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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 121-135 |
| Number of pages | 15 |
| Journal | Police Practice and Research |
| Volume | 16 |
| Issue number | 2 |
| DOIs | |
| State | Published - Mar 4 2015 |
| Externally published | Yes |
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Keywords
- break and entries (BNEs)
- Geographic Information Systems (GIS)
- intelligence led policing
- predictive mapping
- statistical modeling
- Vancouver
ASJC Scopus subject areas
- Law
- Social Sciences (miscellaneous)
Cite this
Predictive crime mapping. / Fitterer, J.; Nelson, Trisalyn; Nathoo, F.
In: Police Practice and Research, Vol. 16, No. 2, 04.03.2015, p. 121-135.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Predictive crime mapping
AU - Fitterer, J.
AU - Nelson, Trisalyn
AU - Nathoo, F.
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 - break and entries (BNEs)
KW - Geographic Information Systems (GIS)
KW - intelligence led policing
KW - predictive mapping
KW - statistical modeling
KW - Vancouver
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UR - http://www.scopus.com/inward/citedby.url?scp=84921024901&partnerID=8YFLogxK
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 -