Resumen
Time geography considers that the motion of moving objects can be expressed using space?time paths. The existing time geography methods construct space-time paths using discrete trajectory points of a moving point object to characterize its motion patterns. However, these methods are not suitable for moving polygon objects distributed by point sets. In this study, we took a type of crime event as the moving object and extracted its representative point at each moment, using the median center to downscale the polygon objects distributed by the point sets into point objects with timestamps. On this basis, space?time paths were generated by connecting the representative points at adjacent moments to extend the application scope of space?time paths, representing the motion feature from point objects to polygon objects. For the case of the City of London, we constructed a space?time path containing 13 nodes for each crime type (n = 14). Then, each edge of the space?time paths was considered as a monthly vector, which was analyzed statistically from two dimensions of direction and norm, respectively. The results showed that crime events mainly shifted to the east and west, and crime displacement was the greatest in April. Therefore, space?time paths as proposed in this study can characterize spatiotemporal trends of polygon objects (e.g., crime events) distributed by point sets, and police can achieve improved success by implementing targeted crime prevention measures according to the spatiotemporal characteristics of different crime types.