Finding Criminal Attractors based on Offenders’Directionality of Crimes

Richard Frank, Martin A. AndresenSchool of Criminology

Simon Fraser UniversityBurnaby, Canada

{rfrank, andresen}@sfu.ca

Connie Cheng, Patricia BrantinghamSchool of Criminology

Simon Fraser UniversityBurnaby, Canada

{cca25, pbrantin}@sfu.ca

Abstract — According to Crime Pattern Theory, individuals allhave routine daily activities which require frequent travelbetween several nodes, with each being used for a differentpurpose, such as home, work or shopping. As people movebetween these nodes, their familiarity with the spatial areaaround the nodes, as well as between nodes, increases. Offendershave the same spatial movement patterns and Awareness Spacesas regular people, hence according to theory an offender willcommit the crimes in their own Awareness Space. This idea isused to predict the location of the nodes within the AwarenessSpace of offenders. The activities of 57,962 offenders who werecharged or charges were recommended against them were usedto test this idea by mapping their offense locations with respect totheir home locations to determine the directions they move. Oncedirectionality to crime was established for each offender, aunique clustering technique, based on K-Means, was used tocalculate their Cardinal Directions through which the awarenessnodes for all offenders were calculated. It was found that, bylooking at the results of various clustering parameters, offenderstend to move towards central shopping areas in a city, andcommit crimes along the way. Almost all cluster centers werewithin one kilometer of a shopping center. This technique offinding Criminal Attractors allows for the reconstruction of thespatial profile of offenders, which allows for narrowing thepossible suspects for new crimes.

Keywords-component; offender profile, directionality,clustering, criminal attractor

I. INTRODUCTION

The movement of people is not random. Each person willroutinely travel along paths between only a handful oflocations, such as their home, work and nearby shopping. Witheach and every trip, they will familiarize their knowledgefurther about the path, and everything along that path. Becausepeople are familiar with the paths they travel frequently, theyare more comfortable in them, as opposed to moving intounfamiliar areas. These familiar areas become the person’sAwareness Space.

The Awareness Space is built from two components: nodesand paths. The nodes, called Awareness Nodes, are formedfrom a person’s frequent destinations, such as their work place,residence or recreation, which provide the end-points of thejourney. From a criminological point of view, the AwarenessNodes are not created equal, with some being crime generatorsattracting a large number of people regardless of their criminal

motivation, and others being crime attractors allowing formany criminal opportunities that offenders are aware of andtake advantage of [1]. Finally, other areas, termed criminalattractors, do not necessarily attract crime, but attract thecriminals themselves, and they commit crimes along the way.

As a person wants to go from a node to another, they willnot follow a random walk [2], but instead will attempt to travelalong the optimal route: the direct path, a straight lineconnecting the two nodes. However, due to boundaries, eithernatural, such as lakes and mountains, or man-made, such ashighways and buildings, they cannot follow the direct path, andinstead must move around on circumscribed paths. In citiesthese circumscribed paths are usually along road-segments.Since the movement is restricted by the topology of a person’senvironment, the person will have a large set of fixed possiblepaths to take from one node to the other, with the majority ofthose paths being completely undesirable (leading away fromthe destination with a significant detour, for example), leavingonly a handful of reasonable paths. The person will travel alongthose select few routes that they consider optimal (or close tooptimal). After the person has established a preference forcertain routes over others, they will become familiar with thatroute, and it will be incorporated into their Awareness Space.

According to Crime Pattern Theory (CPT) [3], offenders acton opportunities within the area they are familiar with and willuse those locations to commit crimes, rather than exploringnew areas with which they are not familiar with. This isintuitively correct. If the offender leaves their Awareness Spacein search of criminal opportunities, they will have to enterunfamiliar territory and hunt for an opportunity; this exercise isunnecessary if the offender is already aware of a similaropportunity within an area they are familiar with. The sub-regions of the Awareness Space where the offender actuallycommits crimes make up the Activity Space. This phenomenonis shown in Figure 1.

The difficulty with applying Crime Pattern Theory is thatthe Awareness Space for an offender is very difficult to obtainwithout direct access to the offender and their willingness toshare such information. Knowing the Awareness Space of anoffender is important as it will highlight the possible ActivitySpaces of the offender, allowing law enforcement to hardenthose areas to prevent further offenses. Unfortunately withoutobserving the offender’s cognitive-map of an area, theAwareness Space cannot be known, only the offenses

committed by the offender (for which they have been caught)are known. Could the Awareness Space be determined byknowing a criminal subset of the Activity Space?

This question has significant meaning when profilingoffenders. Profiling is a way of using the attributes of a crime,or crime location, to determine likely areas where the offendercould be. As mentioned above, crimes happen in areas knownto offenders, which could be along the paths or close to thenodes in their Awareness Space. Knowing the location of thecrime, and the likely nodes in the neighbourhood, a possibleActivity Space could be determined. Projecting the ActivitySpace into the Awareness Space could then highlight importantnodes in the Awareness Space of the offender, regions of spacewhere the offender could be living, for example. When a newcrime occurs, it would be possible to narrow down the numberof suspects by finding offenders whose Awareness Spaceintersects the crime location.

This not only application to offenders but also victims, asthey have the equivalent Awareness Spaces while they alsomove about in their own environment. Further, studies havealso found that victims are victimized most often close to theirhome, within their corresponding Awareness Spaces [4] [5].

One of the components of the Awareness Space, theAwareness Path, is made up of two components: distance anddirection. The distance component of the journey to crime hasbeen researched significantly over the years [6] [7] [8] [9] [10].Research into Crime Pattern Theory (CPT) and the Journey toCrime (JtC) has shown that offenders do not travel very farfrom their home location [11] [12] [13]. The distance to thecrime location (from home) does tend to vary depending onvarious factors, such as the crime type, age of offender(s),layout of the city and the transportation routes available withinit [14] [15] [16] [17]. Although the distance component is wellresearched, the directionality component of the paths has notachieved the same prestige and only few works exist that dealwith it [18] [19].

In this paper, a Directionality-based Criminal AttractorLocator (DCAL) model is proposed. DCAL exploits thedirectionality component of Awareness Paths in order toreconstruct the Awareness Space of offenders from theirActivity Space. It does so by attempting to predict the positionand orientation of the Awareness Paths along which the

offenders were heading when they stopped to commit a crime.Information about all Awareness Paths is then used to find theareas (Nodes) within a city where offenders typically go as partof their (normal non-criminogenic) lives. The eventual goal ofthis model is to be able to profile offenders based on theirknown spatial locations (such as their home) and their crimelocations. DCAL adds to the current literature through thefollowing contributions:

1) Using a popular clustering method in a novel way toperform clustering of directions instead of spatial points.

2) Using clustering techniques to find where people aregoing, based on data about where they offend.

3) Introducing a model capable of reconstructing theAwareness Space of each individual offender based on theknowledge of many offender’s known home and crimelocations.

4) Using real crime data to experimentally evaluate themodel and finding the Criminal Attractors of real offenders.

The paper first discusses the details of the model (SectionII), followed by an outline of the method through which it wasevaluated (Section III) to gather results (Section IV) on a realcity in the Greater Vancouver Regional District in BritishColumbia, Canada. The implications to criminology andpolice of the results are discussed (Section V) and then thepaper is concluded (Section VI).

II. THE MODEL

Why do offenders offend at the locations they do?According to Crime Pattern Theory, the crimes an offendercommits are going to be situated near a path in their AwarenessSpace, where the path leads from one node to another. So, howis the path structured? Where is its origin? Where does itterminate?

The above questions raise two underlying problems. First,for a highly prolific offender, there will be a large number ofJourneys to Crime (JtC), naïvely one to each crime location. Ifmultiple such JtC are headed in the same general direction,called Cardinal Direction (CD), then these must be merged intoa single path. The method proposed in this paper,Directionality-based Criminal Attractor Locator (DCAL), testsfor, and detects, the need to merge multiple paths into a fewCDs (see Section II.A). The second problem is that the(known) crime location is not necessarily at the end of theawareness path, but could be anywhere along the path, thus thelocation of the node at the end of the path remains unknown.DCAL extends each individual path and uses clusteringtechniques to find areas where a lot of paths lead to in order todetermine the locations where offenders are heading when theycommit crimes (See Section II.B). This allows DCAL to locatethe nodes (that is, Criminal Attractors) in the Awareness Spacefor each individual offender, in effect reconstructing theindividual Awareness Spaces for all offenders.

A. Finding Cardinal Directions

Multiple crimes could be the result of the offendertravelling along multiple paths in their Awareness Space.However, prolific offenders could have multiple crimes alongthe same general direction (Cardinal Direction), roughly

Figure 1- Offender’s Awareness Space and the Nodes within it

heading towards the same node, (Figure 2a). It is not likely thatthis offender has different Awareness Paths to each of theircriminal destinations, but rather that there is a singleAwareness path that goes near all of the offenses for thatoffender (Figure 2b). Thus, in general, instead of there being aunique path for each offender, it is expected that some crimesare the result of a single Cardinal Path.

One possible solution to the problem is to simply look forclusters in space, where there is a spatial cluster, there is aCardinal Direction leading there. This approach however is notcorrect. It is possible that the crime locations are in a similardirection, but spread out in space, and hence do not form aspatial cluster. In Figure 3, for example, a spatial clusteringalgorithm would most likely detect approximately five spatialclusters (shown in circles) although it is clear that those fivespatial clusters are the result of only two Cardinal Directions(shown as arrows). Additionally, the four crimes towards thelower right do not make up a spatial cluster, but they are theresult of a single Cardinal Direction. The reason why spatialclustering is not a valid approach in detecting CardinalDirections is because the different clusters (referring to Figure3) are at various distances away from the central node. Spatialclustering is implicitly distance-based, whereas finding

Cardinal Directions requires direction-based clustering. DCALremoves distance from the equation and focuses solely on thedirectionality component. This is done by taking each crimelocation and determining the exact distance between it and thecentral node, then (virtually) moving the crime location suchthat it maintains the exact direction with respect to home, but isonly a unit distance away (Figure 4a). The result is that allcrime locations are on the unit circle with the central node inthe center (Figure 4b). Now, clusters based on directions areapparent, and can be found (Figure 4c).

DCAL finds these clusters, and their cluster-center, usingthe popular K-Means clustering algorithm [20]. Since K-Meansrequires the number n of clusters to be specified by the user asan input parameter, and the number of clusters is not known apriori, thus a large number of clusterings are performed with aniteratively larger number for n. The clustering is stopped whenthe error rate of the clustering drops below 0.1, at which pointthe clustering is assumed to be good with n clusters. Clusters ofone are assumed to be outliers and are dropped1. The remaining

1Clusters of one would indicate a path that is not frequently travelled (at least

from a criminological point of view), and hence do not form a CardinalDirection. The model works identically if these data-points are kept.

a) Individual Paths (one to eachcrime location)

b) The corresponding CardinalDirection

Figure 2 – Individual vs. Aggregate Paths Figure 3 – Crimes spread out around home. No clustering spatially, butclustered directionality-wise.

a) Mapping of crime locations to the unit circle b) Crime locations mapped to the unit circle c) Cluster centers and directions for the activitypaths identified.

Figure 4 – Finding Activity Paths

cluster centers are then used as the directions for the CDs. Inthe example shown in Figure 4c, there would be three CDs: onetowards the upper left, upper right and lower right. A singletoncluster towards the lower left is removed as an outlier.

Due to tractability purposes, a single restriction must beplaced onto the initial model presented in this paper: each pathis assumed to start at the home of the offender (and not one ofthe nodes in their Awareness Space).

B. Finding Nodes in the Awareness Space

According to theory, each path in the Awareness Space iscreated by frequently travelling between two nodes, andcommitting crimes somewhere in between. In other words, theJtC is not necessarily as long as the journey to the CriminalAttractor, but that it is in the same direction with respect to thestarting point. This implies that the node in the AwarenessSpace is going to be found by continuing along the JtC anarbitrary distance beyond the crime location. The challenge isto determine how far beyond it, as the theory does not statewhere along the path the crime is, only that it is along the path.

It is assumed that certain areas will act as the node to a lotof individuals, thus, to determine where the node is beyond thecrime location, the model uses the Cardinal Directions of alloffenders in the region to see which spatial area a lot ofoffenders are moving towards. This is done as follows.

As in the previous step, the Cardinal Directions are foundfor each offender, after which the focus becomes using only theCDs for all offenders simultaneously (Figure 5a). Since the

goal is to determine where the offenders are moving to, theirCDs are extended an arbitrary distance beyond their crimelocation, and are intersected with the CDs of other offenders(Figure 5b). The idea is that the spatial region with a lot ofintersections will have a lot of offenders moving towards it,implying that that region is of interest. Once all intersectionsare calculated, K-Means [20] is applied to the set ofintersection points (Figure 5c). Once again, the appropriatenumber of clusters n is unknown, hence multiple clusterings areperformed while varying n, with the idea that regions of spacewhich consistently contain cluster centers under differentclusterings are stable, and are the true areas of interest. Withstable cluster centers detected, offenders who have a CardinalDirection pointing towards one of those cluster centers havethat node as part of their Awareness Space (Figure 5d).

III. EXPERIMENTAL SETUP

To evaluate DCAL, the model was implemented in VisualStudio 2008, with the data retrieved from DB2, and theclustering process was passed over to Matlab 2009a’s K-Means implementation. The model was then applied to repeatoffenders2 residing in the city of Surrey, a part of the GreaterVancouver Regional District in the South-West corner ofBritish Columbia, Canada. This city was picked specificallyfor this model, since visual analysis of the city’s offenderresidence and crime locations did not yield any patterns, butonly chaotic movement. At the time, it was thought that the

2A repeat offender was considered to have at least 5 offenses.

a) Establish direction of paths b) Extend Cardinal Direction to find intersections

c) Finding Awareness Nodes by looking for clusters of intersection points d) Identify offender’s Awareness Space and Paths

Figure 5 – Finding Awareness Nodes and Paths to find the Awareness Space

city’s grid patterned road-network was distorting any apparentpatterns beyond visual recognition and this made a good testfor an automatic process such as DCAL.

Below is a discussion of Surrey, the city used for the study(Section III.A) and the data used for the evaluation (SectionIII.B). Note that although the experimental evaluation focusedon the city of Surrey, crime and offender data about Surreyand all neighbouring jurisdictions were entered into the model.This was due to the edge-effect [21] [22] where patterns nearthe edges are distorted due to a lack of data on the other sideof the edge. This causes results near the edges to beunderrepresented. Certain types of clustering algorithms, K-Means being one of them, are particularly vulnerable to thisphenomenon. More specifically, the result is a tendency forthe cluster centers to move inwards towards the center of thedataset if data are removed from the edges, as shown in Figure6. Thus, in order to displace the edge effect to the outside ofthe study area, data for neighbouring cities was included.Thus, although data outside of Surrey is part of the model, theresults are only considered valid within Surrey, and not in anyother city, where it’s considered unreliable because ofdistortions in the clustering results due to a lack of dataimmediately outside their respective boundaries.

A. Study Area

As mentioned above, the city of Surrey, a part of theGreater Vancouver Regional District in the South-West cornerof British Columbia, Canada, was chosen as the locale forevaluating DCAL due to the chaotic movement patternsobserved visually within the city. Although the city’s road-network is a grid-network, the crime locations did not at allmatch expectations during an initial visual analysis.

The city, established in 1879, is the second largest city(after Vancouver) in the Province of British Columbia with apopulation of approximately 400,000 [23]. In 2005, Surrey wasnamed Canada’s worst city for car-theft, with a reported 7,654car-theft related calls-for-service to the police3, a decrease of8.2% from the previous year [24]. The city is a suburb ofVancouver, and thus almost half of all housing in Surrey is

3This number included cars recovered in the city of Surrey, but stolen

elsewhere, along with those cars that were stolen from within the borders ofSurrey.

single-detached, according to the results of the 2006 CanadianCensus Survey [25]. Average household income within the citywas approximately $73,000, but varied within the city from$45,000 (in Whalley) to over $96,000 (in South Surrey) [25]. Itis connected to near-by cities to the North and West, andthrough them to Vancouver, by several bridges. Thecommunity of South Surrey is adjacent to the border separatingCanada from the United States.

B. Data

For the analysis presented below, a collection of databases,called the Crime Data-Warehouse (CDW) was used. The CDWis a research database housed at the Institute of Canadian UrbanResearch Studies (ICURS) at Simon Fraser University. Itcontains five years of real-world crime data retrieved for theProvince of British Columbia (BC), Canada, from Canada’snational police, the Royal Canadian Mounted Police (RCMP).The RCMP in BC uses a system called the Police InformationRetrieval System (PIRS), a large database keeping informationfor the regions of the province of BC which is policed by theRCMP. PIRS contains information about crime-events(~4.4million) and people (~9million), in addition to vehicles(~1.4million) and businesses (~1.1million). Crime-events willhereafter be called simply crimes.

For this study, only the crimes and the people associated tothose specific crimes were studied. Information about vehiclesand businesses were not used. The data was restricted to allcrimes occurring between August 1, 2001 and July 31, 2006,committed by offenders within the study area. Linkages exist inthe database between the crimes and people table, thus all thepeople involved in any given crime were identified. This paperfocuses only on property crimes, and for only those offenderswho had at least 5 offenses for which they were charged, in theprocess of being charged, had charges recommended againstthem, or were suspects in the offense.

For the people involved in each crime, the relevantattributes for this model include the full name (first, last andmiddle), home address and the type of their involvement in thecrime. People having the same name were assumed to be thesame person. Linked to each person was the set of crimes,along with the type and location of the crime, that they wereinvolved in. If the location specified in the database was invalidand could not be assigned an XY coordinate, the location wasignored for the analysis. Although for this specific study noother information about offenders was available, such as thelocation of their work or other activities, this type of data couldhave been used to enhance the model’s accuracy.

For the experiments conducted for this paper, it wasassumed that each JtC started at the home location, that is,each path is assumed to start at the home address of theoffender (and not one of the nodes in their Awareness Space).

IV. RESULTS

The home and crime locations of all offenders living inSurrey and its surrounding cities were input into DCAL. Thisresulted in 57,962 repeat offenders (spread across all cities).The CDs for each individual offender were found, with mostof the offenders having 3 CDs. The CDs were extended and

a) Data outside the edges b) No data outside the edges

Figure 6 – Cluster centers move inwards if data from edges are eliminated.

the intersections calculated to yield approximately 10 millionintersections, which were then clustered. Since it was notknown a priori how many clusters were to be found in Surrey,clustering was done many times, with K-Means being told tofind one to ten cluster centers in each iteration. Areas with alot of cluster centers were assumed to show areas of highinterest. The results are shown in Figure 7.

As stated above, cities outside of Surrey were included inthe dataset to eliminate the edge-effect from within Surrey,thus all results (cluster-centers) outside of Surrey are deemed

unreliable. This includes the small cluster-group towards theWest of Surrey, and the few isolates in the North and East.Removing those, there are three clear cohesive cluster-groupswithin Surrey (shown in Figure 8).

The area around the cluster-group towards the North-Westof Surrey, shown in Figure 8a, is a major hub within the city,and contains one of Surrey’s largest shopping malls (CentralCity - identified with an M in the figure), a campus of SimonFraser University, two Skytrain stations, some governmentoffices and tower complexes, along with major commercial

Figure 7 – Cluster centers in Surrey

a) Central City Shopping Center b) Guildford Town Center c) Center of Newton

Figure 8 – Neighbourhood of the cluster groups. M denotes a Shopping Mall, of which there are only three in Surrey.

blocks nearby. Within the group, all but two of the clustercenters are immediately adjacent to the location of theshopping center, while the other two cluster-centers are withinone kilometer along the road-network.

Another cluster-group, towards the North-East of Surrey,shown in Figure 8b, is a bit more spread out. One cluster-center falls right on top of Guildford Town Center, Surrey’slargest shopping mall. While the other cluster-centers aresomewhat removed from the shopping district, they fall ontoseveral Secondary Schools in the area. The furthest cluster-center is approximately two kilometers away (along the road-network) from the mall and is located at a Secondary School.It is possible that offenders whose direction is pointing towardthese locations may frequently commute to either or both ofthese nodes, perhaps even as students of the SecondarySchool. Otherwise the entire region is mainly low-densityresidential housing.

The final cluster-group to the South, Figure 8c, is againnear a shopping center (Center of Newton) with two blocks ofcommercial areas and a hospital surrounding it. Four of thecluster-centers are within a few hundred meters of theshopping center, and the furthest cluster-center from Center ofNewton only a block away, approximately 500 meters.

There was a group of four cluster-centers at the far right ofFigure 7, seemingly in the water, near an industrial areawithout anything significant near it. Although this cluster-group is cohesive, like the other three, it is likely that it isdistorted due to the edge-effect and thus not in the properlocation. If more data had been used in the cities outside ofSurrey, those cluster-centers would most likely have movedslightly further out from Surrey, right into downtown NewWestminster, which is located immediately one kilometernorth of the cluster-group.

V. DISCUSSION

Interestingly, all three cluster-groups were in closeproximity to one of the shopping facilities in Surrey. Althoughthere are two other shopping centers in Surrey, those did notseem to create any cluster-centers at all, regardless of thenumber of clusters sought by K-Means. The reason for this isunknown, but could be that they do not have certain featureswhich attract offenders, that the other three shopping centershave. Further investigation into this phenomenon is required.

The cluster-centers were built from the CDs of offenders,implying that most offenders moved toward a shopping centerwhen they commit crimes. According to Crime Pattern Theory[3], Awareness Space is built around people’s activity nodes.By looking at the cluster centers, it is clear that the majorshopping centers in Surrey are an integral part of offenders’activity nodes. Because the cluster-group on the North-Westside of Surrey (near Central City) is surrounded by twoSkytrain Stations, the stations themselves may be one of theactivity nodes offenders frequent, perhaps in their commute todowntown Vancouver, to which there is a direct Skytrain line.

It is interesting that most offenders commit their crimesalong their journey to a shopping center. According to the ideaof crime generators and attractors [1], shopping malls can be

categorized as both crime generators and crime attractor wherethey are not only bringing in a large amount of people inparticular places and times for crime to occur, but are alsowell-known places for criminal opportunities. Thus, other thanthe explanation that offenders commit their crime on their wayto their daily activity, it is also possible that some offendersintended to commit offenses in these malls but see theopportunity during their journey to the targeted location.Another interesting result is that by looking at the cluster-center, we may be able to determine some of thecharacteristics of the offenders who commit crime in thatparticular area, narrowing down possible suspects. Forinstance, the cluster center on the right in Figure 8b is inbetween a school and a shopping mall. It is possible thatoffenders whose direction points to this cluster center have arelationship with the school. They could be students, workers,or parents from the school. By understanding the relationshipbetween offenders’ choice of direction and the nature of theplaces, it may be able to help the police on their investigationby providing a profile of the offenders.

The most benefit of this type of spatial profiling couldcome when a new crime event occurs. Given that the policehave data similar to what was used for this paper, they coulduse the process proposed in this paper to reconstruct theAwareness Spaces of all offenders in their database. When anew crime event happens, according to theory, it would occurin the Awareness Space of an offender, thus, the police couldsee which offender has an Awareness Space intersecting thelocation of the new crime event, and investigate those as theinitial suspects. If none of the offenders are responsible, themethod from this paper could still yield valuable informationby letting the police see the crime patterns, and homelocations, of other offenders who have also committed a crimein that region. Additionally, it could reveal possible othernodes this new offender could frequent. For example, giventhat the police know of three attractors in Surrey (A, B and C

Figure 9 – Using DCAL for profiling (star indicates new crime location)

on Figure 9), for any new crime the police could determine theAwareness Nodes of offenders who have also committedcrimes near the new crime location (node C, for example) andthen trace the path back from the node, through the crime, tothe possible area where the offender could be living (shadedregion on Figure 9).

VI. CONCLUSIONS

This paper presented an approach to find the AwarenessSpace of offenders by using Crime Pattern Theory to predicteach individual offender’s Awareness Space, based onknowledge gained from the patterns of all offenders. This wasdone by applying clustering techniques in a new way to findthe Cardinal Directions of each individual offender in order todetermine the directions that they tend to go to commit crimes.Given that a crime could occur anywhere on a path, notnecessarily at either end of it, the Cardinal Directions wereextended and intersected with each other with the idea thatintersections represent points where two people are movingtowards, thus, areas which contain a lot of intersectionsrepresent areas where a lot of people want to move towards. K-Means clustering was once again applied to determine areas ofhigh concentrations of intersections, that is, the nodes in theAwareness Space. Given these nodes, the profiles of theoffenders can be reconstructed, if an offender has a crime in thedirection of a node, they were most likely travelling towardsthe node when they committed their crime.

The activities of 57,962 offenders who were charged orcharges were recommended against them were used to test thisidea by mapping their offense locations with respect to theirhome locations to determine the directions they move in. It wasfound that, by looking at the results of various clusteringparameters, offenders tended to move towards central shoppingareas in a city, and commit crimes along the way. Almost allcluster centers were within one kilometer of a shopping center.This finding allows for the reconstruction of the spatial profileof offenders, which will allow for narrowing the possiblesuspects to new crimes.

The model does have some shortcomings, which will be theinvestigated in the continuation of this project. First, the edge-effect is biasing results near the edge of the city. Even thoughdata from the surrounding cities were included, it did not seemenough to completely eliminate its effect from within Surrey.Second, the results presented in this paper are for a single citywithin the GVRD. The approach and experiments should berepeated on several other cities to understand the patterns andattractors each city has. Finally, an entire profiler systemshould be built which incorporates the techniques from thispaper, to actively profile offenders in order to help police.

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