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

Linda Nubani and Jean WinemanAmerican University in Dubai, UAE & University of Michigan, USA

[email protected]

Abstract

Criminologists, planners, and architects are still unable to predict criminals’ preferencesfor committing an offense in one location over another. Criminologists associate crimewith socio-demographic factors such as income, racial composition, youth concentrationand level of education. Architects and planners on the other hand, relate crime to envi-ronmental design factors such as lighting, target hardening, or orientation of entrances,just to name a few. Recently, some work using space syntax has demonstrated statisticalrelationships between properties of spatial layouts and the occurrence of certain types ofcrimes. In this study, Space Syntax measures of accessibility are used to examine geograph-ical patterns of four types of offense behavior: breaking and entering, larceny, vehicle theftand robbery. Crime data, at an address level with the exact date and time, is based on a12 month period for the city of Ypsilanti Michigan (USA). After mapping crime locationsusing GIS, an axial map was prepared using Spatialist, a program developed by Peponisand Wineman. Syntax measures of street accessibility and visibility characteristics wereexamined in relationship to instances of criminal behavior, controlling for such factors asneighborhood socio-economic status. This paper concludes by defining a set of measuresthat identify street segment characteristics that affect the incidence of crime.

1. Introduction

Crime has always been a leading concern of the general public in the United States andfear of becoming a victim is still a major concern. It is the main contributor to the declineof quality of life (Ian Colquhoun, 2004). In 1992, it caused victims to lose $17.6 billionin direct costs and 6.1 million days from work (Klaus, 1994). Crimes referenced in thisstatistic include rape, robbery, assault, household theft, burglary, and car theft.

But what causes crime and what can we do to prevent it? The answer is complex innature and cannot be dealt with adequately in this paper. However, literature that hasdealt with this topic indicates that crime has been associated with poverty, inequalityand pathology (Rengert, 1980). Other known factors include density, racial composition,youth concentration, and family conditions (Bureau of Justice Statistics, 2003).

In addition to sociodemographic factors, some studies were able to link certain aspectsof environmental design with the volume of crime such as building heights and orientationof houses, just to name a few (Newman, 1973). Moreover, there have been few theoreticaldevelopments that address the topic from a spatial perspective. Advocates of this perspec-tive have found strong correlations between certain types of crime and spatial attributeslike street network and layout characteristics (Rengert, 1960). Does this suggest that res-idential neighborhoods can be designed in a way to lessen criminal activity? Is it possibleto predict the street characteristics that attract criminals or how much opportunity they

414 Linda Nubani and Jean Wineman

Figure 195: Map showing locations of crimes in Ypsilanti

offer to crime? This paper focuses on the exploration of the links between crime and spacewhile considering sociodemographic factors.

2. Background literature

2.1. Offenders’ perspective

Generally, most of the ‘design for crime prevention’ work has been grounded in threetheories related to crime: the rational offender theory, the behavioral geography theoryand the routine activities theory (Taylor, 2002). The rational offender theory assumes thatthe benefits of crime influence offending patterns; therefore, it is natural to think thathardening targets will be one solution to decrease opportunities for committing crimes.The behavioral geography theory, on the other hand, considers the fact that places that arecloser to where offenders work or reside are at higher risk of being burglarized than placesthat are not within the offenders’ regular route. One may infer that this theory suggeststhat crime rate is linked to easy accessibility (Taylor, 2002). The routine activities theorylooks at the interaction of three variables: the availability of attractive targets, the absenceof guardians and the presence of motivated offenders (Reid, 2002). If targets are exposedto all three variables, they are at higher risk of being victimized.

To a certain degree, it can be deduced from the afore-mentioned theories that offendersshare four general concerns: how quickly it takes to get to the target, how quickly it takesto run away, how much value the target possibly has, and, how likely the offender is to becaught while committing the crime or leaving the scene (Taylor, 2002; Rengert, 1960). An

The Role of Space Syntax in Identifying the RelationshipBetween Space and Crime 415

Figure 196: Map showing the volume of crime represented by bars. The axial lines in themap vary in thickness where thick lines represent highly integrated street segments andthin lines represent segregated street segments

interesting study by Bennet (1989) highlighted these theories by interviewing 128 offenderscurrently serving sentences in prison about their choice of targets. The subjects were allmale and almost half of them were under 21. In addition to the interview, offenders wereshown a video-recording of 36 dwellings in 4 neighborhoods. The video was recorded froma van traveling at a walking speed. When the offenders assessed the dwellings, resultsshowed that their primary influence was related to the likelihood of being caught. Thedifficulty of entering a particular property was mentioned less. Risk factors included signsof occupancy of the targeted dwelling or the houses nearby. Security locks were seldomconsidered as a risk factor. This suggests that surveillability is an important measure.

Earlier studies by Bennett and Wright (1984) and Jackson and Winchester (1982)supported the finding that surveillability and occupancy influence the burglar’s choiceof targets. Indications of occupancy include a car in the driveway or a security alarmsystem. Bennett and Wright’s study was also the result of an interview with burglars.They concluded that the greatest risk that burglars face is getting caught.

2.2. Crime-design link

A growing body of research has focused on the crime-design link. In the Link BetweenCrime and the Built Environment (Rubenstein,1981), the authors reviewed three types ofrational that might affect crime: the hardware rationale, the community building rationale,and the social surveillance rationale. The hardware rationale focuses on “target hardening”


such as walls around houses, triple locks and so on. It is assumed that “target hardening”might increase the technical difficulty of committing an offense and make the crime lesssuccessful.

The community building rationale is built on the hypothesis that there is a complexrange of physical characteristics that, if controlled, may reduce crime. The list includes,but is not limited to, the following: improved street lighting, increased use of shared pub-lic spaces, reduced number of families per entrance and number of apartments per floor,created hierarchy of zones from public to private, and increased use of symbolic barriersin housing developments. The social surveillance rationale presumes that the layout of thephysical environment helps residents’ awareness of suspicious activities in their neighbor-hood, increases the residents’ ability to recognize strangers and makes strangers feel thatthey are being watched.

The concept of surveillance is not new and can be traced back to early work by OscarNewman and Jane Jacobs in the 1960s Jacobs believed that through the occupation anduse of space, residents come to consider a particular space is theirs and they exert controlover it (Jacobs, 1961).

“The public place of cities is not kept primarily by the police,... it is kept primarily byan intricate, almost unconscious, network of voluntary controls and standards among thepeople themselves, and enforced by the people themselves.” (Jacobs, 1961)

Newman called for the creation of a hierarchy of zones from public to private. Thistype of separation, termed territoriality, allows residents to adopt an attitude that theprivate area is theirs. To achieve this attitude Newman suggests placing walls or fencesaround private areas, or employing symbolic devices such as changes of level, materials,portals or landscaping (Newman, 1973).

It is also interesting to mention that Newman’s ideas formed what currently referredto as Crime Prevention Through Environmental Design, also known as CPTED. CPTEDis defined by Crowe (2000), “as the use of the built environment in reducing fear of crimeand incidence of crime and improving the quality of life.” CPTED is centered around thenotion of Defensible Space, a range of mechanisms popularized by Newman in early 1970s.Briefly, it stresses the importance of creating a sense of territoriality among residents, andproviding natural surveillance through environmental design.

2.3. Space Syntax and Crime

In the past decade, researchers have begun to devote attention to the effect of configu-rational properties on crime. Such studies found correlations between measures of SpaceSyntax, and crime in residential neighborhoods (Shu, 2000; Hillier and Shu, 2000). SpaceSyntax, a group of theories that examine the social use of space, was developed in the late60s by Hillier and Hanson (Hillier and Hanson, 1984). Two Space Syntax measures, knownas Integration and Connectivity, calculate the level of accessibility of street segments fromall other street segments within a spatial system.

Building on the idea that neighborhood layouts provide opportunities and access tocommit a crime, Shu and Huang (2003) investigated the effect of accessibility of residentialneighborhoods in Taiwan. Their research investigated the influence of spatial configurationon the distribution of burglary. In the first part of their analysis, they controlled for socialfactors by looking at three districts in Northern Taiwan inhabited by different socialclasses. The first district was a low density farming district; the second was a mediumdensity historical district; and the third was a densely populated residential area with parks


Figure 197: Table summarizing results of Poisson Regression Modeling

and educational facilities at the periphery. There were a total of 121 neighborhoods withinthe three districts, which were classified into 12 groups according to their income level.The neighborhoods were also categorized into 12 groups according to their mean globalintegration and according to their mean local integration. Police crime data was gatheredfor an 8 month period; there were total number of 241 crime incidents. The results showedweak correlation between burglary rate and income levels and weak correlations betweenburglary rates and global integration. Through correlational analyses within each incomelevel, a strong connection was found between global integration and burglary rates inlow-income neighborhoods. These findings suggested that globally integrated low-incomegroups are safer. Further findings indicated that there were stronger correlations betweenlocal integration and burglary rates than between global integration and burglary rates inmiddle-income groups. The authors proposed that globally and locally integrated middle-income groups are safer than segregated ones. In addition, the authors found no correlationbetween global or local integration and burglary rates in high-income neighborhoods. Thisis possibly explained by the fact that “target hardening” features are more common withinhigh income neighborhoods.

Similar to previous work by Shu and Huang, Jones & Fanek (1997) looked at the effectof spatial configuration on crime in Austin, Texas. They selected four pairs of tracts inwhich each pair had similar income, poverty rates, population and racial composition.Using Axman software, Integration R=3, Integration R=10, Control and Connectivityvalues were calculated for each of the tracts. Correlations were then examined betweensyntax values and crime rates. Results showed that pairs with higher integration valueswere associated with lower crime rates. Three tracts with higher mean integration R=3 andconnectivity values were also associated with lower crimes rates. The authors explainedthat more connected streets will attract higher pedestrian movement, and thus more eyeson the street.


As a result of promising findings using Space Syntax for identifying the spatial distribu-tion of crime, Gosnells, a city in Western Australia consulted the Space Syntax laboratoryat University College London and Murdoch University to identify the spatial distribu-tion of crime (Australia’s National Government Newspaper, 2003). The Space Syntax Labcompared the movement of pedestrians and vehicles to crime statistics and space syntaxmeasures. The results were consistent with previous findings and showed a strong linkbetween spatial configuration and burglary and theft.

3. Method

3.1. Types of crime and description of case study

Generally, different types of crime are associated with different levels of land use andsocial characteristics (Dunn, 1980). Personal attack crimes, for example, occur in lowerclass neighborhoods, while property crimes occur in neighborhoods that are accessibleor close to land uses, or in neighborhoods with higher percentages of underemployed orsingle residents. Arsons, robberies and burglaries share monetary gain objectives and aremore likely to occur in middle- and high-class neighborhoods (Rengert, 1980). For thesereasons, we excluded non-residential neighborhoods. We also excluded organized crimesor crimes that involve acquaintances or for the purpose of revenge such as assaults andmurder. Specifically, we focused on four stranger-to-stranger types of crime. These arelarceny, motor vehicle theft, breaking and entering and robbery.

According to FBI uniform report (1998), larceny, motor vehicle theft and breaking andentering are considered property crimes where the object of the offense is the taking ofproperty without any threat involved. More precisely, larceny is taking away property fromthe possession of another. Purse-snatching and shoplifting are good examples of larceny.Motor vehicle theft is the stealing of a truck, automobile, motorcycles, and any othervehicle. Breaking and entering is defined as the unlawful entry into a property withoutputting people under threat (Hill, 1995). Robbery on the other hand is a violent crime thatinvolves putting victims under threat. It includes taking anything of value from persons(FBI uniform report, 1998).

In this study, we looked at Ypsilanti, a city located within the Metropolitan Detroitarea in Michigan. With a population of approximately 22,362, 1273 crime incidents werereported in year 2003. Crimes in this figure include larceny, breaking and entering, robberyand motor vehicle theft. According to FBI Crime Reports, the crime level in Ypsilanti isworse than the national average particularly for burglaries, robberies, and thefts (YpsilantiMI Crime Statistics, 2002). The crime report was obtained from the Ypsilanti PoliceDepartment and Eastern Michigan University. It includes data on the four types of crimeat an address level with the exact date and time.

3.2. The axial map analysis

Spatial layout was analyzed using space syntax techniques by assigning syntactic valuesto every street segment in the system (e.g. All the street segments in Ypsilanti). Thetwo syntax measures used were Integration and Connectivity. They were calculated usingSpatialist. The Spatialist, a program developed by Peponis and Wineman, runs usingMicroStation. First, the Ypsilanti map was converted into an acceptable format. Anotherlayer was created on top of the map to prepare the axial map. The axial map is a network


of intersecting axial lines. In simple terms, the axial map is represented by the longestlines of sight that can be used to characterize every street segment in the Ypsilanti area.For example, if two people were standing at each end of the line, they will be able to seeeach other. The lines were drawn manually on top of the map using Spatialist. Ypsilanticomprised an average of 634 axial lines.

Second, the program calculated the Integration and Connectivity values of every linein the system (Figure 195). To elaborate on these two measures, Connectivity gives thenumber of lines that are directly connected to a specific line. Integration, on the otherhand, is an indicator of how easily one can reach a specific line. Mathematically speaking,it is the average number of spaces that one needs to pass through to reach a specific linefrom all the axial lines in the system. In other words, these values suggest the extent towhich a selected space in the system is more integrated (can be easily reached from otherspaces), or more segregated (one has to travel through many spaces in order to reach thatselected space).

Since the unit of analysis is the axial line (or the street space), it was necessary toappend sociodemographic data along with crime data to each line. Therefore, a road mapof Ypsilanti was prepared showing 21 block groups using ArcGIS. Data on populationdensity, youth concentration, level of education, percentage of owners, age distributionand racial composition were available from U.S. Census and were appended to each blockgroup in Ypsilanti. The report on crime at an address level was semi-manually enteredinto the same database (Figure 195). Moreover, the original axial map that was preparedusing Spatialist was later converted into an appropriate format and was given accurategeographic coordinates for Ypsilanti. This procedure allowed us to match the Spatialistaxial map with the ArcGIS Ypsilanti road map (Figure 196). The ‘Join Attribute’ featurein ArcGIS allowed us to merge the data on the axial map with the rest of the data.The final database that was produced in ArcGIS was later converted into an acceptableSAS format. SAS is a statistical package that enabled us conduct a Poisson Regressionmodeling of our data since the crime report was collected over a period of a year.

3.3. Statistical Analysis

The MIXED Procedure in SAS (Version 9) was used in these analyses to fit linear mixedmodels to the collected data. Because of the count nature of the response variables, square-root transformations were performed in order to satisfy the assumptions of normality andconstant variance in random errors. In the mixed models, the fixed effects of physical andsociodemographic variables of interest on crime counts collected over one year in givenstreet-spaces or axial lines were estimated. Because of the clustered nature of the data,axial lines were clustered within randomly selected block groups, random intercepts andrandom connectivity effects associated with the randomly sampled block groups were alsoincluded, to test the hypothesis that the crime counts and effects of connectivity on crimecounts tend to randomly vary from one block group to another. Parameters in the modelwere tested using likelihood ratio tests, either based on maximum likelihood (for fixedeffects) or restricted maximum likelihood (for variance parameters associated with therandom effects).


Figure 198: LEFT: Plot showing how the effect of connectivity on crime is moderatedby levels of youth concentration. RIGHT: Plot showing how the effect of connectivity oncrime is moderated by percentages of home ownership

4. Results and analysis

Results of the analysis showed that both local integration and connectivity were highlyassociated with overall crime counts followed by density. Other factors such as medianincome, racial composition and global integration did not feature in the model. However,unlike previous studies by Hillier & Shu and Jones & Fanek, local integration was positivelycorrelated with crime rates. In the model, local integration was significant at the 1%level (P = 0.0001). To elaborate on this finding, street spaces that had low integrationvalues were safer. That is to say neighborhoods that offered highly accessible routes totheir residents apparently also offered criminals easy routes of escape. Table in figure 197summarizes these results.

More interestingly, additional findings showed that the effect of connectivity on crimecount was moderated by levels of youth concentration and the percentage of owners at theblock group level. In the model, connectivity was significant at the 1% level (P= 0002). Theproduct of both connectivity and youth concentration on crime was negative. This is tosay that the higher the percentage of youth concentration, the more negative the relation-ship between connectivity and crime (Figure 198). The same is true for connectivity andpercentage of home owners. The higher the percentage of people who own their residencesat a block group level, the more negative the relationship between connectivity and crime(Figure 198). Perhaps these results can be related to the effects of ‘eyes on the street’.If there are higher levels of home ownership (indicating a more stable population), underconditions of high connectivity (supporting neighboring and ‘eyes on the street’), crimeis lower, while under conditions of low connectivity, crime is higher. Similarly, with highlevels of youths in the neighborhood, high levels of connectivity (supporting neighboringand ‘eyes on the street’), are associated with lower levels of crime.


5. Conclusions and future work

Our review of past work on the crime-design link, together with our space syntax analysisof crime in the Ypsilanti area suggest further opportunities for future work. In summary,some variables suggested by previous research were not significant in this study. These aremedian income, racial composition, and of level of education. Interestingly, both youthconcentration and percentage of owners influenced crime rates only through their interac-tion with connectivity. However, careful explorations into the nature of these interactionsat each of the 21 block groups are needed.

The other recommendation for future research is to examine the differences betweenthe findings of this study and other similar work by Hillier and Shu. In their study,Hillier and Shu (2000) explained that highly integrated streets encouraged more pedestrianmovement, which in turn added more eyes on the street. Thus, integrated streets are morelikely to be safer. However, this explanation is more likely to hold true in places wherewalking behavior is part of the lifestyle. Unfortunately, in the United States, particularlyin Michigan, people are more automobile dependent and walking is rarely used as a modeto commute to work or to grocery stores. Needless to say, the unit of analysis in bothstudies is different. Hillier and Shu (2000) looked at the mean integration and the meanconnectivity of neighborhoods while this study considered axial lines as the unit of analysis.

Finally, a careful investigation into the effect of space syntax measures on differenttypes of crime is also important. Building on previous literature, some of these crimesshare different objectives and criminals have different motives for committing a crimewhether it is to burglarize a property or snatch a purse on the street (Davidson, 1993).Time is also of a critical factor. To conclude, space syntax techniques appear to add apromising new tool to examine the implications of spatial layout characteristics on crimeoutcomes. However, this is a complex issue that will require multi-faceted analyses todevelop tenable solutions.

6. Acknowledgements

We would like to credit Brady West, computer systems consultant at the Center forStatistical Consultation and Research at the University of Michigan, for his assistance inthe statistical analysis.

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