Effectiveness of support vector machine for crime hot-spots prediction

Crime hot-spot location prediction is important for public safety. The output from the predict tion can provide useful information to improve the activities aimed at detecting and preventing safely and security problems. Location prediction is a special case of spatial data mining classification. Tor 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. Support vector machine forms the new generation of machine-learning techniques used to find optimal separability between classes within datasets. We compare the performance of two types of SVMs techniques: two-class SVMs and one-class SVMs. We also compared SVM with a neural network-based approach and spatial auto-regression-based approach. Experiments on two different spatial datasets demonstrate that the former approach performs slightly better and the latter one gives reasonable results. Furthermore, in this study, toe provide a general framework to customize the spatial data classification task for other spatial domains that have datasets similar to the analyzed crime datasets.