Transcript
NEIGHBORHOOD DEPRIVATION
IN ENSCHEDE Application of Geo-tagged Social Media Indicator
YASMIN AFSHARGHOTLI
June, 2015
SUPERVISORS:
Dr. J. A. Martinez Martin
Ing. F.H.M.van den Bosch
Individual final assignment report submitted to the Faculty of Geo-Information
Science and Earth Observation of the University of Twente in partial fulfilment of
the requirements for the Post Graduate Diploma in Geo-information Science and
Earth Observation.
Specialization: Urban Planning and Management
SUPERVISORS:
Dr. J. A. Martinez Martin
Ing. F.H.M.van den Bosch
IFA ASSESSMENT BOARD:
Dr. S. Amer (Chair)
Dr. J. A. Martinez Martin
Ing. F.H.M.van den Bosch
[etc]
NEIGHBORHOOD DEPRIVATION
IN ENSCHEDE Application of Geo-tagged Social Media Indicator
YASMIN AFSHARGHOTLI
Enschede, The Netherlands, June, 2015
DISCLAIMER
This document describes work undertaken as part of a programme of study at the Faculty of Geo-Information Science and
Earth Observation of the University of Twente. All views and opinions expressed therein remain the sole responsibility of the
author, and do not necessarily represent those of the Faculty.
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ABSTRACT
The importance of Multiple Deprivation has raised significantly in Europe since 2010. ‘Europe 20120
Strategy’ is a framework that points the social inclusion as a target and states that social inclusion should be
promoted through poverty reduction by people who are exposed to the risk of poverty (The European
Union, 2012).
The Index of Multiple Deprivation tries to describe the socioeconomic composition of resident’s condition
in a particular spatial unit such as census divisions (Meijer, Engholm, Gritter, & Bloomfield, 2013).
Therefore, different indices of deprivation such as ‘The English Indices of Deprivation’ have executed for
the purpose of poverty measurement in Europe.
This research develops a social economic and physical deprivation index, which was adapted from previous
frameworks to quantify deprivation in Enschede City at Neighbourhood scale. Based on Mpata Wekisa
(2014) a geo-tagged social media indicator is built and further studies are conducted to seek the relationship
between social media indicator and deprivation level in the Enschede’s neighbourhoods.
To this, a quantitative geo-tagged social media indicator based on Instagram data was constructed. The
Factor Analysis categorizes this indicator as a physical indicator. Further, the content of the photo’s captions
extracted from Instagram was examined with Word Clouds analysis method. In this method, the frequency
of positive and negative words in the captions have been discovered.
The geo-visualization for the social economic dimension of Enschede deprivation shows that rural areas of
Enschede have lower social economic deprivation. Also, analysing the individual indices shows that in
deprived neighbourhoods, the percentage of inactive people and people with non-western origin are usually
higher. Moreover, the spatial pattern revealed from geo-visualization of physical deprivation shows that
neighbourhoods located within built-up areas have a lower deprivation. The more distance from the City
Centre neighbourhood located causes the more physical deprivation degree.
Final results showed that at 5% significance level, the final index of social economic deprivation is associated
reversely with the final index of physical deprivation, however, the correlation is not strong.
Keywords: Social economic deprivation, physical deprivation, geo-tagged, social media indicator, Instagram
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ACKNOWLEDGEMENTS
The following research could not be done without the help of my dedicated supervisors Dr. Martinez and
Mr Ing. F.H.M.van den Bosch.
Moreover, I would like to acknowledge my best friend from Geoinformatic department, Mr. Felipe Diniz,
for his countless effort on Instagram’s data extraction needed for execution of this project.
Also, I would like to express my appreciation to my dedicated family for their unlimited passion and strong
support during my academic life especially in the Netherlands.
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TABLE OF CONTENTS
1. Introduction ........................................................................................................................................................... 1
1.1. Urban Poverty and Deprivation ...............................................................................................................................1 1.2. Index of Multiple Deprivation ..................................................................................................................................1 1.3. Geotagging social media .............................................................................................................................................2 1.4. Brief Overview on Enschede ....................................................................................................................................2 1.5. Research Justification ..................................................................................................................................................3 1.6. Objective and questions .............................................................................................................................................4
1.6.1. Objectives ................................................................................................................................................... 4
1.6.2. Questions ................................................................................................................................................... 5
1.7. Report structure ...........................................................................................................................................................5
2. Methodology .......................................................................................................................................................... 7
2.1. Introduction .................................................................................................................................................................7 2.2. Conceptual Framework ..............................................................................................................................................8
2.2.1. Indicators .................................................................................................................................................... 9
2.3. Data and Resources .................................................................................................................................................. 11
2.3.1. Instagram data extraction ..................................................................................................................... 12
2.4. Factor analysis ........................................................................................................................................................... 14 2.5. Standardize and weighting ...................................................................................................................................... 14 2.6. Content analysis ........................................................................................................................................................ 16 2.7. Limitations ................................................................................................................................................................. 17
3. Results and discussion ....................................................................................................................................... 19
3.1. Analysis of the conceptual framework ................................................................................................................. 19
3.1.1. Correlation analysis ................................................................................................................................ 19
3.1.2. Factor Analysis ....................................................................................................................................... 25
3.2. Mapping the indicators ............................................................................................................................................ 26
3.2.1. Social Economic Indicators.................................................................................................................. 26
3.2.2. Physical indicators .................................................................................................................................. 29
3.3. Neighbourhood Deprivation in Enschede .......................................................................................................... 32
3.3.1. Social Economic Deprivation Status in Enschede ........................................................................... 32
3.3.2. Physical Deprivation Status in Enschede ........................................................................................... 33
3.4. Content analysis ........................................................................................................................................................ 34
3.4.1. Word Clouds for Social Economic Deprivation............................................................................... 34
3.4.2. Word Clouds for physical Deprivation .............................................................................................. 35
3.5. Illustrations ................................................................................................................................................................ 36
3.5.1. Social Economic Deprivation status ................................................................................................... 36
3.5.2. Physical Deprivation status .................................................................................................................. 37
4. Conclusion and recomendation ....................................................................................................................... 39
4.1. Conclusion ................................................................................................................................................................. 39 4.2. Research limitations ................................................................................................................................................. 40 4.3. Recommendation ..................................................................................................................................................... 40
Annex 1 ........................................................................................................................................................................ 43
Annex 2 ........................................................................................................................................................................ 44
Annex 3 ........................................................................................................................................................................ 45
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LIST OF FIGURES
Figure 1-1: Trend of unemployment rate in Enschede 2004-2014 ...........................................................................................3
Figure 1-2 : Enschede municipality and Neighbourhood division ............................................................................................4
Figure 2-1: Methodological framework ..........................................................................................................................................7
Figure 2-2: Conceptual framework for Social economic and Physical Deprivation, .............................................................8
Figure 2-3: Instagram data extraction process ........................................................................................................................... 12
Figure 2-4: Spatial distribution of the extracted Instagram photos ........................................................................................ 13
Figure 2-5: Neighbourhoods with unavailable data .................................................................................................................. 17
Figure 3-1: Scatter plot between population density and Instagram photo density ............................................................ 19
Figure 3-2: Correlation between geo-tagged social media indicator ....................................................................................... 21
Figure 3-3: Correlation between geo-tagged social media indicator ....................................................................................... 22
Figure 3-4: Scatter plot between % households with low income and final index of social economic deprivation ..... 22
Figure 3-5: Scatter plot between % of inactive people and final index of social economic deprivation ......................... 22
Figure 3-6: Scatter plot between % of people with non-western origin and final index of social economic deprivation............... 23
Figure 3-7: Scatter plot between Average Distance to secondary eduction and final index of physical deprivation .... 23
Figure 3-8: Scatter plot between geo-tagged social media indicator and average distance to daily necessities .............. 23
Figure 3-9: Correlation between geo-tagged social media indicator and House Affordability .......................................... 23
Figure 3-10: Result of KMO and Bartlett test ............................................................................................................................ 25
Figure 3-11: Factor loadings .......................................................................................................................................................... 25
Figure 3-12: Communalities .......................................................................................................................................................... 25
Figure 3-13: Percentage of people with non-western origin at neighbourhood level in Enschede .................................. 26
Figure 3-14: Percentage of inactive people at neighbourhood level in Enschede ............................................................... 27
Figure 3-15: Percentage of households with low income at neighbourhood level in Enschede ....................................... 28
Figure 3-16: Number of new houses at neighbourhood level in Enschede ......................................................................... 29
Figure 3-17: Average Distance to café teria at neighbourhood level in Enschede .............................................................. 30
Figure 3-18: Instagram photo density at neighbourhood level in Enschede ........................................................................ 31
Figure 3-19: Social Economic Deprivation at neighbourhood level in Ensche ................................................................... 32
Figure 3-20: Physical Deprivation at neighbourhood level in Ensche ................................................................................... 33
Figure 3-21: Scatter plot between Social economic deprivation and Physical deprivation ................................................ 34
Figure 3-22: Word cloud for Cromhoffsbleek-Kotman Neighbourhood ............................................................................. 34
Figure 3-23: Word cloud for Wesselerbrink Zuid-Oost Neighbourhood ............................................................................. 34
Figure 3-24: Word cloud for Ruwenbos Neighbourhood ....................................................................................................... 35
Figure 3-25: Word cloud for Oikos Neighbourhood ............................................................................................................... 35
Figure 3-26: Word cloud for Boekelverd Neighbourhood ...................................................................................................... 35
Figure 3-27: Word cloud for Buurtschap Twekkelo ................................................................................................................. 35
Figure 3-28: Word cloud for Marssteden Neighbourhood ...................................................................................................... 35
Figure 3-29: Word cloud for Boddenkamp Neighbourhood .................................................................................................. 35
Figure 0-1: Percentage of persons 65 years and older at neighbourhood level in Enschede............................................. 45
Figure 0-2: Relative Mortality Rate at neighbourhood level in Enschede ............................................................................. 46
Figure 0-3: Household size at neighbourhood level in Enschede .......................................................................................... 46
Figure 0-4: House affordability at neighbourhood level in Enschede ................................................................................... 46
Figure 0-5: Average Distance to bus stops at neighbourhood level in Enschede ............................................................... 46
Figure 0-6: Average Distance to daily necessities at neighbourhood level in Enschede .................................................... 46
Figure 0-7: Population Density at neighbourhood level in Enschede ................................................................................... 46
Figure 0-8: Average Distance to General Practitioners at neighbourhood level in Enschede .......................................... 46
Figure 0-9: Average Distance to secondary education at neighbourhood level in Enschede ............................................ 46
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LIST OF TABLES
Table 2-1 – Structure of CBS associated data with framework ......................................................................... 11
Table 2-2 - Cost and Benefit .................................................................................................................................... 15
Table 2-3 – Weighting .............................................................................................................................................. 15
Table 3-1: Correlation Matrix ................................................................................................................................... 20
Table 0-1: Meta Data .................................................................................................................................................. 43
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1. INTRODUCTION
1.1. Urban Poverty and Deprivation
Urban poverty and deprivation are often used interchangeably (Noble, Wright, Smith, & Dibben, 2006).
Although there is not clear distinction between these concepts, the most dominant definition for urban
deprivation is presented by Peter Townsend: ‘‘People can be said to be deprived if they lack the types of
diet, clothing, housing, household facilities and fuel and environmental, educational, working and social
conditions, activities and facilities which are customary, or at least widely encouraged and approved, in the
societies to which they belong.’’ (Townsend, 1987, p.125-126).
Followed by Townsend’s definition, European Union Council (EUC) in 1975 adopt a definition of poverty
and social exclusion and extended to more concrete one in 1985: “The persons whose resources(material,
cultural and social) are so limited as to exclude them from the minimum acceptable way of life in the Member
State to which they belong”(EEC, 1985).
According to above definitions, deprivation is a wide range concept that need to be formulated in several
aspects. This concept encompasses not only broad way of different dimensions, but also it includes every
individual who is experiencing deprivation (Noble et al., 2006).
Pacione (1995), discusses the anatomy of deprivation. According to his idea, problems of poverty-related
issues are multidimensional in a way that they interact with each other. He mentions that crime, delinquency,
poor housing, unemployment, increased mortality and mental illnesses are among the poverty effects that
tend to adopt a spatial pattern as well.
Although deprivation is a multidimensional phenomenon, it is a relative concept which can be characterized
by its context (Martínez, 2015). ‘The English Indices of Deprivation’ is an example of governmental report
in United Kingdom which tries to define deprivation in seven domains such as income, employment, health,
education, housing and services, living environment and crime (Noble et al., 2011).
To battle social exclusion, the importance of Multiple Deprivation has raised significantly in Europe since
2010. ‘Europe 20120 Strategy’ is a policy framework that points the social inclusion as one of the most
important objectives. This target articulates that social inclusion should promote through poverty reduction
by people who are exposed to the risk of poverty (European Union, 2012).
1.2. Index of Multiple Deprivation
‘The index of multiple deprivation (IMD)’ is a method that provides a better insight into urban poverty issue
(Baud, Sridharan, & Pfeffer, 2008). In addition, this tool tries to measure the level of disadvantages at small
spatial scale in the context of the western economy (Yuan & Wu, 2014). The Index of Multiple Deprivation
also tries to describe the socioeconomic composition of resident’s condition in a particular spatial unit such
as census divisions (Meijer et al., 2013).
The concept of IMD will reflect various aspects of poverty into discrete domains. Besides, particular groups
of indicators in each domain will address the problem of poverty by means of measuring observables (Yuan
& Wu, 2014).
As it mentioned earlier, ‘The English Indices of Deprivation’, benefits from a combination of seven different
domains that form the index of multiple deprivation. This particular index along with ‘Welsh Index of
Multiple Deprivation’, ‘Northern Ireland Measures of Multiple Deprivation’, and ‘Scottish Indices of
Deprivation’(Noble et al., 2011) are executed for purpose of poverty measurement in British context.
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In order to map the urban poverty, Baud et al. (2008), develop a ‘livelihood assets framework’ to examine
deprivation level in Delhi city, India. This framework benefits from multiple aspects of poverty such as
social capital, human capital, financial capital, and physical capital.
The spatial scale for the index of multiple deprivation will quantify deprivation at a lower level, and assist
policy-makers to identify specific areas which should benefit from allocation of public resources (Noble et
al., 2006).
1.3. Geotagging social media
According to Wikipedia (2015a) geotagging is a metadata information that adds geospatial identification to
different types of media such images, videos, websites and others. The coordinates, usually expressed as
latitude and longitude, can be derived from the smartphone’s GPS, or proximity to places of known
coordinates, or even manual input by the user. On the other hand, Twitter, Instagram, and Flickr are among
the popular social networking services that enable users to share their posts along with geo-referencing that
provides locational information for each tweet or photo.
Instagram is an online photo service, and it gained a drastic popularity among users from 1 million in
December 2010 to over 300 million in December 2014 (Wikipedia, 2015b). This social media also benefits
from different features and tools implemented such as hashtag usage.
The data which is generated by the social media like Foursquare, Flickr, and Twitter can be considered as a
rich data source for spatio-temporal data, since they cover a long time range of different types of data. These
data sources can be used not only to consider social networking and people’s behaviour, but also they have
a spatio-temporal pattern that is observable in particular area (Li, Goodchild, & Xu, 2013).
The research that has done by Quercia & Saez (2014), uses foursquare data as a reliable land use data source
for the city of London. In this research, the possible relationship between certain types of venues and
Neighbourhoods’ socio-economic deprivation have been examined. The results prove that the correlation
between Foursquare categories and Neighbourhood’s index of multiple deprivation is statistically significant.
Moreover, certain types of land use categories are associated with certain deprived or well-off
Neighbourhoods.
In the research initiated by Li et al., (2013) explore the correlation between the density of tweets and photos,
and social economic specifications using Partial least square regression (PLSR) at county level in California,
United States. Moreover, this research identifies the spatial and temporal pattern of geo-referenced tweet
and photos in California. Results show the relationship between tweet densities and percentage of well-
educated people with an advance degree and a good salary, and the correlation between photo densities and
a high percentage of white and Asian people. Spatial distribution of tweets and images, also shows the
concentration of tweets and photo densities along major highways and roads.
1.4. Brief Overview on Enschede
The city of Enschede is located in eastern part of The Netherlands in the urban region of Twente. With the
population of 158000, Enschede is considered as the largest city in both Twente Region and Province
Overijssel (Provincie Overijssel, 2015).
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1Currently, Enschede is facing to problem of stable unemployment. The trend of the unemployment rate is
at its highest point-8 percent- during the last ten years (Fig 1.1).
The municipality of Enschede is divided into 70 Neighbourhood area as the smallest spatial unit, and rural
areas dominant. The share of built-up area in this municipality is only 23.5 percent, and 19 Neighbourhoods
are located in the areas which are more than 50 % rural. Considering the fact that most inhabitants reside in
the built-up area, the average population per Neighbourhood is 2528 in built-up area. Figure 1-2 shows
Enschede built-up area along with Neighbourhood divisions.
Neighbourhoods namely, Buurtschap Zuid-Esmarke, Buurtschap Lonneker-West, Buurtschap
Broekheurne, Noord Esmarke, Boekelerveld, Buurtschap Twekkelo, and Drienerveld-U.T- home of the
University of Twente- are totally located in rural areas.
1.5. Research Justification
Mpata Wekisa (2014), defines the relationship between number of geo-tagged Flickr photo and Multiple
Deprivation Index for the city of Enschede at a Neighbourhood level. The author uses Flickr image hosting
and concludes that social media can be used as a tool to measure urban deprivation.
Therefore, to develop an Index of Multiple Deprivation, this research aims to seek the relationship of the
geo-tagged social media indicator and its contribution to the deprivation level of Enschede’s
Neighbourhood. To this end, Instagram geotagged photos have been adopted.
The spatial distribution of geo-referenced Instagram’s photo illustrate the concentration pattern of social
media indicator and will define the hotspots of photos in city’s Neighbourhood level. Moreover, the current
research will perform a qualitative analysis on the geo-tagged photos in Instagram for a most recent year.
This qualitative analysis not only reveals the contents in Neighbourhood level in Enschede but also is a
strength part of the research since it tries to organize the sample photos into visual word clouds.
1 Data source: CBS data for labor force, active population, and unemployed labor for Enschede municipality 2004-2014.
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2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
per
cen
tage
year
Unemployment Rate
Figure 1-1: Trend of unemployment rate in Enschede 2004-2014
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Figure 1-2 : Enschede municipality and Neighbourhood division
1.6. Objective and questions
The main objectives of this research implementation are described as follow. Further, research questions
are designed in accordance of each objective.
1.6.1. Objectives
To explore geo-tagged social media as an indicator for identifying deprived areas at the
Neighbourhood level in the city of Enschede
Mapping social economic deprivation as well as physical deprivation at Neighbourhood level
Understand the relationship between social economic deprivation and physical deprivation
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1.6.2. Questions
How the geo-tagged social media indicator incorporates with final indices of deprivation?
The answer to this question will construct a social media indicator to assess the deprivation at
Neighbourhood level.
Is there a relationship between geo-tagged social media indicator and final deprivation indices?
This question will test the possible relationships between geo-tagged social media indicator and deprivation
indices.
o What is the content of geo-tagged social media indicator?
The output of this question will present the content of geotagged photos at Neighbourhoods of Enschede
city.
o What is the social economic deprivation status at the Neighbourhood level?
The result of this question will geo-visualize deprivation based on a set of indicators in social economic
dimension.
o What is the physical deprivation status at the Neighbourhood level?
The output of this question will map the deprivation status at Neighbourhood level of Enschede based on
physical dimension.
o To what extent the social economic deprivation is correlated with physical deprivation?
The outcome will investigate the relationship between social economic deprivation and physical deprivation
at the Neighbourhood level for city of Enschede.
1.7. Report structure
This project will be organized in chapters as follow:
1. Introduction
This chapter frames the research problems “Deprivation in Enschede at Neighbourhood level”, and review
fundamental concept about Multiple Deprivation Index (MDI), different types of deprivation, and the role
of social media as a quantitative indicator. Moreover, it presents a brief overview on Enschede Municipality
as a case study area.
2. Methodology
This chapter will present the designated conceptual framework, and discuss methodological steps that
consist of factor analysis, standardize-weighting, and calculating the final index.
3. Results and Discussion
Findings of this research will be set of different maps corresponding to deprivation status in relation to
indicators. Besides, critical discussion on maps will clarify the relationship between social economic
deprivation and physical deprivation. This chapter will carry the most part of this research.
4. Conclusion and recommendation
This chapter tries to answer to main research questions and presenting related recommendations as future
works.
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2. METHODOLOGY
2.1. Introduction
Figure 2.1 presents methodological steps for the construction of Multiple Deprivation Index (MDI) with
the application of geo-tagged social media indicator.
Figure 2-1: Methodological framework
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The work starts with a literature review in previous constructions of Multiple Deprivation Index, focusing in a European context of deprivation. A framework is constructed based on the context of the city of Enschede and data availability. The data sources used are discussed in section 2.3, and its limitations in section 2.7. The proposed conceptual framework and the indicators used are presented in section 2.2.
In section 2.4 the choice of the indicators is evaluated by means of a Factor Analysis. The process is followed
by standardization of the indices and weighting (section 2.5) and construction of the final MDI indices for
Social economic and Physical deprivation.
In addition, content analysis is performed on the captions of the Instagram data. The process of sampling
and word frequency analysis are further explained in Section 2.6. At the end of this chapter are presented
the limitations of the methodology, especially regarding data availability.
2.2. Conceptual Framework
The following conceptual framework tries to measure deprivation at Neighbourhood scale for Enschede.
Therefore, this proposed framework is both theoretical and data driven which consists of three levels.
At first level, the Index of Multiple Deprivation is broken into two major dimensions: social economic, and
physical deprivation. To this end, the final index calculation will result into two different scores for each of
these dimensions. As stated by European Union (2012), social economic dimension of deprivation refers to
insufficient resources and affordability issues while physical deprivation mentions about the availability of
facility and amenities. In addition, Material deprivation includes four major components such as ‘Housing
condition, Basic amenities, Local environment, and Accessibility’.
The second step of this framework, articulates concept of social economic and physical deprivation in
various discrete domains. Population composition, employment, and income are the domains which try to
measure social economic concept. Furthermore, physical deprivation is described by accessibility, housing,
health and living condition, education, and place popularity separate domains.
At the third level, indicators are presented as a measurement tool. According to Martínez & Dopheide
(2014), indicator is defined as: “…qualitative or quantitative data that describe features of a certain
phenomenon and communicate an assessment of the phenomenon involved.” Selection of each indicator
for this research is based on relevancy and data availability regarding to each specific domain.
Figure 2-2: Conceptual framework for Social economic and Physical Deprivation, Adapted(Noble et al., 2011)
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2.2.1. Indicators
This section, presents a rationale for a number of indicators and a brief description on each of the
indicators.
Population composition
In European context there is an association between deprivation and level of social class (Yuan & Wu,
2014). This is defined by economical features such as labour force and social feature such as people and
group composition. Therefore, in this study focus will be on certain groups of people are more vulnerable
to deprivation and poverty.
Percentage of persons 65 years or above
Number of inhabitants of age 65 or older expressed as a percentage of total population.
Relative Mortality
Total number of deaths per 1000 inhabitants. The relative number of deaths is calculated based on
the number of inhabitants on January 1.
Household size
Average household size calculated as the number of persons dwelling in private houses divided by
the total number of private houses.
Percentage of people with non-western origin
Number of immigrants with a non-western origin expressed as a percentage of total population.
Percentage of Inactive
The number of income recipients aged from 15 to 65 with 52 weeks receiving benefit as the main
source of income in the previous year, expressed as a percentage of the total number of income
recipients aged 15 to 65 years. Individuals with unemployment, disabled, and pensioners are among
inactive.
Percentage of households with low income
Total number of private houses classified as low income expressed as a percentage of total private
houses. The definition of low income is based on the 40% lower income households which have a
total annual income lower than 25100 euros.
House affordability
This indicator takes into consideration the fields Average house value (WOZ) and Average income
per year (INK_INW) from CBS data:
WOZ: Average value, in thousands of euros, of residential buildings based on the real
estate determined on the Valuation of Immovable Property Act (Wet Waardering
Onroerende Zaken)
INK_INW: Average value of total annual income based on total population. The indicator is calculated using the following formula:
𝐻𝑜𝑢𝑠𝑒 𝑎𝑓𝑓𝑜𝑟𝑑𝑎𝑏𝑖𝑙𝑖𝑡𝑦 = 𝑊𝑂𝑍𝐼𝑁𝐾_𝐼𝑁𝑊⁄
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Average Distance to bus stops
Average distance from each point inside the Neighbourhood to the closest bus stop. This indicator
was calculated using the attribute Bus Stops from NGR. First Euclidean distance of the point layer
of bus stops was calculated using Euclidean Distance function from Arc Toolbox, then the average
value for the distance inside each Neighbourhood was calculated using the function Zonal Statistics,
also available in Arc Toolbox.
Average distance to daily necessities The average distance of all residents to the nearest groceries shop, bakery, or supermarket, liquor
store, tobacco shop, etc. This distance is calculated following the roads (network distance, not
Euclidean distance)
Population density
Total population divided by the area of the Neighbourhood in km2.
Growth of new housing Total number of dwellings in 2011 minus the total number of dwellings in 2007. The buildings
considered in this indicator are the ones which were built with the purpose of permanent residence.
𝐺𝑟𝑜𝑤𝑡ℎ 𝑜𝑓 𝑛𝑒𝑤 ℎ𝑜𝑢𝑠𝑖𝑛𝑔 = 𝑊𝑂𝑁𝐼𝑁𝐺𝐸𝑁2011 − 𝑊𝑂𝑁𝐼𝑁𝐺𝐸𝑁2007
Assumption: The less number of new houses, the more deprivation.
Average distance to GP
Average distance of all residents to the nearest general practitioner. This distance is calculated
following the roads (network distance, not Euclidean distance).
Average distance to cafeteria Average distance to cafeterias, fast food restaurants, kebab shop, coffee shop, etc. This distance is
calculated following the roads (network distance, not Euclidean distance).
Assumption: The less distance to cafeteria , the more deprivation.
Average distance to secondary education
Average distance of all residents in the Neighbourhood to the nearest government-funded full-time
secondary education. This distance is calculated following the roads (network distance, not
Euclidean distance).
Geo-tagged social media
To eliminate the influence of population in different neighbourhoods, the geo-tagged social
media indicator consider the number of geo-tagged Instagram photos divided by population for
each neighbourhood. Therefore, the geo-tagged social media indicator in this research can be
interpret as a measure for photo density in each neighbourhood.
This indicator consider the amount of geo-tagged Instagram posts between 31may2014 and
31may2015 inside each Neighbourhood divided by the population of that Neighbourhood
(AANT_INW), as presented in the formula below:
𝐺𝑒𝑜 − 𝑡𝑎𝑔𝑔𝑒𝑑 𝑠𝑜𝑐𝑖𝑎𝑙 𝑚𝑒𝑑𝑖𝑎 =#𝐼𝑛𝑠𝑡𝑎𝑔𝑟𝑎𝑚 𝑝ℎ𝑜𝑡𝑜𝑠
𝐴𝐴𝑁𝑇_𝐼𝑁𝑊⁄
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The following assumption for this indicator has been made: Photos have been posted just by
residents of each neighbourhood.
This indicator consider as a cost to deprivation status, so the less Instagram photo density for each
neighbourhood, the more deprived is the neighbourhood.
2.3. Data and Resources
The main data source for this research was the Centraal Bureau Voor de Statistiek (CBS, 2015) for the year
of 2011. This year was chosen due to having a complete range of attributes comparing to the years of 2012,
2013 and 2014 which were available on CBS website. In addition, the attribute WONINGEN was obtained
for the year of 2007 for a comparative analysis. Table 2.1 presents the associated fields of CBS spatial data
and indicators2.
Table 2-1 – Structure of CBS associated data with framework
Complementing CBS data, the coordinates of bus stops for 2011 were obtained in the Nationaal Georegister
website (NGR, 2015).
Also, geo-tagged social media data (from Instagram) was used in addition to the statistical data. The
Instagram API was used for the extraction of geo-tagged photos in the municipality of Enschede between
31 may 2014 and 31 may 2015. The Instagram data extraction is further explained in Section 2.3.1.
2 The full metadata for the data used is presented at Annex 1.
Domain Indicator Data Field Data Field_English
Percentage of persons 65 years or above P_65_EO_JR P_OVER65
Mortality relative P_STERFT MORTALITY
Household size GEM_HH_GR HH_SIZE
Percentage of people with non-western origin P_N_W_AL P_NONWESTERN
Percentage of Inactive P_NIETACT P_INACTIVE
Percentage of households with low income P_LAAGINKH P_LowIncome
House affordability (Houseaff)WOZ/INK_INW Not available in CBS
Average Distance to bus stops AvgDist_bus Not available in CBS
Average distance to daily necessities AF_DAGLMD AvgDist_Daily
Population density BEV_DICHTH POP_DENSITY
Growth of new housing (newhouse)WONINGEN Not available in CBS
Average distance to GP AF_ARTSPR AvgDis_GP
Average distance to cafeteria AF_CAFTAR AvgDist_Cafeteria
Education Average distance to secondary education AF_ONDVRT AvgDist_S_EDU
Place popularity Geo-tagged social media Instagram Not available in CBS
Housing
Health and living condition
Social-economic
Population composition
Employment
Physical
Accessibility
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2.3.1. Instagram data extraction
This section will address more specifically the procedures for obtaining the geo-tagged Instagram data.
Figure 2.3 presents the procedures for the extraction of the geo-tagged Instagram images.
In order to have access to Instagram data, an Instagram account and the creation of a client ID is needed.
The client ID is the authentication for the use of the Instagram Application Program Interface (API). The
Instagram API is an open architecture based on Hypertext Transfer Protocol (HTTP) requests for sharing
data between users and applications (Wikipedia, 2015c).
The Instagram API allows the retrieval of different types of data through endpoints. The Media endpoint
allowed the access of public feed of Instagram photos and was used in this research. This endpoint has a
search method with the following parameters:
o LAT: Latitude of the centre point coordinate of the search area in WGS84.
o LNG: Longitude of the centre point coordinate of the search area in WGS84.
o DISTANCE: Radius of search with a maximum distance of 5km.
o MIN_TIMESTAMP: Minimum time for the search.
o MAX_TIMESTAMP: Maximum time for the search
The response to an API request is a JSON file (Wikipedia, 2015d). Further information on Instagram API
can be obtained at Instagram (2015). From the JSON file response, relevant attributes were selected and
saved to a CSV file. The following attributes were found relevant:
Figure 2-3: Instagram data extraction process
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link of the Instagram post
latitude
longitude
Place: This is a manual location name which is written by users in case of check-in in a particular
location.
Time: Exact time and date for the photo
Photo: An individual URL link for the photo
User caption: Additional image description wrote by user which contain simple text or hashtags.
The function Add XY Data from the software ArcGIS uses CSV file to convert the text into the points.
Each point is a representative for a posted Instagram photo. Further, the CSV file was converted to a GDB
file and, a reprojection from WGS84 3 to RD_New coordinate system was performed to match the
coordinate system with the Neighbourhood boundaries. The data also have improved by removing
duplicate points and removing photos outside Enschede boundary. The duplicates occur due to overlaps of
buffers in photo retrieval, and the ID was used to find duplicates. Also for the same reason exists photos
outside Enschede that were eliminated using ArcGIS Clip function.
In total 42362 Instagram photos were extracted from Enschede Municipality boundary for the period of 31
may 2014 to 31 may 2015. Figure 2.4 illustrates the spatial distribution of geo-tagged photos, and represent
heat locations based on photo densities.
3 WGS84 is a default projection for Instagram.
Figure 2-4: Spatial distribution of the extracted Instagram photos
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In order to generate the final layer with geo-tagged social media indicator, the final step performs a spatial
join between the Neighbourhood layer and the Instagram photos point layer. This is desirable since the
deprivation in this research will be evaluated in Neighbourhood level, using the density of Instagram posts
as an indicator. As a result, an attribute with the number of Instagram photos was created in the
Neighbourhood layer.
2.4. Factor analysis
Factor Analysis (FA) is one of the useful techniques which look into the latent structure of data, and tries
to describe it. Based on conceptual framework discussed earlier, the final index of deprivation is divided
into two dimensions in this research: Social economic and physical deprivation. Factor analysis will perform
to explore the contribution of geo-tagged social media indicator either to social economic deprivation or
physical deprivation. The sample size for this analysis consist of Enschede’s neighbourhoods, and variables
are 15 indicators for measurement. In addition, KMO and Bartlett test will perform within the analysis to
confirm the adequacy of sample size. The result of this analysis will be discussed in chapter 3 respectively.
2.5. Standardization and weighting
In order to perform comparison and computation between indicators, a standardization is needed. This
occurs due the fact that the indicators benefit from various units of measurement. The standardization will
be undertaken in order to determine the direction of each indicator. The standardized indicators (SI) were
calculated with the following formulas whether they are benefit or cost to the concept of deprivation:
𝑆𝐼𝐵𝑒𝑛𝑒𝑓𝑖𝑡 =𝐼 − 𝐼𝑚𝑖𝑛
𝐼𝑚𝑎𝑥 − 𝐼𝑚𝑖𝑛
𝑆𝐼𝐶𝑜𝑠𝑡 = 1 −𝐼 − 𝐼𝑚𝑖𝑛
𝐼𝑚𝑎𝑥 − 𝐼𝑚𝑖𝑛
Where 𝐼 is the indicator value, 𝐼𝑚𝑖𝑛 and 𝐼𝑚𝑎𝑥 are the minimum and maximum values of the indicator
respectively. As a result of standardization, all indices will range between 0 to 1, where 0 value represents
the least deprived and 1 represents the most deprived neighbourhood.
In order to define the direction of each indicator’s contribution to deprivation, the following question has
been asked: ‘What will be the deprivation’s results when the value of each indicator increases?’. In case of
an increase of deprivation, the direction will be “benefit”, and in the event of decrease the direction will be
“cost”. Table 2.2 presents the direction of each indicator as benefit or cost to the final index of deprivation.
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Table 2-2 - Cost and Benefit
For the calculation of the final values for Social economic and Physical final index, a weighting procedure
was applied over the standardized indicators. The equal weighting distribution has been chosen, where all
the indicators within the same domain have received the same weight, and every domain within the same
dimension received the same weight. The final weight per indicator is calculated by multiplying the domain
weight by the indicator weight. Table 2.3 presents the weight for each indicator.
Table 2-3 – Weighting
In conclusion, using the indices final weights the final values, the Social economic and Physical indicator
can be calculated as following:
Domain Indicator Data Field Direction
Percentage of persons 65 years or above P_65_EO_JR benefit
Mortality relative P_STERFT benefit
Household size GEM_HH_GR benefit
Percentage of people with non-western origin P_N_W_AL benefit
Percentage of Inactive P_NIETACT benefit
Percentage of households with low income P_LAAGINKH benefit
House affordability (Houseaff)WOZ/INK_INW benefit
Average Distance to bus stops AvgDist_bus benefit
Average distance to daily necessities AF_DAGLMD benefit
Population density BEV_DICHTH benefit
Growth of new housing (newhouse)WONINGEN cost
Average distance to GP AF_ARTSPR benefit
Average distance to cafeteria AF_CAFTAR cost
Education Average distance to secondary education AF_ONDVRT benefit
Place popularity Geo-tagged social media Instagram cost
Housing
Health and living condition
Employment
Social-economic
Physical
Population composition
Accessibility
Dimension Final weight
%persons 65 years and over 0.25 0.0825
Mortality relative 0.25 0.0825
householdsize 0.25 0.0825
%people with nonwestern origin 0.25 0.0825
Employment 0.33 % of inactive 1 0.33
% household with low income 0.5 0.165
House affordability 0.5 0.165
Average Distance to bus stops 0.5 0.1
Average distance to daily necessities 0.5 0.1
Population density 0.5 0.1
Growth of new housing 0.5 0.1
Average distance to GP 0.5 0.1
Average distance to cafeteria 0.5 0.1
Education 0.2 Average distance to secondary education 1 0.2
Place Popularity 0.2 Geotagged social media 1 0.2
Physical
0.2
0.2
0.2
Housing
Health and living
condition
Accessibility
Socialeconomic
0.33
0.33
Domain Indicator
Population
Composition
Income
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𝐼𝑆𝑜𝑐𝑖𝑜𝑒𝑐𝑜𝑛𝑜𝑚𝑖𝑐 = 0.0825(𝑖𝑎𝑔𝑒65 + 𝑖𝑚𝑜𝑟𝑡 + 𝑖ℎ𝑜𝑢𝑠𝑒𝑠𝑖𝑧 + 𝑖𝑛𝑜𝑛𝑤𝑒𝑠𝑡) + 0.33 𝑖𝑖𝑛𝑎𝑐𝑡𝑖𝑣𝑒 +
+0.165(𝑖𝑙𝑜𝑤𝐼𝑛𝑐 + 𝑖ℎ𝑜𝑢𝑠𝑒𝑎𝑓𝑓)
𝐼𝑝ℎ𝑦𝑠𝑖𝑐𝑎𝑙 = 0.1(𝑖𝑏𝑢𝑠 + 𝑖𝑑𝑎𝑖𝑙𝑦𝑁𝑒𝑐 + 𝑖𝑑𝑒𝑛𝑠𝑖𝑡𝑦 + 𝑖𝑛𝑒𝑤ℎ𝑜𝑢𝑠𝑒 + 𝑖𝑔𝑝 + 𝑖𝑐𝑎𝑓𝑒) +
+0.2( 𝑖𝑠𝑒𝑐𝐸𝑑𝑢 + 𝑖𝑖𝑛𝑠𝑡𝑎𝑔𝑟𝑎𝑚)
The formula used the standardized values for the indicators and the variable 𝑖𝑎𝑔𝑒65 is the % of persons 65
years and older, 𝑖𝑚𝑜𝑟𝑡 is the mortality rate, 𝑖ℎ𝑜𝑢𝑠𝑒𝑠𝑖𝑧 is the household size, 𝑖𝑛𝑜𝑛𝑤𝑒𝑠𝑡 is the % of people with
nonwestern origin, 𝑖𝑖𝑛𝑎𝑐𝑡𝑖𝑣𝑒is the % of inactive population, 𝑖𝑙𝑜𝑤𝐼𝑛𝑐 is the % of people with low income,
𝑖ℎ𝑜𝑢𝑠𝑒𝑎𝑓𝑓 is the house affordability, 𝑖𝑏𝑢𝑠 is the average distance to bus stops, 𝑖𝑑𝑎𝑖𝑙𝑦𝑁𝑒𝑐 is the average
distance to daily necessities, 𝑖𝑑𝑒𝑛𝑠𝑖𝑡𝑦 is the population density, 𝑖𝑛𝑒𝑤ℎ𝑜𝑢𝑠𝑒 is the growth of new housing, 𝑖𝑔𝑝
is the average distance to general practitioner, 𝑖𝑐𝑎𝑓𝑒 is the average distance to cafeteria, 𝑖𝑠𝑒𝑐𝐸𝑑𝑢 is the average
distance to secondary education, and 𝑖𝑖𝑛𝑠𝑡𝑎𝑔𝑟𝑎𝑚 is the geo-tagged social media indicator.
2.6. Content analysis
Content analysis through the word cloud visualization, provides a general overview on the people’s
insight about each of the neighbourhoods. The textual description in Instagram’s caption can be a
representative and provide an insight into the activities of the residents and visitors of each neighbourhoods.
For desired analysis, the least deprived and the most deprived neighbourhoods were sampled in both social
economic and physical derivation. Word clouds were performed based on meaningful words exploring the
presence of negative or positive words.
As mentioned in Section 2.3.1 the caption of the Instagram photos was also retrieved. Instagram’s caption
can provide better information than other social media such as Twitter, since the caption limitation is up to
2200 characters.
Due to the high amount of text descriptions which need to be analysed, not all Instagram captions will be
considered. Based on the final result of the social economic and physical index, neighbourhoods with the
lowest and the highest values will be chosen and a word frequency analysis will be performed individually.
The website Text is Beautiful (Kapiche, 2014), will be used for the word frequency analysis and the result
will visualize as a word cloud (Wikipedia, 2015e). The mentioned website accepts 100000 input characters.
If total amount of characters in each photo caption exceeds the limit value, characters will be removed by
chance until the number of characters reach the specified limit. Another limitation of this website consist
of case sensitivity4 and disability in removing irrelevant words such as conjunction vocabulary in both
English and Dutch languages. Moreover, this website cannot handle special characters5.
4 Word cloud is case sensitive. For example ‘Enschede’ and ‘enschede’ are considered as separate entities. 5 Special characters consist of series of symbols such as @#?!*-_
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2.7. Limitations
The limitations of the adopted methodology are all associated with data in terms of availability and quality.
Neighbourhoods “De Broeid”,’tweldink, and “Koekoekbeekhoek” are among the three neighbourhoods
with lack of data availability. The field values are shown with -99999999 in CBS file. Moreover, the CBS
data associate some fields of some Neighbourhoods with the value -99999997, which are data classified as
secret.
Therefore, 13 other neighbourhoods are excluded from analysis due to the fact that at least had one field
with value classified as secret. These fields consists of indicators for relative mortality rate, percentage of
inactive people, percentage of households with low income, and house affordability 6 . In total, 16
Neighbourhoods (approximate 25% of the Neighbourhoods) are excluded from analysis of this research.
On the other hand, the limitations for Instagram data refers to both geo-tagging accuracy and account
privacy. Moreover, photos with a non-geotagged option cannot be extracted since they do not associate
with geographical coordinates. The extractable Instagram photos are the ones that have the good accuracy
in terms of geo-tagging related to places, and have the public access for API.
Figure 2.5 presents the Neighbourhoods that presents some kind of unavailable data. These neighbourhoods
are not considered in calculation of final index and further analysis such as factor analysis and correlation
analysis. Therefore, among 70 neighbourhoods in Enschede municipality 54 take part in analysis.
6 This indicator is associated with two fields: average house price (WOZ) and average income per year (INKW).
Figure 2-5: Neighbourhoods with unavailable data
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3. RESULTS AND DISCUSSION
3.1. Analysis of the conceptual framework
As it is stated in the first research question, incorporation of social media indicator with the final index of
deprivation-social economic or physical- is desirable. Therefore, the analysis of the conceptual framework
will take place in two steps. Firstly, the bivariate correlation analysis will be conducted to consider the
possible relationships between geo-tagged social media indicator and Final indices of deprivation. Second,
the relevance of geo-tagged social media to final deprivation indices will be determined using Factor analysis
technique.
3.1.1. Correlation analysis
The correlation analysis will be done for following objectives:
1. To explore highly associated variables in analysis.
2. To examine the possible relationship of geo-tagged social media indicator and final indices of
deprivation
3. To examine possible association of all other indicators and final indices of deprivation
4. To examine possible relationship between geo-tagged social media indicator and all other indicators
For analysing mentioned objectives, bivariate correlation has been performed. Table 0-2 presents the
Pearson correlation at 2-tailed significance test generated at SPSS. First the correlation matrix will generate
for 54 neighbourhoods which they cover data availability7. Figure 3-1 demonstrates the scatterplot between
population density and geo-tagged social media indicator. As the figure indicates, the neighbourhood of city
Center shows a different behavior from rest of the neighbourhoods.
Despite the low amount of population for city center neighbourhood, the geo-tagged social media indicator
value is quite high. This shows that this particular neighbourhoods benefits from relatively high number of
Instagram photos from different groups of people rather than residents. The population density for this
particular neighbourhood is 4934 people per square kilometer, and the geo-tagged social media indicator is
1.3.
For the purpose of performing analysis in a more effective way, the City Center neighbourhood will be
excluded as an outlier from further analysis.
7 Refer to Annex 2 for correlation matrix of 54 neighbourhoods including City Center neighbourhood.
Figure 3-1: Scatter plot between population density and Instagram photo density
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At the second step the correlation matrix has been derived based on 53 neighbourhood (Table 3-1).
Table 3-1: Correlation Matrix
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Explore highly associated variables in analysis
According to Field (2005), the acceptable correlation between indicators should be less than 0.9 in bivariate
table. Based on correlation matrix (Table 3-1), two indicators of ‘Percentage of inactive people’ and
‘Percentage of households with low income’ have a high correlation (0.946), which is not acceptable. Besides,
the 2-tailed significance test shows that correlation is acceptable with 1% significance level. Although the
correlation between several indicator pairs are significant, but the correlation value is out of the defined
range by Field.
This correlation can be explained due to the fact that inactive people receives a benefit considered as low
income, so the higher the number of inactive people the higher will be the number of households with low
income.
To examine the relationship between geo-tagged social media indicator and final indices of
deprivation, following assumption has been made:
𝐻0: 𝑇ℎ𝑒𝑟𝑒 𝑖𝑠 𝑛𝑜 𝑠𝑖𝑔𝑛𝑖𝑓𝑖𝑐𝑎𝑛𝑡 𝑐𝑜𝑟𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛
𝐻1: 𝑇ℎ𝑒 𝑐𝑜𝑟𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛 𝑖𝑠 𝑠𝑖𝑔𝑛𝑖𝑓𝑖𝑐𝑎𝑛𝑡
Based on correlation matrix (Table 3-1) the value of 2-tailed Pearson correlation is significant between geo-
tagged social media indicator and Final Physical deprivation index. The value of this correlation is 0.41.
Figure 3-2 illustrates the scatter plot between geo-tagged social media indicator and physical deprivation
final index at 1% significance level. Therefore, null hypothesis would be rejected, and the correlation is
significant. We can conclude that there is a relationship between geo-tagged social media indicator and final
index of physical deprivation, although the relationship might not be strong.
On the other hand, the correlation value between geo-tagged social media indicator and final index of social economic deprivation is low (-0.24) and the correlation is not significant. Therefore, null hypothesis cannot be rejected, and we can conclude that relationship between these particular indicators is not significant (Fig 3-3).
Figure 3-2: Correlation between geo-tagged social media indicator
and Physical deprivation
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To examine possible association of all other indicators and final indices of deprivation
Moreover, the correlation matrix reveals that there are significant and strong correlation between other
indicators such as percentage of household with low income (0.92), percentage of inactive people (0.95) and
percentage of people with non-western origin (0.77) and final index of Social economic Deprivation. Scatter
plots have been made to illustrate this fact in figure 3-4 to 3-6 as follow.
Figure 3-3: Correlation between geo-tagged social media indicator
and Social Economic deprivation
Figure 3-4: Scatter plot between % households with low income and final index of social economic deprivation
Figure 3-5: Scatter plot between % of inactive people and final index of social economic deprivation
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Also, the final index of physical Deprivation has a significant and strong correlation with indicator of
Average distance to secondary education with the value of 0.77 (Figure3-7).
To examine possible relationship between geo-tagged social media indicator and all other indicators
It is important to notice that table 3-1 also shows significant correlations at 99% level between geo-tagged
social media indicator and average distance to daily necessity (0.528), average distance to General
Practitioner (0.542), house affordability (0.561), and average distance to bus stops (0.634). Since the value is
between 0.5-0.7, these correlations can be described as a moderate correlation.
To find out the possible relationship between geo-tagged social media and these four indicators, figures 3-
8 to 3-11 are presented. Although the correlation matrix reveals the positive and significant linear relation
between geo-tagged social media indicator and indicators of average distance to daily necessities, house
affordability, average distance to General Practitioner, and average distance to bus stops, but the scatter
plots reveals that distribution of the neighbourhood does not follow the linear pattern. Therefore, the fact
of positive linear relation between geo-tagged social media indicator and these four indicators cannot be
justified due to the scatter plots.
Figure 3-6: Scatter plot between % of people with non-western origin and final index of social economic deprivation
Figure 3-7: Scatter plot between Average Distance to secondary eduction and final index of physical deprivation
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3.1.2. Factor Analysis
To prove the suitability of Factor analysis for the research sample size which in this case is the number of
neighbourhoods, KMO test has performed. Values of KMO sampling adequacy for this analysis is 0.774
which according to Field (2005), considered as good (more than 0.7) and Factor analysis is acceptable (Figure
-12). Moreover, to maintain analysis consistency with correlation matrix, neighbourhood of City Center is
not considered in FA analysis as well.
At the second step, table of communalities reveals that value of common variance for indicator of growth of
new houses is 0.269. This value shows that common extraction for this indicator is too low, therefore, the
indicator of growth of new houses should be removed for future analysis from the indicator list (Figure 3-
14).
At the end, the factor analysis for the proposed conceptual framework returned to 3 components.
Factor loadings for factor 1 shows the highly associated of variables Average distance to bus stops, Average
distance to daily necessities, Average distance to general practitioner, and house affordability. Based on
associated variables this components can be named provision of services.
Factor 2 is highly associated with percentage of inactive people and percentage of low income households.
Moreover it has a moderate association with household size and percentage of non-western origin. Based
on mentioned factor loading this component will be called livelihood and ethnicity. Factor 3 has moderate
association with percentage of people over age of 65 and relative mortality rate and will be called factor of
age composition.
Due to observable factor loadings, the geo-tagged social media indicator with value of 0.714 has the
moderate association with factor of provision of services (Figure 3-13).
Therefore, based on above results we can conclude that geo-tagged social media indicator contributes to
physical deprivation and factor analysis prove this fact statistically.
Figure 3-10: Result of KMO and Bartlett test
Figure 3-12: Communalities Figure 3-11: Factor loadings
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3.2. Mapping the indicators
To explore the spatial pattern that each of indicators follow, this section will present the map of each indicator result at neighbourhood level in Enschede.8
3.2.1. Social Economic Indicators
% of people with non-western origin
As the figure demonstrate, the concentration of people with non-western origin in Enschede mainly is
in the southern part of the city including neighbourhoods of Cromhoffsbleek-Kotman, and
Wesselerbrinks.
8 This section address to more critical maps and rest of the maps are presented in Annex 3.
Figure 3-13: Percentage of people with non-western origin at neighbourhood level in Enschede
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%inactive people AS the figure shows, the most inactive people are accumulated at neighbourhood of Drienerveld-U.T. (in the area of University of Twente), and the city Center. Also neighbourhood Cromhoffsbleek-Kotman and Wesselerbrink Noord oost also have the high percentage of inactive people.
Figure 3-14: Percentage of inactive people at neighbourhood level in Enschede
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%households with low income As it can be observed from the figure, the percentage of households that receive low income who quite a high in the built-up area of Enschede. This pattern is at its highest in neighbourhood of City center, Drienerveld-U.T, and south west of Enschede.
Figure 3-15: Percentage of households with low income at neighbourhood level in Enschede
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3.2.2. Physical indicators
Growth of new housing
As Figure 3-16 shows, some few neighbourhoods in Enschede such as Drienerveld-U.T, Roombeek-
Roomveldje, and Mekkelholt had a growth in number of new houses in a period of 2007-2011. Since this
indicator considered as a cost to deprivation, therefore, the neighbourhoods with lower growth of new
houses are desirable. These neighbourhoods consists of Velve-Lindenhof and Eekmaat are shown with
red in following map.
Figure 3-16: Number of new houses at neighbourhood level in Enschede
NEIGHBOURHOOD DEPRIVATION IN ENSCHEDE APPLICATION OF GEO-TAGGED SOCIAL MEDIA INDICATOR
30
Distance to cafeteria
The indicator of average distance to cafeteria is also considered as a cost to deprivation. This means that
neighbourhoods which are closer to cafeterias are more deprived, and better neighbourhoods are the ones
which have a distance with the location of Cafeterias. Since most of the cafeteria are concentrated in built-
up area especially the neighbourhoods near city center of Enschede , these neighbourhoods have the least
distance to cafeterias and fast food restaurants.
Figure 3-17: Average Distance to café teria at neighbourhood level in Enschede
NEIGHBOURHOOD DEPRIVATION IN ENSCHEDE APPLICATION OF GEO-TAGGED SOCIAL MEDIA INDICATOR
31
Instagram Photo density Mapping the indicator of Instagram photo density has shown in figure 3-18. Since the Instagram density involves with population for each neighbourhood, the spatial pattern that has emerged from the map can be interpret as follow: The fringe areas such as Buurtschap Twekkelo, Buurtschap Broekheurne, Buurtschap Usselo, and Buurtschap Lonneker-West in Enschede have a very few population. The fact that they benefit from high value of photo density indicator can be interpret because of number of high number of geo-tagged photos in Instagram since these areas are recreational.
Figure 3-18: Instagram photo density at neighbourhood level in Enschede
NEIGHBOURHOOD DEPRIVATION IN ENSCHEDE APPLICATION OF GEO-TAGGED SOCIAL MEDIA INDICATOR
32
3.3. Neighbourhood Deprivation in Enschede
The final result of this study, tries to map both social economic and physical Deprivation status at
neighbourhood level for city of Enschede. Figure 3-19 and 3-20 present different deprivation status
respectively. High deprivation status at each neighbourhood associates with higher value in each map.
3.3.1. Social Economic Deprivation Status in Enschede
The correlation analysis in section 3.1.1 shows that social economic deprivation is highly associated with
percentage of low income households and percentage of inactive people, and has a moderate association
with percentage of people with non-western origin. . The overlay map follow the same pattern excluding
neighbourhoods with non-available data.
Neighbourhoods of City Center, De Bothoven, Wesselerbrink Noord-West, Wesselerbrink Zuid-Oost,
Wesselerbrink Noord-Oost, Cromhoffsbleek-Kotman, Mekkelholt, Dolphia, and Twekkelerveld are
among the most deprived neighbourhoods in Enschede socioeconomically.
In addition, neighbourhoods of Eekmaat West, Oikos, Eilermarke, Hogeland-Zuid, 't Zwering, Bolhaar,
Helmerhoek-Noord, Stroinkslanden Noord-West, Stroinkslanden Noord-Oost, and Ruwenbos are the
least social economic deprived neighbourhoods in Enschede.
Figure 3-19: Social Economic Deprivation at neighbourhood level in Ensche
NEIGHBOURHOOD DEPRIVATION IN ENSCHEDE APPLICATION OF GEO-TAGGED SOCIAL MEDIA INDICATOR
33
3.3.2. Physical Deprivation Status in Enschede
The correlation analysis in section 3.1.1 shows that physical deprivation is highly associated with distance
to secondary education. The overlay map behave in a same way. If secondary education locations
considered as the focal points, the more distance from them causes the most physical deprivation.
Generaly, the neighbourhoods which are located within the built-up area boundary are less deprived since
they have a better accessibility to different facilities.
Neighbourhoods of Buurtschap Twekkelo, Buurtschap Broekheurne, Buurtschap Lonneker-West, Noord
Esmarke, and Boekelerveld are the most physically deprived neighbourhoods since they have a quit high
distance to bus stops, daily necessities, General practitioners, and secondary educations. In addition, neighbourhoods of City Center, De Leuriks, Boddenkamp, Horstlanden-Stadsweide,
Roombeek-Roomveldje, Varvik-Diekman, Walhof-Roessingh, 't Zwering, Cromhoffsbleek-Kotman,
Mekkelholt, and Marssteden are the least physically deprived neighbourhoods in Enschede.
Figure 3-20: Physical Deprivation at neighbourhood level in Ensche
NEIGHBOURHOOD DEPRIVATION IN ENSCHEDE APPLICATION OF GEO-TAGGED SOCIAL MEDIA INDICATOR
34
The value of correlation between final indices is also calculated in Table 3-1. At the 5% significance level
the final index of social economic deprivation is associated to final index of physical deprivation. The
strength of this relationship is (-2.90) which means the relationship between final indices of deprivation is
reverse and not that high. Figure 3-21 has added to clarify the behaviour of each neighbourhoods in both
social economic and physical deprivation. Moreover, this correlation analysis has been done for all the
neighbourhoods with data available in every indicators which consists of 54 neighbourhood in total.
In addition neighbourhoods of Bolhaar and 't Zwering are the least deprived neighbourhoods in both social
economic and physical dimension.
3.4. Content analysis
Content analysis for extracted Instagram photos consists of two parts. At first, a word cloud analysis will
perform on the captions of every photos in least and most deprived areas and try to present the captions
frequency in terms of positive or negative words. On the other hand, second part demonstrate deprived
areas by showing some representative images for both social economic and physical deprivation status.
3.4.1. Word Clouds for Social Economic Deprivation
Figure 3-22 and 3-23 present the word clouds for the two highest values social economic deprivation,
Cromhoffsbleek-Kotman (𝐼𝑆𝑜𝑐𝑖𝑜𝑒𝑐𝑜𝑛𝑜𝑚𝑖𝑐 = 0.59) and Wesselerbrink Zuid-Oost (𝐼𝑆𝑜𝑐𝑖𝑜𝑒𝑐𝑜𝑛𝑜𝑚𝑖𝑐 = 0.52).
Figure 3-21: Scatter plot between Social economic deprivation and Physical deprivation
Figure 3-22: Word cloud for Cromhoffsbleek-Kotman Neighbourhood
Figure 3-23: Word cloud for Wesselerbrink Zuid-Oost Neighbourhood
NEIGHBOURHOOD DEPRIVATION IN ENSCHEDE APPLICATION OF GEO-TAGGED SOCIAL MEDIA INDICATOR
35
Figure 3-24 and 3-25 present the word clouds for the least socioeconomically deprived, Ruwenbos
(𝐼𝑆𝑜𝑐𝑖𝑜𝑒𝑐𝑜𝑛𝑜𝑚𝑖𝑐 = 0.13), and Oikos (𝐼𝑆𝑜𝑐𝑖𝑜𝑒𝑐𝑜𝑛𝑜𝑚𝑖𝑐 = 0.15).
3.4.2. Word Clouds for physical Deprivation
Figure 3-26 and 3-27 present the word clouds for the two highest values for physical deprivation, Boekelverd
(𝐼𝑝ℎ𝑦𝑠𝑖𝑐𝑎𝑙 = 0.67) and Buurtschap Twekkelo (𝐼𝑝ℎ𝑦𝑠𝑖𝑐𝑎𝑙 = 0.66).
Figure 3-28 and 3-29 present the word clouds for the least physically deprived areas, Marssteden(𝐼𝑝ℎ𝑦𝑠𝑖𝑐𝑎𝑙 =
0.36), and Boddenkamp (𝐼𝑝ℎ𝑦𝑠𝑖𝑐𝑎𝑙 = 0.37).
Figure 3-24: Word cloud for Ruwenbos Neighbourhood
Figure 3-25: Word cloud for Oikos Neighbourhood
Figure 3-26: Word cloud for Boekelverd Neighbourhood
Figure 3-27: Word cloud for Buurtschap Twekkelo
Figure 3-28: Word cloud for Marssteden Neighbourhood
Figure 3-29: Word cloud for Boddenkamp Neighbourhood
NEIGHBOURHOOD DEPRIVATION IN ENSCHEDE APPLICATION OF GEO-TAGGED SOCIAL MEDIA INDICATOR
36
3.5. Illustrations
This section will present some of the extracted photos from the Instagram’s Geo Data Base which has been prepared for this research. Digging into each most and least deprived neighbourhoods reveals following images in each dimension:
3.5.1. Social Economic Deprivation status
The following pictures has been extracted from the least deprived neighbourhoods consists of Oikos,
Eilermarke, Ruwenbos. At a glance, photos in this category present a sense of happiness and certain level
of convenience among people’s lives.
On the other hand, the photos that have been extracted from the neighbourhoods of Wesselerbrink
Noord-West, Wesselerbrink Zuid-Oost, and Cromhoffsbleek-Kotman are significantly different with the
first category.
NEIGHBOURHOOD DEPRIVATION IN ENSCHEDE APPLICATION OF GEO-TAGGED SOCIAL MEDIA INDICATOR
37
3.5.2. Physical Deprivation status
As it mentioned earlier, the most deprived neighbourhoods in Enschede are the ones which located in
suburban areas like Buurtschap Twekkelo, Buurtschap Broekheurne, and Boekelerveld. The extraction of
Instagram images, also confirms the final results.
On the other hand, the photos that are extracted for the neighbourhodos of Boddenkamp and Marssteden
shows the better status. These neighbourhoods benefit from the lower values of deprivation.
NEIGHBOURHOOD DEPRIVATION IN ENSCHEDE APPLICATION OF GEO-TAGGED SOCIAL MEDIA INDICATOR
38
NEIGHBOURHOOD DEPRIVATION IN ENSCHEDE APPLICATION OF GEO-TAGGED SOCIAL MEDIA INDICATOR
39
4. CONCLUSION AND RECOMENDATION
4.1. Conclusion
Based on three main research objectives proposed in chapter one, following answers have been found out
to cover the research questions:
To explore geo-tagged social media as an indicator for identification deprived areas at the
Neighbourhood level in city of Enschede.
o How the geo-tagged social media indicator incorporates with final indices of deprivation?
Results of Factor Analysis in section 3.1.2 reveal that geo-tagged social media indicator is
associated with factor of provision of services. Therefore, this indicator can be considered
as a physical indicator in order to quantify levels of deprivation at neighbourhood level.
o Is there a relationship between geo-tagged social media indicator and final deprivation
indices?
Based on proposed conceptual framework in section 2.2 and the results of correlation
analysis in section 3.1.1, there is a relationship between geo-tagged social media indicator
and final index of Physical Deprvation. This relationship is positive and significant but the
strength is not high. Moreover, the relationship between geo-tagged social media indicator
and social economic deprivation is not significant.
o What is the content of geo-tagged social media indicator?
The content of the geo-tagged social media was analyzed in a qualitative inquiry by verifying
the word frequency of positive and negative words used as the caption of Instagram
photos. The result of the frequency analysis was visualized in word clouds. These word
clouds were calculated for the neighbourhoods with highest and lowest deprivation
according to the social economic and physical dimension. For the social economic
dimension there was a significant difference between the words used in a deprived and
well-off neighbourhood, for physical dimension, the contents of fashion and fitness are
among the most observed words in the clouds.
Mapping social economic deprivation as well as physical deprivation at Neighbourhood level
o What is the social economic deprivation status at the Neighbourhood level?
In section 3.3 the geo-visualization for the social economic dimension of Enschede
deprivation mapped which provided a pattern showing that rural areas of Enschede have
lower social economic deprivation. Analyzing the individual indices also shows that in
deprived neighbourhoods the percentage of inactive people and people with non-western
origin are usually high.
o What is the physical deprivation status at the Neighbourhood level?
In section 3.3 the geo-visualization for the physical deprivation was presented. The map
reveals a spatial pattern showing that neighbourhoods which are within built-up areas have
a lower deprivation. The more distance from the center of Enschede causes the more
physical Deprivation. Based on correlation analysis deprived areas have a far distance from
secondary schools. Also, deprived areas have a small distance to cafeterias.
NEIGHBOURHOOD DEPRIVATION IN ENSCHEDE APPLICATION OF GEO-TAGGED SOCIAL MEDIA INDICATOR
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Understand the relationship between social economic deprivation and physical deprivation
o To what extent the social economic deprivation is correlated with physical deprivation at
the Neighbourhood level?
The results showed that at 5% significance level, the final index of social economic
deprivation is associated reversely with the final index of physical deprivation. However,
the correlation is not strong, the fact has been proved by correlation matrix presented in
section 3.1.
4.2. Research limitations
Due to statistical data limitation 16 Neighbourhoods (approximate 25% of the Neighbourhoods) had
missing values for some attributes for security reasons. Therefore the final indices for Social economic and
Physical dimensions was only calculated for 54 Neighbourhoods of Enschede municipality.
During the Factor Analysis was verified a low communality (0.269) for the indicator Growth of new houses.
As this value is lower than 0.3 it should have been removed from the analysis, but due to time constrains
the indicator wasn’t removed. Also in the correlation analysis was verified a 99% statistically significant high
correlation (0.946) between the indices Inactive people and Low Income, to further develop the framework
one of those two indices should be removed.
More precise results for the word cloud can be obtained by using website that takes in consideration
connectives from multiple language (in this case English and Dutch), that cleans for special characters and
that’s case insensitive.
Another limitation for this research refers to data extraction from Instagram’s API. The data extraction
process within Instagram API can be done by specific parameters which does not allow you to differentiate
certain groups of users like residents or tourists. Therefore the spatial distribution of geo-tagged photos are
the results of everyone who geo-tagged photo in Instagram application.
Moreover, this should not be neglected that the number of unrelated photos to deprivation are relatively
high in each neighbourhood and representative photos should be extracted cautiously.
4.3. Recommendation
As a recommendation for future work one could analyse the temporal patterns of geo-tagged social media,
verifying if exists any kind of correlation between the hours and week days of social media activity and
deprivation. Also other kinds of social media could be evaluated, such as Foursquare, which can provide a
rich data of Points of Interest. Analysis in variability of deprivation are lacking research as well, one could
do a temporal comparison of how the deprivation of Enschede changed within a specified time frame.
NEIGHBOURHOOD DEPRIVATION IN ENSCHEDE APPLICATION OF GEO-TAGGED SOCIAL MEDIA INDICATOR
41
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Townsend, P. (1987). Deprivation. Journal of Social Policy, 16(02), 125–146.
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ANNEX 1
Ge
oD
ataBase
DataSe
tC
lassFe
ature
type
Attrib
ute
/Field
De
scriptio
nY
ear
Sou
rce o
f data
BU
_Naam
Nam
e of ea
ch n
eighb
ou
rho
od
20
11
_V3
CB
S
AA
NT_IN
WP
op
ulatio
n o
f each
neigh
bo
urh
oo
d2
01
1C
BS
P_6
5_E
O_JR
%p
erson
s 65
years an
d o
ver2
01
1C
BS
i_age6
5N
orm
alized valu
e of %
perso
ns 6
5 yea
rs and
over
20
11
P_STER
FTM
ortality relative
20
11
CB
S
i_mo
rtN
orm
alized valu
e of m
ortality relative
20
11
GEM
_HH
_GR
ho
useh
old
size2
01
1C
BS
i_ho
usesiz
No
rmalized
value o
f ho
useh
old
size2
01
1
P_N
_W_A
L%
peo
ple w
ith no
nw
estern o
rigin2
01
1C
BS
i_no
nw
estN
orm
alized valu
e for %
peo
ple w
ith no
nw
estern o
rigin2
01
1
P_N
IETAC
T%
of in
active 2
01
1C
BS
i_inactive
No
rmalized
value o
f % o
f inactive peo
ple
20
11
P_LA
AG
INK
H%
ho
useh
old
with lo
w in
com
e2
01
1C
BS
i_low
Inc
No
rmalized
value fo
r % h
ou
seho
ld w
ith low
inco
me
20
11
WO
ZA
verage value o
f residen
tial area2
01
1C
BS
INK
_INW
Average in
com
e per capita
20
11
CB
S
Ho
useaff
Ho
use affo
rdab
ility2
01
1O
wn
i_ho
useaff
No
rmalized
value fo
r ho
use affo
rdab
ility2
01
1
AvgD
ist_bu
sA
verage Distan
ce to b
us statio
n
20
12
NG
R
i_bu
sN
orm
alized valu
e for A
verage Distan
ce to b
us statio
n
20
12
AF_D
AG
LMD
Average distan
ce to d
aily necessities
20
11
CB
S
i_dailyN
ecN
orm
alized valu
e for A
verage distance to
daily n
ecessities2
01
1
BEV
_DIC
HTH
Po
pu
lation
den
sity2
01
1C
BS
i_den
sityN
orm
alized valu
e for p
op
ulatio
n d
ensity
20
11
WO
NIN
GEN
_20
11
Total N
um
ber o
f ho
usin
g 20
11
20
11
CB
S
WO
NIN
GEN
_20
07
Total N
um
ber o
f ho
usin
g 20
11
20
07
CB
S
new
ho
use
Gro
wth o
f new
ho
usin
g 2
00
7-2
01
1O
wn
i_new
ho
use
No
rmalized
value fo
r grow
th of n
ew h
ou
sing
20
07
-20
11
AF_A
RTSP
RA
verage distance to
GP
20
11
CB
S
i_gpN
orm
alized valu
e for A
verage distance to
GP
20
11
AF_C
AFTA
RA
verage distance to
cafeteria2
01
1C
BS
i_cafeN
orm
alized valu
e for A
verage distance to
cafeteria2
01
1
AF_O
ND
VR
TA
verage distance to
secon
dary ed
ucatio
n2
01
1C
BS
i_secEdu
No
rmalized
value fo
r Average distan
ce to seco
nd
ary edu
cation
Geo
-tagged so
cial med
ia2
01
4-2
01
5In
stagram
i_instagram
No
rmalized
value fo
r geo-tagged
social m
edia
20
14
-20
15
SocialEco
no
mic
Final So
cial Econ
om
ic Dep
rivation
Ind
ex2
01
1O
wn
Ph
ysicalFin
al Ph
ysical Dep
rivation
Ind
ex2
01
1O
wn
idIn
stagram's p
ost U
RL
20
14
-15
Place
Man
ual Lo
cation
nam
e2
01
4-1
5In
stagram
time
Date an
d Tim
e of In
stagram p
ost
20
14
-15
image
Image U
RL
20
14
-15
captio
nA
dd
ition
al image descrip
tion
wro
te by In
sta users
20
14
-15
Origin
s of d
ata:
http
s://apigee
.com
/con
sole/in
stagram
http
://ww
w.n
ation
aalgeoregister.n
l
http
://ww
w.cb
sinu
wb
uu
rt.nl/
Hea
lth and
living co
nd
ition
Edu
cation
Place po
pu
larity
Ensch
ede_
Dep
rivation
_20
15
Neigh
bo
urh
oo
ds
Final D
eprivatin
Ind
ices
po
lygon
Ensch
ede_
neigh
bo
urh
oo
d
Po
pu
lation
com
po
sition
Emp
loym
ent
Inco
me
Accessib
ility
Ho
usin
g
Po
int
_20
15
Table 0-1: Meta Data
NEIGHBOURHOOD DEPRIVATION IN ENSCHEDE APPLICATION OF GEO-TAGGED SOCIAL MEDIA INDICATOR
44
ANNEX 2
Table 0-2: Complete Correlation Analysis including City Center Neighbourhood
NEIGHBOURHOOD DEPRIVATION IN ENSCHEDE APPLICATION OF GEO-TAGGED SOCIAL MEDIA INDICATOR
45
ANNEX 3
Figure 0-1: Percentage of persons 65 years and older at neighbourhood level in Enschede
NEIGHBOURHOOD DEPRIVATION IN ENSCHEDE APPLICATION OF GEO-TAGGED SOCIAL MEDIA INDICATOR
46
Figure 0-2: Relative Mortality Rate at neighbourhood level in Enschede
NEIGHBOURHOOD DEPRIVATION IN ENSCHEDE APPLICATION OF GEO-TAGGED SOCIAL MEDIA INDICATOR
47
Figure 0-3: Household size at neighbourhood level in Enschede
NEIGHBOURHOOD DEPRIVATION IN ENSCHEDE APPLICATION OF GEO-TAGGED SOCIAL MEDIA INDICATOR
48
Figure 0-4: House affordability at neighbourhood level in Enschede
NEIGHBOURHOOD DEPRIVATION IN ENSCHEDE APPLICATION OF GEO-TAGGED SOCIAL MEDIA INDICATOR
49
Figure 0-5: Average Distance to bus stops at neighbourhood level in Enschede
NEIGHBOURHOOD DEPRIVATION IN ENSCHEDE APPLICATION OF GEO-TAGGED SOCIAL MEDIA INDICATOR
50
Figure 0-6: Average Distance to daily necessities at neighbourhood level in Enschede
NEIGHBOURHOOD DEPRIVATION IN ENSCHEDE APPLICATION OF GEO-TAGGED SOCIAL MEDIA INDICATOR
51
Figure 0-7: Population Density at neighbourhood level in Enschede
NEIGHBOURHOOD DEPRIVATION IN ENSCHEDE APPLICATION OF GEO-TAGGED SOCIAL MEDIA INDICATOR
52
Figure 0-8: Average Distance to General Practitioners at neighbourhood level in Enschede
NEIGHBOURHOOD DEPRIVATION IN ENSCHEDE APPLICATION OF GEO-TAGGED SOCIAL MEDIA INDICATOR
53
Figure 0-9: Average Distance to secondary education at neighbourhood level in Enschede