TY - JOUR
T1 - Predictive crime mapping
AU - Fitterer, J.
AU - Nelson, T. A.
AU - Nathoo, F.
N1 - Funding Information:
This work was support by NSERC Engage. Thanks to Francis Graf of Latitude Geographic Inc. and Ryan Prox of the Vancouver Police Division who provided direction, data, and input for this work. We would like to thank Susan Kinniburgh from the University of Victoria for help with model coding.
Publisher Copyright:
© 2014 Taylor & Francis.
PY - 2015/3/4
Y1 - 2015/3/4
N2 - 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.
AB - 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.
KW - Geographic Information Systems (GIS)
KW - Vancouver
KW - break and entries (BNEs)
KW - intelligence led policing
KW - predictive mapping
KW - statistical modeling
UR - http://www.scopus.com/inward/record.url?scp=84921024901&partnerID=8YFLogxK
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U2 - 10.1080/15614263.2014.972618
DO - 10.1080/15614263.2014.972618
M3 - Article
AN - SCOPUS:84921024901
VL - 16
SP - 121
EP - 135
JO - Police Practice and Research
JF - Police Practice and Research
SN - 1561-4263
IS - 2
ER -
