Mining the Relationship between Spatial Mobility Patterns and POIs

Passengers move between urban places for diverse interests and drive the metropolitan regions as the aggregation of urban places to group into network communities. This paper aims to examine the relationship between the spatial patterns (represented by the network communities) of mobility flows and places of interest (POIs). Furtherly, it intends to identify the categories of POIs that play the most significant role in shaping the spatial patterns of mobility flows. To achieve these purposes, we partition the study area into disjoint regions and construct the network with each partitioned region as a node and connection between them as links weighted by the mobility flows. The community detection algorithm is implemented on the network to discover spatial mobility patterns, and the multiclass classification based on the logistic regression method is adopted to classify spatial communities featured by POIs. Taking the taxi systems of Shanghai and Beijing as examples, we detect spatial communities based on the movement strengths among regions. Then we investigate their correlations with POIs. It finds that communities’ modularity correlates linearly with POIs; particularly governments, hotels, and the traffic facilities are of the most significance for generating the mobility patterns. This study can provide valuable insight into understanding the spatial mobility patterns from the perspective of POIs