  Predictive hotspot mapping plays a critical role in hotspot policing.
Existing methods such as the popular kernel density estimation (KDE) do not
consider the temporal dimension of crime. Building upon recent works in related
fields, this article proposes a spatio-temporal framework for predictive
hotspot mapping and evaluation. Comparing to existing work in this scope, the
proposed framework has four major features: (1) a spatio-temporal kernel
density estimation (STKDE) method is applied to include the temporal component
in predictive hotspot mapping, (2) a data-driven optimization technique, the
likelihood cross-validation, is used to select the most appropriate bandwidths,
(3) a statistical significance test is designed to filter out false positives
in the density estimates, and (4) a new metric, the predictive accuracy index
(PAI) curve, is proposed to evaluate predictive hotspots at multiple areal
scales. The framework is illustrated in a case study of residential burglaries
in Baton Rouge, Louisiana in 2011, and the results validate its utility.
