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| Kernel Density Crime Mapping× | Crime Hot Spot Analysis× | |
|---|---|---|
| Valdkond | Criminology | Criminology |
| Perekond | Process / pipeline | Process / pipeline |
| Tekkeaasta≠ | 2008 | 1995 |
| Looja≠ | Bernard Silverman (KDE); Spencer Chainey (crime mapping application) | Lawrence Sherman & David Weisburd (policing); Arthur Getis & J. Keith Ord (statistic) |
| Tüüp≠ | Nonparametric density estimation for crime surfaces | Spatial cluster detection for crime concentration |
| Algallikas≠ | 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 ↗ | Sherman, L. W., & Weisburd, D. (1995). General deterrent effects of police patrol in crime "hot spots": A randomized, controlled trial. Justice Quarterly, 12(4), 625–648. DOI ↗ |
| Rööpnimetused | KDE Crime Mapping, Crime Density Surface Mapping, Hot Spot Density Mapping, Kernel Smoothing of Crime Events | Hot Spot Mapping, Crime Hotspot Detection, Getis-Ord Gi* Crime Analysis, Spatial Cluster Analysis of Crime |
| Seotud | 4 | 4 |
| Kokkuvõte≠ | 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. | Crime hot spot analysis identifies the places where crime concentrates far more than chance — the small number of street segments, blocks, or addresses that account for a large share of incidents. Building on Sherman and Weisburd's landmark demonstration that crime clusters tightly in space and that patrolling those clusters deters offending, the method uses spatial statistics such as the Getis-Ord Gi* local statistic to separate genuine, statistically significant clusters from random noise and to classify each place as a hot spot, a cold spot, or neither. |
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