Crime and Space

patterns of offences and offenders’ paths to

crime portrayed by GIS

VÂNIA CECCATO

space in urban criminology

The way spatial information has been approached by literature in crime
analysis varies highly, following both the development of urban crimino­
logy as a discipline (Shaw and McKay 1942, Newman 1972, Cohen and
Felson 1979, Brantingham and Brantingham 1991, Sampson et al. 1997,
Wikström 2003, 2004) and the diffusion of spatial technologies such as
Geographic Information Systems (GIS) in human sciences (Haining
1990, 2003, Anselin 1999, Fotheringham and Rogerson 2002, Chainey
and Ratcliffe 2006). In this article, I review how certain notions of space
have been incorporated into urban criminology research using GIS.

Space is discussed as:

• location, density and distance measures
• mental description of an environment
• discrete demographic and socio-economic entity
• structural backcloth for social interactions
• measure of environmental risk

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The advance of new technologies for data storage and analysis such as
GIS has led to the creation of systems for visualising and analysing the
growing amounts of geocoded crime data both within academia and
police forces. These techniques are making geographical analysis of
crime data more in-depth and interactive than they were in the past
and therefore space can now be addressed more dynamically, both in
time and space. Spatial analysis of crime data often uses information
on the unique location of individual crimes (x,y co-ordinates) or aggre­
gated data (combining individual point data into larger areal units,
such as a city’s statistical units). There are also cases when lines are
used either to portray offenders’ paths into crime or environments
which residents perceive as unsafe. Some of these spatial analyses are
illustrated later in this article. I do not intend however to make a com­
plete review of applications but rather provide a sample of important
areas in this field by showing some of my own research examples. The
article concludes with directions for future research.

space as location, density and distance measures

When a crime event occurs, it happens at a certain location and an
offender who commits a crime can be found at a place near or far from
the crime event. A crime target or victim must be where the offender is
at exactly the same time as the event takes place. Thus, the intersection
of these elements exemplifies how space as location plays a vital role in
understanding crime and how crime occurs. The police have long re­
cognised the inherent geographical component of crime by marking
maps with pins, where each pin represents a crime event. It was how­
ever not until the development of spatial analysis and advent mapping
techniques that the importance of ‘space’ in understanding crime was
further investigated by scholars and practitioners.
Space has extensively been approached in urban criminology as a
measure of concentration or density. In these studies the goal has often
been to identify, predict and in certain cases, manage “an area that is
the target of a higher than expected level of criminal activity” (Rattcliffe

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and McCullagh 2001) sometimes re­

ferred to as ‘hot spots’. Hot spots
evolve or change over time (John­
son and Bowers 2004, Ceccato
2005), are mobile or in transit
(Tremblay and Tremblay 1998, Lou­
kaitou-Sideris et al. 2002, Newton
2004) or even depend on human
perception (Rengert 1995, Rattcliffe
and McCullagh 2001). A number of Figure 1. Cluster of total thefts in Vilnius’
different mapping techniques have Old Town, Lithuania, 2004–2005 using
Nearest Neighbour Hierarchical Cluster.
been developed for identifying Source: Ceccato and Lukyte, 2008:7.
hotspots of crime: from location
quotients, thematic mapping of area based data, spatial ellipses, grid
thematic mapping, kernel density estimation to indicators of global
and local spatial association (Canter 1995, Anselin 1995, Rattcliffe and
McCullagh 1999, Block and Block 1995, Rattcliffe 2004). The global po­
pularity of hot spot analysis is due to the fact that being able to identify
high crime areas is an important crime prevention tool for police for­
ces.
Figure 1 illustrates the use of the Nearest Neighbour Hierarchical
(NNH) cluster technique to identify a high concentration of thefts in
Vilnius Old town, Lithuania. The NNH used here is a clustering techni­
que that defines a threshold distance and compares the threshold to
the distances for all pair of points. In this first criterion, we have chosen
100 meters for the threshold distance. Only points that are closer to
one or more other points than the threshold distance are selected for
clustering (represented by the ellipses in Figure 1). What is evident in
this pattern is how hot spots of thefts follow main roads and areas with
diverse land use, where there is a lack of ‘capable guardians’ despite
being crowded places. These areas mostly comprise transport links
(such as main streets), transport nodes (such as bus stops) and places
where large groups of people gather (close to museums, galleries,

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hotels, theatres, restaurants and hospitals). Similar geography was

previously found in North American and Western European cities
(Brantingham and Brantingham 1991).
Criminology has also focused on space as synonymous with distance
between two places, crime location and offender’s place of residence.
These studies might be simple average distance analysis (a bi-polar
connection of an offender’s residence and crime location) or complex
methodological proposals to predict an individual’s offending behavi­
our. The work of White (1932) is regarded as a basis for many scholars
(Turner 1969, Capone and Nichols 1976, Rhodes and Conly 1981, Phillips
1980, Lundrigan and Canter 2001, Wiles and Costello 2001, Fritzon 2001,
Gore and Pattavina 2004). None of these studies however regard crime
in relation to an individual’s mobility within the city, either prior or
subsequent to the crime event. This is important to highlight because
crime may occur only when motivated offenders, suitable targets and
an absence of responsible guardians intersect in space and time (Co­
hen and Felson 1979).

space as a mental description of en environment

One relevant question is whether crime official statistics reflect people’s
perception of safety. Using GIS, it is possible to create maps of the per­
ceived safety for different groups of residents. These maps constitute
mental descriptions of an environment and can be useful for studying
spatial patterns of crime. For instance, if the perceived pattern of safety
does not correspond to the actual pattern, then important data can be
learned about the perceived and actual environment instigators and
inhibitors to criminal events (Smith and Patterson, 1980, Rengert, 1995,
Rattcliffe and McCullagh, 2001). Figure 2 shows how patterns of offen­
ces match the perceived safety in an urban renewal project of a resi­
dential area (Jordbro) in Stockholm County, Sweden. The data in this
case refers to the people’s perception of their personal safety obtained
from surveys. Those who felt unsafe were invited to indicate on a map
the areas they avoid. These sketch maps were later transferred to the

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vânia ceccato crime and space

Figure 2. Offences per in-

habitants and indication
of ‘unsafe places’ by resi-
dents in Jordbro, Stock-
holm region, Sweden,
1999. Source: Ceccato
and Snickars, 2000.

basic digital map by using GIS. The lines in Figure 2 (right) show the
areas people indicated as unsafe. The map shows that those areas that
are close to the commercial centre, the tram station, and bus stops are
perceived as unsafe places with disturbances, which fairly matches the
pattern from the map based on official statistics (left). Open and less
guarded green areas are also commonly perceived as less safe.
Criminologists have also been interested in looking at space as a
potential landscape for crime. Space, and the factors within it, can be
assessed as a condition that facilitates or deters crime. The central
point of these studies, therefore, is on features of urban space rather
than offenders or criminal events. The scale is at the level of micro
environ­ments, such as facades or street corners, but also the composi­
tion of these parts that make an area more or less susceptible to crime.
Different parts of an urban space can be perceived as vulnerable to vari­
ous types of crime. For example, muggers need busy and semi perme­
able areas, whilst burglars may prefer secluded access. Although these
studies have been fundamental to establishing key factors for situatio­
nal crime prevention (Jacobs 1961, Jeffery 1971, Newman 1972, Hillier
2002), they have also been controversial, especially when the influence
of physical environment in determining crime has been exaggerated by
supporting factions.

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space as a discrete demographic and socio-economic

entity
There has been a long tradition in criminology to approach space as
discrete zones (such as neighbourhoods) in the attempt to understand
crime distribution. Often aggregated data are attached to spatial enti­
ties that are thought to be ecologically exposed to different sorts of cri­
mes. Such data may be ecological (a group-level property), or contex­
tual (an aggregation of a property belonging to the individuals compri­
sing the group). Shaw and McKay (1942) in their seminal work on Chica­
go argued that low economic status, ethnic heterogeneity and residen­
tial instability led to ‘community disorganisation’, which in turn resul­
ted in sub-cultures of violence and high rates of delinquency. Social
disorganisation theory suggests that structural disadvantage breeds
crime. The main focus is placed on offenders and motivation (often in­
dicated by an offender’s place of residence). More recent investigations
have drawn on new concepts (such as social cohesion and collective
efficacy) but are still linked to crime location or an offender’s place of
residence as discrete zones (Rosenfeld et al. 2001, Sampson et al. 1997).
Although ecological studies have continued to reveal strong associa­
tions between characteristics of urban areas and the locations of cer­
tain types of offences, there is little evidence to show how exposure to
different urban environments (beyond place of residence or crime loca­
tion) can influence an individual’s decision to commit a crime. Other
limitations refer to the use of ‘zones’ as a unit of analysis. For instance,
the impact of shape and size of zones on the results (the Modifiable
Area Unit Problem – MAUP) as well as the risk for ecological fallacy
have been extensively documented (Fotheringham and Wong 1991, Ro­
binson 1950). Using cross-sectional aggregated zone data we are able to
ascertain the links between the occurrence of crime and small-area
socioeconomic and demographic characteristics (conclusions are
drawn at ecological level only). However, what cannot be done, is to
observe how these causal mechanisms take place at an individual level
within zones and over time.

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vânia ceccato crime and space

Figure 3. Positive residuals of the Multiple Ordinary

Linear Regression, Car related thefts, Stockholm,
Sweden, 1998. Source: Ceccato et al. 2002

Ecological studies in Western cities have shown that for example,

inner city areas are vulnerable places for car related thefts, especially
those close to deprived neighbourhoods or with diverse land use (Wik­
ström 1991, Evans 1992). In these studies, regression models are often
used to test to what extent socio-economic and/or land use variables
reflect the variation of crime in space. An example of this type of ana­
lysis is depicted by Figure 3, which shows a map of positive residuals of
the Multiple Ordinary Linear Regression model for car related thefts in
Stockholm city, Sweden. The positive residuals indicate areas where car
related thefts are under-predicted by the regression model. In the
Stockholm’s case, they are high-income areas and parts of the inner
city, which may indicate the emergence of a new element in the geo­
graphy of car related theft since the 1980’s, when they occurred in rela­
tively poor neighbourhoods and inner city areas only (Wikström 1991).
High risks of car theft and theft from cars are now seen in more afflu­
ent areas perhaps because of declining levels of guardianship or be­
cause offenders are themselves more mobile, moving beyond the city
boundaries. These findings show that a city’s internal geography is so­
metimes not enough to explain its crime distribution. Crime patterns
can be affected by external forces that go beyond the city’s initial
demographic and socio-economic make up. Figure 4 shows an example
of when a construction of a transport link (a bridge connecting two
cities) impacts on intra-urban crime levels and geography. The map
indicates neighbourhoods of a Southern Swedish city that had an un­
expected increase in car related thefts after the transportation link was
built (the Öresund’s bridge, connecting Malmö to Copen­hagen).

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Figure 4. Changes of car related thefts in Malmö,
Sweden (%), 18 months before and after Öresund’s
bridge. Source: Ceccato and Haining 2004

space as structural backcloth for social interac-

tions
The ‘routine activity theory’, as it is called, suggests that an individual’s
activities and daily habits are rhythmic and comprise repetitive pat­
terns. Space is like a structural backcloth that generates certain types of
social interactions that may lead to crime. The goal of studies based on
routine activity theory is to direct their work away from static ecologi­
cal correlations between socio-economic characteristics and crime
toward a more dynamic view of crime within the context of daily acti­
vity patterns. The dynamic aspect of this theory has however been
empirically limited by the lack of individual level data on a person’s
actions over time and space. Empirical studies have so far taken land
use indicators (e.g. location of city centre, resident population density)
as proxies for an individual’s mobility or potential social interactions
that may lead to crime (Roncek and Maier 1991, Osgood et al. 1996,
Oberwittler 2004). Figure 5 illustrates how land use data can be used as
indicators of the criminogenic conditions of different environments in
Tallinn, Estonia.
Co-coordinates of the location of pubs and clubs, bus/tram/train
stops and transport lines were mapped using GIS. A digital grid cells
map covering the city of Tallinn was used as a basis for the spatial
modelling analysis (500 by 500 meter squares) instead of irregularly
shaped polygons from administrative areas. The map also shows the
intersection of high-risk areas for thefts (dark gray) derived from a

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vânia ceccato crime and space

Figure 5. Areas with relatively high risk (O(i)>E(i))

for thefts in Tallinn, 2004-2005. Source: Ceccato,
2008

standardised offence ratio (Ceccato et al. 2002) with transportation

lines and location of pubs and clubs. One-third of total thefts take
place in the central area of Tallinn, which encompasses about 5 per­
cent of the city’s area. As expected, these areas bring people together,
creating an opportunity for thefts, with potential offenders and victims
present at the same place and time (Cohen and Felson 1979, Wikström
1991). If data were available, the time dimension could be split up into
sections, such as day-time, night-time population, or convergent-diver­
gent times at group level, providing a greater picture of people’s routine
activity and an improved basis from which to ‘explain’ crime patterns.
In this case, GIS in combination with space-temporal techniques (such
as the Kalldorff ’s scan test, (Kulldorff, 1997) can be used to test concen­
tration of crime as clusters in time and space. Since crime clusters
might expand or shrink in size over time (Johnson and Bowers 2004,
Ceccato 2005) police forces may use this information to better tackle
crime by taking decisions on where and more importantly, when to dis­
patch police patrols.

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space as a measure of environmental risk

Traditional urban criminology has during several decades focused
either on the characteristics of crime location or the context of an
offender’s residence to predict crime. Several attempts focused on inter­
actions between these bipolar locations but excluded the complexity of
an individual’s mobility between the two points. Relatively little empiri­
cal evidence exists on whether the quality of the environment and
settings where individuals spend time have any influence on their deci­
sion to commit a crime. Wikström and Loeber (2000) has, for instance,
shown that being exposed to risky environments makes some indivi­
duals more prone to offend. The analysis draws upon Wikström’s Deve­
lopmental Ecological Action Theory of Crime in which acts of crime are
considered to be a result of the interplay of mechanisms linking indivi­
dual characteristics, behaviour settings and community context (Wik­
ström 2003, 2004). The novelty of this analysis lays in the combined
application of GIS and space-time budget techniques. Space-time bud­
gets constitute a technique for data acquisition that provides a basis for
detailed description and analysis of individuals’ behaviour over time
and space (in this case, a longitudinal database sample of juveniles
from Peterborough state schools). The potential for using individual
data in GIS is argued to provide insights on whether, how, where and
when human interactions take place and how this affects actions that
may lead to offending.
One of the GIS techniques tested to represent movement patterns is
space-time prism (or aquarium), which is perhaps the most similar
form of representation to the original Hägerstrand’s daily prisms
(1970:13-14). Hägerstrand used the space-time path to demonstrate how
human spatial activity is often governed by limitations and not by
independent decisions by spatially or temporally autonomous indivi­
duals. This means, for instance, that an individual cannot be in two
places at the same time or travel instantaneously from one location to
another – a certain trade-off must be made between space and time.
The activities of an adolescent on a Monday were mapped using the

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vânia ceccato crime and space

Figure 6. Space-time prism of an adolescent

on a weekday.

co-ordinates of each enumeration districts’ centroids (Figure 6). The

co-ordinates were generated initially in 2-D and then converted to 3-D
shape files together with three other background layers (lines connec­
ting places, time scale and the Enumeration District map). An advan­
tage is that angles of lines connecting places shows the direction of a
path whilst the time scale indicates duration of each activity. Although
the use of individual data mapped at very detailed level (e.g, street add­
ress) has considerable potential for development of person-specific
scales, it still has limitations. The use of multiple paths at once in a
space-time prism can for instance be visually challenging since it is
very difficult to disentangle a path from another (Kwan 2000), failing
to provide a comprehensive picture of activity patterns emanating from
distinct groups. Activity density surface is an alternative technique able
to identify potential differences in spatial paths.
Figure 7 exemplifies the spatial pattern of movement of three young
offenders, where they live, where they committed offence(s) and the
areas that they most likely passed through during that week. This pict­
ure illustrates a combination of points (e.g. home and offence loca­
tions), lines (individual paths) and areas (e.g. kernel home range). Using
default parameters, a fixed kernel home range utilisation distribution
(Worton 1989) as a grid coverage in GIS was calculated for a group of
adolescents living in a southern Peterborough neighbourhood (Orton),
where all three offenders live. The gray polygon represents the area with
a 95% probability where the children moved around (significant differ­
ences in gender can also be found when girls’ and boys’ movement pat­
terns were tested). This map shows that although young offenders’

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Figure 7. Spatial pattern of movement of young

offenders (12-13 years old), Peterborough, UK.
Source: Wikström and Ceccato 2005

movement patterns are strongly influenced by their place of residence,

many activities happen far from where they live (see the gray polygon).
By using space-time budget data (Wikström and Ceccato 2005), their
paths can be broken down by time (hour) and space (output areas) and

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mapped using GIS. A similar approach is used to determine how much

time each individual spends in different parts of the city, including set­
tings or environments that are risky (e.g. measures of environment risk
and setting risk). Individuals are classified into risk groups (based on
morals and self-control), which in combination with their weekly acti­
vity patterns, can be used as a measure of environmental risk and a
predictor for offending.

summary and looking ahead

Crime events are far from being random phenomena. They tend to
occur in particular places; they may occur at certain hours of the day
and even in association with specific demographical, land use, and
socioeconomic aspects of the population. It was not until the advent of
spatial technologies such as GIS that it was recognised that crime could
be explained and understood in more depth by exploring its geographi­
cal components. Spatial analyses have for decades made possible the
identification of patterns and concentrations of crime, the exploration
of factors that explains its geography and, more recently, tracking indi­
viduals over space and indicating risky environments that may affect
their decision to offend. Linking data on offence, victims and offenders
is one of the core issues in modern urban criminology and the GIS
applications shown in this article illustrate where the linkage is crucial
to understanding spatial processes.
An important area of study that has not been covered by this article
is the quality of crime data. Underreporting is a known cause for lack
of reliability within official police offence databases (which varies by
type and crime seriousness). There are other problems of data quality
that take place during the process of recording offences. For instance,
the lack of information about an event from the victim (not knowing
exactly where the offence took place) or by the police officer failing to
record the event properly (missing records on the exact location/time
of the event). There are also risks for inaccuracies in the process of map
creation. Geocoding is the process of matching records in two data­

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bases: the offence address database (without map position informa­

tion) and the reference street map or any other “address dictionary”
(with known map position information). In cases where the matching
of the exact offence location is not possible, a common practice is to
choose a near location (such as midpoint of street) or the polygon
centroid of a region (e.g. district polygon). This practice may create the
so-called “dumping sites” for records, which generates false offence
concentrations and consequently, a poor basis for research or any
police intervention. Future research should devote time to assessing
how these multiple sources of inaccuracy (when reporting, recording
or geocoding) affect the reliability of the outcomes in spatial crime
analysis.

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