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Crime prediction is not limited to predicting what individuals in which situations will turn to delinquency or criminality. In recent years, researchers have employed new technologies and data sources to identify the “where” and “when” of offending. Knowing that certain locations and times are prone to criminal activity has led to the concepts of “hot spots” and “burning times.” Hot spots are clusters of crimes in space; burning times are concentrations of crime at specific repeated moments (Brantingham and Brantingham 1999).

Although the observation that criminal activity is concentrated at some locations and not others is not new, past research focused on criminals' residences as a catalyst for the occurrence of crime (Brantingham and Brantingham 1999)—this despite the fact that crime is often concentrated in other areas. By the mid 1980s, with the computerization of police records and the development of computerbased mapping systems, the geographic mapping of the distribution of reported crimes led to the discovery of concentrations of criminal activity in discrete areas—hot spots.

According to routine activity theory, crime does not occur randomly but, rather, is produced by the convergence in time and space of motivated offenders, suitable targets, and the absence of capable guardians (Koper 1995). This convergence in turn is affected by daily activity, traffic patterns, community organization, and other factors. Sherman et al. (1989) tested the basic premise of routine activity theory—that crime is not randomly distributed—by providing a more complete description of the variation of crime across places. Their assessment of police data in Minneapolis revealed a substantial concentration of all police calls in relatively few hot spots. Over a one-year period, 50.4 percent of all calls to the police came from 3.3 percent of places; all robbery calls were located at 2.2 percent of places, all rape calls at 1.2 percent of places, and all auto theft calls at 2.7 percent of places. Conversely, 95 percent of the city was free from any of these crimes. The concentration of calls in these hot spots was significantly greater than would occur by chance. Moreover, hot spots were not limited to crime in public places; comparable patterns were found for many domestic calls as well. Sherman et al. report that all domestic disturbances were recorded at 9 percent of places.

Identification

There are three ways to identify criminal hot spots. Visual inspection involves the detection of high crime addresses through police calls and mapping; statistical identification uses mathematical tools to identify small areas with disproportionately high crime rates; and theoretical prediction draws on what is known about how environment and routine shape the probability that crime will occur in some locations and not others. Brantingham and Brantingham (1995) suggest that the formation of hot spots can sometimes be predicted by relying on concepts from environmental criminology, where crime is seen as the result of multistaged decisions. That is, the potential that criminal activity will occur at a given location depends on the convergence of several factors, poverty and heavy traffic among them. This information makes it possible to identify crime generators (sites to which large numbers of people are attracted, e.g., shopping malls, housing estates, and parking lots) and crime attractors (sites that create criminal opportunities to which motivated offenders are drawn, e.g., ATMs) and predict the formation of crime hot spots.

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