論文ID: 871
Based on the data of 70 serial residential burglaries, this study examined how areas to commit crimes were developed across a series of crimes. Besides, methods for predicting future crime locations in offender profiling were considered. Size of convex polygon and nearest neighbor index (NNI) were calculated to examine how each offender’s offence space changed with the increase in their crime experience. Furthermore, three strategies proposed for predicting future crime locations in the context of geographical profiling, convex polygon, center of minimum distance (CMD), and kernel density estimation, were compared in terms of prediction precision. As the number of incidents increased, the size of convex polygon increased and NNI decreased, indicating that each offender’s whole crime area tended to spread out, but be more clustered in parallel across a series of crimes. For hit rates within the predicted area equal to the size of the convex polygon, kernel density estimation was most precise, following convex polygon and CMD. Prioritization of multiple clustered spots is essential for more precise prediction of future crime locations of an individual offender.
