importance of crime mapping essay

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Crime mapping.

Crime mapping is the process through which crime analysts and researchers use location information about crime events to detect spatial patterns in criminal activity. Early crime mapping efforts typically involved placing physical markers, such as pins, on maps to designate the locations where crimes occurred. Patterns of criminal activity were determined primarily through visual inspection of these maps. (adsbygoogle = window.adsbygoogle || []).push({});

I. Introduction

Ii. a brief history of crime mapping, iii. theoretical perspectives in crime mapping research, iv. spatial crime research and planning interventions, v. future directions and challenges in crime mapping, vi. conclusion.

Crime is not a random event. Criminological research suggests that certain psychological, social, or economic characteristics are associated with higher levels of criminal involvement. Furthermore, particular lifestyles and patterns of activity place individuals at a heightened risk for victimization. Crime fluctuates temporally as well: More crimes occur in the evening as opposed to the morning, on weekends as opposed to weekdays, and in summer months as opposed to winter months. It comes as no surprise that spatial patterns of crime exist as well. For example, Sherman and colleagues (Sherman, Gartin, & Buerger, 1989) found that approximately 50% of calls for service came from approximately 3% of addresses in Minneapolis, Minnesota.

Crime mapping is the process through which crime analysts and researchers use location information about crime events to detect spatial patterns in criminal activity. Early crime mapping efforts typically involved placing physical markers, such as pins, on maps to designate the locations where crimes occurred. Patterns of criminal activity were determined primarily through visual inspection of these maps. With the advances in computing, geographic information system (GIS) software, such as MapInfo and ArcGIS, enables researchers to convert geographic information (addresses or global positioning system [GPS] coordinates) into coordinates used with virtual maps. Researchers and crime analysts can then use a number of analytic software packages to examine and detect patterns of criminal activity from these virtual maps.

This research paper is designed to offer an overview of the field of crime mapping. First, the history of crime mapping is briefly discussed. After this, a brief overview of several theoretical perspectives that have been used to understand the spatial patterns of crime is provided. Following this, some of the major findings in spatial crime analyses are discussed, particularly in regard to the relevance of implementation strategies designed to combat crime. The research paper concludes with recommendations for future directions in crime mapping research.

Interestingly, the earliest efforts at crime mapping can be traced to the roots of the discipline of criminology itself. In the early 19th century, a number of studies examined the distribution of crime in France and England. Brantingham and Brantingham (1991a) provided an overview of some of the findings of the main studies from this era. Guerry and Quetelet mapped crimes in France at the department level and found that crimes were not distributed evenly across departments. They also found that there was stability over time in both areas with high crime and areas with low crime over time. These findings were echoed in England with studies by Plint, Glyde, and Mayhew.

In the United States, Shaw and McKay’s (1942) seminal study of juvenile delinquency in Chicago made extensive use of crime maps. Shaw and McKay borrowed Park and Burgess’s (1924) ecological model and divided the city into five different zones. They found that the zone adjacent to the central business district, the zone of transition, perpetually suffered from the highest rates of juvenile delinquency and other social problems regardless of the specific ethnic group occupying the zone at the time. This research was instrumental in popularizing social disorganization theory and inspired a number of similar mapping projects in Chicago; Philadelphia; Richmond, Virginia; Cleveland, Ohio; Birmingham, Alabama; Denver, Colorado; Seattle, Washington; and other cities.

Accompanying these early efforts in crime mapping were developments in the profession of policing that provided additional opportunities for crime mapping. In the late 19th and early 20th centuries, the professionalization movement in policing encouraged police organizations to compile statistics documenting the extent of crime in their jurisdictions. In fact, one of the main justifications for the creation of Federal Bureau of Investigation was for the explicit purpose of documenting the extent of crime in the United States through the Uniform Crime Reporting program (Mosher, Miethe, & Phillips, 2002). During this time, many agencies began compiling crime statistics and conducting analyses of crime data. Crime mapping was primarily done using pin maps, which were very time-consuming and provided only a basic visualization of crime patterns.

The late 1960s and early 1970s were critical for the development of crime mapping. In 1966, the Harvard Lab for Computer Graphics and Spatial Analysis developed SYMAP (Synagraphic Mapping System), one of the first widely distributed computerized mapping software programs. The Environmental Science and Research Institute was founded in 1969 and in the subsequent decades emerged as one of the top distributors of GIS software, including the current ArcView and ArcGIS software packages. Also around this time, the U.S. Census Bureau began the ambitious GBF-DIME (Geographic Base Files and Dual Independent Map Encoding) project, which was used to create digitized street maps for all cities in the United States during the 1970 census (Mark, Chrisman, Frank, McHaffie, & Pickles, 1997). These advances were necessary for the development of GIS programs used in crime mapping.

The use of GIS programs for mapping has been the most important advance in the field of crime mapping. There are several important advantages in using virtual maps instead of physical maps. First, computers have dramatically reduced the time and effort required to produce crime maps. Given the relatively low cost and user-friendliness of many of these software programs, it no longer requires a substantial investment for agencies that wish to engage in crime mapping. Second, these GIS programs reduce the amount of error associated with assigning geographic coordinates to crime events. Third, virtual maps are much more flexible than physical maps, allowing researchers and crime analysts to compare the geographic distribution of crimes against other characteristics of the area under investigation (e.g., census bureau information, city planning and zoning maps, and maps produced by other agencies). Finally, GIS and other spatial analysis software provide powerful statistical tools for analyzing and detecting patterns of criminal activity that cannot be detected through simple visual inspection.

In the mid-1970s and early 1980s, a crisis of confidence in traditional police practices emerged following the results of studies, such as the Kansas City Preventative Patrol experiment, that suggested that the police were not effective in combating crime (Weisburd & Lum, 2005). Goldstein’s (1979) problem-oriented policing emerged as a response to this crisis and emphasized that policing should involve identifying emerging crime and disorder problems and working to address the underlying causes of these problems. Academic interests in the field of criminology also began to shift during this time. While many criminologists were concerned with causes of crime that were outside the sphere of influence of police agencies (e.g., economic depravation, differential association, and social bonds), a number of researchers, such as Jeffery (1971), Newman (1972), and Cohen and Felson (1979), began discussing factors that contribute to the occurrence of crime that were more amenable to intervention. The combination of the shift in theoretical focus in criminology and the shift in the philosophy of policing yielded new opportunities for crime mapping and initiated a resurgence of research on both the geography of crime as well as crime prevention strategies involving crime mapping.

Although the first instances of computerized crime mapping occurred in the mid-1960s in St. Louis, Missouri, the adoption of computerized crime mapping across the United States remained relatively slow. Although a number of agencies, in particular in larger jurisdictions, became early adopters of computerized crime mapping technology, the large period of growth in computerized crime mapping did not begin until the late 1980s and early 1990s (Weisburd & Lum, 2005). The rate of adoption of crime mapping among departments greatly increased as desktop computers became cheaper and more powerful and GIS software became easier to use and more powerful. The Compstat program, which started in 1994 in New York City, emphasized crime mapping as a central component to strategic police planning and helped popularize crime mapping among police agencies. With assistance from the Office of Community Oriented Police Services and the National Institute of Justice, a large number of departments adopted computerized crime mapping practices. By 1997, approximately 35% of departments with more than 100 officers reported using crime mapping (Weisburd & Lum, 2005).

As previously noted, the development of tools and techniques of crime mapping have been accompanied by an expanding body of criminological theory oriented toward explaining the geographic patterns of crime. It is important, when discussing theories about the spatial distribution of crime, to distinguish between theories that explain criminality and theories that explain criminal events. Traditional criminological approaches tend to emphasize individual-level social and psychological characteristics as the main factors that lead to criminality, that is, the propensity toward committing criminal acts. These theories focus predominately on explaining why offenders engage and persist in criminal lifestyles. Alternatively, theories that discuss the spatial distribution of crime focus on explaining the patterns seen in criminal events, that is, the occurrences of crime. These theories focus less attention on the motivations of offenders and more attention on factors of the environment that promote crime.

A. Social Disorganization Theory

Although a number of theories have been proposed to explain why particular neighborhoods experience high crime rates, social disorganization theory has been the most influential. Social disorganization theory, as first proposed by Shaw and McKay (1942), can be seen as the first attempt to construct a criminological theory of place. The concept of social disorganization refers to “the inability of local communities to realize the common values of their residents or solve commonly experienced problems” (Bursik, 1988, p. 521). As such, disorganized communities suffer from diminished capacities to exercise social control and are unable to regulate the behavior of community members (see Bursik & Grasmick, 1993). As the capacity of a community to regulate the behavior of its members decreases, the potential for illegal activity increases.

A central tenet of social disorganization theory is that structural conditions within a neighborhood attenuate the social ties that promote social cohesion and enable community members to exercise social control. Economic depravation creates undesirable living conditions that promote residential instability and population heterogeneity. Because social ties require time to form, high residential instability in neighborhoods prevents the development of social ties as residents frequently relocate (Bursik & Grasmick, 1993). In neighborhoods with high levels of population heterogeneity the extensiveness of friendship and acquaintance networks through which social control is exercised is limited because of social and cultural barriers between residents (Bursik & Grasmick, 1993). Structural factors such as these compromise the social integration of neighborhood residents and undermine perceptions of collective efficacy, that is, the collective sense of trust, social cohesion, and willingness to intervene on behalf of the public good (Sampson, Raudenbush, & Earls, 1997). Neighborhoods that have low collective efficacy are likely to experience high levels of crime.

B. Routine Activities Theory

Cohen and Felson’s (1979) routine activities theory has been applied extensively to research on spatial patterns of crime. To Cohen and Felson, crime is a predatory activity and, as such, can subsist only near patterns of legitimate activity. Therefore, to understand crime patterns it is necessary to understand the patterns of conventional routine activities around which crime is organized. Criminal victimization occurs where routine activities produce a convergence in space and time of the three necessary conditions for crime to occur: (1) a suitable target, (2) a motivated offender, and (3) the absence of capable guardians (Cohen & Felson, 1979). Felson (1998) explained that suitable targets have value to the offender, are visible to the offender, are easily moved or removed, and are accessible by the offender. The concept of guardianship has also been extended and includes intimate handlers, who are responsible for monitoring the behavior of offenders; guardians, who are responsible for protecting targets; and place managers, who are responsible for monitoring and controlling access to particular spaces (see Eck, 2001). In applications of this theory to spatial crime analysis, structural features of the city, patterns of land use, and the routine activities associated with particular locations can concentrate motivated offenders and suitable targets into areas with limited guardianship. This, in turn, fosters opportunities for criminal victimization.

C. Crime Prevention Through Environmental Design and Defensible Space Theories

A couple of important theories have been proposed to explain why criminal events occur more frequently at particular sites. Jeffery (1971) was one of the first criminologists to suggest that immediate features of the environment affected crime, with his Crime Prevention Through Environmental Design (CPTED) approach. This approach emphasizes target hardening and surveillance. Contemporaneously, Newman (1972) also emphasized the role of the environment in creating crime with his defensible space theory. Newman argued, in regard to public housing, that it is possible to design the use of space to enhance territorial functioning and to improve the natural surveillance in these environments. Crowe (2000) expanded on both Jefferey’s and Newman’s initial theories. In the current formulation of CPTED, Crowe discussed three strategies that are used to prevent crime: (1) access control to prevent contact between the offender and the target, (2) surveillance to monitor areas and discourage offenders, and (3) territorial reinforcement to promote feelings of ownership among users of the space. CPTED is usually employed along with situational crime prevention (discussed in the next section) to formulate practical strategies for reducing crime.

D. Rational Choice Perspective and Situational Crime Prevention

The rational choice perspective (Cornish & Clarke, 1986) is primarily concerned with understanding offender decision making. This approach assumes that offenders possess limited rationality, meaning that they make rational calculations of the costs and benefits associated with crime but are constrained in their decision making by time, information, context, ability, and prior experiences. This perspective seeks to understand the series of decisions made by the offender that result in a criminal event. Interestingly, unlike many other theories of offending, the rational choice perspective emphasizes that different decisions are involved in the production of different types of crime. Rational choice explanations of criminal offending differ by crime type, instead of ignoring these differences in favor of a general motivation toward engaging in crime, as is common in many other criminological theories. Spatial applications of the rational choice perspective emphasize offender movement, search patterns, and target selection processes that determine the spatial patterns observed in crime.

Situational crime prevention (Clarke, 1997) refers to the application of the rational choice perspective toward developing policy recommendations to reduce crime. Situational crime prevention emphasizes situational-level interventions toward increasing the efforts associated with committing a crime, increasing the perceived risks for engaging in crime, reducing the anticipated rewards from crime, and removing the excuses associated with crime (Clarke, 1997). As with the CPTED and defensible space theories, the policy applications of situational crime prevention focus on practical strategies that are customized to specific settings. Although the successes of this approach are well documented, rarely do the methods used in these studies permit broad conclusions regarding the effectiveness of this approach at reducing crime (see Clarke, 1997, for a discussion).

E. Crime Pattern Theory

Brantingham and Brantingham (1991b, 1993) developed a perspective referred to as crime pattern theory that incorporates elements of the rational choice, routine activities, and other spatial perspectives on crime. According to this perspective, individuals create a cognitive map of their spatial environment with which they are familiar through their routine activities. The action space of an individual consists of (a) nodes, the destinations of travel, such as work, home, and entertainment locations, and (b) paths, the travel routes that individuals take to move from one node to another. Through repeated movement along paths to various nodes, individuals develop an awareness space consisting of the areas in a city with which they are familiar. According to this theory, offenders search for suitable targets primarily within this awareness space by comparing potential targets against templates, or mental conceptualizations of the characteristics of appropriate targets. The likelihood of a particular target being selected by an offender dramatically decreases as an offender moves away from his or her awareness space, a process often referred to as distance decay (see Rengert, Piquero, & Jones, 1999). One interesting application of this theory is geographic profiling, which attempts to narrow the scope of police investigations by using information on repeated crimes to identify the awareness space of a repeat criminal (Rossmo, 2000).

A. Hot Spots

As previously indicated, a large number of studies have demonstrated that criminal events are spatially concentrated. Although the extent of concentration differs between studies, all empirical evidence suggests that a small number of places account for the majority of crime within any given city. Sherman and colleagues (1989) popularized the term hot spot to describe these areas where crime is concentrated. The detection and explanation of these hot spots is a major concern of research in crime mapping. Hot-spot analysis is currently very popular among police agencies because it provides a method to coordinate interventions in emerging problem areas.

A number of studies have demonstrated the benefits of hot-spot analysis to help coordinate police responses to crime. For example, in a randomized experiment in Minneapolis, Sherman and Weisburd (1995) found that concentrated patrol efforts in hot-spot areas produced a significant decline in calls for service. Police responses to crime are not limited to enhanced patrol. In another randomized experiment in Jersey City, New Jersey, Weisburd and Green (1995) found that after identifying drug market hot spots using crime mapping, a coordinated policy of engaging business owners and community members coupled with police crackdowns yielded substantial decreases in disorder calls for service. In fact, a recently conducted meta-analysis on street-level drug enforcement indicated that approaches that focus on community–police partnerships in drug market hot spots were more effective than enforcement-only approaches (Mazerolle, Soole, & Rombouts, 2006). This suggests that the best approach is a coordinated strategy between police officers and community members toward reducing crime in identified hot spots.

B. Community-Level Factors Affecting Crime

When designing strategies to address crime in hot-spot areas, it is important to consider the community context that contributes to emergence and maintenance of hot spots. Neighborhood-level research on spatial crime patterns helps illuminate the factors associated with heightened levels of crime. As previously mentioned, economic depravation, residential mobility, and population heterogeneity all contribute to higher levels of crime in a neighborhood by impeding the development of social ties between residents (Bursik & Grasmick, 1993). Family dissolution and inadequate supervision of adolescents also contribute to increased levels of crime. In fact, the presence of unsupervised adolescents in a community is an important predictor of violent crime in a neighborhood (Veysey & Messner, 1999). Rose and Clear (1998) suggested that prior crime policies that result in mass incarceration may also impair community functioning, because in some communities this represents a substantial loss in the social and human capital on which informal social control depends.

Although many of the structural factors contributing to social disorganization remain outside the control of police agencies, such as concentrated disadvantage and high residential mobility, it remains possible to design interventions to increase social integration and improve collective efficacy. Community policing emphasizes community involvement in responding to crime problems through the creation of police–community partnerships, which should both increase community access to public social control and foster improved trust between community members and police officers. Furthermore, programs designed to increase community integration through increasing resident involvement in local agencies should be helpful in fostering the development of social ties and increasing perceptions of collective efficacy. Finally, if Rose and Clear (1998) are correct, community corrections and offender reintegration efforts should alleviate some of the impact of the mass incarceration policies that have removed offenders from the community. Bursik and Grasmick (1993) provided an extensive discussion on various community-based interventions and provided suggestions for how to improve these programs.

In addition to the previously discussed factors, a fair amount of research has examined the effects of incivilities on crime and the fear of crime within a community. Incivilities, such as poorly tended residences, the accumulation of refuse, graffiti, and public loitering and drunkenness, are signs of disorder. A number of studies have demonstrated that the presence of incivilities in a neighborhood is associated with increased levels of serious crime and with heightened fear of crime among community residents (see Skogan, 1990). Sampson and colleagues (1997), however, suggested that this relationship is spurious and that crime and incivilities result from the same underlying causal process, namely, a lack of collective efficacy. Although the causal role of incivilities in producing crime is in doubt, they may still function as leading indicators of potential crime problems, meaning that mapping incivilities may provide information on communities where hot spots may be emerging.

C. City Features and Crime Locations

In truly comprehensive strategies for addressing crime in hot-spot areas, it is important not only to examine neighborhood-level factors that contribute to the emergence of a crime hot spot but also to consider microlevel place characteristics that promote crime. As Sherman and colleagues (1989) noted, even within high-crime neighborhoods there is substantial variability in the levels of crime. Some places within these neighborhoods experience very low levels of crime, whereas other places are responsible for a substantial amount of the crime.

A number of studies have demonstrated that hot spots of crime tend to emerge around particular features of the urban environment, such as bars and taverns (Roncek & Maier, 1993), fast food restaurants (Brantingham & Brantingham, 1982), schools (Roncek & Faggiani, 1985), public housing (Roncek, Bell, & Francik, 1981), vacant buildings (Spelman, 1993), and public transportation (Block & Davis, 1996). These locations may promote crime by juxtaposing motivated offenders and suitable targets in the absence of capable guardians. Furthermore, the pattern and timing of criminal events in these areas follow the rhythm of legitimate social activity in these areas. For example, crime around bars is more common during evenings and weekends, because more legitimate patrons visit bars during this time. Crime is more common around schools during the school year and after school, because many students interact at this time near school grounds without teacher or parental supervision. Understanding the relationship between the pattern of legitimate social activity and criminal activity around these areas allows researchers and policymakers to design suitable crime prevention strategies.

In addition to identifying the location and timing of criminal events at particular sites, it is important to discern the mechanism through which these areas produce criminal opportunities. Brantingham and Brantingham (1993) discussed the differences between crime generators and crime attractors. Crime generators, such as transit stations, foster criminal activity by bringing both victims and offenders into a location. On the other hand, crime attractors, such as bars and taverns, tend to bring higher proportions of offenders into an area because these locations are tied to patterns of illicit activity. It is important to discern whether a given location functions as a crime generator or a crime attractor, because the appropriateness and effectiveness of intervention strategies may differ by type of location.

There is no shortage of practical policy recommendations for reducing or eliminating criminal opportunities around hot-spot areas. Clarke’s (1997) situational crime prevention model offers a set of 16 different opportunity-reducing strategies. Among those most applicable to location-based interventions are controlling access and entry/exit screening; improving surveillance by officers, civilians, and citizens; deflecting offenders by disrupting routines that promote crime; and facilitating compliance with rules. Use of these strategies to control opportunities for crime may help reduce the risks of victimization in hot-spot areas.

D. Crime Displacement

Unanticipated consequences are always a concern when designing an intervention. For interventions in crime hot spots, crime displacement is of particular importance. After the intervention is implemented and crime opportunities are reduced, it is possible that offenders simply relocate their activities to areas outside the intervention site. For example, if a police crackdown on drug trafficking is initiated at a particular intersection that is a hot spot for drug dealing, it is possible that offenders will simply move to a nearby intersection, and drug sales will continue. Other types of crime displacement, such as offenders committing crime during different times, offenders selecting different targets, or even offenders committing different types of crimes, also are possible. Given the wide ranges of different responses that might constitute crime displacement, it is difficult to conclusively demonstrate that crime displacement did not occur during a particular study. For this reason, any researchers or policymakers implementing place-based intervention strategies should be keen to the possibility of crime displacement. Fortunately, the empirical literature on crime displacement is decidedly mixed, and it appears that many interventions do not lead to appreciable crime displacement effects (Clarke, 1997).

On the basis of the current research on the spatial patterns of crime, a number of avenues of research in crime mapping are worth exploring. Obviously, a major focus for future research in this area will be further development and refinement of the tools needed in crime mapping studies. Although not discussed in this research paper, there are substantial methodological and analytic difficulties that remain in crime mapping research. Beyond this, however, there are a number of substantive research avenues in crime mapping that are worth pursuing.

A first avenue of research is the further development and integration of theories of the spatial distribution of crime. Although there have been some efforts at integrating social disorganization and routine activities theories (see Miethe & Meier, 1994), additional work remains. These theories share considerable conceptual overlap, and linking the two should provide a more comprehensive framework for understanding the relationship between crime at the macroand microlevels. Furthermore, the criminal events perspective (Meier, Kennedy, & Sacco, 2001; Sacco & Kennedy, 2002) provides a mechanism to link other theories of criminality with theories of criminal events. To date, the implications of other theories of criminality for understanding the spatial distribution of crime remains unexplored and may provide useful insights into offender search patterns and the selection of targets and locations.

A second area of research that would be very helpful in regard to policymakers is expanding crime mapping to include additional justice agencies. The vast majority of research in crime mapping has used calls for service and crime report data, and most applications of crime mapping have been applied to police decision making. Researchers should consider broadening the scope of crime mapping efforts to incorporate data from other justice agencies. In a practical sense, mapping efforts involving other agencies can provide assistance with managing caseloads and coordinating the distribution of services. For example, mapping the residences of parolees and probationers can help agencies optimize caseloads and improve the process of referring ex-offenders to nearby treatment facilities. In addition, novel data can provide new measures of concepts that are commonly used in geographic research, raise interesting research questions, and possibly introduce new avenues of research.

A third potentially fruitful area of research would involve increased attention to the differences between types of city features and the production of criminal events. As previously discussed, it is well established that certain city features tend to concentrate criminal events in adjacent areas. What remains to be seen, however, is how other spatial and community features contribute to differential spatial patterns of crime. For example, it is not entirely clear why some bars suffer from high levels of crime problems and others do not. Obviously, design features of the location itself should account for some of the differences, but other features, such as the level of community organization, adjacent land usage, and the level of concentration of other crime generators or attractors, may also be important for differentiating between problematic and nonproblematic bars.

A final recommendation for future research on spatial patterns in crime is to further examine the stability of crime in small areas. Specifically, as Weisburd, Bushway, Lum, and Yang (2004) recognized, few studies have examined the degree to which crime in microlevel areas is stable over time. In their study, conducted in Seattle over a 14-year period, Weisburd et al. found that there was a substantial amount of stability in the level of crime on street segments. Despite the high degree of stability in many places, some street segments exhibited either downward or upward crime trajectories. Obviously, additional research is needed to determine whether this pattern holds generally or is specific to the city of Seattle. This type of research will be very helpful in describing the factors that lead to the development, maintenance, and decline of crime in problematic areas.

The purpose of this research paper was to review some of the current research on crime mapping, the process through which crime analysts and researchers use location information about crime to detect spatial patterns in criminal activity. Although the history of crime mapping can be traced to the beginnings of the field of criminology, it is only recently that researchers and crime analysts have been able to engage in extensive mapping efforts, primarily due to the development of the desktop computer and GIS software. The emergence of the problem-oriented policing model, along with advances in the theory of criminal events, created a niche for crime mapping in police agencies. The popularization of computerized crime mapping through the Compstat program in New York led to a period of rapid adoption of crime mapping that continues today.

Several theories that are widely used in crime mapping research were also discussed in this research paper. Social disorganization theory argues that structural factors can compromise the social networks needed for social integration, which in turn reduces the capacity of communities to regulate the behavior of its members. Routine activities theory states that crime can be understood through the convergence in time and space of suitable targets, motivated offenders, and the absence of capable guardians. Defensible space and CPTED focuses on how the design of a physical space can prevent crime through increasing territorial functioning and enhancing surveillance capabilities. The rational choice and crime pattern theories of crime focus primarily on explaining how patterns of offender routine activities and target-searching strategies can increase the level of crime in particular areas. Taken together, these theories provide the conceptual backdrop for understanding the spatial distribution of crime and designing strategies to combat crime in high-crime areas.

Finally, this research paper aimed to elaborate on some of the major findings in crime mapping and spatial crime research, with particular attention to designing strategies to combat crime problems. It was argued that the best strategy for eliminating crime hot spots requires consideration of causal factors operating at both the neighborhood and site levels. This research paper concluded with a number of suggestions for future researchers examining spatial crime patterns through crime mapping. In particular, crime mapping research may benefit from efforts at theoretical integration, using crime mapping with additional agencies, further examining the source of differences in the production of criminal opportunities between city features, and examining the stability of crime areas over time.

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• Veysey, B. M., & Messner, S. F. (1999). Further testing of social disorganization theory: An elaboration of Sampson and Grove’s “community structure and crime.” Journal of Research in Crime and Delinquency, 36, 156–174.
• Weisburd, D., Bushway, S., Lum, C., & Yang, S. (2004). Trajectories of crime at places:A longitudinal study of street segments in the city of Seattle. Criminology, 42, 283–321.
• Weisburd, D., & Green, L. (1995). Policing drug hot spots: The Jersey City Drug Market Experiment. Justice Quarterly, 12, 711–736.
• Weisburd, D., & Lum, C. (2005). The diffusion of computerized crime mapping in policing: Linking research and practice. Police Practice and Research, 6, 419–434.

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Best Crime Essay Examples

Crime mapping.

608 words | 3 page(s)

Crime mapping has proven itself to be one of the most important innovations in criminal justice over the last couple of decades. It is a process by which departments can predict criminal activity by using technology and by pinpointing where past crimes have taken place. While this does offer many possibilities moving forward, ti also presents several critical challenges that must be addressed in order for mapping to have long-term effectiveness.

One of the ways in which crime mapping can be used positively by police is in allowing departments to route their patrols properly. Departments have to make choices with their somewhat limited resources. They simply cannot patrol every single area of a given place at every time. With this in mind, they must choose the right places, and crime mapping provides a picture of where crimes have taken place.

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In addition, crime mapping provides insights that can allow departments to send the right kinds of police officers to the right places. Police departments are made up of varied groups of officers with different skills. Some officers are more effective on the drug beat, while others are better at dealing with violent crime. With crime mapping, departments can ensure that the rights units are in the right places, thus increasing the quality of policing and prevention in a given place.

In addition, crime mapping allows departments the unique ability to apprehend suspects of past crime. In many instances, the people who commit crimes do so near the places where they live. They tend to stay in their neighborhoods, and one of the keys of crime mapping is that it allows police to dedicate more resources to apprehending people who are dangerous in those neighborhoods. This is especially important in large areas, where people can hide out from police for extended periods of time unless the area is chopped in some meaningful way.

One of the primary challenges associated with this data is what might be deemed the self-scouting effect. One of the best things that police have going for them in trying to stop crime is that they know where they are going to be, while would-be criminals do not know where the police is going to be. However, historical crime data is readily available. Even if people are not able to access the precise data that the police have, they can develop a good idea of where the majority of crime is taking place. With this information in hand, these individuals can then decide to take their criminal activity elsewhere. The data is suggestive, and it can be predictive, but it is not dispositive. Because of the game theory involved in using this data, police could influence where crime takes place, and give individuals the ability to commit crimes out of the sight of police in areas that were previously safe. This assumes a rational, logical criminal, of course, and that impulse may not be true, but this is one of the primary challenges that afflicts departments today.

Crime mapping as a technological solution might benefit society by making policing more transparent. One of the issues right now with policing is that people have little faith in what police are doing, and many are unaware of how police go about their jobs. Crime mapping could bring about a new reality where people are more involved with police. People have more of an understanding of what police do and how they do it. This could bring about more faith in police, which could, in turn, help to heal some of the broken communities around the country that have been afflicted by a lack of trust in modern policing.

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Measurement and Analysis of Crime and Justice: An Introductory Essay

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The accuracy of crime statistics: assessing the impact of police data bias on geographic crime analysis

• Open access
• Published: 26 March 2021
• Volume 18 , pages 515–541, ( 2022 )

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• David Buil-Gil   ORCID: orcid.org/0000-0002-7549-6317 1 ,
• Angelo Moretti   ORCID: orcid.org/0000-0001-6543-9418 2 &
• Samuel H. Langton   ORCID: orcid.org/0000-0002-1322-1553 3  

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Police-recorded crimes are used by police forces to document community differences in crime and design spatially targeted strategies. Nevertheless, crimes known to police are affected by selection biases driven by underreporting. This paper presents a simulation study to analyze if crime statistics aggregated at small spatial scales are affected by larger bias than maps produced for larger geographies.

Based on parameters obtained from the UK Census, we simulate a synthetic population consistent with the characteristics of Manchester. Then, based on parameters derived from the Crime Survey for England and Wales, we simulate crimes suffered by individuals, and their likelihood to be known to police. This allows comparing the difference between all crimes and police-recorded incidents at different scales.

Measures of dispersion of the relative difference between all crimes and police-recorded crimes are larger when incidents are aggregated to small geographies. The percentage of crimes unknown to police varies widely across small areas, underestimating crime in certain places while overestimating it in others.

Conclusions

Micro-level crime analysis is affected by a larger risk of bias than crimes aggregated at larger scales. These results raise awareness about an important shortcoming of micro-level mapping, and further efforts are needed to improve crime estimates.

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Introduction

Police-recorded crimes are the main source of information used by police forces and government agencies to analyze crime patterns, investigate the geographic concentration of crime, and design and evaluate spatially targeted policing strategies and crime prevention policies (Bowers and Johnson 2014 ; Weisburd and Lum 2005 ). Police statistics are also used by criminologists to develop theories of crime and deviance (Bruinsma and Johnson 2018 ). Nevertheless, crimes known to police are affected by selection biases driven by unequal crime reporting rates across social groups and geographical areas (Buil-Gil et al. 2021 ; Goudriaan et al. 2006 ; Hart and Rennison 2003 ; Xie 2014 ; Xie and Baumer 2019a ). The level of police control (e.g., police patrols, surveillance) also varies across areas, which may affect victims’ willingness to report crimes to police and dictate the likelihood that police officers witness incidents in some places more than others (McCandless et al. 2016 ; Schnebly 2008 ). The sources of measurement error that affect the bias and precision of crime statistics is an issue that merits scrutiny, since it affects policing practices, criminal justice policies, and citizens’ daily lives. Yet, it is an understudied issue.

The implications of crime data biases for documenting and explaining community differences in crime and guiding policing operational decision-making processes are mostly unknown (Brantingham 2018 ; Gibson and Kim 2008 ; Kirkpatrick 2017 ). Moreover, police analyses and crime mapping are moving toward using increasingly fine-grained geographic units of analysis, such as street segments and micro-places containing highly homogeneous communities (Groff et al. 2010 ; Weisburd et al. 2009 , 2012 ). Geographic crime analysis based on police-recorded crime and calls for service data is used to identify the micro-places where crime is most prevalent in order to effectively target police resource (Braga et al. 2018 ). In this context, we define “micro-places” as very detailed spatial units of analysis such as addresses, street segments, or clusters of such units (Weisburd et al. 2009 ). Despite the increasing interest in small units of analysis, the extent to which such aggregations impact on the overall accuracy of statistical outputs and spatial analyses remains unknown (Ramos et al. 2020 ). In other words, we do not know whether aggregating crime data at such detailed levels of analysis increases the impact of biases introduced by underreporting. This article presents a simulation study to analyze the impact of data biases on geographic crime analysis conducted at different spatial scales. The open question that this research aims to address is whether aggregating crimes at smaller, more socially homogeneous spatial scales increases the risk of obtaining biased outputs compared with aggregating crimes at larger, more socially heterogeneous geographical levels.

Since the early 1830s, numerous researchers have expressed concern about the limitations of using official statistics to analyze crime patterns across space and time (Kitsuse and Cicourel 1963 ; Skogan 1974 ). Soon after the publication of the first judiciary statistics in France, Alphonse de Candolle ( 1987a [1830], 1987b [1832]) cautioned that the validity of these data was likely to be affected by various sources of measurement error. For instance, crimes may not be discovered by victims, some victims may not report crimes to the authorities, offenders’ identities may remain unknown, and legal procedures may not lead to conviction. Moreover, cross-sectional comparisons of the number of people convicted in court are likely to be affected by changes in prosecution activity, and the proportion of recorded crimes to unknown offences may vary between countries (Aebi and Linde 2012 ). De Candolle ( 1987b [1832]) argued that the number of persons accused of crime was a better indicator of crime incidence than the number of persons convicted, since the former is closer to crime events in terms of legal procedure. This rationale was later used to describe the so-called “Sellin’s dictum” (i.e., “the value of a crime rate for index purposes decreases as the distance from the crime itself in terms of procedure increases,” Sellin 1931 : 346), and it is the main reason why crime incidents known to the police are generally preferred over judiciary statistics when it comes to analyzing crime. Police-recorded crimes, however, are also subject to criticism over the validity of recording and reporting. So much so that such data lost the official designation of National Statistics in the UK in 2014 (UK Statistics Authority 2014 ).

A key issue of concern regarding the use of police records for crime analysis and mapping is the fact that crime reporting rates are unequally distributed across social groups and geographic areas. Crime reporting to police forces is known to be more common among female victims than male victims, and young citizens report crimes less often than adults (Hart and Rennison 2003 ; Tarling and Morris 2010 ). There are also contextual factors that affect crime reporting rates across areas, such as neighborhood economic deprivation, the degree of urbanization, the concentration of minorities, and social cohesion (Berg et al. 2013 ; Goudriaan et al. 2006 ; Slocum et al. 2010 ; Xie and Baumer 2019a , b ; Xie and Lauritsen 2012 ). The demographic and social characteristics of small areas are generally more homogeneous compared with larger scales (e.g., Brattbakk 2014 ; Weisburd et al. 2012 ). Thus, crime aggregates produced at the level of small geographies are more likely to be affected by unequal crime reporting rates across groups compared with aggregates and maps produced at larger, more heterogeneous spatial scales. For instance, Buil-Gil et al. ( 2021 ) show that the variation in the “dark figure of crime” (i.e., all crimes not shown in police statistics) between neighborhoods (within cities) is larger than the variation between cities. We expect the risk of police data bias to be especially large when aggregating crime records at the level of micro-places.

This paper is organized as follows: sect. “ The criminology of place ” introduces the move toward low-level crime analysis in criminology. Section “Geographic crime analysis and measurement error ” discusses the various sources of measurement error that may affect police records and introduce bias into our understanding of community differences in crime. Section “ Data and methods ” introduces the data, methods, and steps taken to generate the synthetic population for our simulation study, and methods used to assess the findings. Section “ Mapping the bias of police-recorded crimes ” reports the results of the simulation study. Finally, sect. “ Discussion and conclusions ” discusses the findings and presents the conclusions and limitations, along with suggestions for future research.

The criminology of place

In the 1980s, several researchers began analyzing the concentration of crime in places and found that a large proportion of crimes known to the police concentrated in a small number of micro-places. Pierce et al. ( 1988 ) showed that 50% of all calls for police services in Boston took place in just 2.6% of addresses, suggesting that a disproportionately large volume of total crime could be attributed to just a handful of places. A year later, Sherman et al. ( 1989 ) conducted similar research in Minneapolis, obtaining almost the same results: 2.5% of addresses in this city generated 50% of all crime calls to the police. These were only two of the first studies looking into the concentration of crime in places. Since then, many other researchers have published remarkably similar findings (see a review in Lee et al. 2017 ). Environmental criminologists argue that the social and contextual conditions that favor crime vary across micro-places, and that opportunities for crime are structured within very small geographic areas (Brantingham and Brantingham 1995 ; Weisburd et al. 2012 ).

Given the persistency of this finding across multiple study sites and countries, Weisburd ( 2015 : 138) argues for a so-called “law of crime concentration” at micro-places, namely, that “for a defined measure of crime at a specific microgeographic unit, the concentration of crime will fall within a narrow bandwidth of percentages for a defined cumulative proportion of crime.” This has served as a basis for police forces all over the world to develop place-based strategies that increase police control over those areas where crime is highly concentrated to efficiently reduce citywide crime (Braga et al. 2018 ; Groff et al. 2010 ; Kirkpatrick 2017 ).

However, the vast majority of research analyzing crime concentration, and evaluating the impact of place-based policing interventions, is based on data about crimes known to the police. For instance, 41 out of 44 studies examining the crime concentration at places reviewed by Lee et al. ( 2017 ) used crime incidents reported to police, and 4 out of 44 analyzed calls for police services (note that some studies used more than one source of data). Both these sources of data depend on citizens’ willingness to report crimes and cooperate with the police, which are known to be affected by the social and demographic characteristics of individuals, but also by variables that operate at the scales of small communities, such as concentrated disadvantage, perceived disorder, and collective efficacy (Jackson et al. 2013 ). Weisburd et al. ( 2012 : 5) argue that “the criminology of place [...] emphasizes the importance of micro-units of geography as social systems relevant to the crime problem.” And yet, these micro-level social systems may also be key in explaining why crime reporting rates—and thus the likelihood of crimes being known to police—are high in some places and low in others, and as such, we might expect that the sources of measurement error that affect police data will vary across micro-places.

Geographic crime analysis and measurement error

There are four primary sources of data bias that may affect the accuracy of community differences in crime documented through police statistics. First, the willingness of residents to report crimes to police is known to be associated with individual and contextual factors that vary across geographic areas (Hart and Rennison 2003 ). There are demographic, social, economic, and environmental factors that affect crime reporting rates. For example, the victims’ sex, age, employment status, education level, and ethnic group are all good predictors of their likelihood to report crimes to the police (Hart and Rennison 2003 ). Since some of these resident characteristics concentrate in particular areas, we also expect crime reporting rates to vary across areas. Generally, deprived neighborhoods and areas with large concentrations of immigrants have lower crime reporting rates than middle-class areas (Baumer 2002 ; Xie and Baumer 2019a ; Goudriaan et al. 2006 ), and crimes that take place in cohesive areas have a higher chance of being known to the police (Goudriaan et al. 2006 ; Jackson et al. 2013 ). Moreover, residents from rural areas are generally more willing to cooperate with police services than urban citizens (Hart and Rennison 2003 ). Research has also found that the incident seriousness and harm are very strongly linked to the reporting decision (Baumer 2002 ; Xie and Baumer 2019b ).

Second, studies have found that the overall crime rate and citizens’ perceptions about police forces, which also vary across areas, affect residents’ willingness to cooperate with the police (e.g., Xie 2014 ). Berg et al. ( 2013 ) show that the most important contextual factor in explaining crime reporting is the level of crime in the area. Jackson et al. ( 2013 ) argue that the level of trust in police fairness and residents’ perceptions of police legitimacy is key to predict the willingness to cooperate with police forces.

Third, unequal police control across areas may inflate crime statistics in some places but not others. Schnebly ( 2008 ) shows that cities with more police officers trained in community-oriented policing generally have higher rates of police notification, whereas McCandless et al. ( 2016 ) argue that poorly handled stop and search practices may discourage residents from engaging with the police.

Fourth, there may be differences between counting rules applied by different police forces (Aebi and Linde 2012 ). This is not expected to be a major source of error in England and Wales, since all 43 police forces follow common counting rules (National Crime Recording Standards and Home Office Counting Rules for Recorded Crime). Nevertheless, we note that, in 2014, Her Majesty’s Inspectorate of Constabulary and Fire & Rescue Services conducted an inspection about police statistics and concluded that the extent to which certain counting practices was followed varied between police forces (HMIC 2014 ).

Some of these sources of measurement error were mentioned by Skogan ( 1977 : 41) to argue that the dark figure of crime “limits the deterrent capability of the criminal justice system, contributes to the misallocation of police resources, renders victims ineligible for public and private benefits, affects insurance costs, and helps shape the police role in society.” Moreover, the UK public administration also acknowledges that “there is accumulating evidence that suggests the underlying data on crimes recorded by the police may not be reliable” (UK Statistics Authority 2014 : 2). As a consequence, in 2014, crime data were removed from the UK National Statistics designation.

Given that many of the factors generating disparities in the bias and precision of police-recorded crime data are non-uniformly distributed across space, even in the same city, it is plausible that the bias affecting crime data varies considerably between small areas. Indeed, issues of bias and precision may even be compounded as the geographic resolution becomes more fine-grained. Oberwittler and Wikström ( 2009 : 41) argue that, in order to analyze crime, “smaller geographical units are more homogeneous, and hence more accurately measure environments. In other words, smaller is better.” Smaller units of analysis are said to be better for explaining criminal behaviors since crime is determined by opportunities that occur in the immediate environment. However, smaller units of analysis may also be preferred to explain the amount of crime which remains hidden in police statistics (either because victims and witnesses fail to report or because the police fail to record). The “aggregation bias,” which argues that what is true for a group should also be true for individuals within such a group, tends to be used to justify the selection of smaller spatial units in geographic crime analysis due to this homogeneity in residential characteristics. And yet, high internal homogeneity and between-unit heterogeneity may generate greater variability in bias and precision between units. It would be paradoxical and self-defeating if, in seeking to avoid aggregation bias with the use of micro-scale units, studies increase the risk of crime statistics being affected by bias and imprecision. This would have significant repercussions for academic endeavor and policing practices that document and explain community differences in crime.

Data and methods

Simulation studies are computer experiments in which data is created via pseudo-random sampling in order to evaluate the bias and variance of estimators, compare estimators, investigate the impact of sample sizes on estimators’ performance, and select optimal sample sizes, among others (Moretti 2020 ). Brantingham and Brantingham ( 2004 ) recommend the use of computer simulations to understand crime patterns and provide policy guidance for crime control (see also Groff and Mazerolle 2008 ; Townsley and Birks 2008 ). In this study, we generate a synthetic dataset of crimes known and unknown to police in Manchester, UK, and aggregate crimes at different spatial scales. This permits an investigation into whether aggregates of crimes known to police at the micro-scale level suffer from a higher risk of bias compared with those at larger aggregations, such as neighborhoods and wards.

Based on parameters obtained from the UK Census 2011 and Index of Multiple Deprivation (IMD) 2010, we simulate a synthetic individual-level population consistent with the characteristics of Manchester. The simulated population reflects the real distributions and parameters of variables related to individuals residing in each area of the city (i.e., mean, proportion, and variance of the citizens’ age, sex, employment status, education level, ethnicity, marriage status, and country of birth). The measure of multiple deprivation captures the overall level of poverty in each area. Then, based on parameters derived from the Crime Survey for England and Wales (CSEW) 2011/2012, we simulate the victimization of these individuals across social groups and areas and predict the likelihood of these crimes being known to the police. This allows us to compare the relative difference between all crimes and police-recorded incidents at the different spatial scales.

The main motivation for using a simulation study with synthetic data, instead of simply using crime records, is because the absolute number of crimes in places is an unknown figure, regardless which source of data we use (see sect. “ Geographic crime analysis and measurement error ”). Police records are affected by a diverse array of sources of error which vary between areas, and the CSEW sample is only designed to allow the production of reliable estimates at the level of police force areas (smaller areas are unplanned domains with very small sample sizes for which analyses based on direct estimates lead to unreliable outputs; Buil-Gil et al. 2021 ). Nevertheless, the analytical steps followed in this article are designed to provide an answer to our research question (namely, whether micro-level aggregates of police-recorded crime are affected by a larger risk of bias compared with larger scales), rather than producing unbiased estimates of crime in places. Future research will explore if the method used here is also a good way to produce accurate estimates of crime in places and compare these estimates with model-based estimates of crime indicators obtained from more traditional methods in small area estimation (Buil-Gil et al. 2021 ). Indeed, unbiased estimates of crime in places are needed to guide evidence-based policing and research.

In this section, we describe the data and methods used to generate the synthetic population of crimes known and unknown to police and evaluate differences between spatial scales. Section “ Generating the population and simulation steps ” outlines the data-generating mechanism and the steps of our simulation study, and in sect. “ Empirical evaluation of simulated dataset of crimes ,” we provide an empirical evaluation of the simulated dataset. We discuss methods to assess the results in sect. “ Assessing the results .”

Generating the population and simulation steps

The simulation of our synthetic population involves three steps which are described in detail below. All analyses have been programmed in R (R Core Team 2020 ), and all data and code used for this simulation study are available from a public GitHub repository (see https://github.com/davidbuilgil/crime_simulation2 ).

Step 1. Simulating a synthetic population from census data

The first step is to generate a synthetic population consistent with the social, demographic, and spatial characteristics of Manchester. We download aggregated data about residents at the output area (OA) level from the Nomis website ( https://www.nomisweb.co.uk/census/2011 ), which publishes data recorded by the UK Census 2011. For consistency, we will conduct all our analyses using information collected in 2011. From Nomis, we obtain census parameters of various variables in each OA in Manchester. OAs are the smallest geographic units for which census data are openly published in the UK. The minimum population size per OA is 40 households and 100 residents, but the average size is 125 households. We will also use other units of geography in further steps: lower layer super output areas (LSOAs), that generally contain between four and six OAs with an average population size of 1500; and middle layer super output areas (MSOAs), which have an average population size of 7200. The largest scale used are wards. In Manchester local authority, there are 1530 OAs, 282 LSOAs, 57 MSOAs, and 32 wards.

Although UK census data achieve nearly complete coverage of the population, and measurement error arising from using these data is likely to be very small, Census data are not problem-free. For instance, census non-response rates vary between age, sex, and ethnic groups (e.g., while more than 97% of females above 55 responded the census, the response rate for males aged 25 to 29 was 86%), and questionnaire items (e.g., non-response rates were 0.4% and 0.6% for sex and age questions, respectively, and 3%, 4%, and 5.7% for ethnicity, employment status, and qualifications questions). In Manchester, the census response rate was 89%. In order to adjust for non-response in census data, the Office for National Statistics used an edit and imputation system and coverage assessment and adjustment process before publishing data in Nomis (Compton et al. 2017 ; Office for National Statistics 2015 ). Census data are widely used as empirical values of demographic domains in areas for academic research and policy (Gale et al. 2017 ). From the census, we obtain the number of citizens living in each OA (i.e., resident population size), the mean and standard deviation of age by OA, and the proportion of citizens in each area with the following characteristics defined by binary variables (in parentheses, we detail the reference category): sex (male), ethnicity (white), employment status (population without any income), education (higher education or more), marriage status (married), and country of birth (born in the UK). We use this information to simulate our synthetic individual-level population and their corresponding social-demographic characteristics within each OA. Moreover, we attach the known IMD 2010 decile in each OA. This ensures that we account for both individual and area-level measures in our simulation. The IMD is a measure of multiple deprivation calculated by the UK Government from indicators of income, employment, health, education, barriers to housing and services, and crime and living environment at the small area level (McLennan et al. 2011 ). Generating these values allows us, in subsequent steps, to simulate crimes experienced by citizens, as well as the likelihood of each crime being known to the police, based on parameters obtained from survey data. We use these specific variables since these are known to be associated with crime victimization and crime reporting rates (see sect. “ Geographic crime analysis and measurement error ”). Thus, the selection of census parameters is driven by the literature review and the availability of data recorded by the census and IMD.

The variables are generated for d  = 1, …, D OAs and i  = 1, …, N d individual citizens according to the distributions detailed below, where N d denotes the population dimension in the d th OA:

\( \mathrm{Ag}{\mathrm{e}}_{di}\sim N\left({\mu}_d^{\mathrm{Age}},{\sigma}_d^{2,\mathrm{Age}}\right),\kern0.5em \) where \( {\mu}_d^{\mathrm{Age}} \) and \( {\sigma}_d^{2,\mathrm{Age}} \) denote the mean and variance of age for the d th OA.

\( \mathrm{Se}{\mathrm{x}}_{di}\sim \mathrm{Bernoulli}\left({\pi}_d^{\mathrm{Male}}\right) \) , where \( {\pi}_d^{\mathrm{Male}} \) denotes the proportion of males in d th OA.

\( {\mathrm{NoInc}}_{di}\sim \mathrm{Bernoulli}\left({\pi}_d^{\mathrm{NoInc}}\right) \) , where \( {\pi}_d^{\mathrm{NoInc}} \) denotes the proportion of citizens without any income in the d th OA.

\( {\mathrm{HE}}_{di}\sim \mathrm{Bernoulli}\left({\pi}_d^{\mathrm{HE}}\right) \) , where \( {\pi}_d^{\mathrm{HE}} \) denotes the proportion of citizens with high education (holding a university degree) in the d th OA.

\( \mathrm{Whit}{\mathrm{e}}_{di}\sim \mathrm{Bernoulli}\left({\pi}_d^{\mathrm{White}}\right) \) , where \( {\pi}_d^{\mathrm{White}} \) denotes the proportion of white citizens in the d th OA.

\( {\mathrm{Married}}_{di}\sim \mathrm{Bernoulli}\left({\pi}_d^{\mathrm{Married}}\right) \) , where \( {\pi}_d^{\mathrm{Married}} \) denotes the proportion of married population in the d th OA.

\( {\mathrm{BornUK}}_{di}\sim \mathrm{Bernoulli}\left({\pi}_d^{BornUK}\right) \) , where \( {\pi}_d^{\mathrm{BornUK}} \) denotes the proportion of population born in the UK in the d th OA.

Thus, we generate N = 503,127 units with their individual and contextual characteristics across D = 1,530 OAs in Manchester. Given that we simulate all individual information based on population parameters obtained from the census using small spatial units of analysis (i.e., OAs), our synthetic population is very similar (in terms of distributions and ranking) to the empirical population of each OA. The Spearman’s rank correlation coefficient of the mean of age, sex, income, higher education, ethnicity, marriage status, and country of birth across areas in census data and our simulated dataset is almost perfect (i.e., larger than 0.99 for all variables).

Step 2. Simulating crime victimization from CSEW data

We use parameters obtained from the CSEW 2011/2012 to generate the crimes experienced by each individual citizen. The CSEW is an annual victimization survey conducted in England and Wales. Its sampling design consists of a multistage stratified random sample by which a randomly selected adult (aged 16 or more) from a randomly selected household is asked about experienced victimization in the last 12 months (Office for National Statistics 2013 ). The survey also includes questions about crime reporting to the police and whether each crime took place in the local area, among others. The main part of the survey is completed face-to-face in respondents’ households, although some questions (about drugs and alcohol use, and domestic abuse) are administered via computer-assisted personal interviewing. The CSEW sample size in 2011/2012 was 46,031 respondents.

In order to simulate the number of crimes faced by each individual unit within our synthetic population of Manchester residents, we first estimate negative binomial regression models of crime victimization from CSEW data and then use the model parameter estimates to predict crime incidence within our simulated population. Given that different crime types are known to be associated with different social and contextual variables (Andresen and Linning 2012 ; Quick et al. 2018 ), and the variables associated with crime reporting to the police also vary according to crime type (Baumer 2002 ; Hart and Rennison 2003 ; Tarling and Morris 2010 ), we estimate one negative binomial regression model by each of four groups of crime types:

Vehicle crimes: includes the number of (a) thefts of motor vehicles, (b) things stolen off vehicles, and (c) vehicles tampered or damaged, all during the last 12 months.

Residence crimes: number of times (a) someone entered a residence without permission to steal, (b) someone entered a residence without permission to cause damage, (c) someone tried to enter a residence without permission to steal or cause damage, (d) anything got stolen from a residence, (e) anything stolen from outside a residence (garden, doorstep, garage), and (f) anything damaged outside a residence. These refer to events happening both at the current and previous households during the last 12 months.

Theft and property crimes (excluding burglary) : number of times (a) something stolen out of hands, pockets, bags, or cases; (b) someone tried to steal something out of hands, pockets, bags, or cases; (c) something stolen from a cloakroom, office, car or anywhere else; and (d) bicycle stolen, all during the last 12 months.

Violent crimes: number of times (a) someone deliberately hit the person with fists or weapon or used force or violence in any way, (b) someone threatened to damage or use violence on the person or things belonging to the person, (c) someone sexually assaulted or attacked the person, and (d) some member of the household hit or used weapon, or kicked, or used force in any way on the person, all during the last 12 months.

Thus, this approach assumes that distributions and slopes observed in the CSEW at a national level apply to crimes that take place in Manchester local authority. The CSEW sample for Manchester is not large enough to estimate accurate regression models, and thus, we use models estimated at a national level to estimate parameters used to generate crimes at a local level. The implications of taking this approach are further discussed in sect. “ Empirical evaluation of simulated dataset of crimes ”. To alleviate the concern about this potential limitation, we show in Appendix Table 7 that the negative binomial regression model of crime victimization estimated from respondents residing in urban and metropolitan areas (excluding London) shows very similar results to model results estimated from all respondents in England and Wales.

The negative binomial regression model is a widely adopted model in this context, which has been proven to adjust well to the skewness of crime count variables (Britt et al. 2018 ; Chaiken and Rolph 1981 ). To estimate the negative binomial regression models, we use the same independent variables described in step 1 (i.e., age, sex, employment status, education level, ethnic group, marriage status, country of birth, IMD decile). However, in this step, these are taken from the CSEW. This allows us to obtain the regression model coefficient estimates and dispersion parameter estimates (Table 1 ), denoted by \( {\hat{\ \alpha}}_p \) for a generic p independent variable and \( \hat{\ \theta } \) , respectively, that will be used to generate the crime counts per person in the synthetic population. Thus, regression models consider individual and area-level variables typically associated with crime victimization risk and crime reporting, but these do not account for other area-level contextual attributes associated with crime and crime reporting, such as the presence of crime generators and attractors in the area (Brantingham and Brantingham 1995 ). Since this is a new methodological approach, we include only a small number of variables recorded in the census and IMD to keep the model parsimonious, avoid multicollinearity, and improve the model accuracy. Models do not consider other important factors, such as individuals’ routine activities and alcohol consumption, because these are not recorded in the census.

Table 1 shows the negative binomial regression models used to estimate crime victimization from CSEW 2011/2012 data. Measures of pseudo- R 2 and normalized root mean squared error (NRMSE) indicate a good fit and accuracy of our models. We use the estimated regression coefficients to generate our synthetic population of crimes, but these also provide some information about which individual characteristics are associated with a higher or lower risk of victimization by crime type. For example, age is negatively associated with crime victimization in all crime types. Being male is a good predictor of suffering vehicle and property crimes, but not residence or violent crimes. With regards to income levels, those with some type of income have a higher risk of victimization by vehicle and violent crimes, whereas respondents without any income have a higher risk of suffering residence crimes. Citizens with a higher education degree generally suffer more property and vehicle crimes than residents without university qualifications, whereas those without higher education certificates are at a higher risk of suffering violent crimes. Married citizens tend to suffer more vehicle crimes, while non-married suffer more property and violent crimes. Citizens born in the UK experience more residence and vehicle crimes than immigrants. And areas with high values of deprivation concentrate more vehicle, residence, and property crimes.

Crime victimization counts for each unit in the simulated population are generated following a negative binomial regression model using the regression coefficient and dispersion parameter estimates obtained from the CSEW (Table 1 ) and the independent variables simulated in step 1. For example, we predict the number of vehicle crimes (Vehi i ) suffered by a given individual i as follows:

where NB denotes the negative binomial distribution, and:

We repeat this procedure for all four crime types. Thus, the variability and relationships between variables observed in the CSEW are reproduced in our simulated population, and we assume that these values represent the true extent of crime victimization in the population of Manchester. We evaluate the quality of the synthetic population of crimes in sect. “ Empirical evaluation of simulated dataset of crimes .”

Step 3. Simulating crimes known to police from CSEW data

The third step consists of estimating whether each simulated crime is known to the police or not. This allows us to analyze the difference between all crimes (generated in step 2), and those crimes known to the police (to be estimated in step 3) for each area in Manchester. First, we create a new dataset in which every crime generated in step 2 becomes the observational unit. Here, our units of analysis are crimes in places, instead of individual citizens; therefore, some residents may be represented more than once (i.e., those who suffered multiple forms of victimization).

In order to estimate the likelihood of each crime being known to the police, we follow a similar procedure as in step 2, but in this case, we make use of logistic regression models for binary outcomes, which are better described by the Bernoulli distribution of crime reporting. First, we estimate a logistic regression model of whether crimes are known to police or not. We use the CSEW dataset of crimes ( n  = 14,758), and fit the model using the same independent variables as in step 2 to estimate the likelihood of crimes being known to the police (see the results of logistic regression models in Table 2 ). We estimate one regression model per crime types to account for the fact that the crime type and incident seriousness are strongly linked to crime reporting (Baumer 2002 ; Xie and Baumer 2019b ). The CSEW asks each victim of each crime whether “Did the police come to know about the matter?” We use this measure to estimate our regression models. Thus, here, we estimate if the police knows about each crime, which is not always due to crime reporting (i.e., estimates from the CSEW 2011/2012 indicate that 32.2% of crimes known to the police were reported by another person, 2.3% were witnessed by the police and 2.2% were discovered by the police by another way).

Second, we estimate whether each crime in our simulated dataset is known to the police, following a Bernoulli distribution from the regression coefficient estimates shown in Table 2 and the independent variables simulated in step 1. As in the previous case, we repeat this procedure for each crime type, since some variables may affect some crime types in a different way than others (Xie and Baumer 2019a ). For example, to estimate whether each vehicle crime j , suffered by an individual i , is known to police (KVehi ji ), we calculate:

\( {\hat{\gamma}}_p \) denotes the regression model coefficient estimate for a p independent variable, and J denotes all simulated crimes. Measures of pseudo-R 2 show a good fit of models.

One important constraint of crime estimates produced from the CSEW is that these provide information about area victimization rates (i.e., number of crimes suffered by citizens living in one area, regardless of where crimes took place), instead of area offence rates (i.e., number of crimes taking place in each area). This may complicate efforts to compare and combine survey-based estimates with police records. Given that our simulated dataset of crimes is based on CSEW parameters and census data about residential population characteristics, our synthetic dataset of crimes is also likely to be affected by this limitation. In order to mitigate the impact of this shortcoming on any results drawn from our study, we follow similar steps as in step 3 in order to estimate whether each crime took place in the residents’ local area or somewhere else and remove from the study all those crimes that do not take place within 15-min walking distance from the citizens’ household (see Appendix 2). Our final sample size is 452,604 crimes distributed across 1530 OAs in Manchester. This facilitates efforts to compare our simulated dataset of crimes with police-recorded incidents, but we note that our synthetic dataset does not account for those crimes that take place in an area but are suffered by persons living in any other place. According to estimates drawn from the CSEW 2011/2012, this represents 26.0% of all crimes, which are likely to be overrepresented in commercial areas and business districts in the city center, where the difference between the workday population and the number of residents is generally very large (e.g., 490.2% in Manchester city center; Manchester City Council 2011 ). We return to this point in the discussion section to discuss ways in which this shortcoming may be further addressed in future research.

Empirical evaluation of simulated dataset of crimes

Once all synthetic data are generated, we use victimization data recorded by the CSEW and data about crimes known to Greater Manchester Police (GMP) to empirically evaluate whether our simulated dataset of crimes matches the empirical values of crime. This is used to evaluate the quality of our synthetically generated dataset of crimes.

First, Table 3 compares the average number of crimes suffered by individuals across socio-demographic groups as recorded by the CSEW 2011/2012 and our simulated dataset. The distribution of the synthetic dataset of crimes is very similar to that of the CSEW, but values appear to be slightly larger in the synthetic population than in the survey data. For instance, citizens younger than 35 suffer the most crimes in both datasets, and males suffer more vehicle, residence, and property crimes. Crime victimization differences by ethnicity, employment status, education level, marriage status, country of birth, and IMD decile shown in the CSEW are also observed in the simulated dataset of crimes. In the case of residence crimes, incidences in our simulated population appear to be slightly larger than those observed in the CSEW. We note that our simulated dataset refers to crimes taking place in Manchester local authority, whereas the CSEW reports data for all England and Wales. In 2011/2012, the overall rate of crimes known to police per 1000 citizens was notably larger in Manchester than in the rest of England and Wales (Office for National Statistics 2019 ), and the Crime Severity Score for 2011/2012 (an index that ranks the severity of crimes in each local authority) was 104.6% larger in Manchester than the average of England and Wales (Office for National Statistics 2020 ). Therefore, the differences observed between CSEW and our synthetic population of crimes are likely to reflect true variations between the crime levels in Manchester and England and Wales as a whole.

Second, Table 4 presents the proportion of crimes that are known to the police grouped by the socio-demographic and contextual characteristics of victims in CSEW and our simulated data. By looking at the table, we see that the proportions related to the CSEW are very similar to the ones obtained on the simulated data. This shows that modeling results are consistent, thus preserving relationships between variables.

Third, we download crime data recorded by GMP ( https://data.police.uk/ ) and compare area-level aggregates of crimes known to GMP with our synthetic dataset of crimes known to the police. To do this, we only consider those simulated crimes that were estimated as being known to police and taking place in the local area. Spearman’s rank correlation and Global Moran’s I coefficients between the area-level aggregates of our synthetic dataset of crimes and crimes known to GMP are reported in Table 5 . Tiefelsdorf’s ( 2000 ) exact approximation of the Global Moran’s I test is used as a measure of spatial dependency between the two measures, to analyze if the number of crimes in our simulated dataset is explained by the value of crimes known to GMP in surrounding areas (Bivand et al. 2009 ).

We aggregate all crimes known to police to each spatial unit using the “sf” package in R (Pebesma 2018 ). Out of the 87,457 crimes known to GMP, 642 could not be geocoded. We note that we obtained slightly different results using two different analytical approaches to aggregating crimes in areas (i.e., counting crimes in OAs and then aggregating from OAs to LSOA, MSOAs, and wards using a lookup table, versus counting crimes in OAs, LSOAs, MSOAs, and wards, respectively), which may be due to errors arising from the aggregation process or inconsistencies in the lookup table. We chose the second approach (i.e., counting points in polygons at the different scales), since, on average, a larger number of offences were registered in each area using this method. Tompson et al. ( 2015 ) demonstrate that open crime data published in England and Wales is spatially precise at the levels of LSOA and MSOA, but that the spatial noise added to these data for the purposes of anonymity means that OA-level maps often have inadequate precision. Thus, we only present and discuss the results obtained at LSOA and larger spatial levels.

Table 5 shows positive and statistically significant coefficients of Spearman’s rank correlation for all crime types at the LSOA level. The index of Global Moran’s I is also statistically significant and positive in all cases. At the MSOA and ward levels, the coefficients of Spearman’s correlation for vehicle crimes are not statistically significant. This is likely to be explained by the small number of MSOAs and wards under study (56 and 32, respectively). Generally speaking, our simulated dataset of synthetic crimes is a good indicator of crimes known to police, although both datasets are not perfectly aligned. Our synthetic dataset of crimes may underestimate crimes known to police in areas with a large difference between workday and residential populations, but it appears to be a precise indicator of crimes known to police in residential areas. In the discussion section, we present some thoughts about how to address this in future research.

Assessing the results

In order to assess the extent to which the number of simulated crimes known to police varies from all simulated crimes at the different spatial scales, we calculate the absolute percentage relative difference (RD) and the percentage relative bias (RB) between these two values for each crime type in each area at four spatial scales.

First, RD is calculated for every area d in the specified level of geography (i.e., Geo = {OA, LSOA, MSOA, wards}), as follows:

where E d denotes the count of all crimes in area d , and K d is the count of crimes known to police in the same area.

Second, RB is computed as follows:

We evaluate the average RD and RB at the different spatial scales, but also their spread, to establish if the measures of dispersion across areas become larger when the geographic scale becomes smaller. This permits a demonstration not just of the mean differences between all crimes and crimes known to police at different spatial scales but also the variability in these differences, to help shed light on whether there is higher variability at fine-grained spatial scales. This is investigated via the standard deviation (SD), minimum, maximum, and mean of the RD and RB at the different scales. In addition, boxplots and maps are shown to visualize outputs.

Mapping the bias of police-recorded crimes

This section presents the results of the simulation study. More specifically, we analyze the mean, minimum, maximum, and SD of the RD and RB between all simulated crimes and those synthetic crimes known to the police. We present analyses at the levels of OAs, LSOAs, MSOAs, and wards for four different crime types, in order to establish if the variability of the RD and RB becomes larger at more fine-grained spatial scales.

First, Table 6 presents the summary statistics of RD and RB for all crime types across the four spatial scales. On average, the RD is close to 62% at all the spatial scales (i.e., on average, 62% of crimes are unknown to police at each spatial scale), but the measures of dispersion—and the minimum and maximum values—vary considerably depending on the spatial level under study. The SD of the RD between all crimes and police-recorded offences is the largest at the level of OAs, whereas it is much smaller when crimes are aggregated at the LSOA level. It becomes almost zero at the level of MSOAs and wards. In other words, the RD has a large variability across small areas, but it is minimal when using larger geographies. In one OA, the police might be aware of the vast majority of crimes, and in another one, very few. Thus, geographic crime analysis produced solely from police records at highly localized spatial scales, such as OAs, and may show high concentrations of crime in some areas, but simply as an artefact of the variability in the crimes known to police. By contrast, the police know roughly the same proportion of crimes in all MSOAs and wards, with little variation around the mean. This is also observed in the minimum and maximum values. As such, documenting community differences in crimes based on police records aggregated at these larger scales will reduce the risk of mistakenly classifying some areas as high-crime density, but not others.

Similarly, the mean RB between all crimes and crimes known to police is roughly the same across all spatial scales, but the SD of the RB varies across levels of analysis. The SD is very large when crimes are aggregated at the level of OAs compared with larger scales.

Results shown in Table 6 , nevertheless, are produced from all crime types merged together and thus are likely to hide important heterogeneity depending on each type of crime under study. Crime research shows that different crime types are affected by different individual and contextual predictors (Andresen and Linning 2012 ; Quick et al. 2018 ), and there are also differences in terms of crime reporting to the police (Tarling and Morris 2010 ). Therefore, some crime types may be less affected by data biases than others, and it may be beneficial to disaggregate results by crime type in order to observe differences that may otherwise remain hidden.

Figure 1 shows boxplots of the RD between all crimes (known and unknown to police) and police-recorded crimes across crime types and spatial scales. Detailed results on this are also shown in Appendix Table 9 . We observe that, on average, the RD is lower for violent crimes than any other crime type. Thus, the proportion of total crime known to police is generally larger in the case of violent crimes. We also see that the measures of dispersion in the RD are much larger in the case of property crimes than all other crime types, while the variance of the RD of residence crimes appears to be the smallest. In the case of property crimes, for example, we observe that there is one OA with a RD equal to zero and another area with a RD equal to 100. In other words, in one OA, all property crimes were known to the police, while in the other small area not a single crime was known to police forces. Regardless of the crime type, larger levels of geography are associated with a smaller variance in the RD between areas, whereas the difference between the RD of crime aggregates for MSOAs or wards is generally small. In summary, geographic analysis produced from police records at larger spatial scales may show a more valid representation of the geographic distribution of crimes (known and unknown to police) than analysis produced for small areas.

Boxplots of RD% between all crimes and crimes known to police at the different spatial scales (simulated dataset)

In order to better illustrate the impact of selection bias on maps produced at the different spatial scales, Fig. 2 visualizes the values of RD between all property crimes and property crimes known to the police at the level of OAs, LSOAs, MSOAs, and wards in Manchester. We produce maps of property crimes since it is the crime type with the most extreme measures of dispersion in terms of RD, but similar—less extreme—results are also observed for other crime types. Figure 2 shows that the RD varies widely across OAs (i.e., in some areas, no crimes are known to police, and in others, nearly every crime is known to the police), while the RD between all crimes and police-recorded crimes becomes very homogeneous when crimes are aggregated at the scales of MSOAs and wards.

Maps of RD% between all property crimes and property crimes known to police at the different spatial scales (simulated dataset). Breaks based on equal intervals

Discussion and conclusions

Crime analysis and crime mapping researchers are moving toward increasingly fine-grained geographic resolutions to study the urban crime problem and to design spatially targeted policing strategies (Braga et al. 2018 ; Groff et al. 2010 ; Kirkpatrick 2017 ; Weisburd et al. 2012 ). Researchers document and explain community differences in crime to generate knowledge about crime patterns, test ideas, and assess interventions. Nevertheless, aggregating crimes known to police at such detailed levels of analysis increases the risk that the data biases inherent in police records reduce the accuracy of research outputs. These biases may contribute to the misallocation of police resources, and ultimately have an impact on the lives of those who reside in places mistakenly defined as high-crime-density or low-crime-density areas (Skogan 1977 ). They may also affect the validity of analyses which test theoretical explanations for the geographic distribution of crime (Gibson and Kim 2008 ).

This issue around the bias of police-recorded crime data largely depends on residents’ willingness to report crimes to police, and the police capacity to control places. Both are known to be affected by social and contextual conditions that are more prevalent in some areas than others (Berg et al. 2013 ; Goudriaan et al. 2006 ; Jackson et al. 2013 ; Slocum et al. 2010 ; Xie and Lauritsen 2012 ). The demographic and social characteristics of micro-places are usually very homogeneous (Brattbakk 2014 ; Oberwittler and Wikström 2009 ), which means that populations unwilling to report crime and cooperate with the police will concentrate in particular places, while other areas may contain social groups that are much more inclined to report crime and work with the police. The influence of these factors is reduced when crimes are aggregated to meso- and macro-levels of spatial analysis with more heterogeneous populations. Our simulation study shows that aggregates of police-recorded crime produced for neighborhoods and wards show a much more accurate—less biased—image of the geography of crime compared with those aggregated to small areas. This can be attributed to greater variability (i.e., between-unit heterogeneity) in the proportion of crimes known to police at fine-grained spatial scales. This study also demonstrates that some crime types are affected by data bias differently, which demonstrates the need to disaggregate analyses by crime types.

However, our simulation study is also affected by some limitations that could be addressed in future research. Namely, our simulated dataset of crimes captures area victimization rates instead of area crime rates and, as a consequence, the empirical evaluation when comparing synthetically generated crimes with actual crimes known to GMP showed that our synthetic dataset could be further improved in those areas with a large difference between workday and residential populations. In order to mitigate against this shortcoming, future research should investigate replicating this analysis using census data for workday populations instead of census data for residential populations. This may allow for the generation of more accurate crime counts, especially in non-residential places where crime is prevalent, such as the city center and commercial districts. Moreover, since the CSEW sample in Manchester is very small, our approach assumed that slopes observed in regressions estimated from the CSEW at a national level apply to crimes in Manchester. Future research may merge several editions of the CSEW to obtain a large enough sample in Manchester. Nevertheless, in such a case, survey and census data would refer to different time periods, and there would be a risk of repeated respondents in the CSEW. There are three further limitations that may have more difficult solutions: (a) the CSEW and most victimization surveys do not record information of so-called victimless crimes (e.g., drug-related offences, corporate crimes) and homicides, for which generating synthetic estimates may be more complicated; (b) the sample of the CSEW consists of adults aged 16 or more, and thus it may be difficult to accurately generate crimes faced by individuals younger than 16 years; and (c) the census is only conducted every 10 years and generating periodic synthetic populations to estimate crime will require the implementation of novel techniques (e.g., spatial microsimulation models; Morris and Clark 2017 ). Future research will also explore the use of other individual and contextual variables recorded in the census and other data sources to further improve the precision of synthetic crime data. Moreover, this approach could be applied to other urban areas with available local crime surveys (e.g., Islington Crime Survey, Metropolitan Police Public Attitudes Survey) which would allow for an empirical evaluation of synthetic crime data generated in each local area.

Those who advocate the need for documenting and explaining micro-level community differences in crime have well-sustained arguments to claim that aggregating crimes at fine-grained levels of spatial analysis allows for better explanations of crime, and more targeted operational policing practices. To mention only a few of their arguments, Oberwittler and Wikström ( 2009 ) show that between-neighborhood crime variance and the statistical power of research outputs increase when smaller units of analysis are used; Steenbeek and Weisburd ( 2016 ) show that most temporal variability in crimes known to police can be attributed to micro-scales; Braga et al. ( 2018 ) show that increasing police control in high-crime-density areas reduces the overall prevalence and incidence of crimes; and Weisburd et al. ( 2012 ) argue that the social systems relevant to understanding the crime problem concentrate in small units of geography. It is not our intention to dismiss the merits of micro-level geographic crime analysis, nor do we directly assess whether the claims made by the advocates of micro-level mapping remain verifiable when analyzing unbiased datasets of crime (this is, perhaps, an area for future research). That said, the results reported in this paper serve to raise awareness about an important shortcoming of micro-level crime analysis. There is a clear need for academics and police administrations to evaluate whether crime rates are associated with conditions external to victimization. In particular, there is a need to make this evaluation with consideration for the spatial scale being used (Ramos et al. 2020 ). The potential sources of bias in police-recorded crime data should always be investigated and acknowledged with this in mind. Further efforts might focus on developing techniques which mitigate against these sources of bias to ensure that geographic crime analysis remains an effective tool in understanding and tackling the crime problem.

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The authors would like to thank Reka Solymosi for comments that greatly improved the manuscript.

This work is supported by the Campion Grant of the Manchester Statistical Society (project title: “Mapping the bias of police records”).

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To estimate whether each crime took place in the victims’ local area or somewhere else, we follow the same procedure as in step 3. First, we estimate a logistic regression model of crimes happening in the local area (as opposed to crimes happening elsewhere) from the CSEW dataset of crimes. We use the same individual independent variables as above (see model results in Table 8 ). Second, we estimate whether each simulated crime took place in the resident’s local area or somewhere else following a Bernoulli distribution from the regression coefficient estimates presented in Table 8 and the independent variables simulated in Step 1. For example, to estimate whether vehicle crime j suffered by person i took place in local area, denoted by AVehi ji , we compute:

where \( {\hat{\beta}}_p \) is the regression model coefficient estimate for a p independent variable.

Then, we remove all those offences that did not take place in the local area from our synthetic dataset of crimes.

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Buil-Gil, D., Moretti, A. & Langton, S.H. The accuracy of crime statistics: assessing the impact of police data bias on geographic crime analysis. J Exp Criminol 18 , 515–541 (2022). https://doi.org/10.1007/s11292-021-09457-y

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How Can Crime Analysis Help Police Reduce Crime?

by Dr. Laura Wyckoff, Bureau of Justice Assistance Fellow

Focusing resources on high-crime places, high-rate offenders, and repeat victims can help police effectively reduce crime in their communities. Doing so reinforces the notion that the application of data-driven strategies, such as hotspots policing, problem-oriented policing, and intelligence-led policing, work. Police must know when, where, and how to focus limited resources, as well as how to evaluate the effectiveness of their strategies. Sound crime analysis is paramount to this success.

What is crime analysis exactly? Crime analysis is not simply crime counts or the change in crime counts—that is just information about crime and not an analysis of crime. Crime analysis is a deep examination of the relationships between the different criminogenic factors (e.g., time, place, socio-demographics) surrounding crime or disorder that helps us understand why it occurs. Sound crime analysis diagnoses problems so a response may be tailored to cure it, or reduce the frequency and severity of such problems.

Data-driven policing and associated crime analysis are still in their infancy and are not typically integrated into the organizational culture as well as traditional policing strategies. Many agencies are still not aware of the advantages of an effective crime analysis unit, and others may not have the resources or knowledge to effectively integrate one. Of those that do employ crime analysis, many may not fully understand or accept this approach, or use it to its potential.

Additionally, police command staff typically are not analysts, so they may be unaware of how to guide this work to provide “actionable” crime analysis products that can be helpful for crime reduction efforts. At the same time, analysts are usually not police officers and may not be aware of how police respond to crime problems (both tactically and strategically), or what types of products will be most useful.

To be more effective at combating crime using data-driven strategies, we need to overcome these barriers and knowledge gaps. That is why the Bureau of Justice Assistance (BJA) established the Crime Analysis on Demand initiative. This initiative has a number of training and technical assistance opportunities focused on increasing crime analysis capacity in agencies across the nation. BJA’s National Training and Technical Assistance Center (NTTAC) is providing police agencies access to crime analysis experts that provide recommendations, training, and technical assistance to help agencies improve their application of crime analysis.

Additionally, the Police Foundation’s recent Crime Mapping and Analysis News publication provides a synopsis of the different services offered through this initiative. Other resources for crime analysis can be found on the International Association of Crime Analysts and the International Association of Law Enforcement Intelligence Analysts’ websites.

Dr. Laura Wyckoff is the Crime Analysis Fellow for the Bureau of Justice Assistance (BJA), where she assists in guiding BJA and the field in crime analysis best practices.

BJA NTTAC has a training and technical assistance (TTA) initiative focused on Crime Analysis on Demand and is offering TTA resources that will help police department leaders assess their needs related to crime analysis. If you know a police agency that would benefit from TTA or if you are a police agency with a critical need, click here to complete the online TTA request form. If you are interested in authoring a TTA Today blog post on the work of your organization or jurisdiction, please email us at [email protected] .

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In This Article Expand or collapse the "in this article" section Contextual Analysis of Crime

Introduction, general overviews.

• Neighborhood Context and Delinquency
• School Context and Delinquency
• Individual Risk-Factors and Neighborhood Context
• Individual Risk-Factors and School Context
• Situational Opportunity, Unstructured Socialization, and Delinquency
• Statistical Modeling
• Measuring “Context”

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• Community Change and Crime
• Community Disadvantage and Crime
• Land Use and Crime
• Social Disorganization
• Social Ecology of Crime

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Contextual Analysis of Crime by Matt Vogel , Brittany Jaecques LAST REVIEWED: 26 May 2016 LAST MODIFIED: 26 May 2016 DOI: 10.1093/obo/9780195396607-0200

Criminologists have a long-standing interest in the relationship between community characteristics and crime. Much of this research has focused on how the social processes at work within neighborhoods influence aggregate rates of crime and delinquency. When most people think of neighborhoods and crime, it is usually this broad “neighborhood effects” literature. A second, albeit less common approach to studying neighborhood influences in criminology has been to examine how neighborhood characteristics affect individual behaviors. This is commonly referred to as “contextual analyses” or “contextual effects” research. It is worth noting that social context can refer to any social environment external to the individual, including families, peer groups, or schools. In this sense, many criminological perspectives point to the role of contextual factors in crime causation. However, the term “contextual effect” is most often used to describe how community characteristics, such as economic disadvantage, informal social control, and collective efficacy are related to individual variation in offending. In recent years, contextual studies have expanded to (1) consider the role of other social contexts, particularly schools, in crime causation and (2) examine how contextual factors mitigate or exacerbate the associations between individual risk-factors and offending. The broader criminological research on neighborhood effects, as well as some of the recent research examining neighborhood effects on individual behavior, has been succinctly summarized in earlier articles in this Modules (for instance, see “ Situational Action Theory ” and “ Social Ecology of Crime ”). Where appropriate, this article refers the reader to these summaries for a more comprehensive treatment of the subtopics covered here. In an effort to avoid substantial overlap, this article instead focuses on what the authors see as the most pressing developments in the study of contextual influences on crime and delinquency––namely, the increasing focus on school context, the growing person-context perspective, and methodological considerations unique to this area of research.

Unlike some of the other topics covered in this series, the study of contextual effects does not have the same coherent, canonical set of references to which scholars customarily refer. There are several classic texts on communities and crime that underscore the importance of social context in crime causation. The chapter Bursik and Grasmick 1996 provides a detailed overview of these earlier works. Liska 1990 highlights the importance of both aggregate dependent variables and contextual causal variables in the field, explaining that while these variables may explain only a small percentage of the variance in offending, they contribute significantly to understanding macro-micro linkages in theory. More contemporary works, such as Wikström and Sampson 2003 , Wikström 2004 , and Zimmerman and Messner 2012 , help elucidate the mechanisms linking broader social environments with variation in individual behavior. Likewise, Leventhal and Brooks-Gunn 2000 , which focuses on neighborhood context and child well-being, further emphasizes the enduring importance of neighborhoods on a variety of psychosocial domains, above and beyond criminal behavior. Kubrin and Weitzer 2003 and Sampson, et al. 2002 review the state of neighborhood studies more generally and provide roadmaps for future work in this area. Finally, Gottfredson 2001 provides a comprehensive overview of school context and delinquency, laying the groundwork for more recent analyses examining the influence of school context on student behavior.

Bursik, Robert J., and Harold Grasmick. 1996. Use of contextual analysis in models of criminal behavior. In Delinquency and crime: Current theories . Edited by J. David Hawkings, 236–267. New York: Cambridge Univ. Press.

This chapter provides a detailed overview of the unique theoretical and methodological issues that arise in the contextual analysis of crime. The authors focus specifically on issues related to choosing appropriate aggregations of “context,” measuring features of these contexts, and appropriately modeling individual and contextual process simultaneously.

Gottfredson, Denise C. 2001. Schools and delinquency . New York: Cambridge Univ. Press.

Provides a comprehensive review of the research on schools and delinquency. Identifies a number of challenges to school-based research (including the need for person-context research) and discusses potential avenues for school-based delinquency prevention.

Kubrin, Charis, and Ronald Weitzer. 2003. New directions in social disorganization research. Journal of Research in Crime and Delinquency 40:374–402.

DOI: 10.1177/0022427803256238

This article “takes stock” of the literature on social disorganization and crime. The authors provide a comprehensive overview of neighborhood-effects research and identify several notable gaps in the literature. In particular, their discussion of neighborhood effects on individual outcomes and the issues with measuring spatial processes remain relevant today.

Leventhal, Tama, and Jeanne Brooks-Gunn. 2000. The neighborhoods they live in: The effects of neighborhood residence on child and adolescent outcomes. Psychological Bulletin 126:309–337.

DOI: 10.1037/0033-2909.126.2.309

Provides a review of the research on neighborhood effects on child outcomes. Topics include methodological issues, theoretical implications, and key findings regarding the relationship between neighborhoods and outcomes.

Liska, Allen E. 1990. The significance of aggregate dependent variables and contextual independent variables for linking macro and micro theories. Social Psychology Quarterly 292–301.

DOI: 10.2307/2786735

The value of aggregate-dependent variables and contextual-causal variables should not be judged solely by the amount of variance they explain, but by their theoretical implications. Aggregate dependent variables are important as they are properties of social units and capture patterns and relationships that individual level factors do not.

Sampson, Robert J., Jeffrey D. Morenoff, and Thomas Gannon-Rowley. 2002. Assessing “Neighborhood effects”: Social processes and new directions in research. Annual Review of Sociology 28:443–478.

DOI: 10.1146/annurev.soc.28.110601.141114

Reviews the literature of neighborhood effects from the mid-1990s to 2001, and discusses methodological issues in the area, particularly selection bias. Final comments address directions for future research and strategies that may prove useful.

Wikström, Per Olaf. 2004. Crime as alternative: Towards a cross-level situational action theory of crime causation. In Beyond empiricism: Institutions and intentions in the study of crime, advances in criminological theory . Edited by J. McCord, 1–38. New Brunswick, NJ: Transaction.

This chapter further elucidates the mechanisms through which individual risk factors (e.g., morality, low self-control) interact with broader environmental factors leading to criminogenic behavior settings (temptations, provocations, weak deterrence) to influence offending.

Wikström, Per Olaf H., and Robert J. Sampson. 2003. Social mechanisms of community influences on crime and pathways in criminality. In The causes of conduct disorder and serious juvenile delinquency . Edited by B. Lahey, T. Moffitt, and A. Caspi, 118–148. New York: Guilford.

A comprehensive overview of community context and crime. Provides a detailed description of the pathways through which neighborhoods influence behavior. The authors demarcate and situate influences and encourage researchers to focus on (1) measurement, (2) mechanisms, and (3) the interactions between individual and contextual risk factors.

Zimmerman, Gregory M., and Steven F. Messner. 2012. Person-in-context: Insights and issues in research on neighborhoods and crime. In The future of criminology . Edited by Brandon C. Welsh and Rolf Loeber, 70–78. New York: Oxford Univ. Press.

DOI: 10.1093/acprof:oso/9780199917938.003.0009

Provides a succinct summary of person-context research in criminology and catalogues findings from recent research examining the interactions between neighborhood characteristics, individual risk-factors, and crime.

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3 3 The Theoretical Importance of Place in Crime Prevention

• Published: September 2010
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This chapter develops the theoretical basis for policing crime hot spots by exploring more closely our understanding of the relationship between place and crime. A series of empirical studies suggests that crime is concentrated at very small geographic units of analysis, such as street segments or small groups of street blocks. While the larger worlds of communities and neighborhood have been the primary focus of crime prevention theory and research in the past, there is a growing recognition of the importance of shifting that focus to the small worlds in which the attributes of place and its routine activities combine to develop crime events. This chapter presents complementary theoretical perspectives that influence our understanding of the importance of place in criminology. It also presents findings from criminological research that identifies facilities, such as taverns and convenience stores, and site features, such as easy access, the presence of valuable goods, and a lack of guardianship, in influencing the presence of criminal opportunities at particular places.

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