Spatial Cluster Detection in Spatial Flow Data

As a typical form of geographical phenomena, spatial flow events have been widely studied in contexts like migration, daily commuting, and information exchange through telecommunication. Studying the spatial pattern of spatial flow data serves to reveal essential information about the underlying process generating the phenomena. Most methods of global clustering pattern detection and local clusters detection analysis are focused on single-location spatial events or fail to preserve the integrity of spatial flow events. In this research a new spatial statistical approach of detecting clustering (clusters) of flow data that extends the classical local K-function, while maintaining the integrity of flow data was introduced. Through the appropriate measurement of spatial proximity relationships between entire flows, the new method successfully upgraded the classical hot spot detection method to the stage of “hot flow” detection. Spatial proximity of flows was measured by a four-dimensional distance. Several specific aspects of the method were discussed to provide evidence of its robustness and expandability, such as the multiscale issue, relative importance control and adaptive scale detection, using a real dataset of vehicle theft and recovery location pairs in Charlotte, NC.