Length L‐function for network‐constrained point data

Network-constrained points are constrained by and distributed on road networks, for example, taxi pick-up drop-off locations. The aggregation pattern (clustering) of network-constrained (significantly denser than randomly distributed) along roads may indicate spatial anomalies. For detecting quantifying the with highest intensity (i.e., number per network length) can reveal high demand. K-function its derivative (incremental K-function) have been utilized to identify point aggregations measure scale, yet only radius-based planar-scale results, thereby mis-estimating patterns owing topology configuration heterogeneity. Specifically, complex networks (e.g., intersections) incur despite their low intensity. This study constructs length L-function points, using first quantify true-to-life scale local function extract aggregations. Synthetic practical data experiments show innovative detection at length-based intensity, providing a new approach analysis unaffected topological complexity.