방법 비교

	Randomized Controlled Trial in Criminology	Crime Hot Spot Analysis
분야	Criminology	Criminology
계열	Process / pipeline	Process / pipeline
기원 연도	1995	1995
창시자	Lawrence W. Sherman & David Weisburd	Lawrence Sherman & David Weisburd (policing); Arthur Getis & J. Keith Ord (statistic)
유형	Experimental impact evaluation of justice interventions	Spatial cluster detection for crime concentration
원전	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.	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.
별칭	Criminological Field Experiment, Experimental Criminology Trial, Place-Based Randomized Trial, Justice RCT	Hot Spot Mapping, Crime Hotspot Detection, Getis-Ord Gi* Crime Analysis, Spatial Cluster Analysis of Crime
관련	4	4
요약	A randomized controlled trial (RCT) in criminology evaluates a justice intervention — such as hot-spots policing, a deterrence message, or a reentry program — by randomly assigning units (places, people, or cases) to receive the intervention or to serve as controls. Because assignment is by chance, treatment and control groups are statistically equivalent at baseline, so any later difference in crime or reoffending can be attributed to the intervention rather than to selection. Sherman and Weisburd's 1995 Minneapolis hot-spots patrol experiment helped establish the design as the gold standard of experimental criminology.	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.