Abstract
As part of a contemporary look at the intersection of geography and crime, the current chapter offers a new conceptual definition of predictive policing, an overview of some of the most commonly used methods to identify crime “hot spots” retrospectively, prospective hot spot analysis, and predictive policing analytics. Police and researchers have mapped crime and disorder for decades, consistently finding that these incidents clusters in areas commonly referred to as hot spots. Understanding where and when crime and disorder cluster, both spatially and temporally, provides vital information necessary for community leaders to design and implement effective crime-reduction strategies and community-safety initiatives. Until recently, however, researchers and analysts have operated retrospectively—using incident location information to describe historical crime patterns through the use of data visualization techniques or to design reactionary policing strategies based on the assumption that historical crime patterns are reliable indicators of future problem areas. Unlike traditional crime hot spot analysis, predictive analytics identify patterns in crime data that relate to criminal activity in the near future. The aim of this approach is to exploit patterns identified in data dynamically and to prevent crime through proactive resource allocation. This approach is distinct from how crime analysis and intelligence-led policing has historically been conducted.
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Notes
- 1.
In KDE, bandwidths are defined as either “adaptive” or “fixed interval.” Adaptive bandwidths are typically used when a sample of point locations are used in lieu of all points located within the study area, which is generally not the case in crime analysis. If adaptive bandwidths are used, the minimum sample size is an additional parameter that must be defined. Fixed interval bandwidths, on the other hand, are used when the entire population of events is analyzed (e.g., all crimes within a given timeframe). When a fixed interval bandwidth is selected, the size of the bandwidth must be defined (Brunsdon 1995).
- 2.
Instead of asking users to define the actual cell size, most GIS applications that include KDE as an analytic method for identifying hot spots (e.g., ArcGIS and MapInfo) ask the user to indicate how many columns the grid overlay should be divided into. This input value is then used to calculate the actual size of each grid cell.
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Hart, T.C. (2020). Hot Spots of Crime: Methods and Predictive Analytics. In: Lersch, K., Chakraborty, J. (eds) Geographies of Behavioural Health, Crime, and Disorder. GeoJournal Library, vol 126. Springer, Cham. https://doi.org/10.1007/978-3-030-33467-3_5
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