Significant Linear Hotspot Discovery

Given a spatial network and a collection of activities (e.g., pedestrian fatality reports, crime reports), Significant Linear Hotspot Discovery (SLHD) finds all shortest paths in the spatial network where the concentration of activities is statistically significantly high. SLHD is important for societal applications in transportation safety or public safety such as finding paths with significant concentrations of accidents or crimes. SLHD is challenging because 1) there are a potentially large number of candidate paths ( $\sim10^{16}$ ) in a given dataset with millions of activities and road network nodes and 2) test statistic (e.g., density ratio) is not monotonic. Hotspot detection approaches on euclidean space (e.g., SaTScan) may miss significant paths since a large fraction of an area bounded by shapes in euclidean space for activities on a path will be empty. Previous network-based approaches consider only paths between road intersections but not activities. This paper proposes novel models and algorithms for discovering statistically significant linear hotspots using the algorithms of neighbor node filter, shortest path tree pruning, and Monte Carlo speedup. We present case studies comparing the proposed approaches with existing techniques on real data. Experimental results show that the proposed algorithms yield substantial computational savings without reducing result quality.