Time Series from Clustering: An Approach to Forecast Crime Patterns

Abstract

This chapter presents an approach to forecast criminal patterns that combines the time series from clustering method with a computational intelligence-based prediction. In this approach, clusters of criminal events are parametrized according to simple geometric prototypes. Cluster dynamics are captured as a set of time series. The size of this set corresponds to the number of clusters multiplied by the number of parameters per cluster. One of the main drawbacks of clustering is the difficulty of defining the optimal number of clusters. The paper also deals with this problem by introducing a validation index of dynamic partitions of crime events that relates the optimal number of clusters with the foreseeability of time series by means of non-linear analysis. The method as well as the validation index was tested over two cases of reported urban crime. Our results showed that crime clusters can be predicted by forecasting their representative time series using an evolutionary adaptive neural fuzzy inference system. Thus, we argue that the foreseeability of these series can be anticipated satisfactorily by means of the proposed index.