A spatio-temporal kernel density estimation framework for predictive crime hotspot mapping and evaluation

Predictive hotspot mapping plays a critical role in hotspot policing. Existing methods such as the popular kernel density estimation (KDE) do not consider the temporal dimension of crime. Building upon recent works in related fields, this article proposes a spatio-temporal framework for predictive h...

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Vydáno v:Applied geography (Sevenoaks) Ročník 99; s. 89 - 97
Hlavní autoři: Hu, Yujie, Wang, Fahui, Guin, Cecile, Zhu, Haojie
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.10.2018
Témata:
Baton rouge
case studies
crime
Crime hotspot prediction
Louisiana
Optimal bandwidth
Predictive accuracy index (PAI) curve
Residential burglary
Significance test
Spatio-temporal kernel density estimation (STKDE)
Louisiana
Significance test
Predictive accuracy index (PAI) curve
Spatio-temporal kernel density estimation (STKDE)
Crime hotspot prediction
Optimal bandwidth
Residential burglary
Baton rouge
ISSN:0143-6228, 1873-7730
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Shrnutí:Predictive hotspot mapping plays a critical role in hotspot policing. Existing methods such as the popular kernel density estimation (KDE) do not consider the temporal dimension of crime. Building upon recent works in related fields, this article proposes a spatio-temporal framework for predictive hotspot mapping and evaluation. Comparing to existing work in this scope, the proposed framework has four major features: (1) a spatio-temporal kernel density estimation (STKDE) method is applied to include the temporal component in predictive hotspot mapping, (2) a data-driven optimization technique, the likelihood cross-validation, is used to select the most appropriate bandwidths, (3) a statistical significance test is designed to filter out false positives in the density estimates, and (4) a new metric, the predictive accuracy index (PAI) curve, is proposed to evaluate predictive hotspots at multiple areal scales. The framework is illustrated in a case study of residential burglaries in Baton Rouge, Louisiana in 2011, and the results validate its utility. •Predictive hotspot mapping plays a critical role in hotspot policing.•A spatio-temporal kernel density estimation (STKDE) method is developed.•The likelihood cross-validation is used to select the best bandwidths in STKDE.•A statistical significance test filters out false hotspots.•The predictive accuracy index (PAI) curve evaluates predictive hotspots.•A case study examines residential burglaries in Baton Rouge, Louisiana in 2011.
Bibliografie:ObjectType-Article-1
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ISSN:0143-6228
1873-7730
DOI:10.1016/j.apgeog.2018.08.001