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Kernel Density Crime Mapping

Kernel density crime mapping turns a scatter of geocoded crime points into a smooth, continuous surface that shows where incidents concentrate. Each event is spread out over a small neighborhood by a kernel function, and the overlapping contributions are summed across a fine grid so that areas with many nearby crimes glow as peaks. Chainey, Tompson, and Uhlig (2008) showed that, among common hot-spot mapping techniques, kernel density estimation is one of the most accurate at predicting where future crime will occur, which is why it became the default crime-mapping surface in policing.

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Kernel Density Estimation for Crime Mapping

Taxonomisk metodpost · process-pipeline / criminology

Chainey, S., Tompson, L., & Uhlig, S. (2008). The utility of hotspot mapping for predicting spatial patterns of crime. Security Journal, 21(1–2), 4–28. · DOI 10.1057/palgrave.sj.8350066
Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall. · ISBN 9780412246203

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Same method familyCrime Hot Spot Analysismachine-suggested · Relational suggestion, not evidence.Taxonomic bucketCrime Mappingmachine-suggested · Relational suggestion, not evidence.Same method familyRisk Terrain Modeling (Criminology)machine-suggested · Relational suggestion, not evidence.See alsoSpace-Time Kernel Density Estimationmachine-suggested · Relational suggestion, not evidence.