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Risk Terrain Modeling×Krimināllietu saistības analīze×
NozareKriminālistikaKriminālistika
SaimeProcess / pipelineProcess / pipeline
Izcelsmes gads20112002
AutorsJoel CaplanCraig Bennell
TipsGeographic information systems and crime science methodCrime science and offender profiling method
PirmavotsCaplan, J. M., Kennedy, L. W., & Miller, J. (2011). Risk terrain modeling: Brokering criminological theory and GIS methods for crime forecasting. Journal of Research and Practice in Criminal Justice, 17(1), 56-69. link ↗Bennell, C., Canter, D. V., & Alison, L. J. (2002). Linking commercial burglaries by modus operandi: Tests using regression and ROC analysis. Science and Justice, 42(3), 153-164. DOI ↗
Citi nosaukumienvironmental criminology, RTM analysis, crime risk mappingcase linkage, offender linking, serial crime attribution
Saistītās33
KopsavilkumsRisk Terrain Modeling (RTM) is a geospatial crime prediction method that identifies high-risk locations by analyzing environmental and geographic features that attract or facilitate crime. Developed by Joel Caplan, Lichen Kennedy, and James Miller in 2011, RTM bridges environmental criminology theory with geographic information systems (GIS) to create predictive risk maps. Unlike methods that predict offender location (e.g., geographic profiling), RTM predicts where crimes are likely to occur based on terrain characteristics, infrastructure, and social environmental factors.Crime linkage analysis is a forensic method that determines whether a series of crimes were committed by the same offender based on behavioral and modus operandi (MO) similarities. Developed systematically by Craig Bennell and colleagues in the early 2000s, crime linkage applies statistical and similarity-matching techniques to establish offender attribution. The method is essential in serial crime investigation, where establishing linkage enables consolidation of investigation resources, geographic profiling, and offender-focused surveillance.
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ScholarGateSalīdzināt metodes: Risk Terrain Modeling · Crime Linkage Analysis. Izgūts 2026-06-19 no https://scholargate.app/lv/compare