Efficiently mining maximal co-locations in a spatial continuous field under directed road networks

Abstract

Extracting useful spatial co-location patterns from urban service facilities can help planners allocate limited resources effectively. These facilities are mostly distributed within man-made spatial fields with road-network constraints. To promote urban-space adaptivity, co-location algorithms for this network circumstance have been designed with distance decay effects and topological relationships of roads. However, these algorithms neglect the traffic direction, which affects the accuracy of the results. Moreover, the efficiency problem is more severe than with the traditional algorithm (i.e., no constraints). To address these problems, we propose an efficient maximal co-location mining algorithm with directed road-network constraints and spatial-continuity consideration (CMDS). To improve the accuracy, we design a network-based prevalence index, combined with both distance decay effects and road direction interference, to measure the significance of a pattern. To promote the execution speed, we use a key-node-separating approach and an improved shortest-path batch task for the co-location mining process. The experiments with both the synthetic and real datasets show that the CMDS algorithm is more efficient and accurate than the state-of-the-art network co-location when applied to problems in an urban space.