Crime Theory Evaluation Using Simulation Modelsof Residential Burglary

Bryan Chastain1& Fang Qiu1

& Alex R. Piquero2

Received: 2 December 2015 /Accepted: 15 January 2016# Southern Criminal Justice Association 2016

Abstract This paper provides new insights on the study of crime modeling through thedevelopment of a hybrid cellular automaton (CA) and Multi-agent System (MAS)simulation model that is able to combine components of multiple criminologicaltheories to forecast the locations of residential burglary targets: journey to crime(JTC), social disorganization (SD) theory, and routine activity (RA) theory. In orderto combine individual factors from each theory into a unified model, AnalyticHierarchy Process (AHP) was employed for hierarchical parameter selection. Themodel is then evaluated using data on offenders obtained from the Dallas PoliceDepartment to examine how different crime theories perform in the prediction ofresidential burglary. Compared to the SD- and RA-weighted models, the JTC-weighted model performed the best when comparisons were made to actual burglarylocations. The findings demonstrate that the simulation models of crime provide testbeds for research into the explanatory power of various crime theories.

Keywords Crime simulation . GIS . Agent-basedmodeling . AHP

Am J Crim JustDOI 10.1007/s12103-016-9336-8

* Fang Qiuffqiu@utdallas.edu

Bryan Chastainchastain@utdallas.edu

Alex R. Piqueroapiquero@utdallas.edu

1 Program in Geospatial Information Sciences, EPPS, University of Texas at Dallas, 800 W.Campbell Road, GR31, Richardson, TX 75080, USA

2 Program in Criminology, EPPS, University of Texas at Dallas, 800 W. Campbell Road, GR31,Richardson, TX 75080, USA

Introduction

In the United States, the rate of residential burglaries has been falling since the 1980s.Despite this, the rate of residential burglary in many metropolitan areas is still quite high(FBI, 2012), and burglaries remain to be a challenging problem for law enforcementofficers to address. One of the more difficult practical and policy issues that police face isthe lack of understanding about what triggers a potential offender to make his or herjourney to the crime location, so that areas where these burglaries are likely to occur canbe identified and preventive measures can be implemented. In order to overcome theseobstacles, police seek the answers to the following questions: What factors affect anoffender’s ability and willingness to travel a certain distance from his/her home to apotential target to commit a burglary?What other factors, in addition to distance traveled,does the offender consider when selecting a residence to burglarize? Which of theseconsiderations are more important than others during their decision-making process?

As well, research on these questions is relevant for extant criminological theories(see Wright & Decker, 1994). Three criminological theories in particular have beenapplied to the study of residential burglary for answering these questions: theories forJourney to Crime (JTC) modeling, Routine Activity theory (RA), and SocialDisorganization theory (SD). While each of these assesses different aspects of crime,they are not necessarily mutually exclusive. For example, the neighborhood character-istics of the target used in SD are closely related to the factors that determine theattractiveness of committing a burglary used in RA, such as desirability and lack ofplace guardianship (Shaw & Mckay, 1942). In order to assess how each of the theoriescan aid in explaining and predicting residential burglary, researchers have begun toemploy computer simulation models to study this phenomenon.

Different methodologies have been proposed for building crime models in the past,among which models of complex systems are receiving increasing attention in theliterature (Gunderson & Brown, 2000; Brantingham, Gläser, Kinney, Singh, &Vajihollahi, 2005; Groff, 2007; Johnson & 2007; Townsley & Birks, 2008; Johnson& Bowers, 2010; Birks, Townsley, & Stewart, 2012). Cellular automata (CA) andmulti-agent system (MAS) are two unique approaches of complex systems (Batty &Torrens, 2001; Malleson, Heppenstall, & See, 2010). In terms of crime modeling, CA isbetter suited for modeling the aspects of crime with fixed locations, such as thoserelated to neighborhood characteristics (e.g., target desirability and place guardianship),while MAS is more appropriate for simulating the movement of the involving agents,in this case the offender’s journey to crime. The current study proposes a hybrid CA/MAS model in order to take advantage of the neighborhood structure of CA, whilesimulating individual offenders on top of this spatial structure using MAS.

An important issue in the field of simulationmodeling is the determination of appropriateparameters for each factor embedded in the model (Clarke &Gaydos 1998; Li &Yeh 2001;Wu, 2002). Such factors are usually integrated using a method known as Weighted LinearCombination (WLC) into simulation models (Eastman, Kyem, Toledano, & Jin, 1993;Malczewski, 2000). Since models are notoriously sensitive to WLC parameters, manymethods have been devised to find ideal parameters for simulation models (Li & Yeh,2001). Indeed, Berk (2008) has stated that the validity of the crimemodel is dependent uponwhether or not it is able to reproduce empirical phenomena accurately. In order for a set ofWLC parameters to be considered valid, they need to be chosen in an intelligent and

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consistent manner and then tested with real world data. One popular method widely adoptedfor selecting weights of WLC in the literature is the Analytic Hierarchy Process (AHP; seeMalczewski, 2002; Marinoni, 2004; Rinner & Taranu, 2006; Zhang, Yang, & Yu, 2006).AHP is applied by arranging the factors in a hierarchic order; making numerical pairwisecomparisons based on subjective judgments by a domain expert on the relative importanceof each factor; and then synthesizing the judgments to reach a consistent relative importanceto be assigned to these factors (Saaty, 1977).

The current study aims to: (1) use a simulation model to implement various criminolog-ical theories to demonstrate that these theories can be used in combination to addressresidential burglary; (2) derive the model parameters using AHP weights to exploit thehierarchical nature of factors of burglary; and (3) evaluate the contributions of each theory tothe modeling of crime based on the weights achieved when the prediction results best matchthe real world outcomes. This model may not only present a novel method for using JTC,SD theory, and RA theory to forecast residential burglary, but may also serve as a futureplatform for other theories to be evaluated in their power of describing various types ofcrime. Simulations such as the one undertaken in the present study provide a complimentaryapproach to experimental and, as Eck and Liu (2008) recently noted, also compare favorablywith empirical experiments (see also Berk et al., 2001; Birks et al., 2012).1

Background

Exploring the distance traveled between an offender’s residence and the actual crimelocation is the emphasis of the study of journey to crime (JTC; Rossmo, 1993).Criminologists have found that most offenders are motivated to commit crimes close totheir homes, with the average crime trip ranging only in the short range from 0.5 to 2 milesfrom home (Fig. 1a) (White, 1932; Turner, 1969; Gottheil &Gabor, 1984; Costello &Wiles2001). However, when only property crimes are examined, it has been shown that they areconsistently committed farther from the home than crimes against persons (White, 1932;Pyle, 1976; Rhodes & Conley, 1981; Rand, 1986; Defrances & Smith, 1994). Brantinghamand Brantingham (1981) showed that in property crime, there often exists a relatively lowcrime zone near the offender’s home, beyondwhich the criminal activity spikes to its highestpoint and then decreases according to a distance decay model (Fig. 1b; see also Rengert,Piquero, & Jones, 1999). Rossmo (1993, 1995) built upon their work by developing a JTCcomputer model that employed this idea of a base zone near the offender’s home anddistance decay outside the zone. Although very important, several modifications could bemade to Rossmo’s JTC model. For example, Rossmo’s model assumes that all offendershave the same JTC probability and distance decay, regardless of individuals with differentmotivations. However, research has shown that differences in offender’s age (Turner, 1969),race (Sampson & Wilson, 1995), and gender (Hayslett-Mccall, Qiu, Curtin, Chastain,Schubert, & Carver, 2008), for example, influence how much they travel, and a variety ofother factors, such as perceived risks and rewards, have also been found to be related to

1 Increasingly, the use of simulations and agent-based modeling has been of interest to criminologists, asshown for example in a special issue of the Journal of Experimental Criminology (Groff and Mazerolle 2008).As well, computer simulation modeling has been considered in theoretical testing and explanatory develop-ment in criminology (Sullivan 2013).

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different offender motivation. Due to its sole focus on the distance an offender journeys,Rossmo’s model, like most JTC models, also neglects the effect of target attractiveness,which can also influence how far an offender is willing to travel to commit a burglary.

Research has demonstrated that offenders commit crimes usually close to home,although considerations of target attractiveness must account for a victim selectionprocess (Rengert & Wasilchick, 1985; Wright & Decker, 1994; Piquero & Rengert,1999; Rengert et al., 1999; Bernasco & Luykx, 2003; Bernasco & Nieuwbeerta, 2005).Target attractiveness has two main aspects: target desirability and lack of guardianshipof the neighborhood. Target desirability deals with the perceived value of the targethousehold and the offender’s familiarity with the neighborhood, while lack of guard-ianship is established, among other things, by how little people know their neighborsand how little time is spent at home, both of which make the target more attractive. Theattractiveness of an area greatly impacts the target selection and timing of burglaries,which aim at achieving great reward with very little risk (Rengert, 1981). Burglarscommence their search in some general area, from which they select a specific target(Wright & Decker, 1994). The desirability of a target location is partially based uponthe potential reward of goods burglarized. Offenders may ascertain this informationthrough their judgment of the relative wealth of the neighborhood and their familiaritywith the neighborhood. However, opportunistic offenders may place less emphasis onthis aspect of desirability. Guardianship is commonly reflected by how well peopleknow their neighbors and how much time their residents spend at home, for whichlengthy residential tenure and short commute times make good proxies (Hunter, 1985;Sampson, 1987; Sampson & Groves, 1989; Bursik & Grasmick, 1993). Guardianshipplays an important role in determining the timing of burglaries. Burglars target resi-dential and public spaces at opposite times of the day. People often perceive crime ashappening most often under the darkness of night, which is generally true for publiclocations, such as offices and stores, which are usually empty late at night. However, inthe case of residential burglary, an offender’s target is a place of residence (house,apartment, trailer, etc.). Residential burglary occurs mostly during the day because theguardianship is at its lowest level while the residential owners are at work (Ratcliffe &Mccullagh, 2001). Since offenders commit residential burglaries within a community, itis useful to analyze the individual level factors in the context of those at the neighbor-hood level, for example, using JTC to theorize individual behavior on top of neigh-borhood dynamics defined by RA and/or SD (Hayslett-Mccall et al., 2008).

Fig. 1 a Traditional crime distance decay; b Residential burglary distance decay

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RA states that there are three required elements for crime: a motivated offender, asuitable target, and a lack of a capable guardian (Cohen & Felson, 1979). In otherwords, crime happens when a target is not sufficiently protected, and if the reward isworth it. The availability of opportunity is related to people’s every-day activities, suchas the routine patterns of work, shopping and leisure (Liu, Wang, Eck, & Liang, 2005).Rational Choice (RC) shares common ground with RA theory, but does not assume thatall offenders are motivated all of the time and for the same reasons; instead, RChighlights the process of weighing perceived risks and rewards associated with thedecision-making circumstance and hypothesizes that the likelihood of offending in-creases when the perceived benefits outweigh the perceived risks (Cornish & Clarke,1986; Piquero, Paternoster, Pogarsky, & Loughran, 2011).

SD seeks to understand community differences in crime rates. The theory examinesthe community characteristics such as income, racial composition, commute time, andresidential tenure, and draws on the breakdown in informal social controls as anintervening mechanism in order to help explain why these characteristics contributeto high crime rates (Hunter, 1985; Sampson, 1987; Sampson & Groves, 1989).

Both SD and RA take into consideration the attractiveness of an area. Although thetwo theories have some differences, both share compatible interpretations of howresidential burglary occurs within neighborhoods. Both theories, for example, presumethat offenders need to somehow be prevented or deterred from offending. SD purportsthat control mechanisms present in neighborhoods have varying degrees of efficacy inpreventing potential offenders from actually committing crime. Similarly, RA considerscontrol by assuming that motivated offenders will commit crime against attractivetargets when opportunities become available due to a lack of a capable guardian.When these controls are in place, both SD and RA concur that potential offenders willbe less likely to commit a crime, even when the reward is perceived to be high. In SD,this control includes the three levels of social control (private, parochial, and public)described by Hunter (1985; Bursik & Grasmick, 1993), 2 while in RA, control isdiscussed in terms of the presence (or lack) of a capable guardian (Cohen & Felson,1979).

Each factor of these models (offender motivation, target desirability, and neighbor-hood guardianship) may be divided into several sub-factors that help to better define itsvarious important aspects. For example, race, age, and sex can be sub-factors that aresignificant in determining the offender motivation in traveling a certain distance tocommit residential burglary (Turner, 1969; Sampson & Wilson, 1995; Hayslett-Mccallet al., 2008). In order to combine these factors and their associated sub-factors in ameaningful way, a model that can handle the complexities of all their dynamicinteractions is needed. One widely-used approach to combine a series of interconnectedfactors into a meaningful output is that of modeling complex systems.

A complex system can be defined as “an entity that is coherent in some recognizableway but whose elements, interactions, and dynamics generate structures admittingsurprise and novelty that cannot be defined a priori” (Batty & Torrens, 2005:745). Inother words, a complex system takes a set of interconnected inputs and produces aresult that is more than the sum of its parts. Some of the most popular examples for

2 Of course, recent variants of SD, in particular collective efficacy with its focus on social cohesion andwillingness to intervene, also focus on informal control structures (see Sampson et al. 1997).

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replicating complex systems include cellular automata (CA) and multi-agent systems(MAS).

Although popular in other disciplines for many years, the CA model was not used incrime modeling until the 2000s (Liang, 2001; Liu et al., 2005; Dietzel & Clarke, 2006).CA models divide an action space into grids of either a regular or irregular tessellation,with each containing a finite set of states unique to a given location. The cells of thisaction space are fixed in space and cannot be moved (Wolfram, 1986; Takeyama &Couclelis, 1997; Clarke & Gaydos, 1998). They are designed to represent stationaryobjects in the real world, such as land use units, parcels, or city blocks. Their states areupdated iteratively by a set of transition rules that determine the next state of a cellbased on its current state and/or that of its neighboring cells. After several iterations,these transition rules can lead to a steady-state and cause complex global patterns toemerge (Batty & Xie, 1994). CA’s modeling of fixed cells and strict neighborhoodrelationships are its distinguishing characteristic.

There has also been some growing popularity in the literature for models that useMcFadden’s (1973) discrete choice model to examine the factors that influence anoffender’s spatial decision making process (Bernasco & Nieuwbeerta, 2005; Bernasco& Block, 2009; Clare, Fernandez, & Morgan, 2009). Townsley and Sidebottom (2010)have taken this approach and used it to investigate the influence of RA and SDvariables after controlling for the fact that offenders generally tend to commit offensesclose to home. Their research has provided a novel way of thinking about how tocombine multiple theories for maximal benefit. Unfortunately, these recent discretechoice models are limited to using a single estimate for the influence of distance for allindividuals (Bernasco & Block, 2009), but a more realistic approach may be toincorporate a multilevel structure so that the influence of distance may be estimatedat the individual level rather than globally. Multi-Agent Systems (MAS) provide theability to consider individual-level interactions of agent based transition rules, whichallow autonomous behaviors to be defined without being limited to a rigid neighbor-hood (Benenson & Torrens, 2004). These autonomous agents can be designed to notonly make human-like decisions (to a certain degree), but also to be capable of movingfreely through space, which is the key feature distinguishing it from CA, whereautomata are fixed. The fixed spatial relationships between agents can also be relaxedin MAS, allowing for interaction between agents regardless of adjacency (Benenson &Torrens, 2004).

Due to its structure, CA is able to model static spatial neighborhoods, but isincapable of directly modeling individual autonomously mobile entities, such ashumans. Conversely, MAS is able to represent dynamic mobile entities, but it isinefficient in providing an underlying spatial structure at the neighborhood level. Amore ideal solution than CA or MAS alone would, therefore, be one where these twoapproaches are closely integrated with one another, jointly simulating interactions atboth the individual and neighborhood levels. A hybrid model of the two would providethe CA capabilities of fixed automata with the MAS dynamics of non-fixed agents. Inthe context of residential burglary, this would allow for modeling the offender’sdecision-making process of choosing a potential target. As individual agents in MAS,offenders have their independent motivations to target certain locations based on theirpersonal preferences such as demographic characteristics as well as neighborhoodcharacteristics and target desirability.

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The Weighted-Linear Combination (WLC) approach, one of the most popular GIS-based decision support methods, widely employed in areas such as land suitabilityanalysis, site selection, and resource allocation (Tomlin, 1990; Eastman et al., 1993;Malczewski, 2000) has been used to build transition rules for spatial crime simulationmodels (Hayslett-Mccall et al. 2008). The determination of proper parameter weights iscrucial for WLC to generate reliable results (Li & Yeh, 2001). Each variable in a spatialmodel makes a certain contribution to the simulation, and its relative importance isgiven by its associated weight. This is especially important when multiple criminolog-ical theories are integrated into a single model, as a variety of factors relevant to thesetheories are considered simultaneously. The idea of using weights corresponding toimportance lends itself to the comparison of the effectiveness of different theories inpredicting residential burglary. If the weight parameter corresponding to the factors of aspecific theory is high and the model output matches the real-world data, this mayindicate the effectiveness—or at least potential usefulness—of that theory in explainingand/or forecasting the crime. Simulation models often involve many variables, and theresults of these models are exceptionally sensitive to these weights (Wu, 2002).

To deal with the complex nature involved in determining WLC weights, a logicalprocess known as the Analytic Hierarchy Process (AHP) was developed by Saaty(1977). AHP is a multi-objective, multi-criteria decision-making approach whichenables modelers to arrive at a scale of preference drawn from a set of alternatives,and has been applied to numerous areas, such as decision theory and conflict resolution(Banai, 1993; Charnpratheep, Zhou, & Garner, 1997). AHP uses a procedure ofpairwise comparisons to consider the trade-offs of multiple criteria variables. There isa considerable body of literature on the application of AHP in ecological and landscapesuitability estimation (Charnpratheep et al., 1997; Malczewski, 2002; Marinoni, 2004),as well as in urban study (Dai, Lee, & Zhang, 2001; Rinner & Taranu, 2006; Zhang etal., 2006), but to date there has been little research that incorporates AHP in crimesimulation (see Yan et al., 2011).

In classic AHP, a domain expert is called upon to rate the pairwise comparisonsbetween all variables on a scale from 1 to 9, where 1 implies the two variables areperceived to be of “equal importance,” while 9 indicates one is perceived to have“absolute importance” over the other (Saaty, 1977). Saaty’s scale of nine units is basedon research in psychology that found people can deal with information that simulta-neously involves only a few facts: seven plus or minus two (Saaty, 1977). The matrix ofpairwise importance ratings is a reciprocal matrix; in other words, if the pairwisecomparison score between i and h, rih = 9, then rhi = 1/9. If the expert decides a factoris of relatively low importance, he/she would rate that factor as less than one (1/9–1/2).Conversely, if the factor was determined to be more important, it would be rated greaterthan one (2–9). After all pairwise comparisons have been made, the principal eigen-vector is calculated, and the individual elements of the eigenvector correspond to ascaled perception of importance for the model variables.

Current Focus

The majority of the aforementioned studies on crime theories that have identifiedseveral relationships between demographic characteristics and crime have well-docu-mented weaknesses (such as sample size, bias, etc.) and results in different contexts

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often result in mixed outcomes. It is these assertions, organized around several theo-retical frameworks that our proposed model aims to test.

Previously, Hayslett-McCall et al. (2008) developed a simulation model by using asingle criminological theory, the journey to residential burglary. The weights wereselected subjectively, resulting in predicted locations that were much more clusteredthan the actual burglar crime distribution. In the current study, the simulation modelwas constructed by integrating JTC, RA theory, and SD theory. Combining multipletheories in a WLC model can be a complex task, as each theory can have multiplecomponents and sub-components. This paper proposes that using AHP to impose ahierarchical structure on these components will not only make more sense organiza-tionally, but will also make the WLC more computationally efficient by requiring fewertotal number of parameters to evaluate during the simulation. Instead of comparing aparameter to every other parameter, AHP allows us to reduce the complexity by onlycomparing parameters to those that are nested in the same branch of the hierarchy. Ananalysis of AHP-derived weights employed with empirical crime data allows us toevaluate the contribution of each theory to the efficacy of the model. Understandingthese relative contributions allows future researchers to better understand how tradi-tional criminological theories interact to explain real-world residential burglaryphenomena.

Data & Methods

The prediction of residential burglary is primarily based on offender motivation, targetdesirability, and neighborhood guardianship. While JTC assumes that everyone is apotential offender, some may be more motivated than others, leading us to suspect thatoffenders may be more motivated to travel further based on, for example, their age,race, and gender. In order to properly model the problem of residential burglary, severalkey types of data are needed. First, the historical records of residential burglaryoffenders and victims with spatial reference are necessary to build the model. A subsetof data documenting the locations of residential burglaries and those of their offendersconvicted in the year 2000 was obtained from the Dallas Police Department. Detailedinformation on the offender was also provided, including race, gender, and age, and issummarized in Table 1. Preliminary analysis suggests that white offenders usuallytravel longer distances to commit a crime than black and Hispanic offenders (Table 1).This is in agreement with findings from the literature showing that minorities tend totravel shorter distances to commit crime (Sampson & Wilson, 1995). As for age, theliterature has shown that the age of an offender is positively correlated with his or hermobility (Turner, 1969; Gottheil & Gabor, 1984). Younger offenders are less likely tohave their own automobile, and they also have less experience in general, so they preferto be closer to areas they know. Little work has been done to examine the difference inmale and female journeys to crime (Hayslett-Mccall et al., 2008), but in these data,females were likely to travel further to commit residential burglary (Table 1). The onlyinformation available about the victims in the Dallas data is limited to just the streetaddresses of their homes.3

3 Additional details may be found in Chastain (2011).

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The other key component of the model is socioeconomic data. For this, UnitedStates Census data at the block group level was employed, including information suchas racial composition, median household income, mean travel time to work, and meanhousehold tenure. Neighborhood racial composition is used in comparison to theoffender’s race, which partially defines the desirability for the target. Research hasshown that criminals prefer to commit crimes in neighborhoods composed mainly oftheir own race (Reppetto, 1974) or at least in mixed race neighborhoods (Sampson &Wilson, 1995). Median household income is used as another surrogate for targetdesirability, as richer households are more likely to have valuable goods to steal. Akey tenant of SD is that residential mobility precipitates the disruption of communitysolidarity and the development of informal social control, which in turn, increases thelikelihood of crime (Shaw & Mckay, 1942). Residential tenure and commute time arevital factors for examining guardianship in SD (Hunter, 1985; Sampson, 1987;Sampson & Groves, 1989; Bursik & Grasmick, 1993). Longer periods of residentialtenure within a neighborhood strengthen the effectiveness of guardianship. The longerthe time it takes neighborhood residents to travel to work, the fewer available guardiansand the weaker the guardianship capacity for the neighborhood. To the extent that theyare available, individual residence characteristics, such as presence of a security systemand visibility, can provide useful insights into a neighborhood’s guardianship.Unfortunately, they were not readily available for this study area.

Model Building

A residential burglary model was developed consisting of three main components:neighborhood modeling, offender modeling, and AHP weight selection. Similar toother agent-based models, this model utilizes cellular space, which consists of

Table 1 Summary statistics of individual offender characteristics (n = 148), Dallas, Texas (2000)

Components Properties Representation

Offender: motivation Race* Average distance (and standard deviation) in miles:

• Whites: 3.23 (3.0065)

• Blacks: 2.96 (4.4130)

• Hispanic: 1.87 (3.6456)

Age* Average distance (and standard deviation) in miles:

• 16–19: 2.13 (3.47)

• 20–24: 3.51 (5.09)

• 25–29: 3.32 (4.70)

• 30–34: 1.07 (1.44)

• 35–39: 3.83 (4.39)

• 40+: 2.19 (2.12)

Gender* Average distance (and standard deviation) in miles:

• Males: 2.49 (3.74)

• Females: 5.49 (6.03)

*For each category, differences were found to be significant at the α = 0.01 level by ANOVA testing

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interconnected cells. These become the places within which the targets reside. Theoffenders are modeled as intelligent agents in the simulation, and their ability to moveat any distance and in any direction is due to the flexibility provided by vector-basedMAS.

The neighborhood and offender modeling portions of the model are closelyintertwined. Motivation is derived by determining the distance an offender is willingto travel, based upon their race, age, sex, and knowledge of the target areas. Since themotivation usually increases with the distance within a certain range, and after that,willingness to travel further becomes diminished with the distance, one of the commonJTC functions, the Gaussian function, was chosen to model the offender’s motivation asa function of distance from their home address. This function was also recentlyidentified by Canter, Hammond, Youngs, and Juszczak (2013) as performing well ingeographical offender profiling. For each offender, this Gaussian function can beparameterized based on the burglar’s personal characteristics. To obtain these param-eters, descriptive statistics on the Dallas Police data were analyzed to derive the meanand standard deviation distance traveled for each age, race and gender group (Table 1).For this particular dataset, one offender agent is created per each actual offender, whichresulted in 148 agents created from the 2000 Dallas Police data.

Next, desirability is determined through the neighborhood characteristics of medianhousehold income and racial composition. Offenders often select targets that have ahigher income level than their own and avoid impoverished areas which do not havemuch of value to steal. However, this is not always the case, because occasionallyoffenders may select targets within their own income bracket or even lower (Clare etal., 2009). Additionally, some high income residences may be inaccessible due to gatedcommunities, security guards, and/or alarm systems. While generally higher incometargets are more desirable to offenders, a simple linear function is not appropriate forthis model. Again, the Gaussian function was used to model the relationship betweendesirability and the income level based on the statistics obtained from Dallas censusblock group data. In this way, both very low and very high median household incomeswould have a low desirability while medium and medium high household incomeswould have a high desirability. Additionally, race is a consideration in desirability, as ithas been shown that offenders tend to commit crimes within a neighborhood of similarracial makeup or within a mixed race neighborhood (Wilson, 1987). In order todetermine a neighborhood’s desirability based on race, the census statistics for thepercentage of population that is white, black, and Hispanic are combined and weightedin such a fashion as to reflect an offender’s general tendency to stay in a neighborhoodof similar racial diversity as his or her own (Table 2).

The lack of guardianship in this study is defined by mean commute time and meanresidential tenure (% of less than 5 years’ tenure). In general, the longer the commutetimes of neighborhood residents and the higher the percentage of new comers to thecommunity, the higher degree the lack of guardianship. In order to maintain a normaldistribution throughout all parameters and through the final WLC (Singer & Willett,2003), both variables are transformed from raw block group census data into normally-distributed values using a power transformation (shown in Table 2). The output are thenrescaled to the range from 0 (meaning very low degree of lack of guardianship) to 1(indicating extremely high degree of lack of guardianship). This guardianship score isalso influenced by recent crimes in the neighborhood. Neighborhoods that have had a

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crime occur may see an increased guardianship for a period of time, due to increasedawareness of crime (neighborhood crime watches, increased police patrols, etc.).

The desirability and guardianship components of the model, therefore, comprise theattributes on which the CA cells/neighborhoods are built. If the crimes in the neigh-borhood reach a certain level, adjacent neighborhoods may also receive increasedguardianship, through the use of CA’s transition rules. This, in turn, can affect theoffender agents’ decisions during the next iteration of the simulation, providing a richinterplay between offender and environment.

Our work focuses on the theories of residential burglary by imitating the relationshipbetween the offender’s motivation, the target’s desirability, and the neighborhood’sguardianship, each of which produces its own likelihood surface. The interaction ofthese three elements determines the final likelihood of a residential burglary, defined bya weighted linear combination, which is generated across all cells in the study area. Arandom surface based on Monte Carlo methods is then produced programmatically andcompared with each offender agent’s likelihood surface. If the likelihood is greater thanthe random value at that location, the location will be selected as a potential target. Themodel chooses the location with the highest likelihood among the potential targetsselected as the final target for the current burglar and then starts over for the nextoffender.

The rationale for the stochastic nature of this model is due to the imperfect process ofhuman decision-making (see Piquero et al., 2011). Potential burglary offenders willlikely not choose the optimal target since they do not have complete information aboutthe target residence and its neighborhood. The randomization also models the arbitrar-iness of the human thought process. When an offender has a list of possible targets,sometimes the final decision is merely an arbitrary one. The effect of this randomselection process is that the residential burglary model becomes a stochastic process,producing different results with each execution, just as a real offender may target adifferent house each time she/he chooses to commit a crime.

While CA and MAS differ in many ways, their underlying handling of time andscheduling are practically identical (Benenson & Torrens, 2004). In general, there arethree different techniques employed in CA and MAS for scheduling: synchronous,asynchronous, and event-driven (Brown, Riolo, Robinson, North, & Rand, 2005). Withsynchronous scheduling, the events occur in a step-wise fashion, meaning that all

Table 2 Variable transformations for Desirability and Guardianship model components

Components Properties Representation

Target:Desirability

Race composition • For whitePw = 0.6 × %white + 0.3 × %Hispanic + 0.1 × %black• For blackPb = 0.1 × %white + 0.3 × %Hispanic + 0.6 × %black• For a HispanicPh = 0.2 × %white + 0.6 × %Hispanic + 0.2 × %black

Place:Lack of guardianship

Length of tenure (Y0.5 − 1)/0.5This result is then rescaled to fall between 0 and 1

Commute time (Y0.4 − 1)/0.4This result is then rescaled to fall between 0 and 1

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automata are programmed to perform their tasks once per iteration. However, inasynchronous scheduling, each automaton acts independently during each modeliteration, with the current automata observing reality as left by previous automata.The third style, event-driven scheduling, leads automata to only take action when aspecific event occurs. For example, during a crime simulation, a researcher might set upan event-driven schedule for neighborhoods based on their crime frequency, in orderthat they may change their neighborhood guardianship (e.g., neighborhood watches,increased police patrols,) only when the occurrence of crimes becomes too high (andperceived safety becomes an issue) or too low (and complacency becomes an issue).Unlike synchronous and asynchronous scheduling, actions are not directly dependentupon iteration time steps. Instead, automata are only directed to act once their specifiedrequirements have been met.

The choice of which scheduling method to use can have significant consequences inthe results of the model (Liu & Andersson, 2004), but it is important to note that thesethree methods are not mutually exclusive (Sloot, Kaandorp, Hoekstra, & Overeinder,2002). In this research, both asynchronous and event-driven scheduling are employed.Asynchronous scheduling is used for the MAS modeling of the offender agents andevent-driven scheduling for the neighborhood guardianship. For the offender agents,each offender will attempt a burglary one at a time. The neighborhood event-drivenscheduling increases the guardianship after a certain number of crimes occur. If thiscrime frequency threshold is not met after a certain number of days by the offenders,then no action is taken on the neighborhood’s part.

This residential burglary model assumes that the time it takes for a target householdto realize the offense has occurred and file a police report will be approximately oneday, so the time interval used in this model is set to one day per iteration. While thesimulation can, in theory, be executed over any number of iterations in both CA andMAS, this particular model terminates after the 366th day, as the Dallas crime data thatare being used for comparison come from a period lasting from January 1, 2000 toDecember 31, 2000. Although this means the asynchronous offender agents will becalled 366 times (once per day), there is no pre-set number of actions for the CA-drivenneighborhoods. Due to their event-driven nature and the stochastic nature of thesimulation itself, the neighborhoods will only be called upon to update their guardian-ship information when a certain number of crimes occur, which should amount toconsiderably less than 366 occurrences.

AHP is used to determine the weights of the motivation, desirability and guardianshipelements of the model. The values chosen are based on which theory is being emphasized ina given weighting scheme. In weighting schemes favoring one theory over another, higherpairwise preference scores will be given to those model components. For example, aweighting scheme favoring RA may place higher importance on target desirability overguardianship. In that case, a pairwise preference score of 9 (the maximum) for desirabilityover guardianship might be appropriate. This type of fine-tuning is designed to allowresearchers to have more control over how crime theories are represented in their model.In order to evaluate these weights, the locations of predicted targets are compared to those ofthe actual targets to assess the model’s goodness of fit. This is computed by calculating theroot mean squared error (RMSE) between actual and predicted burglary locations.

After completion, the modeled household burglary targets are presented for inspec-tion. After running the simulation using different weighting schemes, users will be able

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to assess the efficacy of each theory by comparing the final RMSEs. The weightingscheme that produces the lowest amount of error should identify the theory or theoriesthat provided the greatest explanatory power in predicting residential burglary for thesedata.

Results

The model developed to simulate burglary crimes embraced JTC, SD theory and RAtheory. To assess these theories, the information about themotivation of the offenses (i.e.,age, race, and gender) was obtained and modeled at the individual level. The variablesused to derive target desirability and lack of guardianship of place (i.e., income, racecomposition, commute time, and % of less than 5 years tenure) are presently onlyavailable at the neighborhood level. The model was developed in the C# programminglanguage, using Esri’s ArcObjects library for spatial reference and analysis.

Four different weighting schemes were examined (Table 3), one with uniform AHPpreference scores (all theories weighted equally), one with AHP preference scoresfavoring JTC components (high score for Offender Motivation; low scores for every-thing else), one with scores favoring RA components (high score for desirability;medium score for guardianship; low score for remainder), and one with scores favoringSD components (high score for guardianship; low scores for everything else).

Figure 2 shows the results derived from various weighting schemes and the actualcrime locations, while Table 4 shows the results of the four models compared to theactual burglary locations from the year 2000, using three different RMSE measures.The first, “Journey RMSE”, measures the difference in JTC distance of the offenderfrom the actual offense and the predicted location. As can be seen, both SD- and RA-favored models resulted in higher Journey RMSE error values than the JTC-weightedmodel (as lower scores are preferred). This is likely due to an undervaluing of the JTCparameters. The JTC-favored weighting scheme, however, led to a much less error-prone (Table 4) and much less clustered (Fig. 2d) result than the other two theoreticalmodels.

Table 3 AHP scoring schemes for Journey to Crime (JTC), Routine Activity (RA), and Social Disorgani-zation (SD) weighted models

Pairwise relationship AHP Score

JTC RA SD

Motivation-desirability 9/1 1/9 1/1

Guardianship-desirability 1/1 1/2 9/1

Guardianship-motivation 1/9 5/1 9/1

Age-race 1/1 1/1 1/1

Age-gender 5/1 5/1 5/1

Gender-race 1/5 1/5 1/5

Income-race 5/1 5/1 5/1

Commute-tenure 1/1 1/1 1/1

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In order to avoid the trap of presupposed emergence where outcomes are the resultof programming decisions (such as the model’s offender motivation simulation) insteadof real relationships (Townsley & Birks, 2008), two other error measures were

Fig. 2 Predicted residential burglaries in Dallas compared to actual burglaries, using a equal weights, b RA-favored weights, c SD-favored weights, and d JTC-favored weights

Table 4 Root-Mean Square Errors for each of the four different weighting schemes

Weighting scheme JourneyRMSE

Nearest neighborRMSE

Burglary distanceRMSE

Uniform weights 8046.94 4817.21 8866.28

JTC-favored weights 6945.24 3172.30 7143.66

RA-favored weights 8998.09 3354.98 7780.27

SD-favored weights 9010.28 3780.56 8294.27

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employed. The second measure, “Nearest Neighbor RMSE,” is calculated by looking atthe difference between each actual recorded burglary and its nearest predicted crime.The rationale for this method is that while it might not measure individual error well, itprovides a better picture of the overall crime pattern match between the observed andpredicted crimes. Table 4 shows that the JTC-weighted model also performed betterthan the SD- and RA-weighted models, but the difference was much smaller than withthe Journey RMSE. This can be attributed to the inherent bias of the Journey RMSEtowards the JTC-weighted model, but the JTC-weighted model does maintain thelowest error even in the Nearest Neighbor RMSE measure.

The final RMSE method, “Burglary Distance RMSE,” is similar to the NearestNeighbor RMSE, but instead of measuring distance to the nearest burglary, it measuresthe distance from the actual burglary location to the predicted burglary for eachoffender. The RMSEs are understandably higher here than the Nearest NeighborRMSE across the board, as it is always farther to the corresponding predicted burglarythan the nearest neighboring burglary. Despite this, the JTC-weighted model maintainsa slight advantage over both the RA- and SD-weighted models.

For comparison, the results of a uniformly-weighted model are also shown in Table4. The uniformly weighted model performs poorly in all three measures. This poorperformance resulted from the uninformed nature of equal weighting also indicates thatthe three theories do not pose the same explanatory power to the burglary crimes.

Conclusion

The complexities of neighborhood phenomena, such as residential burglary, are diffi-cult to describe with a single theory. Unlike past research, this research has presented anapproach based upon journey-to-crime (JTC), routine activity (RA) theory, and socialdisorganization (SD) theory. JTC offers insights into offender motivation and thelocation of the offense. RA helps to explain the target desirability for residentialburglary. When examining residential burglary, RA and SD theories both provideexplanation on the protection of the residence regarding the expansion of guardianshipinto the larger geographical space of a neighborhood.

GIS simulation tools have made it possible to model the residential burglaryprocess by incorporating both the behavioral characteristics of the offenders them-selves as well as the neighborhoods they move within. By emphasizing the spatialprocess involved in the offender target selection process, crime dynamics in aneighborhood landscape were modeled using a hybrid CA/MAS model. The hybridnature allowed for the simulation of MAS-style dynamic actors (offenders) on top ofa fixed spatial landscape (neighborhoods) from CA. The successful integration ofthese two methodologies lays the foundation for GIS-based simulation of crime, andprovides a good basis for potentially adding more complex agents, such as parcelsand victims, into the simulations. The powerful spatial analysis tools in GIS canprovide a viable alternative for criminologists to build more mechanism-orientedsimulation models.

Unlike the traditional approach for understanding the effects of social contexts oncrime analysis, which has been focused on static empirical deduction, the newerapproach of spatial crime simulation seeks to use existing knowledge of criminology

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to forecast and prevent future crimes while providing a test-bed for crime theories, suchas RA, SD, and JTC. While the traditional approach is a vital part of the search forunderstanding the effects of social context on crime, computational models are alsoneeded for reasoning about most likely scenarios (Brantingham et al., 2005).Simulation models, such as the one presented here, offer a rigorous tool for under-standing and experimenting with crime patterns and for “evidence-based policy mak-ing” (Brantingham et al., 2005; Groff &Mazerolle, 2008). Additionally, simulations areinherently dynamic and are effective approaches to predictive modeling in the presenceof conflict, hidden intentions by the participants, and change (Gunderson & Brown,2000). While simulations can be successfully employed in a static environment, theirreal power derives from predictions in the presence of change. At the same time, theAHP model should only be used to determine the sufficiency of a theory in describingreal world crimes, not its necessity. As Groff and Mazerolle (2008) warns, crimesimulation researchers should be aware of the “equifinality problem,” which states thatmore than one set of model parameters can lead to the same result. For example, it maybe the case that multiple different theoretical AHP weightings could lead to similarlyrealistic results.

There are several opportunities for future research based upon this model.Currently, the model only examines straight-line distance when calculating offendermotivation, and also when calculating the error. A potentially more realistic modelwould use network distance or cost for these measures. Future research should testthis model in other parts of the country and world to investigate the effect that localehas on these (and other) theories. As well, the model may be enhanced by addinginformation on the amount of goods stolen from each actual residential burglary.Another important addition would be to address the inherent limitation of a com-bined CA/MAS model, namely the lack of existing techniques in the literature forsubstantive uncertainty and sensitivity analyses for this uncommon approach. Webelieve there are some promising parameter calibration techniques to explore thatexamine which individual factors and combination of factors lead to more accuratepredictions of crime. This, in turn, could then lead to a better understanding of howeach theory contributes to the model. In order to better model the offender spatialdecision making process, it would be beneficial to include additional assessment(s)suitability which have been used in other crime models, such as risk of detection, theeffort involved in victimizing, and a more detailed representation of reward (Cornish& Clarke, 1986; Birks et al., 2014). It would also be desirable to simultaneouslycollect information gleaned from offenders’ narratives to better understand theirdecision-making processes, from their perspectives, in concert with the knowledgeyielded from the types of modeling strategies proposed in this paper. Finally,replication of our work with more recent data would be useful in an effort tocorroborate our results and also examine the extent to which the patterning of crime(and crime type) may have changed in the City of Dallas over time. In this regard,care should be taken to ensure that the quality of the data obtained and the proxiesused to assess various theories are as optimal as possible.

Going forward, we believe, as do others (cf. Berk et al., 2001; Birks et al., 2012; Eck& Liu, 2008; Sullivan, 2013), that the use of simulations provide a complementaryapproach to experimentation and should go hand-in-hand with such research, especiallywhen carrying out experiments is initially too costly or prohibitive.

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Bryan Chastain is currently an adjunct professor in the Geospatial Information Sciences program at theUniversity of Texas at Dallas. Dr. Chastain’s research and teaching areas are crime mapping, Web GIS, andGIS application development. He is the winner of multiple teaching and best paper awards. When notteaching, Dr. Chastain consults with the U.S. federal government on matters of cloud computing, high-performance GIS, and geospatial data science.

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Fang Qiu is currently a Professor and Head of Geospatial Information Sciences at the University of Texas atDallas. Dr. Qiu’s research and instructional areas are remote sensing digital image processing, spatial analysisand modeling, crime analysis, GIS application software development, and web and mobile based mapping.His research work at the University of Texas at Dallas has been funded by major federal government agencies,such as National Science Foundation (NSF), National Aeronautics and Space Administration (NASA),Environmental Protection Agency (EPA) and Centers for Disease Control and Prevention (CDC), etc. Hewas the Winner of ERDAS Award for Best Scientific Paper in Remote Sensing in 2013 by American Societyof Photogrammetry and Remote Sensing, and Winner of the Remote Sensing Special Group Award in 2011and 2013 by American Association of Geographers.

Alex R. Piquero is the Ashbel Smith Professor of Criminology and Associate Dean for Graduate Programs inthe School of Economic, Political and Policy Sciences at the University of Texas at Dallas. His researchinterests include criminal careers, criminological theory, and quantitative research methods. He has receivedseveral research, teaching, and service awards, and he is Fellow of both the American Society of Criminologyand the Academy of Criminal Justice Sciences. In 2014, he received The University of Texas System Regents’Outstanding Teaching Award.

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