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The Role of Space Syntax in Identifying the Relationship Between ...

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<strong>The</strong> <strong>Role</strong> <strong>of</strong> <strong>Space</strong> <strong>Syntax</strong> <strong>in</strong> Identify<strong>in</strong>g <strong>the</strong> <strong>Relationship</strong><strong>Between</strong> <strong>Space</strong> and Crime 419<strong>of</strong> <strong>in</strong>tersect<strong>in</strong>g axial l<strong>in</strong>es. In simple terms, <strong>the</strong> axial map is represented by <strong>the</strong> longestl<strong>in</strong>es <strong>of</strong> sight that can be used to characterize every street segment <strong>in</strong> <strong>the</strong> Ypsilanti area.For example, if two people were stand<strong>in</strong>g at each end <strong>of</strong> <strong>the</strong> l<strong>in</strong>e, <strong>the</strong>y will be able to seeeach o<strong>the</strong>r. <strong>The</strong> l<strong>in</strong>es were drawn manually on top <strong>of</strong> <strong>the</strong> map us<strong>in</strong>g Spatialist. Ypsilanticomprised an average <strong>of</strong> 634 axial l<strong>in</strong>es.Second, <strong>the</strong> program calculated <strong>the</strong> Integration and Connectivity values <strong>of</strong> every l<strong>in</strong>e<strong>in</strong> <strong>the</strong> system (Figure 195). To elaborate on <strong>the</strong>se two measures, Connectivity gives <strong>the</strong>number <strong>of</strong> l<strong>in</strong>es that are directly connected to a specific l<strong>in</strong>e. Integration, on <strong>the</strong> o<strong>the</strong>rhand, is an <strong>in</strong>dicator <strong>of</strong> how easily one can reach a specific l<strong>in</strong>e. Ma<strong>the</strong>matically speak<strong>in</strong>g,it is <strong>the</strong> average number <strong>of</strong> spaces that one needs to pass through to reach a specific l<strong>in</strong>efrom all <strong>the</strong> axial l<strong>in</strong>es <strong>in</strong> <strong>the</strong> system. In o<strong>the</strong>r words, <strong>the</strong>se values suggest <strong>the</strong> extent towhich a selected space <strong>in</strong> <strong>the</strong> system is more <strong>in</strong>tegrated (can be easily reached from o<strong>the</strong>rspaces), or more segregated (one has to travel through many spaces <strong>in</strong> order to reach thatselected space).S<strong>in</strong>ce <strong>the</strong> unit <strong>of</strong> analysis is <strong>the</strong> axial l<strong>in</strong>e (or <strong>the</strong> street space), it was necessary toappend sociodemographic data along with crime data to each l<strong>in</strong>e. <strong>The</strong>refore, a road map<strong>of</strong> Ypsilanti was prepared show<strong>in</strong>g 21 block groups us<strong>in</strong>g ArcGIS. Data on populationdensity, youth concentration, level <strong>of</strong> education, percentage <strong>of</strong> owners, age distributionand racial composition were available from U.S. Census and were appended to each blockgroup <strong>in</strong> Ypsilanti. <strong>The</strong> report on crime at an address level was semi-manually entered<strong>in</strong>to <strong>the</strong> same database (Figure 195). Moreover, <strong>the</strong> orig<strong>in</strong>al axial map that was preparedus<strong>in</strong>g Spatialist was later converted <strong>in</strong>to an appropriate format and was given accurategeographic coord<strong>in</strong>ates for Ypsilanti. This procedure allowed us to match <strong>the</strong> Spatialistaxial map with <strong>the</strong> ArcGIS Ypsilanti road map (Figure 196). <strong>The</strong> ‘Jo<strong>in</strong> Attribute’ feature<strong>in</strong> ArcGIS allowed us to merge <strong>the</strong> data on <strong>the</strong> axial map with <strong>the</strong> rest <strong>of</strong> <strong>the</strong> data.<strong>The</strong> f<strong>in</strong>al database that was produced <strong>in</strong> ArcGIS was later converted <strong>in</strong>to an acceptableSAS format. SAS is a statistical package that enabled us conduct a Poisson Regressionmodel<strong>in</strong>g <strong>of</strong> our data s<strong>in</strong>ce <strong>the</strong> crime report was collected over a period <strong>of</strong> a year.3.3. Statistical Analysis<strong>The</strong> MIXED Procedure <strong>in</strong> SAS (Version 9) was used <strong>in</strong> <strong>the</strong>se analyses to fit l<strong>in</strong>ear mixedmodels to <strong>the</strong> collected data. Because <strong>of</strong> <strong>the</strong> count nature <strong>of</strong> <strong>the</strong> response variables, squareroottransformations were performed <strong>in</strong> order to satisfy <strong>the</strong> assumptions <strong>of</strong> normality andconstant variance <strong>in</strong> random errors. In <strong>the</strong> mixed models, <strong>the</strong> fixed effects <strong>of</strong> physical andsociodemographic variables <strong>of</strong> <strong>in</strong>terest on crime counts collected over one year <strong>in</strong> givenstreet-spaces or axial l<strong>in</strong>es were estimated. Because <strong>of</strong> <strong>the</strong> clustered nature <strong>of</strong> <strong>the</strong> data,axial l<strong>in</strong>es were clustered with<strong>in</strong> randomly selected block groups, random <strong>in</strong>tercepts andrandom connectivity effects associated with <strong>the</strong> randomly sampled block groups were also<strong>in</strong>cluded, to test <strong>the</strong> hypo<strong>the</strong>sis that <strong>the</strong> crime counts and effects <strong>of</strong> connectivity on crimecounts tend to randomly vary from one block group to ano<strong>the</strong>r. Parameters <strong>in</strong> <strong>the</strong> modelwere tested us<strong>in</strong>g likelihood ratio tests, ei<strong>the</strong>r based on maximum likelihood (for fixedeffects) or restricted maximum likelihood (for variance parameters associated with <strong>the</strong>random effects).

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