Simulating Crime Events and Crime Patterns in a RA/CA Model

This chapter presents an innovative approach for simulating crime events and crime patterns. The theoretical basis of the crime simulation model is routine activities (RA) theory. Offenders, targets and crime places, the three basic elements of routine activities, are modeled as individual agents. The properties and behaviors of these agents change in space and time. The interactions of these three types of agents are modeled in a cellular automaton (CA). Tension, measuring the psychological impact of crime events to human beings, is the state variable of the CA. The model, after being calibrated by using a real crime data set in Cincinnati, is able to generate crime patterns similar to real patterns. Results from experimental runs of the model conform to known criminology theories. This type of RA/CA simulation model has the potential of being used to test new criminology theories and hypotheses.

Integrating GIS, GPS and MIS on the Web

Computer applications for conducting complex spatial analysis of crime data are widely used by law enforcement agencies. By combining sophisticated geographic information systems with global positioning satellite tracking devices, a new tool is emerging that will remove the criminal anonymity of probationers, parolees and offenders on pretrial release. Every year, an ever-increasing number of offenders are set free on either probation or parole within our nation’s communities. As the number of offenders on our streets grows, the need for the criminal justice system to hold these offenders accountable and exert some level of control also increases. Florida’s Electronic Monitoring Protection and Crime Tracking (EMPACT) project is breaking new ground in an effort to use technology as an effective way to remove the anonymity of crime. Through the automated correlation of GPS tracking data and local crime incident data, participating criminal justice agencies are able to determine if a tracked offender was at the scene of a crime when it occurred. In addition, because EMPACT uses a Web-based interface, participating agencies also have access to each other’s data. This creates a crime-mapping environment where crime analysts and investigators have the opportunity to evaluate, at the click of a button, multi-jurisdictional crime patterns and offender track data.

Geographic Surveillance of Crime Frequencies in Small Areas

In this chapter, I describe a system for monitoring crime frequencies for a set of small areas. The objective is to detect as quickly as possible any increase in any area’s crime frequency, relative to a specified expected frequency. The system uses a cumulative sum approach, cumulating differences between the observed and expected frequencies of crime in each area. The approach is illustrated using 1996 burglary data from Buffalo, which is available by census tract. Computer code associated with the geosurveillance program is provided in the appendix.

Garbage In, Garbage Out

Advances in computing technology and analytical techniques have given crime analysts increasingly powerful toolboxes with which to unlock the spatial patterns and processes of crime. However, the utility of such tools is still bounded by the “garbage in, garbage out,” maxim, whereby analytical output is only as reliable as the analytical input. Therefore, this chapter reviews some of the sources of spatial data inaccuracy that must be considered when analyzing crime. Given the prevalence of street addresses as a spatial location identifier for crime events, particular attention is given to the accuracy and optimum parameters for geographically referencing address data. Example data drawn from burglary records in the city of Wollongong, Australia, illustrate the significance of the issues and the impact that poor address management can have on the analysis of crime. The chapter emphasizes the practical, by outlining address correction options and summarizing recent research that identifies optimum settings for geocoding software tools.

Measuring Crime in and around Public Housing Using GIS

For many people, the phrase “public housing” conjures up images of serious violent crime. However, the neighborhood surrounding public housing may be a greater factor in crime than the housing itself. Because most police departments do not routinely keep statistics on small parcels of land like public housing developments or neighborhoods, measuring the incidence of crime in public housing has proved difficult. Consequently, there is little hard evidence with respect to whether public housing is more or less crime-ridden than the neighborhoods that surround it. This chapter explores the application of geographic information systems (GIS) technology in measuring reported crime levels in and around public housing developments. GIS technology was used to extract crime counts from police data bases of reported incidents for (1) public housing developments and (2) the surrounding neighborhoods. Rates of reported Part I crimes in public housing developments are compared with those in the surrounding neighborhoods and in the respective municipal jurisdictions. Odds ratios are used to compare the risk of victimization in public housing with that in the respective neighborhood and municipal catchment zones. The GIS-based analysis of reported crime in and around public housing communities reveals that risk of falling victim to aggravated assault in public housing communities is much higher than in the surrounding neighborhoods or in the parent jurisdictions as whole. Conversely, risk of property crimes such as burglary, larceny and car theft appears to be much lower. These crime patterns are discussed in the context of routine activity theory.

Web GIS for Mapping Community Crime Rates

This chapter describes a prototype Web geographic information system (GIS) and spatial model application for mapping person crime rates in Brisbane, Australia. Our application, which integrates GIS functionality, a clustering model, client/server technology and the Internet, can generate useful documents such as maps and tables to examine and present crime patterns in space and time. Our chapter also demonstrates the usefulness and appeal of the Web GIS application as an information dissemination and spatial data analysis tool for promoting public awareness of social conditions. This chapter argues that Web-based data access is a better approach to delivering large volumes of crime data and geographical information to the public. We expect that police, community workers and citizens could utilize the application and associated maps to facilitate and enhance crime prevention activities. We note, however, that further development of Web-based GIS applications need to answer a number of pertinent questions regarding system maintenance, data integrity and neighborhood crime prevention.

Integrating GIS and Maximal Covering Models to Determine Optimal Police Patrol Areas

This chapter presents a new method for determining the most efficient spatial distribution of police patrols in a metropolitan region, termed the police patrol area covering (PPAC) model. This method employs inputs from geographic information systems (GIS) data layers, analyzes that data through an optimal covering model formulation, and provides alternative optimal solutions for presentation to decision makers. The goal of this research is to increase the level of police service by finding more efficient spatial allocations of the available law enforcement resources. Extensions to the model that incorporate variations in the priority of calls for service based on the type of crime being committed, and the need for an equitable distribution of workload among police officers are discussed. Examples of the inputs from – and outputs to – GIS are provided through a pilot study of the city of Dallas, Texas.

Application of Tracking Signals to Detect Time Series Pattern Changes in Crime Mapping Systems

Tracking signals are widely used in industry to monitor inventory and sales demand. These signals automatically and quickly detect departures in product demand, such as step jumps and outliers, from “business-as-usual”. This chapter explores the application of tracking signals for use in crime mapping to automatically identify areas that are experiencing changes in crime patterns and thus may need police intervention. Detecting such changes through visual examination of time series plots, while effective, creates too large a workload for crime analysts, easily on the order of 1,000 time series per month for medium-sized cities. We demonstrate the so-called smoothed-error-term tracking signal and carry out an exploratory validation on 10 grid cells for Pittsburgh, Pennsylvania. Underlying the tracking signal is an extrapolative forecast that serves as the counterfactual basis of comparison. The approach to validation is based on the assumption that we wish tracking signal behavior to match decisions made by crime analysts on identifying crime pattern changes. We present tracking signals in the context of crime early warning systems that provide wide-area scanning for crime pattern changes and detailed drill-down maps for crime analysis. Based on preliminary results, the tracking signal is a promising tool for crime analysts.

Constructing Geographic Areas for Analysis of Homicide in Small Populations

The rate estimates for rare events like homicide in small populations are very susceptible to data errors, and thus compromise the validity of inferences. This chapter discusses two geographic information systems (GIS)-based methods for constructing geographic areas with sufficiently large base populations to permit reliable estimates of homicide rates to be obtained. One is the spatial order method, and the other is the ISD method (after the Information & Statistics Division of the Health Service in Scotland, where it was devised). Both methods construct new analysis areas based on spatial proximity of basic units. For demonstration, the methods are applied to testing the herding-culture-of-honor hypothesis proposed by Nisbett and Reaves, and the result shows that the herding-culture-of-honor proposition is merely an artifact of unreliable estimate of homicide rates. The methodology, in general, is applicable to analysis of any rates with small base populations.

Remote Sensing and Spatial Statistics as Tools in Crime Analysis

This chapter explores the feasibility and utility of using aerial photography or remotely sensed satellite imagery to identify geographic or “place” features that may be associated with criminal activity. It assesses whether or not variables derived from satellite images can provide surrogate relationships between land use and crime. A review of the remote sensing literature suggests two basic approaches to the use of remotely sensed images in law enforcement: (1) tactical; and (2) analytical. The tactical approach uses the imagery as a background to the maps and other spatial information that an officer on the beat might have as he or she is investigating a crime or emergency situation. The analytical approach uses the remotely sensed images to create new variables that may serve as proxies for the risk of crime in particular locations. In this study we employ the analytical approach to the use of remotely sensed images, classifying images according to the presence or absence of vegetation within a pixel, as well as the classification of specific urban attributes, such as parking lots. We also employ spatial statistics to quantify the relationship between features of the images and crime events on the ground, and these analyses may be particularly useful as input to policy decisions about policing within the community.