The Role of Space Syntax in Identifying the Relationship Between

The Role of Space Syntax in Identifying the Relationship Between Space and
Crime
Linda Nubani and Jean Wineman
American University in Dubai, UAE & University of Michigan, USA
[email protected]
Abstract
Criminologists, planners, and architects are still unable to predict criminals’ preferences
for committing an offense in one location over another. Criminologists associate crime
with socio-demographic factors such as income, racial composition, youth concentration
and level of education. Architects and planners on the other hand, relate crime to environmental design factors such as lighting, target hardening, or orientation of entrances,
just to name a few. Recently, some work using space syntax has demonstrated statistical
relationships between properties of spatial layouts and the occurrence of certain types of
crimes. In this study, Space Syntax measures of accessibility are used to examine geographical patterns of four types of offense behavior: breaking and entering, larceny, vehicle theft
and robbery. Crime data, at an address level with the exact date and time, is based on a
12 month period for the city of Ypsilanti Michigan (USA). After mapping crime locations
using GIS, an axial map was prepared using Spatialist, a program developed by Peponis
and Wineman. Syntax measures of street accessibility and visibility characteristics were
examined in relationship to instances of criminal behavior, controlling for such factors as
neighborhood socio-economic status. This paper concludes by defining a set of measures
that 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 and
fear of becoming a victim is still a major concern. It is the main contributor to the decline
of quality of life (Ian Colquhoun, 2004). In 1992, it caused victims to lose $17.6 billion
in direct costs and 6.1 million days from work (Klaus, 1994). Crimes referenced in this
statistic 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 in
nature and cannot be dealt with adequately in this paper. However, literature that has
dealt with this topic indicates that crime has been associated with poverty, inequality
and 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 aspects
of environmental design with the volume of crime such as building heights and orientation
of houses, just to name a few (Newman, 1973). Moreover, there have been few theoretical
developments that address the topic from a spatial perspective. Advocates of this perspective have found strong correlations between certain types of crime and spatial attributes
like street network and layout characteristics (Rengert, 1960). Does this suggest that residential neighborhoods can be designed in a way to lessen criminal activity? Is it possible
to predict the street characteristics that attract criminals or how much opportunity they
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Figure 195: Map showing locations of crimes in Ypsilanti
offer to crime? This paper focuses on the exploration of the links between crime and space
while considering sociodemographic factors.
2. Background literature
2.1. Offenders’ perspective
Generally, most of the ‘design for crime prevention’ work has been grounded in three
theories related to crime: the rational offender theory, the behavioral geography theory
and the routine activities theory (Taylor, 2002). The rational offender theory assumes that
the benefits of crime influence offending patterns; therefore, it is natural to think that
hardening 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 are
closer to where offenders work or reside are at higher risk of being burglarized than places
that are not within the offenders’ regular route. One may infer that this theory suggests
that crime rate is linked to easy accessibility (Taylor, 2002). The routine activities theory
looks at the interaction of three variables: the availability of attractive targets, the absence
of guardians and the presence of motivated offenders (Reid, 2002). If targets are exposed
to 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 offenders
share four general concerns: how quickly it takes to get to the target, how quickly it takes
to run away, how much value the target possibly has, and, how likely the offender is to be
caught while committing the crime or leaving the scene (Taylor, 2002; Rengert, 1960). An
The Role of Space Syntax in Identifying the Relationship
Between Space and Crime
415
Figure 196: Map showing the volume of crime represented by bars. The axial lines in the
map vary in thickness where thick lines represent highly integrated street segments and
thin lines represent segregated street segments
interesting study by Bennet (1989) highlighted these theories by interviewing 128 offenders
currently serving sentences in prison about their choice of targets. The subjects were all
male and almost half of them were under 21. In addition to the interview, offenders were
shown a video-recording of 36 dwellings in 4 neighborhoods. The video was recorded from
a van traveling at a walking speed. When the offenders assessed the dwellings, results
showed that their primary influence was related to the likelihood of being caught. The
difficulty of entering a particular property was mentioned less. Risk factors included signs
of occupancy of the targeted dwelling or the houses nearby. Security locks were seldom
considered 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 choice
of targets. Indications of occupancy include a car in the driveway or a security alarm
system. 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 Between
Crime and the Built Environment (Rubenstein,1981), the authors reviewed three types of
rational that might affect crime: the hardware rationale, the community building rationale,
and the social surveillance rationale. The hardware rationale focuses on “target hardening”
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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 less
successful.
The community building rationale is built on the hypothesis that there is a complex
range 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 public 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 barriers
in housing developments. The social surveillance rationale presumes that the layout of the
physical environment helps residents’ awareness of suspicious activities in their neighborhood, increases the residents’ ability to recognize strangers and makes strangers feel that
they are being watched.
The concept of surveillance is not new and can be traced back to early work by Oscar
Newman and Jane Jacobs in the 1960s Jacobs believed that through the occupation and
use of space, residents come to consider a particular space is theirs and they exert control
over it (Jacobs, 1961).
“The public place of cities is not kept primarily by the police,... it is kept primarily by
an intricate, almost unconscious, network of voluntary controls and standards among the
people themselves, and enforced by the people themselves.” (Jacobs, 1961)
Newman called for the creation of a hierarchy of zones from public to private. This
type of separation, termed territoriality, allows residents to adopt an attitude that the
private area is theirs. To achieve this attitude Newman suggests placing walls or fences
around 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 referred
to as Crime Prevention Through Environmental Design, also known as CPTED. CPTED
is defined by Crowe (2000), “as the use of the built environment in reducing fear of crime
and incidence of crime and improving the quality of life.” CPTED is centered around the
notion 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, and
providing 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 configurational properties on crime. Such studies found correlations between measures of Space
Syntax, and crime in residential neighborhoods (Shu, 2000; Hillier and Shu, 2000). Space
Syntax, a group of theories that examine the social use of space, was developed in the late
60s by Hillier and Hanson (Hillier and Hanson, 1984). Two Space Syntax measures, known
as Integration and Connectivity, calculate the level of accessibility of street segments from
all other street segments within a spatial system.
Building on the idea that neighborhood layouts provide opportunities and access to
commit a crime, Shu and Huang (2003) investigated the effect of accessibility of residential
neighborhoods in Taiwan. Their research investigated the influence of spatial configuration
on the distribution of burglary. In the first part of their analysis, they controlled for social
factors by looking at three districts in Northern Taiwan inhabited by different social
classes. The first district was a low density farming district; the second was a medium
density historical district; and the third was a densely populated residential area with parks
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Figure 197: Table summarizing results of Poisson Regression Modeling
and educational facilities at the periphery. There were a total of 121 neighborhoods within
the 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 global
integration and according to their mean local integration. Police crime data was gathered
for an 8 month period; there were total number of 241 crime incidents. The results showed
weak correlation between burglary rate and income levels and weak correlations between
burglary rates and global integration. Through correlational analyses within each income
level, a strong connection was found between global integration and burglary rates in
low-income neighborhoods. These findings suggested that globally integrated low-income
groups are safer. Further findings indicated that there were stronger correlations between
local integration and burglary rates than between global integration and burglary rates in
middle-income groups. The authors proposed that globally and locally integrated middleincome groups are safer than segregated ones. In addition, the authors found no correlation
between global or local integration and burglary rates in high-income neighborhoods. This
is possibly explained by the fact that “target hardening” features are more common within
high income neighborhoods.
Similar to previous work by Shu and Huang, Jones & Fanek (1997) looked at the effect
of spatial configuration on crime in Austin, Texas. They selected four pairs of tracts in
which each pair had similar income, poverty rates, population and racial composition.
Using Axman software, Integration R=3, Integration R=10, Control and Connectivity
values were calculated for each of the tracts. Correlations were then examined between
syntax values and crime rates. Results showed that pairs with higher integration values
were associated with lower crime rates. Three tracts with higher mean integration R=3 and
connectivity values were also associated with lower crimes rates. The authors explained
that more connected streets will attract higher pedestrian movement, and thus more eyes
on the street.
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As a result of promising findings using Space Syntax for identifying the spatial distribution of crime, Gosnells, a city in Western Australia consulted the Space Syntax laboratory
at University College London and Murdoch University to identify the spatial distribution of crime (Australia’s National Government Newspaper, 2003). The Space Syntax Lab
compared the movement of pedestrians and vehicles to crime statistics and space syntax
measures. The results were consistent with previous findings and showed a strong link
between 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 and
social characteristics (Dunn, 1980). Personal attack crimes, for example, occur in lower
class neighborhoods, while property crimes occur in neighborhoods that are accessible
or close to land uses, or in neighborhoods with higher percentages of underemployed or
single residents. Arsons, robberies and burglaries share monetary gain objectives and are
more likely to occur in middle- and high-class neighborhoods (Rengert, 1980). For these
reasons, we excluded non-residential neighborhoods. We also excluded organized crimes
or crimes that involve acquaintances or for the purpose of revenge such as assaults and
murder. Specifically, we focused on four stranger-to-stranger types of crime. These are
larceny, motor vehicle theft, breaking and entering and robbery.
According to FBI uniform report (1998), larceny, motor vehicle theft and breaking and
entering are considered property crimes where the object of the offense is the taking of
property without any threat involved. More precisely, larceny is taking away property from
the 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 other
vehicle. Breaking and entering is defined as the unlawful entry into a property without
putting people under threat (Hill, 1995). Robbery on the other hand is a violent crime that
involves 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 Detroit
area in Michigan. With a population of approximately 22,362, 1273 crime incidents were
reported in year 2003. Crimes in this figure include larceny, breaking and entering, robbery
and motor vehicle theft. According to FBI Crime Reports, the crime level in Ypsilanti is
worse than the national average particularly for burglaries, robberies, and thefts (Ypsilanti
MI Crime Statistics, 2002). The crime report was obtained from the Ypsilanti Police
Department and Eastern Michigan University. It includes data on the four types of crime
at 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 values
to every street segment in the system (e.g. All the street segments in Ypsilanti). The
two syntax measures used were Integration and Connectivity. They were calculated using
Spatialist. The Spatialist, a program developed by Peponis and Wineman, runs using
MicroStation. First, the Ypsilanti map was converted into an acceptable format. Another
layer was created on top of the map to prepare the axial map. The axial map is a network
The Role of Space Syntax in Identifying the Relationship
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of intersecting axial lines. In simple terms, the axial map is represented by the longest
lines 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 see
each other. The lines were drawn manually on top of the map using Spatialist. Ypsilanti
comprised an average of 634 axial lines.
Second, the program calculated the Integration and Connectivity values of every line
in the system (Figure 195). To elaborate on these two measures, Connectivity gives the
number of lines that are directly connected to a specific line. Integration, on the other
hand, 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 line
from all the axial lines in the system. In other words, these values suggest the extent to
which a selected space in the system is more integrated (can be easily reached from other
spaces), or more segregated (one has to travel through many spaces in order to reach that
selected space).
Since the unit of analysis is the axial line (or the street space), it was necessary to
append sociodemographic data along with crime data to each line. Therefore, a road map
of Ypsilanti was prepared showing 21 block groups using ArcGIS. Data on population
density, youth concentration, level of education, percentage of owners, age distribution
and racial composition were available from U.S. Census and were appended to each block
group in Ypsilanti. The report on crime at an address level was semi-manually entered
into the same database (Figure 195). Moreover, the original axial map that was prepared
using Spatialist was later converted into an appropriate format and was given accurate
geographic coordinates for Ypsilanti. This procedure allowed us to match the Spatialist
axial map with the ArcGIS Ypsilanti road map (Figure 196). The ‘Join Attribute’ feature
in 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 acceptable
SAS format. SAS is a statistical package that enabled us conduct a Poisson Regression
modeling 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 mixed
models to the collected data. Because of the count nature of the response variables, squareroot transformations were performed in order to satisfy the assumptions of normality and
constant variance in random errors. In the mixed models, the fixed effects of physical and
sociodemographic variables of interest on crime counts collected over one year in given
street-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 and
random connectivity effects associated with the randomly sampled block groups were also
included, to test the hypothesis that the crime counts and effects of connectivity on crime
counts tend to randomly vary from one block group to another. Parameters in the model
were tested using likelihood ratio tests, either based on maximum likelihood (for fixed
effects) or restricted maximum likelihood (for variance parameters associated with the
random effects).
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Figure 198: LEFT: Plot showing how the effect of connectivity on crime is moderated
by levels of youth concentration. RIGHT: Plot showing how the effect of connectivity on
crime is moderated by percentages of home ownership
4. Results and analysis
Results of the analysis showed that both local integration and connectivity were highly
associated with overall crime counts followed by density. Other factors such as median
income, racial composition and global integration did not feature in the model. However,
unlike previous studies by Hillier & Shu and Jones & Fanek, local integration was positively
correlated 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 integration
values were safer. That is to say neighborhoods that offered highly accessible routes to
their residents apparently also offered criminals easy routes of escape. Table in figure 197
summarizes these results.
More interestingly, additional findings showed that the effect of connectivity on crime
count was moderated by levels of youth concentration and the percentage of owners at the
block group level. In the model, connectivity was significant at the 1% level (P= 0002). The
product of both connectivity and youth concentration on crime was negative. This is to
say that the higher the percentage of youth concentration, the more negative the relationship between connectivity and crime (Figure 198). The same is true for connectivity and
percentage of home owners. The higher the percentage of people who own their residences
at 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), under
conditions of high connectivity (supporting neighboring and ‘eyes on the street’), crime
is lower, while under conditions of low connectivity, crime is higher. Similarly, with high
levels of youths in the neighborhood, high levels of connectivity (supporting neighboring
and ‘eyes on the street’), are associated with lower levels of crime.
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5. Conclusions and future work
Our review of past work on the crime-design link, together with our space syntax analysis
of 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 are
median income, racial composition, and of level of education. Interestingly, both youth
concentration and percentage of owners influenced crime rates only through their interaction with connectivity. However, careful explorations into the nature of these interactions
at each of the 21 block groups are needed.
The other recommendation for future research is to examine the differences between
the 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 pedestrian
movement, which in turn added more eyes on the street. Thus, integrated streets are more
likely to be safer. However, this explanation is more likely to hold true in places where
walking behavior is part of the lifestyle. Unfortunately, in the United States, particularly
in Michigan, people are more automobile dependent and walking is rarely used as a mode
to commute to work or to grocery stores. Needless to say, the unit of analysis in both
studies is different. Hillier and Shu (2000) looked at the mean integration and the mean
connectivity 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 different
types of crime is also important. Building on previous literature, some of these crimes
share different objectives and criminals have different motives for committing a crime
whether 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 a
promising new tool to examine the implications of spatial layout characteristics on crime
outcomes. However, this is a complex issue that will require multi-faceted analyses to
develop tenable solutions.
6. Acknowledgements
We would like to credit Brady West, computer systems consultant at the Center for
Statistical Consultation and Research at the University of Michigan, for his assistance in
the statistical analysis.
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