Using “Barriers” in Kernel Density Estimation to Improve the Predictive Accuracy of Crime Forecasts: A Case Study of Three Florida Cities

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dc.contributor.advisorDr. Timothy C. Hart,
dc.contributor.advisorDr. Gabriel R. Paez
dc.contributor.advisorDr. Chivon H. Fitch
dc.contributor.authorCoates, Kehara
dc.date.accessioned2022-11-11T13:43:52Z
dc.date.available2022-11-11T13:43:52Z
dc.date.issued2022-12
dc.description.abstractKernel density estimation (KDE) is one of the most popular crime hot spot mapping methods used to reduce and prevent crime. However, this technique does not consider where crime cannot occur within a study area when a crime risk surface is interpolated. Therefore, a knowledge gap exists as to how effective incorporating barriers into KDE analysis can be in producing more accurate prospective crime hot spot maps. Therefore, the current study investigated whether the predictive accuracy of crime forecasts based on KDE will improve when barriers to crime are incorporated into the analytic process.en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11868/3671
dc.language.isoen_USen_US
dc.publisherMSCC, The University of Tampaen_US
dc.subjectBarriersen_US
dc.subjectKernel Density Estimationen_US
dc.subjectPredictive Policingen_US
dc.subjectCrime Analysisen_US
dc.titleUsing “Barriers” in Kernel Density Estimation to Improve the Predictive Accuracy of Crime Forecasts: A Case Study of Three Florida Citiesen_US
dc.typeThesisen_US

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