Predictive crime mapping

Abstract

Geographic Information Systems (GIS) have emerged as a key tool in intelligence-led policing and spatial predictions of crime are being used by many police services to reduce crime. Break and entries (BNEs) are one of the most patterned and predictable crime types, and may be particularly amendable to predictive crime mapping. A pilot project was conducted to spatially predict BNEs and property crime in Vancouver, Canada. Using detailed data collected by the Vancouver Police Department on where and when observed crimes occur, the statistical model was able to predict future BNEs for residential and commercial locations. Ideally implemented within a mobile GIS, the automated model provides continually updated predictive maps and may assist patrol units in self-deployment decisions. Future research is required to overcome computational and statistical limitations, and to preform model validation.

Interactive Maps of Social Problems and Security Threats Illustrated with an Example of Solutions Currently Used in Upper Silesia

“…It is based on Figure 2. Widely adopted throughout the US after implementation in New York, CompStat has been described as a paradigm shift in operational policing [24], offering the possibility of presenting different data: (a) crimes per 1000 residents by precinct; (b) crimes per location; (c) concentration of crimes; (d) burglaries per location.…”

“…The first use of computerized crime mapping in applied crime analysis most likely occurred in the mid-1960s in St. Louis [23]. Thus began a long process of evolution from mere crime mapping to crime forecasting [24]. A geographic information system (GIS), thanks to the use of geography and computer-generated maps as an interface for integrating and accessing massive amounts of location-based information, allows police to plan effectively for emergency response situations, determine mitigation priorities, analyze historical events, and predict future events.…”

Examining the Local Spatial Variability of Robberies in Saint Louis Using a Multi-Scale Methodology

“…Raster grids or fishnets provide equally distributed, micro-level geographic areas to spatially analyze crime and associated explanatory factors. Using raster grids in spatial crime analysis to create micro-level geographic scales is a common and accepted methodology (Caplan et al 2011(Caplan et al , 2012Kennedy et al 2010Kennedy et al , 2015Barnum et al 2017;Hart and Zandbergen 2014;He et al 2018;Fitterer et al 2015;Stucky and Ottensmann 2009;Dmowska and Stepinski 2014;Maimaitijiang et al 2015;Patterson and Doyle 2009;Ratcliffe and McCullagh 1999;Drawve et al 2016). Grid-based methods offer the ability to accurately capture the spatial crime environment considering very small geographic areas while also addressing the impacts associated with irregular polygons, such as the Modifiable Areal Unit Problem (MAUP) (Caplan et al 2012) or topology constraints associated with defined geographies when utilizing spatial statistics (Grubesic 2006).…”

“…The purpose of the current research is to use ESDA techniques to explore the locations of spatial clustering of robbery rates and to observe the global and local relationships of race and neighborhood stability with robbery rates, in the City of St. Louis, Missouri. Specifically, the American Community Survey (ACS) 2012 to 2016 five-year estimates data were collected at both the census tract and block group level and disaggregated to the 250-and 500-m raster grid level (Caplan et al 2011(Caplan et al , 2012Kennedy et al 2010Kennedy et al , 2015Barnum et al 2017;Hart and Zandbergen 2014;He et al 2018;Fitterer et al 2015;Stucky and Ottensmann 2009;Dmowska and Stepinski 2014;Maimaitijiang et al 2015;Patterson and Doyle 2009;Ratcliffe and McCullagh 1999;Drawve et al 2016). With data standardized and spatial autocorrelation confirmed, the Spatial Lag Model (SLM) was applied to determine which independent variables had a global, significant relationship with robbery rates.…”

Complex-Network Tools to Understand the Behavior of Criminality in Urban Areas

“…Other works include the proposals of Galbrun et al [8], who focus on crime mapping to estimate the probability of a crime occurrence within a street segment; of Fitterer et al [9], who use a statistical model to predict crimes in the city of Vancouver; and of Bogomolov et al [10], who predict crime hotspots in London by using mobile data. Our work differs from theirs because (i) we consider crimes within a more complete and up-to-date period; (ii) we map crimes directly, i.e.…”