This article reports on research that developed a new method of cyclically adjusted, spatio-temporal kernel density estimation method, by considering the cyclic trend in the temporal dimension of crime patterns in order to further improve the predictive crime hotspot analysis; it presents methodology and data, the authors’ implementation of the cSTKDE method and performance assessment compared with KDE and STKDE methods, and implications in public policy and police practice.
This paper presents a new method for predictive crime hotspot analysis that further improves the kernel density estimation (KDE) method and the spatio-temporal kernel density estimation (STKDE) method by accounting for temporal crime cycles and is therefore termed the ‘cyclically adjusted STKDE (cSTKDE) method’. The case study on robbery incidents in Baton Rouge, Louisiana, shows a temporal cycle with a 6-month period of statistical significance from January 2010 to May 2018. This identified period is incorporated into the temporal kernel function of the new cSTKDE method. For validation, the Forecast Accuracy Index (FAI) and Forecast Precision Index (FPI) were used to evaluate the performance across 52 weeks in 2013. For 11 consecutive weeks since the beginning of 2013, the cSTKDE method outperformed the STKDE by 89 percent lower average abs(1-FAI) and 17 percent higher average FPI, and outperformed the KDE by 90 percent lower average abs(1-FAI) and 8 percent higher average FPI. Overall, the scenario with the best predictive accuracy by the cSTKDE is recommended over the traditional KDE or STKDE method as most feasible and effective in implementation of hotspot policing in practice. Publisher Abstract Provided