ISSN 1675-7009 SCIENTIFIC RESEARCH JOURNAL - UiTM IRir.uitm.edu.my/11109/1/AJ_ASRA HOSSEINI SRJ 12 1.pdf · ISSN 1675-7009 SCIENTIFIC RESEARCH JOURNAL RESEARCH MANAGEMENT INSTITUTE

VOLUME 9 NO. 2

DEC 2 0 1 2

ISSN 1675-7009

SCIENTIFIC RESEARCH

JOURNAL

RESEARCH MANAGEMENT INSTITUTE i^Riffi

SCIENTIFIC RESEARCH JOURNAL

Chief Editor Zaiki Awang

Universiti Teknologi MARA, Malaysia

Managing Editor Hajah Razidah Ismail

Universiti Teknologi MARA, Malaysia

International Editor David Shallcross, University of Melbourne, Australia

Ichsan Setya Putra, Bandung Institute of Technology, Indonesia K.lto,Chiba University, Japan

Luciano Boglione, University of Massachusetts Lowell, USA Vasudeo Zambare, South Dakota School of Mines and Technology, USA

Editorial Board

Abu Bakar Abdul Majeed, Universiti Teknologi MARA, Malaysia Halila Jasmani, Universiti Teknologi MARA, Malaysia

Hamidah Mohd. Saman, Universiti Teknologi MARA, Malaysia Jamil Salleh, Universiti Teknologi MARA, Malaysia

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Scientific Research Journal

Volume 9 No. 2 Dec 2012 ISSN 1675-7009

Spatial Autocorrelation in the Study of Neighbourhoods 1 towards Smart Cities: Empirical Evidence from Kerman, Iran Asra Hosseini

The Effect of Temperature on the Dispersion of a-Mangostin in 21 PNIPAM Microgel System Madihah Ahmad, Bohari M. Yamin andAzwan Mat Lazim

Influence of Metakaolin as Partially Cement Replacement 33 Minerals on the Properties of Cement and Concrete Muhd Norhasri Mohd Sidek, Mohd Fadzil Arshad, Megat Azmi Megat Johari, Zaid Mohd Yazid and Amir Khomeiny R.

Electrical Resistivity, Thermal Stability and Tensile 49 Strength of Rice Husk Flour-Plastic Waste Composites ShahrilAnuar Bahari, Kamrie Kamlon and Masitah Abu Kassim

Saving Primary Energy Consumption Through Exergy 65 Analysis of Combine Distillation and Power Plant Alhassan Salami Tijani, Nazri Mohammed and Werner Witt

A Crystallographic Study of Bis [S-benzyl-5-bromo-2- 87 oxoindolin-3-ylidenemethanehydrazonthioate] Disulfide Solvated with Dimethylsulfoxide Mohd Abdul Fatah Abdul Manan, M. Ibrahim M Tahir, Karen A. Crouse, Fiona N.-F How and David J. Watkin

Spatial Autocorrelationin the Study of Neighbourhoodstowards Smart Cities: Empirical

Evidence from Kerman, Iran

Asra Hosseini

Department ofUrban and Regional Planning,Faculty ofBuilt Environments,

University ofMalayaEmail: asra.hosseinitiosiswa.um.edu.my

ABSTRACT

From earliest cities to the present, spatial division into residential zones andneighbourhoods is the universal feature ofurban areas. This study exploredissue ofmeasuring neighbourhoods through spatial autocorrelation methodbased on Moran's I index in respect of achieving to best neighbourhoods'model for forming cities smarter. The research carried out by selection of35 neighbourhoods only within central part of traditional city of Kermanin Iran. The results illustrate, 75% ofneighbourhoods, area in the inner cityof Kerman had clustered pattern, and it shows reduction in Moran 's indexis associated with disproportional distribution of density and increasingin Moran's I and Z-score have monotonic relation with more dense areasand clustered pattern. It may be more efficient for urban planner to focuson spatial autocorrelation to foster neighbourhood cohesion rather thanemphasis on suburban area. It is recommended characteristics of historicneighbourhoods can be successfully linked to redevelopment plans towardmaking city smarter, and also people's quality of life can be related to theway that neighbourhoods' patterns are defined.

Keywords: Neighbourhoods, Smart Cities, SpatialAutocorrelation, IranianTraditional Cities .

SciENTIFIC ReSEARCH JOURNAL

INTRODUCTION

From earliest cities to the present, spatial division into residential zonesand neighbourhoods is the universal feature of urban areas. The roleof neighbourhoods and face-to-face interaction is probably strong instructuring smarter cities . Nowadays its influence on liveability of urbanpeople has become clear and suburban living is often blamed for causingincreasing dependence on the car [1]. The dispersal procedure encouragesthe growth ofcar traffic and the polarisation ofneighbourhoods, low incomehouseholds in poorer neighbourhoods yet suffer higher levels of traffic andenvironmental damage, whereas they have far lower levels ofcar ownershipthan average [2]. Most probably urban design influence where and withwhom people engage in physical activity [3], and the planning criteria forforemost infrastructures such as campuses, shopping centres, etc. will needto be immediately redefined to enable the transformation of cities towardsbeing smarter and the use of renewable energy, readjusted for extremeclimatic events such as floods, storm surges, global temperature increases,sea-level rise, water shortages [4,5], the studies show our generation need toconduct more studies on this topic. This study explored issue ofmeasuringneighbourhoods through spatial autocorrelation method based on Moran'sI index in respect of achieving to best neighbourhoods' model for formingcities smarter.

LITERATURE REVIEW

The painful story of urban planning development in Iran from 1920 to1941 is discussed in a paper by Eckert Ehlers and Willem Floor [6]. Thisresearch showed how dualism and interstice between traditional city andmodem context of that, started 80 years ago under imitation of westernurban design and had an effect on the lives of Iranian. They have shownhow significant centres such as mosques and schools which were the heart

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of the city, were changed to places like bank, cinemas and hospitals [6].There are so many studies that show how neighbourhoods or other urbanelements can be useful for built environment and public health, but lack ofdata could be the limitation of research on this field [7-9].

As studies show, city clusters are denser and compact than theirequals in the developed countries in the Europe and United State [10], Lifeof people can change remarkably in neo-traditional developed area withemphasis on street network and also it reduces congestion or even theirhealth and happiness [11-13]. The critical need for work on sustainablecommunities and need is obviously recognisable in Table1.Also a researchin the context of developing countries found that, higher density, mixedland use and income are not really sufficient to predict pedestrian volumeand site-design characteristics including streets, block size, length ofsidewalks, and pedestrian route travelled [7]. Kitamura and Mokhtarianwith a different view on pedestrian routes and they expressed the attitudeof resident influences travel behaviour and they have pointed some keycharacter such as measures of residential density, mixed land use, publictransit accessibility and existence ofsidewalks in trip generation. Also, theymentioned the promoting ofland use with higher density and mixture valueis not possible without change ofresident's attitude [14], equated with thosewho don 't , impact ofurban form on frequency ofwalk ing and bicycling asa form ofphysical activity emphasised [11].

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Table 1: Lack of Physical Activities can Cause to Increasethe Number of Overweight and Obese People

7,06l.475,879

908,515,628

1,561,896,966

520,632 ,322

27,328

7,196,489

$ 425,260,915

$ 105,082,U2

$ 4,886,108

$ 31,767,407

rurrent total world population

undernourished people in the worldright now

overweight people in the world rightnow

obese people in the world right now

people who died of hunger today

people who died of hunger this year

Economics

money spent due to obesity relateddiseases in the USA today

spending on food purchased and thentossed by UShouseholds today

spending on global food aid today

amount that would allow to feed thehungry today

$ 168,932 ,933spending on weight-loss programs andproducts in the U~A today

soarces: World Populetion Clock, State of Food Insecurity in the World 2006, WHO, Timothy Jones,University of Arizona (UA) in Tucson,August 2012.

In addition, Ewing et al. measure urban sprawl and with defining asprawl index of 448 counties across the United States, they account lowdevelopment density, segregated land uses, lack of significant centres andpoor street accessibility and sparse street network are the main causes ofurban sprawl [15]. Even more, a small but detailed study also have shownin two San Diego neighbourhoods which physical activity was assessedby self-report and by accelerometer, residents in the "high workability"neighbourhoods had almost 70 more minutes per week ofphysical activityand had lower obesity incidence than those in the "low workability"

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neighbourhoods after regulating for age and education [16] . Thiscomparative study explored how characteristics ofhistoric neighbourhoodscan be successfully linked to redevelopment towards making smarter Iraniancities and whether health and happiness ofpeople can be related to the waythat neighbourhoods' patterns are defined.

METHODS

Data Processing and Area of Study

As there are many researches on smart growth ofcities, neighbourhoodsand their measuring in urban areas such as Tehran and Esfahan [17-21] andalso there are many studies on neighbourhoods' accessibility measuring, herethere is a closer look to spatial dependency in the city ofKerman which hascritical situation and facing with lots of challenges in near future. The cityof Kerman is a place ofhistorical value with no master plans implementedcarefully, in addition to the lack of planning policies the city is locatedin a critical geographical location. With this regards, the research carriedout by selection of 35 neighbourhoods only within central part of urbanarea which is also traditional part of Kerman. Summaries of its data haveoffered in Table 2. The location maps of both city and neighbourhoods haspresented in Figure 1.

Table 2: Summaries of the Statistical Data

Kerman Central Part

Population 515 ,114 27,584

Men 263 ,674 14,038

Women 251,640 13,800

Ratio 104.7 101.7

Families 127,936 6,991

Area (m2) 12,611 451.3

Density Personl 40.8 58.2Hectare

%

5.5

5.5

3.6

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SciENTIFIC RESEARCH JOURNAL

Figure 1: location Map of Inner Cityof Kerman and Neighbourhoods Boundaries

Old neighbourhoods boundaries are divided based on the physical andinstitutional characteristics of the neighbourhood, their class , race, ethniccomposition, symbolic neighbourhood identities and resident's sense ofdependency which can compromise neighbourhood boundaries and identity[22]. The concept ofneighbourhoods in Iran is almost equal to the westerndefinition [21] but with the difference that neighbourhoods in Iranian citieshave evolved in an organic, and not a predetermined or planned, mannerinto an equivalent of quarters in the West.

In 2003, Kerizek discussed and focused on changes in travel behaviouras triggered by change in urban form variables, he presented a model whichdecreases the total distance ofhousehold travel. It referenced the cost ofeachtrip is less for household in areas with more access and strong definitionof neighbouring, they may make more of them [23].So neighbourhoods,walking and cycling define as a daily life activity and can be caused to enjoyconsiderable health benefits Many plans presented for Kerman but yet noplans could organise the area even it caused to destroy it more because ofshortage in policies. The first plan for city ofKerman was presented in Oct.1936, by municipality of Kerman Figure 2.

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Figure 2: A Suggested Plan for Kerman in 1936, PredictedStreets Network has Presented in Yellow Lines

The main objective of this plan was predicting of construction anddevelopment for new streets and alleys in the purpose of easy access to allplaces over city. There were four more plans for developing Kerman from1965s, 1975s, 1983s and 1994s respectively from private organisations andeven the University ofTehran but all those did not implement in practice. Infact, unsuccessful plans caused to consternation in central part ofKerman,with this regards evaluating of spatial autocorrelation level has a greaterimpact on the performance ofneighbourhoods and whole city structure aswell. This is the first attempt to promote spatial autocorrelation method ata neighbourhood scale in a city, especially in developing countries like Iranand it tried to find:

1. which context can spatial autocorrelations have reflection in the citywith respect to challenges of the future

2. ifthere is any inequality between spatial auto correlations in differentzones, if the answer is positive what might be the causes

3. if spatial autocorrelation can help the measuring growth of smartercity in developing countries and particularly in Iran

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This study will attempt to answer the research questions mentionedabove; however analysis of the sources of variation in neighbourhoodsand how they can be effectible in smarter city drew on additional methods.The methods which have been proposed in this study will measurespatial autocorrelation based on both feature locations and feature valuessimultaneously. It evaluates whether the neighbourhood pattern is clustered,dispersed, or random. Previous research have demonstrated that spatialautocorrelation analysis can be used to access measuring network [24] evento determine land use (change) and to give cause to unravel its complexityeven more from a spatial perspective [25], also researchers showed with thismethod we are able to quantity segregation even during time [26-27] andapplied to distributional archaeological data [28]. Spatial autocorrelation ischaracterised by a correlation in a signal among nearby locations in space.

As one of the most challenging issues in Iranian cities is dispersingof urban forms [29], statistically, Moran's I implemented as a degree ofmeasuring of spatial autocorrelation, developed by Patrick AP. Moran[30]. Indeed, Moran's method can implement for multi-dimensions and itis more complex than one but here I used only the one dimension and itcould expand in future research.

Measuring Neighbourhoods' Spatial Autocorrelation

Spatial autocorrelation is a powerful technique for the analysis of thespatial patterning in variant values which has been successfully applied[26] [31-32]. Moran's I for measuring neighbourhoods auto correlation isdefined as:

where N the number of units in each neighbourhood and it is indexed

by iandj; Xis the variable of areas; Xis the mean ofX ;and Wij is anelement of a matrix of spatial weights.

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Based on data set if (X [1], X [2], ... , X [N]) be spatial units in eachneighbourhood boundaries, The mean is:

X = (X [1] + X [2] + ... + X [N])/N.

The deviations would beR[i] = X[i] - X, i = 1,2, ..., N.

The variance isV = (R[lY2 + R[2y'2 + ... + R[N]"2)/N.

The standard deviation isS = Square (V).

The normalized values therefore areZ[i] = (X[i] - X)/S, i = 1, 2, ..., N.

The ordered pairs are entire N*N possible combinations(Z[i], Z[j]), 1 <= i <= Nand 1 <= j <= N.

In general, if we have values Y[l], ..., Y[M] and correspondingweights W[l], ..., W[M], then the weighted average of the Y's is given bymultiplying the Y's by the weights, adding them up, and dividing by thesum of the weights.

Let the weight associated with the ij pair be W[ij]. We will need thesum of the weights, which is:

W = Sum over all (i.j) ofW[ij].

The weighted correlation--Moran's I--therefore is given byI = {Sum over all (ij) of Z[i]*Z[j]*W[ij]} / W.

That is, it is the weighted average of the products Z[i]*Z[j].

The range ofMoran's index starts from -1 indicating perfect dispersionto +I indicating perfect correlation. A zero value indicates a randomspatial pattern. For statistical hypothesis testing, Moran's I values can be

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transformed to Z-scores in which values greater than 1.96 or smaller than-1.96 indicate spatial autocorrelation that is significant at the 5% level.After calculating z-scores and drawing Moran's I index ofneighbourhoodswe need to reclassify them. This study was based on Moran's Index I andresults ofanalysis divided neighbourhoods into three categories: clustered,random and dispersed. Afterwards, spatial autocorrelation, neighbourhoodstructures and forms have been analysed and outcome has interpreted. AlsoArcGIS promoted to drawing location maps ofneighbourhoods, land use andstreet network in each neighbourhood for more comparisons and evaluations.

RESULTS AND DISCUSSION

The results have been illustrated, 75% of neighbourhood' areas in theinner city ofKerman have clustered pattern with the Z-score between 2.74and 12.32. The location of these neighbourhoods presented in Figure 3.Based on the calculation of Moran's I which is presented in Table 3, and itshowed Z scores of these boundaries felled outside the range, because thenormal range of Z-score is from -2.58 to +2.58 , as the pattern exhibitedis too unusual to be version of random chance. It is possible to reject thenull hypothesis and proceed with figuring out what might be causing thestatistically significant clustered pattern.

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Table 3: Summaries of the Spatial Autocorrelation Method's Calculations andMoran's I Values in 35 Neighbourhoods of Inner City of Kerman. * Six RandomSelected Neighbourhoods which Three of them (1, 12, 35) have ClusteredPattern and Left (14.15,16) have Random Pattern

ill

28

29

30

Name Z-Score Area

11

Length Density Patern

SciENTIFIC RESEARCH JOURNAL

< ':'

Figure 3: Location Map of Neighborhoodswith Clusterd Pattern in Central Part of Kerman

The next category consists offour neighbourhoods with the portion of8% that fall in a same category ofclustered pattern but with different Z-scorefrom 2.07 to 1.76. In the third group, there have been seven neighbourhoodsout of thirty-nine which have random pattern with Z-score between 1.32and -1.07 that it has 16% of total area, as it defined before they are neitherclustered nor dispersed. Interestingly, this category located exactly oncentral part ofthe city surrounded by two streets , Sharya'ti and Emam, nearcommercial district of main bazaar. Finally, only one neighbourhood withZ-score -2.81 which is the least one had dispersed pattern.

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Six neighbourhoods out ofthirty-five selected for further investigationwhich have presented in Figure 5,which three of the have the clusteredpattern (I , 12, 35) and left (14, 15, 16) have the random pattern , it canbe founded reducing in Moran's index is associated with disproportionaldistribution ofdensity. In comparison, two neighbourhoods Meidanghaleh(lD= I) and Golbazkhan (ID= 15) Figure 4, though have the same value inMoran's index, area and units as well, but one considered with clusteredpattern and other one as dispersed pattern, with considering Z-score, itwould be observed neighbourhoods have a big difference in amount ofZ-score though they are near each other and almost are same in profiledata. Moran 's I value and Z-score in Golbazkhan neighbourhood is 7.04,0.24 respectively and the same for Meidanghale are -0.11 and -0.0 l. Thestructure ofneighbourhoods boundaries illustrated, scattering ofdensity inGhobbehsabz neighbourhood (ID= I) with Moran's value 0.21 and Z-score6.2 is steadier than Meidanghale neighbourhood (ID=15) with Moran'svalue -0.0 I And Z-score 0.11 and it could be said increasing in Moran's Iand Z-score have monotonic relation with more dense areas and clusteredpattern.

In attempting to find out the reasons for the statistically significantclustered pattern in the neighbourhood of Meidanghale, it was found there

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is a big difference in density, street pattern and land use. There has beenthe dilapidated field that is in neighbourhood Golbazkhan, Adversely,land use of neighbourhood Meidanghale with random patterns has morestreet connection and it has a central part for easy accessibility and blocksare divided nicely and access to other parts is easily possible, In moredetail, Figure 5 presents how spatial auto correlations change in differentneighbourhood boundaries with change in street pattern and even density.

Neighbourhoods'Boundaries Frequency of Land use Freque ncy of Density

l§:I. b N. : • I i, .. '........... , I

N: SU Nli1: , "Mille: 5280 S: 768s4"M: i.3o

N: W Mil\: 10 M.x:61'9S : 16<116 M ; 107

N:354 Min: 3 Mu: 119'95:40055 M: 113

§!,§:1=' ' ,' .

~i;;:. :••,:~ .:::/;1.:':":"'f~ '· y .:

~ t"~:>!....~~~~·~1~N: 3U Min: 2 MiX: IOU S: 39954 M: U4

CH

"

./.

I , '

: : .~.;~ .._..a: ~::~..I~~~-~~~~~~

.I:I.::

=l:i""'-:~~~~::""!

N: 405 Min: 3 Mn: 1199 S: !2.Ua M: us

: .. ... : .. i :.

~f",:~-t~~~-·~~:;"'·:tl~·~N: 162 Mk --#)Max: 1770S :~2032a M: us

N: 403 Min: 1 M.Jx: 11335 5 : 69205 M: 171

" .:~;fi~

·N:356 Mil: 2 Mn:23«) S:4;0Q3 M: 137

035. Glia etnlim, Ranaom N:655 Min: 3M..: 2331 5:70S33 M: 108 N:481M": .3M": ~J~46350 M :lI6

Fjgure5 . Scatte r PkJt5 of LBnd Use and Dens ity In Six S.'ect~ Nejgh boumood in Centra l Pan of Kerman

-N=Number of Po lygons, S=S urn. M= l 4e. " .

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As it has claimed level of smart cities has direct relation with density[33-37], it could be concluded that if we raise the pattern with high Z-score,we can make cities smarter. But increasing smarty possible with changingwhich can be studies in more research. Study of spatial autocorrelationin neighbourhoods showed variety of pattern in a city can cause to havesmarter city instead of cost for affordable housing in Iran (Mehr Maskan).

CONCLUSION

This study explored the issue of measuring neighbourhoods' spatialautocorrelation based on Moran's I index in respect of achieving to bestneighbourhood's model to forming smarter cities. The results could be veryconstructive for modelling of smart cities and it enables us to select thebest spatial pattern for neighbourhoods depending on the cities conditionsand profiles.

This study is the first known attempt to use spatial autocorrelation inthe study ofneighbourhoods in developing countries and in Iran as well. Thestatistical and graphical analyses exploit in depth , at first it tried to calculateMoran's I degree and Z-score then to analyse the neighbourhoods basedon their spatial autocorrelations and finally to compare neighbourhoodswith each other and to expand it for finding appropriate pattern forneighbourhoods especially in cities ofIran. Also this study was carried out toillustrate how spatial autocorrelation measures could be applied to incidentsor events in a neighbourhood and having done that, how these could beinterpreted. This study suggests it may be more efficient for urban plannerto focus on spatial autocorrelation to foster neighbourhood cohesion ratherthan emphasis on suburban area. It is also recommended characteristics ofhistoric neighbourhoods can be successfully linked to redevelopment planstoward making city smarter, and also people's quality of life can be relatedto the way that neighbourhoods ' patterns are defined.

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