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| Crime Displacement and Diffusion Analysis× | Crime Hot Spot Analysis× | |
|---|---|---|
| Област | Criminology | Criminology |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 2003 | 1995 |
| Създател≠ | Kate Bowers & Shane Johnson | Lawrence Sherman & David Weisburd (policing); Arthur Getis & J. Keith Ord (statistic) |
| Тип≠ | Quasi-experimental spatial impact assessment of crime prevention | Spatial cluster detection for crime concentration |
| Основополагащ източник≠ | Bowers, K. J., & Johnson, S. D. (2003). Measuring the geographical displacement and diffusion of benefit effects of crime prevention activity. Journal of Quantitative Criminology, 19(3), 275–301. 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 ↗ |
| Други названия | Crime Displacement Analysis, Diffusion of Benefits Analysis, Weighted Displacement Quotient, WDQ Analysis | Hot Spot Mapping, Crime Hotspot Detection, Getis-Ord Gi* Crime Analysis, Spatial Cluster Analysis of Crime |
| Свързани | 4 | 4 |
| Резюме≠ | Displacement and diffusion analysis evaluates what happens around a crime-prevention intervention: does crime simply move to nearby areas, times, or targets (displacement), or do the benefits spill over so that crime also falls in surrounding untreated areas (diffusion of benefits)? Bowers and Johnson's weighted displacement quotient (WDQ) provides a simple, widely used metric that compares pre/post crime change in a target area, a surrounding buffer, and a control area. | 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. |
| ScholarGateНабор от данни ↗ |
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