The Influence of Data Classification Methods on Predictive Accuracy of Kernel Density Estimation Hotspot Maps

When it comes to hot spot identification, spatial analysis techniques come to the fore. One of such techniques, that has gained great popularity among crime analysts, is the Kernel Density Estimation (KDE). Small variation in KDE parameters can give different outputs and hence affect predictive accuracy of hotspot map. The influence these parameters have on KDE hotspot output sparked many researches, mostly analyzing the influence of the cell size and bandwidth size. Yet, the influence of different classification methods applied to calculated cell values, including the choice of threshold value, on the KDE hotspot predictive accuracy remained neglected. The objective of this research was to assess the influence of different classification methods to KDE predictive accuracy. In each KDE computation, calculated cell values were divided into five thematic classes, using three the most common (default) classification methods provided by Environmental Systems Research Institute (ESRI) Geographical Information System (Arc GIS) (equal interval classification, quantile classification and natural breaks classification) and incremental multiples of the grid cells' mean. Based upon calculated hit rates, predictive accuracy indices and recapture rate indices and taking into account the necessity that mapping output should satisfy some operational requirements as well as statistical rules, this research suggest that incremental mean approach with hotspot threshold of 3 and above multiples of the grid cell's mean, should be used.