Trivariate Kernel Density Estimation of Spatiotemporal Crime Events with Case Study for Lithuania

Michael Govorov (), Giedrė Beconytė () and Gennady Gienko
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Michael Govorov: Department of Geography, Vancouver Island University, Nanaimo, BC V9R 5S5, Canada
Giedrė Beconytė: Institute of Geosciences, Vilnius University, LT-10223 Vilnius, Lithuania
Gennady Gienko: Department of Geomatics, University of Alaska Anchorage, Anchorage, AK 99508, USA

Sustainability, 2023, vol. 15, issue 11, 1-17

Abstract: The paper presents the results of the investigation of the applicability of spatiotemporal kernel density estimation (KDE) methods for density mapping of violent crime in Lithuania. Spatiotemporal crime research helps to understand and control specific types of crime, thereby contributing to Sustainable Development Goals. The target dataset contained 135,989 records of the events registered by the police of Lithuania from 2015–2018 that were classified as violent. The research focused on choosing appropriate KDE functions and their parameters for modeling the spatiotemporal point pattern of this particular type of crime. The aim was to estimate density, mass, and intensity function(s) so that they can be used in further confirmatory spatial modeling. The application-driven objective was to obtain reliable and practically interpretable KDE surfaces of crime events. Several options for improving and extending the investigated KDE methods are demonstrated.

Keywords: crime events; spatial point pattern; probability mass and density functions; bandwidth selectors; relative risk estimator (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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