Crime hot-spot location prediction is important for public safety. The output from the prediction

can provide useful information to improve the activities aimed at detecting and preventing

safety and security problems. Location prediction is a special case of spatial data mining classification.

For instance, in the public safety domain, it may be interesting to predict location(s) of

crime hot spots. In this study, we present a support vector machine (SVM)-based approach to predict

the location as an alternative to existing modeling approaches.