Mapping the Spatial Influence of Crime Correlates: A Comparison of Operationalization Schemes and Implications for Crime Analysis and Criminal Justice Practice
Joel M. Caplan
, Rutgers University
As with the articles in this issue, this introduction reflects the views of the authors and does not necessarily reflect the views of the U.S. Department of Housing and Urban Development.
Decades of criminological research have identified a variety of independent variables that correlate significantly with particular crime outcomes. Less is understood about how these correlates, or factors, of crime can be operationalized to maps in ways that best represent their spatial influences on the emergence of nearby crime events. A geographic information system (GIS) enables the exploration of spatial influence, which refers to the way in which features of a landscape affect places throughout the landscape. For example, empirical knowledge that bars correlate with locations of violent crimes can be mapped in several ways to show more or less crime-prone places, such as places with bars, places within certain distances from bars, or places with higher concentrations of bars. Rather than just a feature’s presence, its influence on space is important because context affects criminal behavior. GIS enables analysts to move beyond just creating maps of points that coexist with crime to creating visual narratives of how settings become conducive to crime. With growing use of spatial risk assessments and predictive modeling in the criminal justice community, operationalizing crime correlates to geographic units across a landscape is an important task that requires careful consideration. This article presents (1) a detailed discussion of the theoretical framework relevant to risk analysis and spatial influence and (2) three primary methods for operationalizing criminogenic features to a geographic map. One of these maps is included in a risk terrain model (RTM) along with three other spatially operationalized maps of different criminogenic features to produce a composite map of criminogenic contexts for shootings in Irvington, New Jersey. The RTM is then deconstructed to show how each spatially operationalized map layer adds to the overall predictive validity. Finally, the article demonstrates how producing an RTM of theoretically grounded operationalizations of spatial influence from many risk factors can be used as a control measure of environmental context when evaluating the spatial effect of place-based interventions on future crime events. Tending to the detail of mapping the spatial influence of crime correlates is particularly important and necessary for maximizing the reliability and validity of assessments of the likelihood of crime to occur at certain places within a study area.
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