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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Sources
- 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
How to cite this page
ScholarGate. (2026, June 22). Kernel Density Estimation for Crime Mapping. ScholarGate. https://scholargate.app/en/criminology/kernel-density-crime-mapping
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Crime Hot Spot AnalysisCriminology↔ compare
- Crime MappingCriminology↔ compare
- Risk Terrain Modeling (Criminology)Criminology↔ compare
- Space-Time Kernel Density EstimationSpatial analysis↔ compare