

Durham Durham Tees Valley Airport 161 092 0,07%

Edinburgh Edinburgh Airport 9 775 443 4,22%

West Bridgford East Midlands Airport 4 334 117 1,87%

Exeter Exeter International Airport 741 465 0,32%

Glasgow Glasgow International Airport 7 363 764 3,18%

Glasgow Glasgow Prestwick Airport 1 145 836 0,50%

Guernsey Guernesey Airport 886 396 0,38%

Humberside Humberside Airport 236 083 0,10%

Inverness Inverness Airport 608 184 0,26%

Isle of Man Isle of Man Airport 739 683 0,32%

St. Marys Isles of Scilly Airport 89 170 0,04%

Jersey Jersey Airport 1 453 863 0,63%

Kirkwall Kirkwall Airport 159 325 0,07%

Leeds Leeds Bradford International Airport 3 318 358 1,43%

Liverpool Liverpool John Lennon Airport 4 187 493 1,81%

Manchester Manchester Airport 20 751 581 8,97%

Newcastle upon Tyne

Newcastle Airport 4 420 839 1,91%

Newquay Newquay Cornwall Airport 174 891 0,08%

Norwich Norwich International Airport 463 401 0,20%

Scatsta Scatsta Airport 298 308 0,13%

Stornoway Stornoway Airport 122 410 0,05%

Sumburgh Sumburgh Airport 212 233 0,09%

Southampton Southampton Airport 1 722 758 0,74% [1] Statistics Austria, Civil Aviation Statistics, 2013 [2] Belgium AIP, 2013 [3] Bulgarian AIP, 2013 [4] Croatian Bureau of Statistics, Traffic in Airports, 2013 [5] Department of Civil Aviation, Republic of Cyprus, 2011 [6] Czech AIP, 2012 [7] Statistics Denmark, 2013 [8] Tallinn Airport Statistics, 2013 [9] Finland AIP, 2011 [10] Résultats d’activité des aéroports Français 2013, Statistiques de traffic, Union des Aéroports Français [11] German Airport Statistics, 2013 [12] Hellenic Republic, Ministry of Infrastructure, Transport and Network, 2013 [13] Hungarian Central Statistical Office, 2013 [14] Ireland Statistics, 2010

[15] Assaeroporti, 2012 [16] Latvian AIP, 2014 [17] AZWorldAirports, 2012 and Lithuanian AIP, 2014 (Vilnius Airport) [18] Belgian AIP, 2013 [19] Maltese AIP, 2013 [20] Statistics Netherlands – Aviation, 2013 [21] Warsaw: Civil Aviation Office, 2013 [22] ANA Relatório de Contas, 2013 [23] Website of each airport (2013) [24] Slovakia AIP, 2013 and Poprad-Tatry Airport, 2013 [25] Slovenian AIP, 2013 and AZWorldAirports, 2011 (Portoroz Airport) [26] AENA, 2013 [27] Swedavia, Swedish Airports, 2013 [28] CAA Statistics, 2013


100


101

Annex 3 – Airport Destinations in the EU

AT

BE

BG

HR

CY

CZ

DK

EE

FI

FR

DE

EL

HU

IE

IT

LV

LT

AT Vienna International Airport 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1

BE Brussels and Charleroi Airports 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1

BG Sofia Airport and Burgas Airport 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0

HR Dubrovnik, Split and Zagreb Airports 1 1 0 0 1 1 0 1 1 1 1 1 1 1 0 0

CY Larnaca and Paphos International Airports 1 1 1 0 0 0 0 0 1 1 1 1 0 1 1 1

CZ Václav Havel Airport Prague 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

DK Copenhagen Airport 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1

EE Tallinn Airport 0 1 0 0 0 0 1 1 1 1 0 0 0 1 1 1

FI Helsinki Airport 1 1 0 0 0 1 1 1 1 1 0 1 0 1 1 1

FR Charles de Gaulle Airport 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

DE Frankfurt Airport 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

EL Athens International Airport 1 1 1 0 1 1 1 0 0 1 1 1 0 1 0 0

HU Budapest Ferenc Liszt International Airport 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 0

IE Dublin Airport 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

IT Leonardo da Vinci - Fiumicino Airport 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

LV Riga International Airport 1 1 0 0 0 1 1 1 1 1 1 0 1 1 1 1

LT Kaunas and Vilnius Airports 1 1 0 0 1 1 1 1 1 1 1 1 0 1 1 1

LU Luxembourg Findel Airport 1 0 1 1 0 1 0 0 1 1 1 1 1 1 1 0 0

MT Malta International Airport 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0

NL Amsterdam Airport Schiphol 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

PL Warsaw Chopin Airport 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1

PT Lisbon Portela Airport 1 1 0 1 0 1 1 1 1 1 1 0 1 1 1 0 0

RO Henri Coanda International Airport 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1 1 0

SK Bratislava Airport 1 1 1 0 0 1 0 0 0 1 0 1 0 1 1 0 0

SI Ljubljana Joze Pucnik Airport 1 1 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0

ES Adolfo Suarez Madrid - Barajas Airport 1 1 1 0 0 1 1 0 1 1 1 1 1 1 1 0 0

SE Stockholm Arlanda Airport 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1

UK London Heathrow Airport 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0

Percentage of connections to each country main airport 9

6,3

0

96

,30

62

,96

59

,26

55

,56

92

,59

85

,19

51

,85

81

,48

10

0,0

0

96

,30

81

,48

85

,19

81

,48

96

,30

70

,37

55

,56


102

LU

MT

NL

PL

PT

RO

SK

SI

ES

SE

UK


one of the other countries

AT Vienna International Airport 1 0 1 1 1 1 0 1 1 1 1 85,19

BE Brussels and Charleroi Airports 0 1 1 1 1 1 1 1 1 1 1 92,59

BG Sofia Airport and Burgas Airport 1 0 1 1 0 1 1 0 1 0 1 74,07

HR Dubrovnik, Split and Zagreb Airports 0 0 1 1 1 0 1 0 1 1 1 66,67

CY Larnaca and Paphos International Airports 0 1 1 1 0 1 0 0 0 1 1 59,26

CZ Václav Havel Airport Prague 0 1 1 1 1 1 1 1 1 1 1 96,30

DK Copenhagen Airport 1 0 1 1 1 1 0 0 1 1 1 81,48

EE Tallinn Airport 0 0 1 1 0 0 0 0 0 1 1 44,44

FI Helsinki Airport 0 0 1 1 1 0 0 0 1 1 1 62,96

FR Charles de Gaulle Airport 1 0 1 1 1 1 0 1 1 1 1 92,59

DE Frankfurt Airport 1 1 1 1 1 1 0 1 1 1 1 96,30

EL Athens International Airport 0 1 1 1 0 1 0 0 1 1 1 62,96

HU Budapest Ferenc Liszt International Airport 0 1 1 1 1 1 0 0 1 1 1 77,78

IE Dublin Airport 1 1 1 1 1 1 1 1 1 1 1 100,00

IT Leonardo da Vinci - Fiumicino Airport 1 1 1 1 1 1 0 0 1 1 1 92,59

LV Riga International Airport 0 0 1 1 0 1 1 0 1 1 1 70,37

LT Kaunas and Vilnius Airports 0 1 1 1 0 0 0 0 1 1 1 70,37

LU Luxembourg Findel Airport 1 1 1 1 1 0 0 1 1 1 70,37

MT Malta International Airport 1 1 1 0 1 1 1 1 1 1 88,89

NL Amsterdam Airport Schiphol 1 1 1 1 1 0 1 1 1 1 96,30

PL Warsaw Chopin Airport 0 0 1 1 1 1 1 1 1 1 88,89

PT Lisbon Portela Airport 1 0 1 1 1 0 0 1 1 1 70,37

RO Henri Coanda International Airport 0 0 1 1 1 0 0 1 1 1 70,37

SK Bratislava Airport 0 0 0 0 0 0 0 1 0 1 37,04

SI Ljubljana Joze Pucnik Airport 0 0 1 0 0 0 0 0 0 1 29,63

ES Adolfo Suarez Madrid - Barajas Airport 1 1 1 1 1 1 1 0 1 1 77,78

SE Stockholm Arlanda Airport 1 1 1 1 1 1 0 0 1 1 88,89

UK London Heathrow Airport 1 1 1 1 1 1 0 0 1 1 81,48

Percentage of connections to each country main airport 4

8,1

5

51

,85

96

,30

92

,59

66

,67

77

,78

33

,33

33

,33

88

,89

88

,89

10

0,0

0


103

Annex 4 – Distance between European Capitals

AT

BE

BG

HR

CY

CZ

DK

EE

FI

FR

DE

EL

HU

AT 913 817 268 2012 253 870 1362 1439 1034 524 1283 216

BE 913 1696 1022 2898 715 765 1599 1647 263 649 2087 1128

BG 817 1696 680 1203 1066 1636 1862 1944 1758 1318 527 628

HR 268 1022 680 1878 490 1123 1623 1702 1080 769 1080 300

CY 2012 2898 1203 1878 2255 2773 2768 2843 2949 2488 914 1809

CZ 253 715 1066 490 2255 634 1229 1301 882 280 1535 446

DK 870 765 1636 1123 2773 634 836 882 1026 355 2136 1014

EE 1362 1599 1862 1623 2768 1229 836 82 1857 1041 2387 1380

FI 1439 1647 1944 1702 2843 1301 882 82 1907 1105 2468 1461

FR 1034 263 1758 1080 2949 882 1026 1857 1907 877 2097 1246

DE 524 649 1318 769 2488 280 355 1041 1105 877 1803 691

EL 1283 2087 527 1080 914 1535 2136 2387 2468 2097 1803 1122

HU 216 1128 628 300 1809 446 1014 1380 1461 1246 691 1122

IE 1681 776 2472 1797 3674 1463 1238 2002 2022 778 1315 2853 1895

IT 763 1171 894 515 1954 921 1531 2123 2199 1107 1182 1050 806

LV 1102 1453 1584 1359 2518 994 724 279 361 1702 844 2109 1107

LT 947 1464 1339 1186 2256 895 813 529 610 1696 819 1861 911

LU 763 186 1525 847 2725 596 802 1613 1669 288 601 1905 978

MT 1375 1846 1070 1107 1704 1576 2202 2718 2798 1748 1848 850 1340

NL 936 175 1743 1085 2945 709 620 1456 1500 429 576 2164 1147

PL 556 1158 1073 803 2133 517 671 833 914 1366 517 1599 547

PT 2296 1711 2753 2198 3760 2241 2476 3309 3358 1452 2310 2849 2468

RO 855 1768 294 808 1200 1080 1573 1669 1750 1870 1294 746 640

SK 55 966 775 273 1966 292 893 1350 1429 1089 553 1250 163

SI 277 916 793 116 1989 448 1078 1635 1711 965 723 1175 380

ES 1807 1317 2251 1698 3278 1771 2073 2893 2947 1055 1868 2365 1972

SE 1244 1281 1885 1512 2908 1056 524 378 395 1544 813 2410 1321

UK 1217 305 1997 1321 3198 1014 940 1768 1806 333 913 2375 1433


104

IE

IT

LV

LT

LU

MT

NL

PL

PT

RO

SK

SI

ES

SE

UK

AT 1681 763 1102 947 763 1375 936 556 2296 855 55 277 1807 1244 1217

BE 776 1171 1453 1464 186 1846 175 1158 1711 1768 966 916 1317 1281 305

BG 2472 894 1584 1339 1525 1070 1743 1073 2753 294 775 793 2251 1885 1997

HR 1797 515 1359 1186 847 1107 1085 803 2198 808 273 116 1698 1512 1321

CY 3674 1954 2518 2256 2725 1704 2945 2133 3760 1200 1966 1989 3278 2908 3198

CZ 1463 921 994 895 596 1576 709 517 2241 1080 292 448 1771 1056 1014

DK 1238 1531 724 813 802 2202 620 671 2476 1573 893 1078 2073 524 940

EE 2002 2123 279 529 1613 2718 1456 833 3309 1669 1350 1635 2893 378 1768

FI 2022 2199 361 610 1669 2798 1500 914 3358 1750 1429 1711 2947 395 1806

FR 778 1107 1702 1696 288 1748 429 1366 1452 1870 1089 965 1055 1544 333

DE 1315 1182 844 819 601 1848 576 517 2310 1294 553 723 1868 813 913

EL 2853 1050 2109 1861 1905 850 2164 1599 2849 746 1250 1175 2365 2410 2375

HU 1895 806 1107 911 978 1340 1147 547 2468 640 163 380 1972 1321 1433

IE 1885 1952 2047 952 2521 755 1825 1640 2536 1732 1690 1454 1628 478

IT 1885 1865 1700 987 689 1296 1314 1862 1136 781 488 1361 1977 1420

LV 1952 1865 262 1440 2446 1330 561 3149 1398 1087 1378 2712 443 1660

LT 2047 1700 262 1417 2245 1366 393 3118 1140 921 1223 2660 677 1705

LU 952 987 1440 1417 1666 320 1080 1710 1612 817 738 1279 1325 474

MT 2521 689 2446 2245 1666 1980 1886 2106 1365 1376 1128 1660 2619 2075

NL 755 1296 1330 1366 320 1980 1093 1862 1787 985 989 1483 1126 340

PL 1825 1314 561 393 1080 1886 1093 2757 944 533 833 2288 812 1430

PT 1640 1862 3149 3118 1710 2106 1862 2757 2973 2346 2094 504 2988 1593

RO 2536 1136 1398 1140 1612 1365 1787 944 2973 803 924 2469 1744 2072

SK 1732 781 1087 921 817 1376 985 533 2346 803 303 1855 1248 1270

SI 1690 488 1378 1223 738 1128 989 833 2094 924 303 1596 1497 1212

ES 1454 1361 2712 2660 1279 1660 1483 2288 504 2469 1855 1596 2595 1270

SE 1628 1977 443 677 1325 2619 1126 812 2988 1744 1248 1497 2595 1420

UK 478 1420 1660 1705 474 2075 340 1430 1593 2072 1270 1212 1270 1420


105

Annex 5 – GDP of each European Country

Country GDP per capita

% GDP per capita comparing with

EU average

GDP (PPS-million €)

% EU GDP

AT 34 231,3 32,43% 288 569,7 2,15%

BE 31 461,7 21,71% 350 113,0 2,61%

BG 12 426,6 -51,93% 90 787,2 0,68%

HR 15 743,8 -39,09% 67 211,1 0,50%

CY 25 580,8 -1,04% 22 100,4 0,16%

CZ 21 750,0 -15,86% 228 609,9 1,70%

DK 32 784,3 26,83% 183 315,5 1,37%

EE 18 684,7 -27,72% 24 714,2 0,18%

FI 30 430,8 17,72% 164 751,3 1,23%

FR 28 533,1 10,38% 1 867 017,0 13,91%

DE 33 065,9 27,92% 2 659 349,2 19,82%

EL 19 598,9 -24,18% 217 406,0 1,62%

HU 17 299,2 -33,08% 171 614,7 1,28%

IE 34 683,0 34,17% 159 087,5 1,19%

IT 27 245,3 5,40% 1 622 180,4 12,09%

LV 16 346,6 -36,76% 33 254,3 0,25%

LT 18 495,3 -28,45% 55 259,7 0,41%

LU 68 610,2 165,42% 36 428,3 0,27%

MT 22 941,9 -11,25% 9 623,1 0,07%

NL 34 784,1 34,56% 582 806,3 4,34%

PL 17 327,8 -32,97% 667 741,3 4,98%

PT 20 029,6 -22,51% 210 607,8 1,57%

RO 13 787,8 -46,66% 276 556,4 2,06%

SK 19 700,7 -23,79% 106 532,9 0,79%

SI 21 797,8 -15,67% 44 841,5 0,33%

ES 24 735,5 -4,31% 1 156 953,7 8,62%

SE 33 411,1 29,25% 318 052,7 2,37%

UK 28 293,3 9,45% 1 802 292,5 13,43%

Average 25 849,3 Sum 13 417 777,6 100,00%


106


107

Annex 6 – Airports to consider in the case study “Brazil”

State City Airport Passengers Country

Passengers %

AC

Cruzeiro do Sul

Cruzeiro do Sul International Airport 70 216 448 346

15,66%

Rio Branco Plácido de Castro - Rio Branco International Airport

378 130 84,34%

AL Maceió Zumbi dos Palmares International Airport 1 893 488 1 893 488 100,00%

AP Macapá Macapá International Airport 624 716 624 716 100,00%

AM

Manaus Eduardo Gomes International Airport 3 077 077

3 204 892

96,01%

Tabatinga Tabatinga International Airport 60 678 1,89%

Tefé Tefé International Airport 67 137 2,09%

BA

Ilhéus Jorge Amado Airport 502 390

9 092 948

5,53%

Paulo Afonso Paulo Afonso Airport 895 0,01%

Salvador Dep. Luís Eduardo Magalhães International Airport

8 589 663 94,47%

CE Fortaleza Pinto Martins International Airport 5 952 535

6 340 525 93,88%

Juazeiro do Norte

Cariri Regional Airport 387 990 6,12%

DF Brasília Brasília International Airport 16 610 000 16 610 000 100,00%

ES Vitória Eurico de Aguiar Sallles Airport 3 450 695 3 450 695 100,00%

GO Goiânia Saint Genoveva Airport 3 000 592 3 000 592 100,00%

MA Imperatriz Imperatriz Airport 338 283

2 078 939 16,27%

São Luis Marechal Cunha Machado International Airport

1 740 656 83,73%

MT Cuiabá Marechal Rondon International Airport 2 995 676 2 995 676 100,00%

MS

CampoGrande Campo Grande International Airport 1 496 288

1 591 157

94,04%

Corumbá Corumbá International Airport 31 231 1,96%

Dourados Dourados Regional Airport 63 638 4,00%

MG

Belo Horizonte Carlos Prates - Belo Horizonte Airport 35 921

12 707 484

0,28%

Confins Tancredo Neves International Airport 10 002 477 78,71%

Montes Claros Montes Claros Airport 326 702 2,57%

Pampulha Pampulha Airport 989 332 7,79%

Uberlândia Uberlândia Airport 1 205 687 9,49%

Ponta Porã Ponta Porã International Airport 2 210 0,02%

Uberaba Uberaba Airport 145 155 1,14%

PA

Altamira Altamira Airport 206 506

4 791 817

4,31%

Belém Val de Cans International Airport 3 473 945 72,50%

Parauapebas Carajás Airport 124 663 2,60%

Belém Brigadeiro Protásio Airport 29 009 0,61%

Marabá João Correa da Rocha - Marabá Airport 458 106 9,56%

Santarém Santarém International Airport 499 588 10,43%

PB

Campina Grande

Campina Grande Airport 143 766 1 354 636

10,61%

João Pessoa Presidente Castro Pinto International Airport 1 210 870 89,39%


108

PR São José dos Pinhais

Afonso Pena International Airport 6 740 024 10 264 276 65,66%

PE Recife Guararapes International Airport 6 817 790 7 397 924 92,16%

PI Teresinha Senador Petrônio Portella Airport 1 091 173

1 092 254 99,90%

Parnaíba Perfeito Dr. João Silva Filho - Parnaíba International Airport

1 081 0,10%

RJ

Rio de Janeiro Galeão International Airport 17 109 590

26 922 394

63,55%

Rio de Janeiro Santos Dumont Airport 9 102 187 33,81%

Macaé Macaé Airport 433 299 1,61%

Rio de Janeiro Jacarepaguá Airport 145 155 0,54%

Campos dos Goytacazes

Campos dos Goytacazes Airport 132 163 0,49%

RN Natal Augusto Severo International Airport 2 375 771 2 375 771 100,00%

RS

Pelotas Pelotas International Airport 37 754

8 033 158

0,47%

Bagé Bagé International Airport 2 001 0,02%

Uruguaiana Rubem Berta - Uruguaiana International Airport

239 0,00%

Porto Alegre Salgado Filho International Airport 7 993 164 99,50%

RO Porto Velho Governador Jorge Teixeira International Airport

905 103 905 103 100,00%

RR Boa Vista Boa Vista International Airport 350 195 350 195 100,00%

SC

Joinville Joinville Airport 512 742

5 657 828

9,06%

Criciúma Criciúma Airport 69 824 1,23%

Florianópolis Hercílio Luz International Airport 3 872 637 68,45%

Navegantes Navegantes International Airport 1 202 625 21,26%

SP

Campinas Viracopos International Airport 9 294 446

66 200 985

14,04%

Ribeirão Preto Rbeirão Preto Airport 1 096 285 1,66%

São José do Rio Preto

São José do Rio Preto Airport 758 513 1,15%

São Paulo Campo de Marte Airport 303 392 0,46%

São Paulo Congonhas Airport 17 119 530 25,86%

São Paulo Guarulhos International Airport 36 678 452 55,40%

Presidente Prudente

Presidente Prudente Airport 266 123 0,40%

Sorocaba Sorocaba Airport 50 244 0,08%

Araçatuba Araçatuba Airport 164 981 0,25%

Bragança Paulista

Bragança Paulista Airport 37 510 0,06%

São José dos Campos

São José dos Campos Airport 150 958 0,23%

Marília Marília Airport 75 747 0,11%

Jundiaí Jundiaí Airport 16 605 0,03%

Itanhaém Itanhaém Airport 12 897 0,02%

Assis Assis Airport 6 408 0,01%

Ubatuba Ubatuba Airport 5 422 0,01%

Bauru Bauru-Arealva Airport 138 424 0,21%

Araraquara Araraquara Airport 25 048 0,04%


109

SE Aracaju Santa Maria International Airport 1 343 899 1 343 899 100,00%

TO Palmas Palmas Airport 778 245 778 245 100,00%


110


111

Annex 7 – Airport Destinations in Brazil

AC

AL

AP

AM

BA

CE

DF

ES

GO

MA

MT

MS

MG

PA

AC Plácido de Castro - Rio Branco International Airport

0 0 1 0 1 1 0 0 0 0 0 1 1

AL Zumbi dos Palmares International Airport 0 0 0 1 1 1 0 0 0 0 0 1 0

AP Macapá International Airport 0 0 0 1 1 1 0 1 0 0 0 0 1

AM Eduardo Gomes International Airport 1 0 0 1 1 1 1 0 1 1 1 0 1

BA Dep. Luís Eduardo Magalhães International Airport

0 1 0 1 1 1 1 0 1 0 0 1 1

CE Pinto Martins International Airport 1 1 1 1 1 1 0 1 1 1 0 1 1

DF Brasília International Airport 1 1 1 1 1 1 1 1 1 1 1 1 1

ES Eurico de Aguiar Sallles Airport 0 0 0 0 1 0 1 1 0 0 0 1 0

GO Saint Genoveva Airport 0 0 0 0 0 1 1 1 0 1 1 1 0

MA Marechal Cunha Machado International Airport

0 0 0 1 1 1 1 0 0 0 0 1 1

MT Marechal Rondon International Airport 0 0 0 1 0 0 1 0 1 0 1 1 0

MS Campo Grande International Airport 0 0 0 1 0 0 1 0 0 0 1 1 0

MG Tancredo Neves International Airport 0 1 0 1 1 1 1 1 1 1 1 1 1

PA Val de Cans International Airport 1 0 1 1 1 1 1 0 1 1 0 0 1

PB Presidente Castro Pinto International Airport

0 0 0 0 1 1 1 0 0 0 0 0 0 0

PR Afonso Pena International Airport 0 1 0 1 1 1 1 0 1 0 1 1 1 0

PE Guararapes International Airport 0 1 1 1 1 1 1 0 1 1 0 0 1 1

PI Senador Petrônio Portella Airport 0 0 0 0 0 1 1 0 0 1 0 0 0 1

RJ Galeão and Santos Dumont Airports 0 1 1 1 1 1 1 1 1 1 1 1 1 1

RN Augusto Severo International Airport 0 1 0 1 1 1 1 0 1 0 0 0 1 1

RS Salgado Filho International Airport 0 1 0 1 1 0 1 0 1 0 0 1 1 0

RO Governador Jorge Teixeira International Airport

1 0 0 1 0 1 1 0 0 0 1 1 1 1

RR Boa Vista International Airport 0 0 0 1 0 0 1 0 0 0 0 0 1 0

SC Hercílio Luz and Navegantes International Airports

0 0 0 0 1 0 1 0 1 0 0 0 0 0

SP Congonhas and Guarulhos Airports 0 1 0 1 1 1 1 1 1 1 1 1 1 1

SE Santa Maria International Airport 0 1 0 1 1 1 1 0 0 1 0 0 1 1

TO Palmas Airport 0 0 1 0 0 1 1 0 1 1 1 0 1 1


19

,23%

42

,31%

23

,08%

69

,23%

69

,23%

76

,92%

10

0,0

0%

26

,92%

57

,69%

46

,15%

42

,31%

38

,46%

80

,77%

61

,54%


112

PB

PR

PE

PI

RJ

RN

RS

RO

RR

SC

SP

SE

TO

Percentage of connections

from the main airport to each

one of the other states

AC Plácido de Castro - Rio Branco International Airport

0 1 0 0 0 0 0 1 0 0 1 0 0 30,77%

AL Zumbi dos Palmares International Airport 0 0 1 1 1 1 1 0 0 0 1 1 0 42,31%

AP Macapá International Airport 0 1 1 0 1 0 0 0 0 0 1 0 1 38,46%

AM Eduardo Gomes International Airport 0 1 1 0 1 1 1 1 1 0 1 1 0 69,23%

BA Dep. Luís Eduardo Magalhães International Airport

1 1 1 1 1 1 1 0 0 1 1 1 0 69,23%

CE Pinto Martins International Airport 1 0 1 0 1 1 1 1 0 0 1 0 1 73,08%

DF Brasília International Airport 1 1 1 1 1 1 1 1 1 1 1 1 1 100,00%

ES Eurico de Aguiar Sallles Airport 0 0 0 0 1 0 1 0 0 0 1 0 0 26,92%

GO Saint Genoveva Airport 0 0 1 0 1 0 1 0 0 1 1 0 1 46,15%

MA Marechal Cunha Machado International Airport

0 0 1 1 1 1 0 0 0 0 1 1 0 46,15%

MT Marechal Rondon International Airport 0 1 0 0 1 0 0 1 0 0 1 0 0 34,62%

MS Campo Grande International Airport 0 1 0 0 1 0 1 1 0 0 1 0 0 34,62%

MG Tancredo Neves International Airport 0 1 1 0 1 1 1 0 0 0 1 0 1 69,23%

PA Val de Cans International Airport 0 1 1 0 1 1 1 1 0 0 1 0 1 65,38%

PB Presidente Castro Pinto International Airport 0 0 0 1 0 0 0 0 0 1 0 0 19,23%

PR Afonso Pena International Airport 1 1 0 1 1 1 1 0 1 1 0 0 65,38%

PE Guararapes International Airport 1 1 1 1 1 1 0 0 0 1 1 0 69,23%

PI Senador Petrônio Portella Airport 0 0 1 0 0 0 0 0 0 1 0 0 23,08%

RJ Galeão and Santos Dumont Airports 1 1 1 0 1 1 1 0 1 1 1 0 84,62%

RN Augusto Severo International Airport 0 1 1 0 1 0 0 0 0 1 1 0 50,00%

RS Salgado Filho International Airport 0 1 1 0 1 1 0 0 1 1 0 0 50,00%

RO Governador Jorge Teixeira International Airport

0 1 0 0 1 0 1 0 0 1 0 0 46,15%

RR Boa Vista International Airport 0 0 0 0 1 0 0 0 0 1 0 0 19,23%

SC Hercílio Luz and Navegantes International Airports

0 1 0 0 1 0 1 0 0 1 0 1 30,77%

SP Congonhas and Guarulhos Airports 1 1 1 1 1 1 1 1 0 1 1 1 88,46%

SE Santa Maria International Airport 0 0 1 0 1 0 0 0 0 0 1 0 42,31%

TO Palmas Airport 0 0 0 0 0 0 0 0 0 1 1 0 38,46%


26

,92%

61

,54%

65

,38%

23

,08%

88

,46%

50

,00%

61

,54%

38

,46%

7,6

9%

30

,77%

10

0,0

0%

34

,62%

30

,77%


113

Annex 8 – Distance between Brazilian states Capitals

AC

AL

AP

AM

BA

CE

DF

ES

GO

MA

MT

MS

MG

PA

AC 3538 2009 964 3272 3257 2379 3308 2285 2649 1595 2061 2940 2225

AL 3538 2005 2777 476 730 1482 1282 1655 1234 2302 2353 1432 1680

AP 2009 2005 1051 1995 1450 1785 2538 1858 801 1815 2300 2330 325

AM 964 2777 1051 2605 2383 1937 2864 1912 1745 1453 2013 2547 1291

BA 3272 476 1995 2605 1028 1056 839 1224 1323 1915 1906 957 1687

CE 3257 730 1450 2383 1028 1686 1855 1853 653 2329 2547 1883 1134

DF 2379 1482 1785 1937 1056 1686 942 175 1525 879 881 610 1595

ES 3308 1282 2538 2864 839 1855 942 1023 2023 1745 1490 382 2276

GO 2285 1655 1858 1912 1224 1853 175 1023 1661 740 706 659 1692

MA 2649 1234 801 1745 1323 653 1525 2023 1661 1943 2284 1921 482

MT 1595 2302 1815 1453 1915 2329 879 1745 740 1943 560 1368 1778

MS 2061 2353 2300 2013 1906 2547 881 1490 706 2284 560 1117 2213

MG 2940 1432 2330 2547 957 1883 610 382 659 1921 1368 1117 2099

PA 2225 1680 325 1291 1687 1134 1595 2276 1692 482 1778 2213 2099

PB 3719 340 2039 2903 814 619 1786 1618 1961 1239 2575 2665 1771 1715

PR 2841 2260 2828 2734 1785 2671 1080 1076 974 2600 1303 781 828 2665

PE 3629 202 2002 2833 675 629 1654 1484 1828 1209 2453 2530 1632 1677

PI 2771 930 1075 1921 994 496 1312 1713 1466 329 1862 2132 1641 750

RJ 3170 1672 2679 2849 1210 2190 929 413 937 2267 1576 1212 352 2451

RN 3602 434 1872 2764 876 435 1773 1706 1948 1071 2524 2654 1822 1551

RS 3092 2776 3333 3132 2303 3214 1619 1536 1499 3142 1679 1119 1350 3188

RO 473 3091 1720 762 2809 2856 1906 2836 1814 2274 1138 1634 2470 1887

RR 915 2856 1111 79,1 2684 2457 2009 2939 1981 1817 1505 2064 2618 1360

SC 3060 2402 3074 2982 1931 2858 1313 1161 1217 2822 1544 1007 982 2905

SP 2915 1928 2656 2689 1454 2369 871 742 812 2349 1326 894 498 2463

SE 3401 201 1962 2673 277 815 1288 1102 1461 1226 2122 2155 1234 1642

TO 2169 1380 1167 1510 1113 1297 625 1414 727 960 1033 1325 1169 970


114

PB

PR

PE

PI

RJ

RN

RS

RO

RR

SC

SP

SE

TO

AC 3719 2841 3629 2771 3170 3602 3092 473 915 3060 2915 3401 2169

AL 340 2260 202 930 1672 434 2776 3091 2856 2402 1928 201 1380

AP 2039 2828 2002 1075 2679 1872 3333 1720 1111 3074 2656 1962 1167

AM 2903 2734 2833 1921 2849 2764 3132 762 79,1 2982 2689 2673 1510

BA 814 1785 675 994 1210 876 2303 2809 2684 1931 1454 277 1113

CE 619 2671 629 496 2190 435 3214 2856 2457 2858 2369 815 1297

DF 1786 1080 1654 1312 929 1773 1619 1906 2009 1313 871 1288 625

ES 1618 1076 1484 1713 413 1706 1536 2836 2939 1161 742 1102 1414

GO 1961 974 1828 1466 937 1948 1499 1814 1981 1217 812 1461 727

MA 1239 2600 1209 329 2267 1071 3142 2274 1817 2822 2349 1226 960

MT 2575 1303 2453 1862 1576 2524 1679 1138 1505 1544 1326 2122 1033

MS 2665 781 2530 2132 1212 2654 1119 1634 2064 1007 894 2155 1325

MG 1771 828 1632 1641 352 1822 1350 2470 2618 982 498 1234 1169

PA 1715 2665 1677 750 2451 1551 3188 1887 1360 2905 2463 1642 970

PB 2598 139 988 2010 190 3115 3286 2980 2742 2267 537 1603

PR 2598 2459 2363 676 2645 547 2413 2792 251 339 2062 1697

PE 139 2459 934 1874 254 2977 3191 2910 2604 2129 398 1495

PI 988 2363 934 1980 844 2909 2363 1997 2574 2092 903 832

RJ 2010 676 1874 1980 2085 1124 2707 2918 748 357 1483 1515

RN 190 2645 254 844 2085 3173 3179 2840 2802 2321 604 1524

RS 3115 547 2977 2909 1124 3173 2706 3183 376 852 2580 2226

RO 3286 2413 3191 2363 2707 3179 2706 751 2641 2464 2947 1714

RR 2980 2792 2910 1997 2918 2840 3183 751 3039 2753 2752 1588

SC 2742 251 2604 2574 748 2802 376 2641 3039 489 2208 1935

SP 2267 339 2129 2092 357 2321 852 2464 2753 489 1731 1496

SE 537 2062 398 903 1483 604 2580 2947 2752 2208 1731 1233

TO 1603 1697 1495 832 1515 1524 2226 1714 1588 1935 1496 1233


115

Annex 9 – GDP of each Brazilian state

State GDP per

capita (R$)


Brazilian average

GDP (million R$)

% Brazil GDP

AC 12 690 -35,04% 9 629 0,22%

AL 9 333 -52,22% 29 545 0,67%

AP 14 914 -23,65% 10 420 0,24%

AM 17 855 -8,60% 64 120 1,46%

BA 11 832 -39,43% 167 727 3,82%

CE 10 473 -46,39% 90 132 2,05%

DF 64 653 230,97% 171 236 3,90%

ES 29 996 53,55% 107 329 2,44%

GO 20 134 3,07% 123 926 2,82%

MA 8 760 -55,16% 58 820 1,34%

MT 25 945 32,82% 80 830 1,84%

MS 21 744 11,31% 54 471 1,24%

MG 20 324 4,04% 403 551 9,19%

PA 11 678 -40,22% 91 009 2,07%

PB 10 151 -48,04% 38 731 0,88%

PR 24 194 23,85% 255 927 5,83%

PE 13 138 -32,75% 117 340 2,67%

PI 8 137 -58,35% 25 721 0,59%

RJ 31 064 59,02% 504 221 11,48%

RN 12 249 -37,30% 39 544 0,90%

RS 25 779 31,97% 277 658 6,32%

RO 18 466 -5,47% 29 362 0,67%

RR 15 577 -20,26% 7 314 0,17%

SC 27 771 42,16% 177 276 4,04%

SP 33 624 72,12% 1 408 904 32,08%

SE 13 180 -32,53% 27 823 0,63%

TO 13 775 -29,48% 19 530 0,44%

Average 19 534,7 Sum 4 392 096 100,00%


116


117

Annex 10 – Airports to consider in the case study “United States of America”

State City Airport Passengers Country

Passengers %

AL

Birmingham Birmingham–Shuttlesworth International Airport

1 335 215

2 334 798

57,19%

Dothan Dothan Regional Airport 48 423 2,07%

Huntsville Huntsville International Airport 505 541 21,65%

Mobile Mobile Regional Airport 287 661 12,32%

Montgomery Montgomery Regional Airport 157 958 6,77%

AK

Anchorage Ted Stevens Anchorage International Airport 2 325 030

3 967 193

58,61%

Aniak Aniak Airport 14 334 0,36%

Barrow Wiley Post–Will Rogers Memorial Airport 51 568 1,30%

Bethel Bethel Airport 152 084 3,83%

Cordova Merle K. (Mudhole) Smith Airport 15 772 0,40%

Deadhors Deadhorse Airport 48 588 1,22%

Dillingham Dillingham Airport 26 632 0,67%

Fairbanks Fairbanks International Airport 457 372 11,53%

Galena Edward G. Pitka Sr. Airport 14 141 0,36%

Gustavus Gustavus Airport 14 141 0,36%

Haines Haines Airport 10 106 0,25%

Homer Homer Airport 37 705 0,95%

Hoonah Hoonah Airport 10 468 0,26%

Juneau Juneau International Airport 321 573 8,11%

Kenai Kenai Municipal Airport 99 821 2,52%

Ketchikan Ketchikan International Airport 109 433 2,76%

King Salmon King Salmon Airport 3 545 0,09%

Kodiak Kodiak Airport 7 993 0,20%

Kotzebue Ralph Wien Memorial Airport 61 274 1,54%

Nome Nome Airport 5 802 0,15%

Petersburg Petersburg James A. Johnson Airport 20 046 0,51%

Sitka Sitka Rocky Gutierrez Airport 67 989 1,71%

St. Mary's St. Mary's Airport 13 949 0,35%

Unalakleet Unalakleet Airport 14 011 0,35%

Unalaska Unalaska Airport 28 556 0,72%

Valdez Valdez Airport 13 318 0,34%

Wrangell Wrangell Airport 11 807 0,30%

Yakutat Yakutat Airport 10 135 0,26%

AZ

Bullhead City Laughlin/Bullhead International Airport 109 647

22 256 307

0,49%

Flagstaff Flagstaff Pulliam Airport 58 323 0,26%

Grand Canyon Grand Canyon National Park Airport 126 364 0,57%

Mesa Phoenix–Mesa Gateway Airport 725 048 3,26%

Page Page Municipal Airport 2 526 0,01%

Peach Springs Grand Canyon West Airport 59 846 0,27%

Phoenix Phoenix Sky Harbor International Airport 19 525 829 87,73%

Tucson Tucson International Airport 1 570 329 7,06%


118

Yuma Yuma International Airport / MCAS Yuma 78 395 0,35%

AR

Fayetteville Northwest Arkansas Regional Airport 558 218

1 729 450

32,28%

Fort Smith Fort Smith Regional Airport 82 742 4,78%

Little Rock Bill and Hillary Clinton National Airport 1 055 608 61,04%

Texarkana Texarkana Regional Airport 32 882 1,90%

CA

Arcata Arcata Airport 56 682

89 040 919

0,06%

Bakersfield Meadows Field 143 175 0,16%

Burbank Bob Hope Airport 1 919 005 2,16%

Carlsbad McClellan–Palomar Airport 52 561 0,06%

Chico Chico Municipal Airport 16 835 0,02%

Crescent City Del Norte County Airport 12 136 0,01%

Fresno Fresno Yosemite International Airport 684 849 0,77%

Long Beach Long Beach Airport 1 438 948 1,62%

Los Angeles Los Angeles International Airport 32 427 115 36,42%

Mammoth Lakes

Mammoth Yosemite Airport 3 097 0%

Modesto Modesto City–County Airport 1 131 0%

Monterey Monterey Regional Airport 205 838 0,23%

Oakland Oakland International Airport 4 771 830 5,36%

Ontario Ontario International Airport 1 970 538 2,21%

Palm Springs Palm Springs International Airport 876 428 0,98%

Redding Redding Municipal Airport 24 875 0,03%

Sacramento Sacramento International Airport 4 255 145 4,78%

San Diego San Diego International Airport 8 876 777 9,97%

San Francisco San Francisco International Airport 21 706 567 24,38%

San Jose Norman Y. Mineta San José International Airport

4 317 896 4,85%

San Luis Obispo

San Luis Obispo County Regional Airport 135 844 0,15%

Santa Ana John Wayne Airport – Orange County 4 542 376 5,10%

Santa Barbara Santa Barbara Municipal Airport 365 036 0,41%

Santa Maria Santa Maria Public Airport 51 395 0,06%

Santa Rosa Charles M. Schulz–Sonoma County Airport 113 083 0,13%

Stockton Stockton Metropolitan Airport 71 757 0,08%

CO

Aspen Aspen-Pitkin County Airport 208 682

26 926 648

0,78%

Colorado Springs

City of Colorado Springs Municipal Airport 658 318 2,44%

Denver Denver International Airport 25 497 348 94,69%

Durango Durango-La Plata County Airport 192 797 0,72%

Eagle Eagle County Regional Airport 168 535 0,63%

Grand Junction

Grand Junction Regional Airport 21 127 0,08%

Gunnison Gunnison-Crested Butte Regional Airport 3 078 0,01%

Hayden Yampa Valley Airport 92 184 0,34%

Montrose Montrose Regional Airport 84 579 0,31%

CT Hartford Bradley International Airport 2 681 718

2 719 152 98,62%

New Haven Tweed New Haven Regional Airport 37 434 1,38%

DE Wilmington Wilmington-Philadelphia Regional Airport 52 456 52 475 99,96%


119

Georgetown Sussex County Airport 19 0,04%

DC NO AIRPORTS 0%

FL

Daytona Beach

Daytona Beach International Airport 627 917

76 313 877

0,82%

Fort Lauderdale

Fort Lauderdale–Hollywood International Airport

11 079 402 14,52%

Fort Myers Southwest Florida International Airport 7 205 205 9,44%

Gainesville Gainesville Regional Airport 41 352 0,05%

Jacksonville Jacksonville International Airport 2 549 712 3,34%

Key West Key West International Airport 403 021 0,53%

Melbourne Melbourne International Airport 211 702 0,28%

Miami Miami International Airport 16 194 277 21,22%

Orlando Orlando International Airport 17 614 745 23,08%

Orlando Orlando Sanford International Airport 805 661 1,06%

Panama City Beach

Northwest Florida Beaches International Airport

391 893 0,51%

Pensacola Pensacola International Airport 744 259 0,98%

Punta Gorda Punta Gorda Airport 171 121 0,22%

Sarasota/ Bradenton

Sarasota–Bradenton International Airport 595 423 0,78%

St. Augustine Northeast Florida Regional Airport 18 255 0,02%

St. Petersburg St. Petersburg International Airport 814 595 1,07%

Tallahassee Tallahassee Regional Airport 336 129 0,44%

Tampa Tampa International Airport 13 306 354 17,44%

Valparaiso Northwest Florida Regional Airport 353 953 0,46%

West Palm Beach

Palm Beach International Airport 2 848 901 3,73%

GA

Albany Southwest Georgia Regional Airport 31 276

45 780 671

0,07%

Atlanta Hartsfield-Jackson Atlanta International Airport

45 308 685 98,97%

Augusta Augusta Regional Airport 261 079 0,57%

Brunswick Brunswick Golden Isles Airport 3 245 0,01%

Columbus Columbus Metropolitan Airport 59 675 0,13%

Savannah Savannah/Hilton Head International Airport 79 897 0,17%

Valdosta Valdosta Regional Airport 36 814 0,08%

HI

Hilo Hilo International Airport 640 411

15 865 694

4,04%

Honolulu Honolulu International Airport 9 466 995 59,67%

Kahului Kahului Airport 2 955 304 18,63%

Kailua/Kona Kona International Airport at Keahole 1 376 641 8,68%

Kaunakakai Molokai Airport 63 879 0,40%

Lanai City Lanai Airport 47 323 0,30%

Lihue Lihue Airport 1 315 141 8,29%

ID

Boise Boise Airport 1 313 741

1 627 792

80,71%

Hailey Friedman Memorial Airport 52 393 3,22%

Idaho Falls Idaho Falls Regional Airport 147 073 9,04%

Lewiston Lewiston-Nez Perce County Airport 62 209 3,82%

Pocatello Pocatello Regional Airport 23 775 1,46%

Twin Falls Magic Valley Regional Airport 28 601 1,76%


120

IL

Belleville MidAmerica St. Louis Airport 13 542

43 095 921

0,03%

Bloomington/ Normal

Central Illinois Regional Airport at Bloomington-Normal

211 957 0,49%

Champaign/ Urbana

University of Illinois - Willard Airport 84 853 0,20%

Chicago Chicago O'Hare International Airport 32 278 906 74,90%

Chicago Chicago Midway International Airport 9 919 985 23,02%

Marion Williamson County Regional Airport 11 241 0,03%

Moline Quad City International Airport 384 198 0,89%

Peoria General Downing - Peoria International Airport

491 0%

Quincy Quincy Regional Airport 10 679 0,02%

Rockford Chicago Rockford International Airport 109 384 0,25%

Springfield Abraham Lincoln Capital Airport 70 685 0,16%

IN

Evansville Evansville Regional Airport 161 279

4 321 229

3,73%

Fort Wayne Fort Wayne International Airport 294 968 6,83%

Indianapolis Indianapolis International Airport 3 535 579 81,82%

South Bend South Bend International Airport 329 403 7,62%

IA

Cedar Rapids The Eastern Iowa Airport 52 036

1 210 987

4,30%

Des Moines Des Moines International Airport 1 079 189 89,12%

Dubuque Dubuque Regional Airport 33 465 2,76%

Sioux City Sioux Gateway Airport 25 313 2,09%

Waterloo Waterloo Regional Airport 20 984 1,73%

KS

Garden City Garden City Regional Airport 24 456

163 761

14,93%

Manhattan Manhattan Regional Airport 65 683 40,11%

Wichita Wichita Dwight D. Eisenhower National Airport

73 622 44,96%

KY

Covington Cincinnati/Northern Kentucky International Airport

2 776 377

5 092 212

54,52%

Lexington Blue Grass Airport 604 091 11,86%

Louisville Louisville International Airport 1 669 470 32,78%

Owensboro Owensboro-Daviess County Regional Airport 21 751 0,43%

Paducah Barkley Regional Airport 20 523 0,40%

LA

Alexandria Alexandria International Airport 183 899

5 856 865

3,14%

Baton Rouge Baton Rouge Metropolitan Airport 401 035 6,85%

Lafayette Lafayette Regional Airport 233 498 3,99%

Lake Charles Lake Charles Regional Airport 65 281 1,11%

Monroe Monroe Regional Airport 115 757 1,98%

New Orleans Louis Armstrong New Orleans International Airport

4 577 498 78,16%

Shreveport Shreveport Regional Airport 279 897 4,78%

ME

Bangor Bangor International Airport 265 245

1 140 417

23,26%

Bar Harbor Hancock County-Bar Harbor Airport 10 625 0,93%

Portland Portland International Jetport 837 335 73,42%

Presque Isle Northern Maine Regional Airport at Presque Isle

11 488 1,01%

Rockland Knox County Regional Airport 15 724 1,38%

MD Baltimore Baltimore/Washington International Thurgood Marshall Airport

11 134 130 11 153 338 99,83%


121

Salisbury Salisbury-Ocean City Wicomico Regional Airport

6 267 0,06%

Hagerstown Hagerstown Regional Airport 12 941 0,12%

MA

Boston Gen. Edward Lawrence Logan International Airport

14 721 693

15 073 021

97,67%

Hyannis Barnstable Municipal Airport 88 055 0,58%

Nantucket Nantucket Memorial Airport 184 618 1,22%

New Bedford New Bedford Regional Airport 10 604 0,07%

Provincetown Provincetown Municipal Airport 11 288 0,07%

Vineyard Haven

Martha's Vineyard Airport 56 763 0,38%

MI

Alpena Alpena County Regional Airport 15 914

18 025 847

0,09%

Charlevoix Charlevoix Municipal Airport 16 929 0,09%

Detroit / Romulus

Detroit Metropolitan Wayne County Airport 15 683 787 87,01%

Escanaba Delta County Airport 1 511 0,01%

Flint Bishop International Airport 398 132 2,21%

Grand Rapids Gerald R. Ford International Airport 1 123 257 6,23%

Hancock Houghton County Memorial Airport 25 312 0,14%

Iron Mountain/ Kingsford

Ford Airport 11 271 0,06%

Kalamazoo/ Battle Creek

Kalamazoo/Battle Creek International Airport 129 211 0,72%

Lansing Capital Region International Airport 216 925 1,20%

Marquette/Gwinn

Sawyer International Airport 42 355 0,23%

Muskegon Muskegon County Airport 1 802 0,01%

Pellston Pellston Regional Airport of Emmet County 27 281 0,15%

Saginaw MBS International Airport 120 689 0,67%

Sault Ste. Marie

Chippewa County International Airport 21 827 0,12%

Traverse City Cherry Capital Airport 189 644 1,05%

MN

Bemidji Bemidji Regional Airport 22 819

16 629 377

0,14%

Brainerd Brainerd Lakes Regional Airport 15 654 0,09%

Duluth Duluth International Airport 155 455 0,93%

Hibbing Range Regional Airport 11 669 0,07%

International Falls

Falls International Airport 15 796 0,09%

Minneapolis Minneapolis–St. Paul International Airport 16 282 038 97,91%

Rochester Rochester International Airport 110 104 0,66%

St. Cloud St. Cloud Regional Airport 15 842 0,10%

MS

Columbus/West Point/Starkville

Golden Triangle Regional Airport 4 114

980 389

0,42%

Gulfport/Biloxi Gulfport-Biloxi International Airport 369 597 37,70%

Hattiesburg/ Laurel

Hattiesburg-Laurel Regional Airport 10 633 1,08%

Jackson Jackson-Evers International Airport 596 045 60,80%

MO

Columbia Columbia Regional Airport 45 714

11 487 988

0,40%

Joplin Joplin Regional Airport 23 329 0,20%

Kansas City Kansas City International Airport 4 836 221 42,10%


122

Springfield Springfield-Branson National Airport 368 752 3,21%

St. Louis Lambert-St. Louis International Airport 6 213 972 54,09%

MT

Billings Billings Logan International Airport 387 368

1 359 029

28,50%

Bozeman Bozeman Yellowstone International Airport 442 788 32,58%

Butte Bert Mooney Airport 2 949 0,22%

Great Falls Great Falls International Airport 18 239 1,34%

Helena Helena Regional Airport 9 731 0,72%

Kalispell Glacier Park International Airport 199 701 14,69%

Missoula Missoula International Airport 298 253 21,95%

NE

Grand Island Central Nebraska Regional Airport 57 165

2 196 683

2,60%

Kearney Kearney Regional Airport 13 096 0,60%

Lincoln Lincoln Airport 138 787 6,32%

Omaha Eppley Airfield 1 977 480 90,02%

Scottsbluff Western Nebraska Regional Airport 10 155 0,46%

NV

Boulder City Boulder City Municipal Airport 103 972

21 701 656

0,48%

Elko Elko Regional Airport 1 951 0,01%

Las Vegas McCarran International Airport 19 923 594 91,81%

Reno Reno/Tahoe International Airport 1 672 139 7,71%

NH Lebanon Lebanon Municipal Airport 10 953

1 201 035 0,91%

Manchester Manchester–Boston Regional Airport 1 190 082 99,09%

NJ

Atlantic City Atlantic City International Airport 534 204

18 196 599

2,94%

Trenton Trenton Mercer Airport 148 256 0,81%

Newark Newark Liberty International Airport 17 514 139 96,25%

NM

Albuquerque Albuquerque International Sunport 2 477 960

2 607 930

95,02%

Farmington Four Corners Regional Airport 14 263 0,55%

Hobbs Lea County Regional Airport 17 246 0,66%

Roswell Roswell International Air Center 32 616 1,25%

Santa Fe Santa Fe Municipal Airport 65 845 2,52%

NY

Albany Albany International Airport 1 196 753

45 771 671

2,61%

Binghamton Greater Binghamton Airport 9 521 0,02%

Buffalo Buffalo Niagara International Airport 2 568 018 5,61%

Elmira/Corning Elmira/Corning Regional Airport 129 749 0,28%

Islip Long Island MacArthur Airport 662 612 1,45%

Ithaca Ithaca Tompkins Regional Airport 103 722 0,23%

New York John F. Kennedy International Airport 25 036 855 54,70%

New York LaGuardia Airport 13 353 365 29,17%

Newburgh Stewart International Airport 163 815 0,36%

Niagara Falls Niagara Falls International Airport 98 958 0,22%

Plattsburgh Plattsburgh International Airport 151 235 0,33%

Rochester Greater Rochester International Airport 1 209 532 2,64%

Syracuse Syracuse Hancock International Airport 991 663 2,17%

Watertown Watertown International Airport 18 818 0,04%

White Plains Westchester County Airport 77 055 0,17%

NC Asheville Asheville Regional Airport 342 731

27 970 347 1,23%

Charlotte Charlotte/Douglas International Airport 21 347 428 76,32%


123

Fayetteville Fayetteville Regional Airport 244 345 0,87%

Greensboro Piedmont Triad International Airport 860 124 3,08%

Greenville Pitt-Greenville Airport 6 002 0,02%

Jacksonville Albert J. Ellis Airport 167 528 0,60%

New Bern Coastal Carolina Regional Airport 121 479 0,43%

Raleigh Raleigh-Durham International Airport 4 482 973 16,03%

Wilmington Wilmington International Airport 397 737 1,42%

ND

Bismarck Bismarck Municipal Airport 238 929

1 143 222

20,90%

Dickinson Dickinson Theodore Roosevelt Regional Airport

34 979 3,06%

Fargo Hector International Airport 403 786 35,32%

Grand Forks Grand Forks International Airport 148 663 13%

Minot Minot International Airport 220 787 19,31%

Williston Sloulin Field International Airport 96 078 8,40%

OH

Akron/Canton Akron-Canton Regional Airport 852 332

9 690 068

8,80%

Cleveland Cleveland-Hopkins International Airport 4 375 822 45,16%

Columbus Port Columbus International Airport 3 065 569 31,64%

Columbus Rickenbacker International Airport 17 765 0,18%

Dayton James M. Cox Dayton International Airport 1 244 841 12,85%

Toledo Toledo Express Airport 86 221 0,89%

Youngstown/ Warren

Youngstown-Warren Regional Airport 47 518 0,49%

OK

Lawton Lawton–Fort Sill Regional Airport 55 526

3 169 876

1,75%

Oklahoma City Will Rogers World Airport 1 790 407 56,48%

Tulsa Tulsa International Airport 1 323 943 41,77%

OR

Eugene Eugene Airport 434 095

8 184 438

5,30%

Klamath Falls Klamath Falls Airport 13 433 0,16%

Medford Rogue Valley International-Medford Airport 30 645 0,37%

North Bend Southwest Oregon Regional Airport 16 864 0,21%

Portland Portland International Airport 7 453 098 91,06%

Redmond Redmond Municipal Airport 236 303 2,89%

PA

Allentown Lehigh Valley International Airport 301 969

19 162 440

1,58%

Erie Erie International Airport 10 952 0,06%

Harrisburg Harrisburg International Airport 65 765 0,34%

Latrobe Arnold Palmer Regional Airport 12 704 0,07%

Philadelphia Philadelphia International Airport 14 705 014 76,74%

Pittsburgh Pittsburgh International Airport 3 813 007 19,90%

State College University Park Airport 13 122 0,07%

Wilkes-Barre/Scranton

Wilkes-Barre/Scranton International Airport 216 536 1,13%

Williamsport Williamsport Regional Airport 23 371 0,12%

RI

Providence/ Warwick

Theodore Francis Green State Airport 1 951 566 1 962 968

99,42%

Westerly Westerly State Airport 11 402 0,58%

SC

Charleston Charleston International Airport 2 593 063

4 873 497

53,21%

Columbia Columbia Metropolitan Airport 491 921 10,09%

Florence Florence Regional Airport 67 745 1,39%


124

Greer Greenville-Spartanburg International Airport 936 288 19,21%

Hilton Head Island

Hilton Head Airport 61 705 1,27%

Myrtle Beach Myrtle Beach International Airport 722 775 14,83%

SD

Aberdeen Aberdeen Regional Airport 20 089

674 840

2,98%

Pierre Pierre Regional Airport 14 686 2,18%

Rapid City Rapid City Regional Airport 284 126 42,10%

Sioux Falls Sioux Falls Regional Airport 355 939 52,74%

TN

Bristol/Johnson City/Kingsport

Tri-Cities Regional Airport 202 114

10 399 776

1,94%

Chattanooga Chattanooga Metropolitan Airport 29 283 0,28%

Knoxville McGhee Tyson Airport 804 917 7,74%

Memphis Memphis International Airport 4 930 935 47,41%

Nashville Nashville International Airport 4 432 527 42,62%

TX

Abilene Abilene Regional Airport 73 605

67 544 782

0,11%

Amarillo Rick Husband Amarillo International Airport 394 593 0,58%

Austin Austin-Bergstrom International Airport 4 201 136 6,22%

Beaumont/Port Arthur

Jack Brooks Regional Airport 17 394 0,03%

Brownsville Brownsville/South Padre Island International Airport

84 401 0,12%

College Station

Easterwood Airport 72 188 0,11%

Corpus Christi Corpus Christi International Airport 339 193 0,50%

Dallas Dallas Love Field 3 783 407 5,60%

Dallas-Fort Worth

Dallas/Fort Worth International Airport 27 100 656 40,12%

Del Rio Del Rio International Airport 1 318 0%

El Paso El Paso International Airport 1 509 093 2,23%

Fort Hood/Killeen

Killeen-Fort Hood Regional Airport 243 861 0,36%

Harlingen Valley International Airport 373 438 0,55%

Houston George Bush Intercontinental Airport 19 528 631 28,91%

Houston William P. Hobby Airport 4 357 835 6,45%

Laredo Laredo International Airport 11 252 0,02%

Longview East Texas Regional Airport 2 183 0%

Lubbock Lubbock Preston Smith International Airport 508 858 0,75%

McAllen McAllen-Miller International Airport 344 302 0,51%

Midland Midland International Airport 445 043 0,66%

San Angelo San Angelo Regional Airport 56 021 0,08%

San Antonio San Antonio International Airport 3 916 320 5,80%

Tyler Tyler Pounds Regional Airport 74 357 0,11%

Waco Waco Regional Airport 61 401 0,09%

Wichita Falls Wichita Falls Municipal Airport 44 296 0,07%

UT

Provo Provo Municipal Airport 1 124

9 999 947

0,01%

Salt Lake City Salt Lake City International Airport 9 910 493 99,11%

St. George St. George Municipal Airport 37 596 0,38%

Wendover Wendover Airport 50 734 0,51%


125

VT Burlington Burlington International Airport 64 079 64 079 100%

VA

Charlottesville Charlottesville–Albemarle Airport 197 776

24 466 050

0,81%

Lynchburg Lynchburg Regional Airport 93 772 0,38%

Newport News Newport News/Williamsburg International Airport

519 906 2,13%

Norfolk Norfolk International Airport 1 663 294 6,80%

Richmond Richmond International Airport 1 651 131 6,75%

Roanoke Roanoke Regional Airport 316 478 1,29%

Staunton/ Waynesboro/ Harrisonburg

Shenandoah Valley Regional Airport 10 408 0,04%

Washington, D.C.

Ronald Reagan Washington National Airport 8 736 804 35,71%

Washington, D.C.

Washington Dulles International Airport 11 276 481 46,09%

WA

Bellingham Bellingham International Airport 398 368

17 884 275

2,23%

Friday Harbor Friday Harbor Airport 12 381 0,07%

Pasco Tri-Cities Airport 312 915 1,75%

Port Angeles William R. Fairchild International Airport 10 616 0,06%

Pullman/Mosco

Pullman/Moscow Regional Airport 35 248 0,20%

Seattle King County International Airport 33 656 0,19%

Seattle/Tacoma

Seattle–Tacoma International Airport 15 406 243 86,14%

Spokane Spokane International Airport 1 545 115 8,64%

Walla Walla Walla Walla Regional Airport 29 064 0,16%

Wenatchee Pangborn Memorial Airport 46 837 0,26%

Yakima Yakima Air Terminal 53 832 0,30%

WV

Charleston Yeager Airport 264 818

414 317

63,92%

Clarksburg North Central West Virginia Airport 10 694 2,58%

Huntington Tri-State Airport 115 263 27,82%

Lewisburg Greenbrier Valley Airport 12 293 2,97%

Morgantown Morgantown Municipal Airport 11 249 2,72%

WI

Appleton Outagamie County Regional Airport 25 934

5 336 419

0,49%

Eau Claire Chippewa Valley Regional Airport 18 762 0,35%

Green Bay Austin Straubel International Airport 410 348 7,69%

La Crosse La Crosse Regional Airport 111 462 2,09%

Madison Dane County Regional Airport 728 075 13,64%

Milwaukee General Mitchell International Airport 3 861 333 72,36%

Wausau Central Wisconsin Airport 154 312 2,89%

Rhinelander Rhinelander-Oneida County Airport 26 193 0,49%

WY

Casper Casper/Natrona County International Airport 74 167

505 243

14,68%

Cheyenne Cheyenne Regional Airport 16 697 3,30%

Cody Yellowstone Regional Airport 25 863 5,12%

Gillette Gillette-Campbell County Airport 28 232 5,59%

Jackson Jackson Hole Airport 305 566 60,48%

Laramie Laramie Regional Airport 10 371 2,05%

Riverton Riverton Regional Airport 17 035 3,37%


126

Rock Springs Rock Springs – Sweetwater County Airport 25 541 5,06%

Sheridan Sheridan County Airport 1 771 0,35%

Source: FAA Airports, Calendar Year 2011 Enplanements for U.S. Airports, by State


127

Annex 11 – Airport Destinations in the USA

AL

AK

AZ

AR

CA

CO

CT

DE

DC

FL

GA

HI

ID

IL

IN

AL Birmingham-Shuttlesworh and Huntsville International Airports

0 0 0 0 1 0 0 1 1 0 0 1 0

AK Ted Stevens Anchorage International Airport

0 1 0 1 1 0 0 0 1 1 0 1 0

AZ Phoenix Sky Harbor International Airport 0 1 1 1 1 0 0 1 1 1 1 1 1

AR Northwest Arkansas and Bill and Hillary Clinton Airports

0 0 1 1 1 0 0 1 1 0 0 1 0

CA Los Angeles and San Francisco International Airports

0 1 1 1 1 0 0 1 1 1 1 1 1

CO Denver International Airport 1 1 1 1 1 0 0 1 1 1 1 1 1

CT Bradley International Airport 0 0 0 0 0 1 0 1 1 0 0 1 0

DE Wilmington-Philadelphia Regional Airport 0 0 0 0 0 0 0 0 1 0 0 0 0

DC

FL Miami and Orlando International Airports 1 0 1 0 1 1 0 0 1 0 0 1 1

GA Hartsfield-Jackson Atlanta International Airport

1 0 1 1 1 1 1 0 1 1 0 1 1

HI Honolulu International Airport 0 1 1 0 1 1 0 0 0 1 0 1 0

ID Boise Airport (Boise Air Terminal) (Gowen Field)

0 0 1 0 1 1 0 0 0 0 0 1 0

IL Chicago O'Hare and Chicago Midway International Airports

1 1 1 1 1 1 1 0 1 1 1 1 1

IN Indianapolis International Airport 0 0 1 0 1 1 0 0 1 1 0 0 0

IA Des Moines International Airport 0 0 1 0 1 1 0 0 1 1 0 0 1 0

KS Manhattan and Wichita Dwight Airports 0 0 0 0 1 1 0 0 0 1 0 0 1 0

KY Cincinnati/Northern Kentucky and Louisville International Airports

0 0 1 1 1 1 1 0 1 1 0 0 1 0

LA Louis Armstrong New Orleans International Airport

0 0 1 0 1 1 0 0 1 1 0 0 1 1

ME Bangor and Portland International Airports 0 0 0 0 0 0 0 0 1 1 0 0 1 0

MD Baltimore/Washington International Thurgood Marshall Airport

1 0 1 1 1 1 1 0 1 1 0 0 1 1

MA Gen. Edward Lawrence Logan International Airport

0 0 1 0 1 1 0 0 1 1 0 0 1 1

MI Detroit Metropolitan Wayne County Airport 1 0 1 1 1 1 1 0 1 1 1 0 1 1

MN Minneapolis–St. Paul International Airport (Wold–Chamberlain Field)

1 1 1 0 1 1 1 0 1 1 1 1 1 1

MS Gulfport-Biloxi and Jackson-Evers International Airports

0 0 0 0 0 0 0 0 1 1 0 0 1 0

MO Kansas City and Lambert-St. Louis International Airports

0 0 1 1 1 1 0 0 1 1 0 0 1 1

MT Billings Logan, Bozeman Yellowstone and Missoula International Airports

0 0 1 0 1 1 0 0 0 1 0 0 1 0

NE Eppley Airfield 0 0 1 0 1 1 0 0 1 1 0 0 1 0

NV McCarran International Airport 1 0 1 1 1 1 1 0 1 1 1 1 1 1

NH Manchester–Boston Regional Airport 0 0 1 0 0 0 0 0 1 1 0 0 1 0

NJ Newark Liberty International Airport 0 0 1 1 1 1 1 0 1 1 1 0 1 1

NM Albuquerque International Sunport 0 0 1 0 1 1 0 0 1 1 0 0 1 0

NY John F. Kennedy and La Guardia Airports 1 1 1 0 1 1 1 0 1 1 0 0 1 1


128

IA

KS

KY

LA

ME

MD

MA

MI

MN

MS

MO

MT

NE

NV

NH


0 0 0 0 0 1 0 1 0 0 0 0 0 1 0


0 0 0 0 0 0 0 0 1 0 0 0 0 1 0

AZ Phoenix Sky Harbor International Airport 1 0 1 1 0 1 1 1 1 0 1 0 1 1 0


0 0 0 0 0 1 0 1 1 0 0 0 0 1 0


1 1 1 1 0 1 1 1 1 0 1 1 1 1 0

CO Denver International Airport 1 1 1 1 0 1 1 1 1 0 1 1 1 1 0

CT Bradley International Airport 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0

DE Wilmington-Philadelphia Regional Airport 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

DC

FL Miami and Orlando International Airports 0 0 1 1 0 1 1 1 1 0 1 0 1 1 1


1 1 1 1 0 1 1 1 1 1 1 1 1 1 1

HI Honolulu International Airport 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0


0 0 0 0 0 0 0 0 1 0 0 0 0 1 0


1 1 1 1 0 1 1 1 1 1 1 1 1 1 1

IN Indianapolis International Airport 0 0 0 1 0 1 1 1 1 0 0 0 0 1 0




0 0 1 0 1 0 1 1 0 1 0 0 1 0


0 0 1 0 1 0 1 1 0 1 0 0 1 0



0 0 1 1 1 1 1 1 0 1 0 0 1 1


0 0 1 1 1 1 1 1 0 1 0 0 1 1



1 1 1 1 0 1 1 1 0 1 1 1 1 0


0 0 0 0 0 1 0 0 0 0 0 0 0 0


1 1 1 1 0 1 1 1 1 0 0 1 1 0


0 0 0 0 0 0 0 0 1 0 0 0 1 0




NJ Newark Liberty International Airport 0 0 1 1 1 1 1 1 1 0 1 0 0 0 1

NM Albuquerque International Sunport 0 0 0 0 0 1 0 0 1 0 1 0 0 1 0

NY John F. Kennedy and La Guardia Airports 1 0 1 1 1 1 1 1 1 0 1 0 1 1 1


129

NJ

NM

NY

NC

ND

OH

OK

OR

PA

RI

SC

SD

TN

TX


0 0 1 1 0 0 0 0 1 0 0 0 0 1


0 0 0 0 0 0 0 1 0 0 0 0 0 1

AZ Phoenix Sky Harbor International Airport 0 1 1 1 0 1 1 1 1 0 0 0 1 1


1 0 1 1 0 1 0 0 0 0 0 0 0 1


1 1 1 1 1 1 1 1 1 0 0 1 1 1

CO Denver International Airport 1 1 1 1 1 1 1 1 1 0 0 1 1 1

CT Bradley International Airport 1 0 0 1 0 1 0 0 1 0 0 0 0 1

DE Wilmington-Philadelphia Regional Airport 0 0 1 1 0 0 0 0 1 0 0 0 0 0

DC

FL Miami and Orlando International Airports 1 1 1 1 0 1 1 1 1 0 1 0 1 1


1 1 1 1 0 1 1 1 1 1 1 0 1 1

HI Honolulu International Airport 1 0 0 0 0 0 0 1 0 0 0 0 0 1


0 0 0 0 0 0 0 1 0 0 0 0 0 0


1 1 1 1 1 1 1 1 1 1 1 1 1 1

IN Indianapolis International Airport 0 0 1 1 0 0 0 0 1 0 1 0 0 1




1 0 1 1 0 0 0 0 1 0 1 0 1 1


1 0 1 1 0 1 0 0 1 0 0 0 1 1



1 1 1 1 0 1 1 0 1 1 1 0 1 1


1 0 1 1 0 1 0 1 1 0 1 0 1 1



1 1 1 1 1 1 1 1 1 1 0 1 1 1


0 0 0 1 0 0 0 0 0 0 0 0 1 1


1 1 1 1 0 1 1 1 1 0 0 0 1 1


1 0 1 0 0 0 0 1 0 0 0 0 0 1




NJ Newark Liberty International Airport 0 1 1 0 1 1 1 1 1 1 0 1 1

NM Albuquerque International Sunport 0 0 0 0 0 0 1 0 0 0 0 0 1

NY John F. Kennedy and La Guardia Airports 0 0 1 0 1 0 1 1 1 1 0 1 1


130

UT

VT

VA

WA

WV

WI

WY




0 0 0 0 0 0 0 22,45%


1 0 0 1 0 0 0 24,49%

AZ Phoenix Sky Harbor International Airport 1 0 0 1 0 1 0 65,31%


0 0 0 0 0 0 0 30,61%


1 0 1 1 0 1 1 79,59%

CO Denver International Airport 1 0 1 1 0 1 1 81,63%

CT Bradley International Airport 0 0 1 0 0 0 0 26,53%

DE Wilmington-Philadelphia Regional Airport 0 0 1 0 0 0 0 10,20%

DC 0,00%

FL Miami and Orlando International Airports 1 0 1 1 0 0 0 63,27%


1 0 1 1 1 1 0 83,67%

HI Honolulu International Airport 1 0 0 1 0 0 0 28,57%


1 0 0 1 0 0 0 18,37%


1 1 1 1 1 1 1 95,92%

IN Indianapolis International Airport 1 0 0 0 0 1 0 36,73%

IA Des Moines International Airport 0 0 1 0 0 0 0 28,57%

KS Manhattan and Wichita Dwight Airports 0 0 1 0 0 0 0 18,37%


1 0 1 1 0 1 0 51,02%


1 0 1 1 0 1 0 48,98%

ME Bangor and Portland International Airports 0 0 1 0 0 0 0 22,45%


1 0 1 1 0 1 0 69,39%


1 1 1 1 0 1 0 61,22%

MI Detroit Metropolitan Wayne County Airport 1 1 1 1 1 1 0 87,76%


1 0 1 1 0 1 1 83,67%


0 0 1 0 0 0 0 16,33%


1 0 1 1 0 1 0 65,31%


1 0 0 1 0 0 0 26,53%

NE Eppley Airfield 1 0 1 1 0 0 0 32,65%

NV McCarran International Airport 1 0 1 1 0 1 1 85,71%

NH Manchester–Boston Regional Airport 0 0 1 0 0 0 0 26,53%

NJ Newark Liberty International Airport 1 1 1 1 0 1 0 69,39%

NM Albuquerque International Sunport 1 0 1 1 0 0 0 30,61%

NY John F. Kennedy and La Guardia Airports 1 1 1 1 1 1 0 73,47%


131

AL

AK

AZ

AR

CA

CO

CT

DE

DC

FL

GA

HI

ID

IL

IN

NC Charlotte/Douglas International Airport 1 0 1 1 1 1 1 0 1 1 0 0 1 1

ND Bismarck and Hector Airports 0 0 0 0 1 1 0 0 0 0 0 0 1 0

OH Cleveland-Hopkins and Port Columbus International Airports

0 0 1 0 1 1 1 0 1 1 0 0 1 1

OK Will Rogers World and Tulsa Airports 0 0 1 0 1 1 0 0 1 1 0 0 1 0

OR Portland International Airport 0 1 1 0 1 1 0 0 0 1 1 1 1 0

PA Philadelphia International Airport 1 1 1 0 1 1 1 0 1 1 0 0 1 1

RI Theodore Francis Green State Airport 0 0 1 0 0 0 0 0 1 1 0 0 1 0

SC Charleston International Airport / Charleston AFB

0 0 0 0 0 0 0 0 1 1 0 0 1 0

SD Rapid City and Sioux Falls Airports 0 0 1 0 0 1 0 0 1 1 0 0 1 0

TN Memphis and Nashville Airports 1 0 1 1 1 1 1 0 1 1 0 0 1 1

TX Dallas/Fort Worth and George Bush International Airports

1 1 1 1 1 1 1 0 1 1 1 0 1 1

UT Salt Lake City International Airport 0 1 1 0 1 1 0 0 1 1 1 1 1 1

VT Burlington International Airport 0 0 0 0 0 0 0 0 1 0 0 0 1 0

VA Ronald Reagan Washington and Washington Dulles International Airports

1 0 1 0 1 1 1 0 1 1 0 0 1 1

WA Seattle–Tacoma International Airport 0 1 1 0 1 1 0 0 1 1 1 1 1 0

WV Yeager and Tri-State Airports 0 0 0 0 0 0 0 0 1 1 0 0 1 0

WI General Mitchell International Airport 0 0 1 0 1 1 0 0 1 1 0 0 1 1

WY Jackson Hole Airport 0 0 0 0 1 1 0 0 0 0 0 0 1 0


28

,57%

24

,49%

77

,55%

28

,57%

77

,55%

83

,67%

30

,61%

0,0

0%

0,0

0%

81

,63%

91

,84%

28

,57%

18

,37%

95

,92%

46

,94%


132

IA

KS

KY

LA

ME

MD

MA

MI

MN

MS

MO

MT

NE

NV

NH

NC Charlotte/Douglas International Airport 0 0 1 1 1 1 1 1 1 1 1 0 0 1 1

ND Bismarck and Hector Airports 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0


0 0 1 0 1 1 1 1 1 0 1 0 0 1 1

OK Will Rogers World and Tulsa Airports 0 0 0 0 0 1 0 1 1 0 1 0 0 1 0

OR Portland International Airport 0 0 0 0 0 0 1 1 1 0 1 0 0 1 0

PA Philadelphia International Airport 0 0 1 1 1 1 1 1 1 0 1 0 0 1 1

RI Theodore Francis Green State Airport 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0


0 0 0 0 0 1 0 1 0 0 0 0 0 0 0

SD Rapid City and Sioux Falls Airports 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0

TN Memphis and Nashville Airports 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0


1 1 1 1 0 1 1 1 1 1 1 0 1 1 0

UT Salt Lake City International Airport 0 0 1 1 0 1 1 1 1 0 1 1 1 1 0

VT Burlington International Airport 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0


1 0 1 1 1 0 1 1 1 1 1 0 1 1 1

WA Seattle–Tacoma International Airport 0 0 1 0 0 1 1 1 1 0 1 1 0 1 0

WV Yeager and Tri-State Airports 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0

WI General Mitchell International Airport 1 0 1 0 0 1 1 1 1 0 1 0 1 1 0

WY Jackson Hole Airport 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0


28

,57%

20

,41%

51

,02%

44

,90%

18

,37%

73

,47%

51

,02%

79

,59%

83

,67%

14

,29%

57

,14%

18

,37%

32

,65%

81

,63%

26

,53%


133

NJ

NM

NY

NC

ND

OH

OK

OR

PA

RI

SC

SD

TN

TX

NC Charlotte/Douglas International Airport 1 0 1 0 1 0 1 1 1 1 0 1 1

ND Bismarck and Hector Airports 0 0 0 0 0 0 0 0 0 0 0 0 0


1 0 1 1 0 0 1 1 1 0 1 1

OK Will Rogers World and Tulsa Airports 1 0 0 0 0 0 0 0 0 0 0 1 1

OR Portland International Airport 1 1 1 1 0 0 0 1 0 0 0 0 1

PA Philadelphia International Airport 1 0 1 1 0 1 0 1 1 1 0 1 1

RI Theodore Francis Green State Airport 1 0 1 1 0 1 0 0 1 0 0 1 0


1 0 1 1 0 0 0 0 1 0 0 0 1

SD Rapid City and Sioux Falls Airports 0 0 0 0 0 0 0 0 0 0 0 0 1

TN Memphis and Nashville Airports 1 0 1 1 0 1 1 0 1 1 0 0 1


1 1 1 1 0 1 1 1 1 0 1 1 1

UT Salt Lake City International Airport 1 1 1 0 1 0 1 1 1 0 0 1 1 1

VT Burlington International Airport 1 0 1 0 0 1 0 0 1 0 0 0 0 0


1 1 1 1 0 1 1 1 1 1 1 0 1 1

WA Seattle–Tacoma International Airport 1 1 1 1 0 1 0 1 0 0 0 0 1 1

WV Yeager and Tri-State Airports 0 0 1 1 0 0 0 0 0 0 1 0 0 0

WI General Mitchell International Airport 1 0 1 1 0 1 0 0 1 0 0 0 1 1

WY Jackson Hole Airport 0 0 0 0 0 0 0 0 0 0 0 0 0 1


69

,39%

30

,61%

79

,59%

75

,51%

12

,24%

55

,10%

33

,33%

48

,98%

67

,35%

26

,53%

32

,65%

16

,33%

55

,10%

85

,71%


134

UT

VT

VA

WA

WV

WI

WY



NC Charlotte/Douglas International Airport 0 0 1 1 1 1 0 69,39%

ND Bismarck and Hector Airports 1 0 0 0 0 0 0 12,24%


0 1 1 1 0 1 0 60,42%

OK Will Rogers World and Tulsa Airports 1 0 1 0 0 0 0 32,65%

OR Portland International Airport 1 0 1 1 0 0 0 44,90%

PA Philadelphia International Airport 1 1 1 1 0 1 0 69,39%

RI Theodore Francis Green State Airport 0 0 1 0 0 0 0 32,65%


0 0 1 0 0 0 0 22,45%

SD Rapid City and Sioux Falls Airports 1 0 0 0 0 0 0 18,37%

TN Memphis and Nashville Airports 1 0 1 1 0 1 0 69,39%


1 0 1 1 0 1 1 81,63%

UT Salt Lake City International Airport 0 1 1 0 0 1 67,35%

VT Burlington International Airport 0 1 0 0 0 0 18,37%


1 1 1 1 1 0 77,55%

WA Seattle–Tacoma International Airport 1 0 1 0 1 0 57,14%

WV Yeager and Tri-State Airports 0 0 1 0 0 0 20,41%

WI General Mitchell International Airport 0 0 1 1 0 0 51,02%

WY Jackson Hole Airport 1 0 0 0 0 0 12,24%


67

,35%

16

,33%

77

,55%

63

,27%

12

,24%

46

,94%

14

,29%


135

Annex 12 – Distance between the capitals of the states of the USA

AL AK AZ AR CA CO CT DE DC FL GA HI ID IL IN IA KS

AL 4593 2403 619 3238 1865 1592 1228 1105 286 235 7075 2882 878 823 1214 1128

AK 4593 3224 4048 2391 2939 4585 4628 4562 4877 4575 4521 2065 3761 3962 3388 3486

AZ 2403 3224 1818 1011 935 3552 3308 3178 2636 2554 4672 1176 2110 2403 1851 1588

AR 619 4048 1818 2621 1248 1879 1568 1435 893 737 6485 2270 609 781 770 565

CA 3238 2391 1011 2621 1425 4103 3932 3813 3499 3346 3955 714 2730 3026 2381 2226

CO 1865 2939 935 1248 1425 2713 2517 2395 2141 1945 5369 1025 1308 1605 979 801

CT 1592 4585 3552 1879 4103 2713 376 488 1628 1358 8056 3520 1443 1155 1734 1964

DE 1228 4628 3308 1568 3932 2517 376 134 1252 993 7885 3393 1212 914 1552 1735

DC 1105 4562 3178 1435 3813 2395 488 134 1146 871 7763 3283 1088 790 1436 1609

FL 286 4877 2636 893 3499 2141 1628 1252 1146 367 7302 3162 1146 1049 1495 1413

GA 235 4575 2554 737 3346 1945 1358 993 871 367 7222 2946 816 686 1189 1168

HI 7075 4521 4672 6485 3955 5369 8056 7885 7763 7302 7222 4554 6677 6974 6335 6162

ID 2882 2065 1176 2270 714 1025 3520 3393 3283 3162 2946 4554 2234 2515 1855 1780

IL 878 3761 2110 609 2730 1308 1443 1212 1088 1146 816 6677 2234 298 390 525

IN 823 3962 2403 781 3026 1605 1155 914 790 1049 686 6974 2515 298 660 822

IA 1214 3388 1851 770 2381 979 1734 1552 1436 1495 1189 6335 1855 390 660 332

KS 1128 3486 1588 565 2226 801 1964 1735 1609 1413 1168 6162 1780 525 822 332

KY 661 4167 2495 770 3168 1743 1110 817 684 862 495 7106 2683 449 207 836 944

LA 507 4501 1999 488 2904 1624 2072 1718 1591 660 733 6652 2644 1045 1129 1257 1041

ME 1952 4554 3802 2198 4282 2928 370 746 853 1996 1719 8217 3664 1712 1441 1957 2219

MD 1147 4587 3224 1482 3856 2438 448 86,6 47,1 1181 912 7807 3322 1132 834 1477 1654

MA 1739 4632 3687 2026 4221 2839 149 517 634 1768 1504 8170 3627 1580 1295 1860 2099

MI 1163 3817 2600 1115 3122 1735 982 854 762 1365 997 7075 2552 538 356 758 1020

MN 1516 3153 2060 1136 2443 1134 1682 1581 1484 1785 1448 6383 1839 639 808 375 689

MS 367 4384 2043 336 2902 1563 1870 1524 1394 599 564 6713 2588 833 905 1076 900

MO 872 3721 1870 427 2534 1110 1689 1440 1311 1157 879 6468 2080 255 535 357 309

MT 2687 2004 1432 2099 1165 935 3150 3054 2952 2972 2717 4965 452 1951 2213 1561 1560

NE 1318 3283 1582 775 2128 714 2000 1805 1685 1604 1337 6079 1633 606 898 270 212

NV 3093 2379 929 2474 163 1267 3941 3772 3653 3356 3196 4117 576 2571 2866 2220 2069

NH 1764 4538 3652 2020 4164 2792 186 560 664 1808 1530 8109 3561 1551 1274 1816 2066

NJ 1350 4587 3374 1662 3969 2562 245 135 245 1383 1115 7924 3412 1267 971 1588 1792

NM 1847 3272 607 1241 1409 461 2945 2701 2572 2098 1978 5246 1242 1502 1795 1256 982

NY 1585 4450 3466 1828 3995 2614 135 415 500 1644 1353 7945 3404 1362 1082 1636 1879

NC 800 4726 3051 1246 3769 2345 838 462 370 792 572 7694 3295 1061 792 1446 1544

ND 2023 2575 1756 1517 1915 856 2291 2209 2114 2305 2000 5810 1256 1190 1415 813 956

OH 897 4127 2674 1010 3289 1872 888 645 524 1065 703 7240 2763 569 272 912 1093

OK 1093 3704 1348 480 2145 808 2259 1982 1850 1357 1216 6007 1831 842 1108 759 429

OR 3445 1683 1576 2832 720 1586 4019 3915 3810 3724 3505 4115 562 2779 3054 2395 2338

PA 1216 4459 3194 1492 3792 2383 389 169 153 1278 983 7747 3241 1087 792 1411 1612

RI 1681 4653 3656 1980 4204 2816 104 456 576 1705 1446 8155 3617 1547 1259 1837 2068

SC 494 4729 2830 1012 3603 2190 1149 774 667 479 283 7495 3176 980 772 1368 1398

SD 1806 2789 1572 1270 1868 641 2253 2127 2020 2091 1806 5807 1269 1017 1274 627 705


136

KY LA ME MD MA MI MN MS MO MT NE NV NH NJ NM NY NC

AL 661 507 1952 1147 1739 1163 1516 367 872 2687 1318 3093 1764 1350 1847 1585 800

AK 4167 4501 4554 4587 4632 3817 3153 4384 3721 2004 3283 2379 4538 4587 3272 4450 4726

AZ 2495 1999 3802 3224 3687 2600 2060 2043 1870 1432 1582 929 3652 3374 607 3466 3051

AR 770 488 2198 1482 2026 1115 1136 336 427 2099 775 2474 2020 1662 1241 1828 1246

CA 3168 2904 4282 3856 4221 3122 2443 2902 2534 1165 2128 163 4164 3969 1409 3995 3769

CO 1743 1624 2928 2438 2839 1735 1134 1563 1110 935 714 1267 2792 2562 461 2614 2345

CT 1110 2072 370 448 149 982 1682 1870 1689 3150 2000 3941 186 245 2945 135 838

DE 817 1718 746 86,6 517 854 1581 1524 1440 3054 1805 3772 560 135 2701 415 462

DC 684 1591 853 47,1 634 762 1484 1394 1311 2952 1685 3653 664 245 2572 500 370

FL 862 660 1996 1181 1768 1365 1785 599 1157 2972 1604 3356 1808 1383 2098 1644 792

GA 495 733 1719 912 1504 997 1448 564 879 2717 1337 3196 1530 1115 1978 1353 572

HI 7106 6652 8217 7807 8170 7075 6383 6713 6468 4965 6079 4117 8109 7924 5246 7945 7694

ID 2683 2644 3664 3322 3627 2552 1839 2588 2080 452 1633 576 3561 3412 1242 3404 3295

IL 449 1045 1712 1132 1580 538 639 833 255 1951 606 2571 1551 1267 1502 1362 1061

IN 207 1129 1441 834 1295 356 808 905 535 2213 898 2866 1274 971 1795 1082 792

IA 836 1257 1957 1477 1860 758 375 1076 357 1561 270 2220 1816 1588 1256 1636 1446

KS 944 1041 2219 1654 2099 1020 689 900 309 1560 212 2069 2066 1792 982 1879 1544

KY 1034 1430 731 1256 505 1013 814 638 2397 1053 3010 1251 898 1891 1059 612

LA 1034 2423 1635 2221 1484 1619 224 907 2524 1253 2770 2236 1834 1495 2051 1305

ME 1430 2423 816 242 1202 1839 2216 1965 3268 2226 4119 189 613 3199 373 1208

MD 731 1635 816 593 794 1519 1439 1357 2989 1728 3695 628 204 2618 469 398

MA 1256 2221 242 593 1104 1788 2019 1829 3247 2128 4058 102 389 3081 225 977

MI 505 1484 1202 794 1104 727 1261 791 2202 1027 2960 1058 861 1997 879 920

MN 1013 1619 1839 1519 1788 727 1427 712 1476 544 2281 1726 1585 1498 1565 1584

MS 814 224 2216 1439 2019 1261 1427 720 2431 1108 2760 2030 1635 1499 1843 1131

MO 638 907 1965 1357 1829 791 712 720 1838 458 2378 1803 1504 1263 1613 1235

MT 2397 2524 3268 2989 3247 2202 1476 2431 1838 1380 1022 3177 3061 1297 3028 3004

NE 1053 1253 2226 1728 2128 1027 544 1108 458 1380 1968 2085 1848 991 1904 1663

NV 3010 2770 4119 3695 4058 2960 2281 2760 2378 1022 1968 4001 3808 1276 3833 3613

NH 1251 2236 189 628 102 1058 1726 2030 1803 3177 2085 4001 426 3047 192 1021

NJ 898 1834 613 204 389 861 1585 1635 1504 3061 1848 3808 426 2766 283 596

NM 1891 1495 3199 2618 3081 1997 1498 1499 1263 1297 991 1276 3047 2766 2860 2456

NY 1059 2051 373 469 225 879 1565 1843 1613 3028 1904 3833 192 283 2860 867

NC 612 1305 1208 398 977 920 1584 1131 1235 3004 1663 3613 1021 596 2456 867

ND 1613 1997 2412 2149 2388 1355 630 1847 1152 859 744 1755 2318 2209 1311 2169 2203

OH 256 1290 1189 567 1031 332 994 1070 805 2443 1161 3128 1015 700 2067 823 599

OK 1166 816 2547 1896 2401 1377 1117 764 587 1714 597 2000 2381 2060 762 2190 1703

OR 3228 3202 4135 3848 4117 3065 2342 3149 2636 869 2184 696 4046 3926 1769 3897 3840

PA 725 1687 737 148 538 694 1421 1482 1325 2896 1670 3631 550 180 2587 370 518

RI 1211 2165 309 534 66,5 1083 1778 1965 1793 3243 2103 4041 154 331 3049 215 914

SC 565 1001 1518 702 1291 1012 1578 844 1096 2923 1549 3450 1330 905 2249 1166 319

SD 1459 1743 2415 2059 2363 1282 577 1604 935 939 496 1705 2302 2143 1078 2139 2065


137

ND OH OK OR PA RI SC SD TN TX UT VT VA WA WV WI WY

AL 2023 897 1093 3445 1216 1681 494 1806 425 1112 2457 1778 986 3487 787 1221 1910

AK 2575 4127 3704 1683 4459 4653 4729 2789 4230 4177 2522 4396 4650 1476 4340 3511 2823

AZ 1756 2674 1348 1576 3194 3656 2830 1572 2318 1397 803 3579 3143 1756 2778 2235 1062

AR 1517 1010 480 2832 1492 1980 1012 1270 527 707 1838 1990 1368 2883 1035 961 1302

CA 1915 3289 2145 720 3792 4204 3603 1868 3057 2351 855 4063 3815 950 3439 2727 1448

CO 856 1872 808 1586 2383 2816 2190 641 1640 1237 596 2705 2392 1653 2016 1350 157

CT 2291 888 2259 4019 389 104 1149 2253 1366 2574 3249 278 624 3960 851 1376 2661

DE 2209 645 1982 3915 169 456 774 2127 1044 2247 3080 618 248 3879 537 1237 2484

DC 2114 524 1850 3810 153 576 667 2020 910 2115 2962 703 156 3779 404 1134 2366

FL 2305 1065 1357 3724 1278 1705 479 2091 677 1293 2731 1845 1004 3770 909 1474 2191

GA 2000 703 1216 3505 983 1446 283 1806 345 1315 2541 1547 752 3531 567 1122 1972

HI 5810 7240 6007 4115 7747 8155 7495 5807 6966 6026 4809 8002 7757 4239 7382 6680 5402

ID 1256 2763 1831 562 3241 3617 3176 1269 2625 2198 477 3450 3307 646 2936 2159 974

IL 1190 569 842 2779 1087 1547 980 1017 474 1284 1883 1492 1085 2778 710 366 1288

IN 1415 272 1108 3054 792 1259 772 1274 404 1487 2176 1226 794 3043 421 455 1581

IA 813 912 759 2395 1411 1837 1368 627 844 1308 1528 1733 1453 2389 1082 385 933

KS 956 1093 429 2338 1612 2068 1398 705 847 991 1391 1997 1595 2366 1220 691 808

KY 1613 256 1166 3228 725 1211 565 1459 282 1471 2326 1227 653 3225 282 662 1733

LA 1997 1290 816 3202 1687 2165 1001 1743 752 634 2187 2236 1482 3276 1235 1411 1707

ME 2412 1189 2547 4135 737 309 1518 2415 1699 2901 3433 223 993 4056 1190 1578 2859

MD 2149 567 1896 3848 148 534 702 2059 957 2161 3004 672 181 3816 451 1171 2408

MA 2388 1031 2401 4117 538 66,5 1291 2363 1514 2722 3367 245 765 4051 999 1495 2782

MI 1355 332 1377 3065 694 1083 1012 1282 754 1812 2267 979 834 3026 546 396 1679

MN 630 994 1117 2342 1421 1778 1578 577 1110 1677 1597 1620 1539 2299 1199 361 1040

MS 1847 1070 764 3149 1482 1965 844 1604 531 751 2145 2024 1295 3208 1025 1199 1628

MO 1152 805 587 2636 1325 1793 1096 935 546 1051 1700 1746 1291 2654 917 552 1113

MT 859 2443 1714 869 2896 3243 2923 939 2381 2173 629 3059 2993 830 2631 1819 823

NE 744 1161 597 2184 1670 2103 1549 496 1002 1172 1278 2003 1691 2197 1315 653 683

NV 1755 3128 2000 696 3631 4041 3450 1705 2903 2230 693 3900 3656 912 3280 2565 1287

NH 2318 1015 2381 4046 550 154 1330 2302 1517 2721 3312 144 805 3976 1005 1444 2731

NJ 2209 700 2060 3926 180 331 905 2143 1142 2349 3115 484 380 3882 627 1253 2521

NM 1311 2067 762 1769 2587 3049 2249 1078 1724 970 765 2976 2541 1888 2173 1641 617

NY 2169 823 2190 3897 370 215 1166 2139 1327 2530 3141 203 650 3835 818 1270 2557

NC 2203 599 1703 3840 518 914 319 2065 733 1878 2932 1071 217 3834 386 1223 2341

ND 1620 1288 1728 2048 2384 2166 274 1655 1853 1115 2201 2168 1673 1820 987 707

OH 1620 1371 3294 520 991 683 1506 539 1716 2437 980 550 3272 216 633 1842

OK 1288 1371 2391 1884 2362 1487 1016 971 576 1382 2330 1803 2461 1446 1096 892

OR 1728 3294 2391 3759 4112 3732 1789 3181 2737 1019 3927 3843 235 3475 2678 1535

PA 2048 520 1884 3759 488 802 1973 977 2185 2938 568 308 3720 461 1083 2344

RI 2384 991 2362 4112 488 1229 2351 1465 2674 3349 287 703 4050 949 1476 2762

SC 2166 683 1487 3732 802 1229 1995 552 1596 2785 1368 526 3745 486 1228 2206

SD 274 1506 1016 1789 1973 2351 1995 1463 1580 1026 2197 2056 1769 1692 890 511


138

AL AK AZ AR CA CO CT DE DC FL GA HI ID IL IN IA KS

TN 425 4230 2318 527 3057 1640 1366 1044 910 677 345 6966 2625 474 404 844 847

TX 1112 4177 1397 707 2351 1237 2574 2247 2115 1293 1315 6026 2198 1284 1487 1308 991

UT 2457 2522 803 1838 855 596 3249 3080 2962 2731 2541 4809 477 1883 2176 1528 1391

VT 1778 4396 3579 1990 4063 2705 278 618 703 1845 1547 8002 3450 1492 1226 1733 1997

VA 986 4650 3143 1368 3815 2392 624 248 156 1004 752 7757 3307 1085 794 1453 1595

WA 3487 1476 1756 2883 950 1653 3960 3879 3779 3770 3531 4239 646 2778 3043 2389 2366

WV 787 4340 2778 1035 3439 2016 851 537 404 909 567 7382 2936 710 421 1082 1220

WI 1221 3511 2235 961 2727 1350 1376 1237 1134 1474 1122 6680 2159 366 455 385 691

WY 1910 2823 1062 1302 1448 157 2661 2484 2366 2191 1972 5402 974 1288 1581 933 808

KY LA ME MD MA MI MN MS MO MT NE NV NH NJ NM NY NC

TN 282 752 1699 957 1514 754 1110 531 546 2381 1002 2903 1517 1142 1724 1327 733

TX 1471 634 2901 2161 2722 1812 1677 751 1051 2173 1172 2230 2721 2349 970 2530 1878

UT 2326 2187 3433 3004 3367 2267 1597 2145 1700 629 1278 693 3312 3115 765 3141 2932

VT 1227 2236 223 672 245 979 1620 2024 1746 3059 2003 3900 144 484 2976 203 1071

VA 653 1482 993 181 765 834 1539 1295 1291 2993 1691 3656 805 380 2541 650 217

WA 3225 3276 4056 3816 4051 3026 2299 3208 2654 830 2197 912 3976 3882 1888 3835 3834

WV 282 1235 1190 451 999 546 1199 1025 917 2631 1315 3280 1005 627 2173 818 386

WI 662 1411 1578 1171 1495 396 361 1199 552 1819 653 2565 1444 1253 1641 1270 1223

WY 1733 1707 2859 2408 2782 1679 1040 1628 1113 823 683 1287 2731 2521 617 2557 2341

ND OH OK OR PA RI SC SD TN TX UT VT VA WA WV WI WY

TN 1655 539 971 3181 977 1465 552 1463 1208 2234 1501 842 3200 516 799 1655

TX 1853 1716 576 2737 2185 2674 1596 1580 1208 1723 2695 2030 2844 1724 1601 1362

UT 1115 2437 1382 1019 2938 3349 2785 1026 2234 1723 3213 2968 1123 2593 1873 596

VT 2201 980 2330 3927 568 287 1368 2197 1501 2695 3213 853 3853 1001 1355 2637

VA 2168 550 1803 3843 308 703 526 2056 842 2030 2968 853 3822 376 1181 2373

WA 1673 3272 2461 235 3720 4050 3745 1769 3200 2844 1123 3853 3822 3461 2646 1581

WV 1820 216 1446 3475 461 949 486 1692 516 1724 2593 1001 376 3461 837 1998

WI 987 633 1096 2678 1083 1476 1228 890 799 1601 1873 1355 1181 2646 837 1287

WY 707 1842 892 1535 2344 2762 2206 511 1655 1362 596 2637 2373 1581 1998 1287


139

Annex 13 – GDP of each USA state

State GDP per capita (chained 2009

dollars)


USA average

GDP (millions of current dollars)

% USA GDP

AL 37 389,0 -25,58% 193 566,0 1,15%

AK 70 113,0 39,55% 59 355,0 0,35%

AZ 39 526,0 -21,33% 379 024,0 2,26%

AR 39 111,0 -22,15% 124 218,0 0,74%

CA 53 497,0 6,48% 2 202 678,0 13,11%

CO 51 956,0 3,41% 294 443,0 1,75%

CT 65 070,0 29,51% 249 251,0 1,48%

DE 62 683,0 24,76% 62 703,0 0,37%

DC 163 145,0 224,72% 113 362,0 0,67%

FL 38 384,0 -23,60% 800 492,0 4,76%

GA 42 494,0 -15,42% 454 532,0 2,71%

HI 49 934,0 -0,61% 75 235,0 0,45%

ID 35 375,0 -29,59% 62 247,0 0,37%

IL 52 119,0 3,74% 720 692,0 4,29%

IN 44 775,0 -10,88% 317 102,0 1,89%

IA 48 703,0 -3,06% 165 767,0 0,99%

KS 45 665,0 -9,11% 144 062,0 0,86%

KY 38 830,0 -22,71% 183 373,0 1,09%

LA 47 997,0 -4,47% 253 576,0 1,51%

ME 38 517,0 -23,34% 54 755,0 0,33%

MD 54 351,0 8,18% 342 382,0 2,04%

MA 52 866,0 5,22% 446 323,0 2,66%

MI 41 252,0 -17,89% 432 573,0 2,57%

MN 53 340,0 6,17% 312 081,0 1,86%

MS 32 421,0 -35,47% 105 163,0 0,63%

MO 42 708,0 -14,99% 276 345,0 1,64%

MT 39 251,0 -21,88% 44 040,0 0,26%

NE 52 582,0 4,66% 109 614,0 0,65%

NV 44 407,0 -11,61% 132 024,0 0,79%

NH 48447 -3,57% 67848 0,40%

NJ 57203 13,86% 543071 3,23%

NM 40431 -19,53% 92245 0,55%

NY 62420 24,24% 1310712 7,80%

NC 44646 -11,14% 471365 2,81%

ND 68804 36,95% 56329 0,34%

OH 45476 -9,49% 565272 3,36%

OK 42670 -15,07% 182086 1,08%

OR 53750 6,98% 219590 1,31%

PA 47274 -5,91% 644915 3,84%

RI 47515 -5,43% 53184 0,32%


140

SC 36059 -28,23% 183561 1,09%

SD 48696 -3,08% 46732 0,28%

TN 41503 -17,39% 287633 1,71%

TX 52465 4,43% 1532623 9,12%

UT 45165 -10,10% 141240 0,84%

VT 44241 -11,94% 29509 0,18%

VA 51623 2,75% 452585 2,69%

WA 54654 8,78% 408049 2,43%

WV 36963 -26,43% 73970 0,44%

WI 45993 -8,46% 282486 1,68%

WY 67857 35,06% 45432 0,27%

Average 50 241,5 Total 16 801 415,0


141

Annex 14 – Airport Websites

Name of the Airport Website Adress Date of

visitation

EUROPEAN UNION AIRPORTS

Adolfo Suarez Madrid – Barajas Airport

http://www.aeropuertomadrid-barajas.com/ 06-01-2015

Amsterdam Airport Schiphol http://www.schiphol.nl/ 06-01-2015

Athens International Airport https://www.aia.gr/traveler/ 06-01-2015

Bratislava Airport http://www.bts.aero/en/passengers 06-01-2015

Brussels Airport http://www.brusselairport.be/en/ 06-01-2015

Brussels South Charleroi Airport http://www.charleroi-airport.com/ 06-01-2015

Budapest Ferec Liszt International Airport

http://www.bud.hu/english 06-01-2015

Burgas Airport http://www.bourgas-airport.com/ 06-01-2015

Charles de Gaulle Airport http://www.aeroportsdeparis.fr/ 06-01-2015

Copenhagen Airport http://www.cph.dk/en/ 06-01-2015

Dublin Airport http://dublinairport.com/ 06-01-2015

Dubrovnik Airport http://www.airport-dubrovnik.hr 06-01-2015

Frankfurt Airport http://www.frankfurt-airport.de/ 06-01-2015

Helsinki Airport http://www.finavia.fi/en/helsinki-airport/ 06-01-2015

Henri Coanda International Airport http://www.bucharestairports.ro/otp/ 06-01-2015

Kaunas Airport https://www.kaunas-airport.lt/ 06-01-2015

Larnaca International Airport http://www.hermesairports.com/en/larnakahome 06-01-2015

Leonardo da Vinci – Fiumicino Airport https://www.adr.it/fiumicino 06-01-2015

Lisbon Portela Airport http://www.ana.pt/ 06-01-2015

Ljubljana Joze Pucnik Airport http://www.lju-airport.si/ 06-01-2015

London Heathrow Airport http://www.heathrowairport.com/ 06-01-2015

Luxembourg Findel Airport http://www.lux-airport.lu/ 06-01-2015

Malta International Airport http://maltairport.com/en/home.htm 06-01-2015

Paphos International Airport http://www.hermesairports.com/en/pafoshome 06-01-2015

Riga International Airport http://www.riga-airport.com/ 06-01-2015

Sofia Airport http://www.sofia.airport.bg/ 06-01-2015

Split Airport http://www.split-airport.hr/ 06-01-2015

Stockholm Arlanda Airport http://www.swedavia.com/arlanda/ 06-01-2015

Tallinn Airport http://www.tallinn-airport.ee/ 06-01-2015

Václav Havel Airport Prague http://www.prg.aero/en/ 06-01-2015

Vienna International Airport http://www.viennaairport.com/ 06-01-2015

Vilnius Airport http://www.vilnius-airport.lt/ 06-01-2015

Warsaw Chopin Airport http://www.lotnisko-chopina.pl/ 06-01-2015

Zagreb Airport http://www.zagreb-airport.hr/ 06-01-2015

BRAZILIAN AIRPORTS

Afonso Pena International Airport http://www.aeroportocuritiba.net/en/ 15-02-2015

Augusto Severo International Airport http://www.infraero.gov.br/ 15-02-2015

Boa Vista International Airport http://www.infraero.gov.br/ 15-02-2015


142

Brasília International Airport http://www.aeroportobrasilia.net/en/ 15-02-2015

Campo Grande International Airport http://www.infraero.gov.br/index.php/aeroportos/mato-grosso-do-sul/aeroporto-internacional-de-campo-grande.html

15-02-2015

Congonhas Airport http://www.aeroportocongonhas.net/en/ 15-02-2015

Dep. Luís Eduardo Magalhães International Airport

http://www.aeroportosalvador.net/en/ 15-02-2015

Eduardo Gomes International Airport http://www.infraero.gov.br/index.php/br/aeroportos/amazonas/aeroporto-internacional-eduardo-gomes.html

15-02-2015

Eurico de Aguiar Sallles Airport http://www.infraero.gov.br/index.php/br/aeroportos/espirito-santo/aeroporto-de-vitoria-eurico-de-aguiar-salles-vitoria-es.html

15-02-2015

Galeão and Santos Dumont Airports http://www.aeroportogaleao.net/en 15-02-2015

Governador Jorge Teixeira International Airport

http://www.infraero.gov.br/index.php/br/aeroportos/rondonia/aeroporto-internacional-porto-velho.html

15-02-2015

Guararapes International Airport http://www.aeroportorecife.net/en/recife-international-airport-rec

15-02-2015

Guarulhos International Airport http://www.aeroportoguarulhos.net/en/ 15-02-2015

Hercílio Luz International Airport http://www.aeroportoflorianopolis.net/en 15-02-2015

Macapá International Airport http://www.infraero.gov.br/index.php/br/aeroportos/amapa/aeroporto-internacional-de-macapa/contatos.html

15-02-2015

Marechal Cunha Machado International Airport

http://www.infraero.gov.br/index.php/aeroportos/maranhao/aeroporto-marechal-cunha-machado.html

15-02-2015

Marechal Rondon International Airport http://www.infraero.gov.br/index.php/br/aeroportos/mato-grosso/aeroporto-internacional-marechal-rondon.html

15-02-2015

Navegantes International Airport http://www.infraero.gov.br/index.php/br/aeroportos/santa-catarina/aeroporto-de-navegantes.html

15-02-2015

Palmas Airport http://www.infraero.gov.br/index.php/br/aeroportos/tocantins/aeroporto-de-palmas.html

15-02-2015

Pinto Martins International Airport http://www.aeroportofortaleza.net/en/ 15-02-2015

Plácido de Castro - Rio Branco International Airport

http://www.infraero.gov.br/index.php/br/aeroportos/acre/aeroporto-branco-placido-de-castro.html

15-02-2015

Presidente Castro Pinto International Airport

http://www.infraero.gov.br/index.php/br/aeroportos/paraiba/aeroporto-internacional-presidente-castro-pinto.html

15-02-2015

Saint Genoveva Airport http://www.infraero.gov.br/index.php/br/aeroportos/goias/aeroporto-de-goiania.html

15-02-2015

Salgado Filho International Airport http://www.aeroportoportoalegre.net/en/porto-alegre-international-airport-poa

15-02-2015

Santa Maria International Airport http://www.infraero.gov.br/index.php/br/aeroportos/sergipe/aeroporto-de-aracaju-santa-maria.html

15-02-2015

Senador Petrônio Portella Airport http://www.infraero.gov.br/index.php/br/aeroportos/piaui/aeroporto-de-teresina.html

15-02-2015

Tancredo Neves International Airport http://www.aeroportoconfins.net/en/ 15-02-2015

Val de Cans International Airport http://www.infraero.gov.br/index.php/aeroportos/para/aeroporto-internacional-de-belem.html#

15-02-2015

Zumbi dos Palmares International Airport

http://www.infraero.gov.br/index.php/aeroportos/alagoas/aeroporto-internacional-de-maceiozumbi-dos-palmares.html

15-02-2015


143

UNITED STATES OF AMERICA AIRPORTS

Albuquerque International Sunport https://www.cabq.gov/airport/ 18-03-2015

Baltimore/Washington International Thurgood Marshall Airport

http://www.bwiairport.com/ 16-03-2015

Bangor International Airport http://www.flybangor.com/ 16-03-2015

Bill and Hillary Clinton National Airport (Adams Field)

http://www.clintonairport.com/ 14-03-2015

Billings Logan International Airport http://www.flybillings.com/ 17-03-2015

Birmingham–Shuttlesworth International Airport

http://www.flybirmingham.com/ 14-03-2015

Bismarck Municipal Airport http://www.bismarckairport.com/ 18-03-2015

Boise Airport (Boise Air Terminal) (Gowen Field)

http://www.iflyboise.com/ 15-03-2015

Bozeman Yellowstone International Airport

http://www.bozemanairport.com/ 17-03-2015

Bradley International Airport http://www.bradleyairport.com/ 14-03-2015

Burlington International Airport http://www.burlingtonintlairport.com/ 20-03-2015

Charleston International Airport / Charleston AFB

https://www.chs-airport.com/ 19-03-2015

Charlotte/Douglas International Airport http://charmeck.org/city/charlotte/Airport/Pages/default.aspx

18-03-2015

Chicago Midway International Airport http://www.flychicago.com/midway/ 15-03-2015

Chicago O'Hare International Airport http://www.flychicago.com/ohare/ 15-03-2015

Cincinnati/Northern Kentucky International Airport

http://www.cvgairport.com/ 16-03-2015

Cleveland-Hopkins International Airport

http://www.clevelandairport.com/ 18-03-2015

Dallas/Fort Worth International Airport https://www.dfwairport.com/ 20-03-2015

Denver International Airport http://www.flydenver.com/ 14-03-2015

Des Moines International Airport http://www.dsmairport.com/ 15-03-2015

Detroit Metropolitan Wayne County Airport

http://www.metroairport.com/ 16-03-2015

Eppley Airfield http://www.flyoma.com/ 17-03-2015

Gen. Edward Lawrence Logan International Airport

https://www.massport.com/logan-airport/ 16-03-2015

General Mitchell International Airport http://www.mitchellairport.com/ 20-03-2015

George Bush Intercontinental Airport http://www.fly2houston.com/ 20-03-2015

Gulfport-Biloxi International Airport http://www.flygpt.com/ 17-03-2015

Hartsfield-Jackson Atlanta International Airport

http://www.atlanta-airport.com/ 15-03-2015

Hector International Airport http://www.fargoairport.com/ 18-03-2015

Honolulu International Airport http://hawaii.gov/hnl 15-03-2015

Huntsville International Airport (Carl T. Jones Field)

http://www.flyhuntsville.com/portal/#.VW4gpM9VikY

14-03-2015

Indianapolis International Airport http://www.indianapolisairport.com/ 15-03-2015

Jackson Hole Airport http://www.jacksonholeairport.com/ 20-03-2015

Jackson-Evers International Airport http://jmaa.com/ 17-03-2015

John F. Kennedy International Airport https://www.panynj.gov/airports/jfk.html 18-03-2015

Kansas City International Airport http://www.flykci.com/ 17-03-2015

LaGuardia Airport https://www.panynj.gov/airports/laguardia.html 18-03-2015

Lambert-St. Louis International Airport http://www.flystl.com/ 17-03-2015

Los Angeles International Airport http://www.lawa.org/welcomeLAX.aspx 14-03-2015


144

Louis Armstrong New Orleans International Airport

http://www.flymsy.com/ 16-03-2015

Louisville International Airport (Standiford Field)

http://www.flylouisville.com/ 16-03-2015

Manchester–Boston Regional Airport http://www.flymanchester.com/ 18-03-2015

Manhattan Regional Airport http://www.flymhk.com/ 16-03-2015

McCarran International Airport https://www.mccarran.com/ 17-03-2015

Memphis International Airport http://www.mscaa.com/ 19-03-2015

Miami International Airport http://www.miami-airport.com/ 15-03-2015

Minneapolis–St. Paul International Airport (Wold–Chamberlain Field)

https://www.mspairport.com/ 17-03-2015

Missoula International Airport http://www.flymissoula.com/ 17-03-2015

Nashville International Airport (Berry Field)

http://www.flynashville.com/about/pages/bna-history.aspx

19-03-2015

Newark Liberty International Airport https://www.panynj.gov/airports/newark-liberty.html

18-03-2015

Northwest Arkansas Regional Airport http://www.flyxna.com/ 14-03-2015

Orlando International Airport http://www.orlandoairports.net/ 15-03-2015

Philadelphia International Airport http://www.phl.org/Pages/HomePage.aspx 19-03-2015

Phoenix Sky Harbor International Airport

https://skyharbor.com/ 14-03-2015

Port Columbus International Airport http://flycolumbus.com/ 18-03-2015

Portland International Airport http://www.flypdx.com/PDX 19-03-2015

Portland International Jetport http://www.portlandjetport.org/ 16-03-2015

Rapid City Regional Airport http://www.rcgov.org/Airport/ 19-03-2015

Ronald Reagan Washington National Airport

http://www.metwashairports.com/reagan/reagan.htm

20-03-2015

Salt Lake City International Airport http://www.slcairport.com/ 20-03-2015

San Francisco International Airport http://www.flysfo.com/pt 14-03-2015

Seattle–Tacoma International Airport https://www.portseattle.org/Sea-Tac/Pages/default.aspx

20-03-2015

Sioux Falls Regional Airport (Joe Foss Field)

http://www.sfairport.com/ 19-03-2015

Ted Stevens Anchorage International Airport

http://www.dot.state.ak.us/anc/ 14-03-2015

Theodore Francis Green State Airport http://www.pvdairport.com/ 19-03-2015

Tri-State Airport (Milton J. Ferguson Field)

http://www.tristateairport.com/ 20-03-2015

Tulsa International Airport http://www.tulsaairports.com/ 19-03-2015

Washington Dulles International Airport

http://www.metwashairports.com/dulles/dulles.htm

20-03-2015

Wichita Dwight D. Eisenhower National Airport

http://www.flywichita.com/ 16-03-2015

Will Rogers World Airport http://www.flyokc.com/ 19-03-2015

Wilmington-Philadelphia Regional Airport

http://www.newcastleairportilg.com/ 15-03-2015

Yeager Airport http://www2.yeagerairport.com/ 20-03-2015


145

Exploring Connectivity in Air Transport as an Equity Factor

Documents