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