Σύγκριση μεθόδων

	Crime Displacement and Diffusion Analysis	Crime Hot Spot Analysis
Πεδίο	Criminology	Criminology
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2003	1995
Δημιουργός	Kate Bowers & Shane Johnson	Lawrence Sherman & David Weisburd (policing); Arthur Getis & J. Keith Ord (statistic)
Τύπος	Quasi-experimental spatial impact assessment of crime prevention	Spatial cluster detection for crime concentration
Θεμελιώδης πηγή	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.	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.
Εναλλακτικές ονομασίες	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
Συναφείς	4	4
Σύνοψη	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.