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Mining the Relationship between Spatial Mobility Patterns and POIs

机译:挖掘空间移动模式与POI之间的关系

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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.
机译:乘客为了不同的兴趣在城市之间移动,并将大都市地区作为城市的聚集地,聚集成网络社区。本文旨在研究流动流的空间模式(由网络社区表示)与兴趣点(POI)之间的关系。此外,它还打算确定在塑造流动性流的空间模式方面发挥最重要作用的POI类别。为了达到这些目的,我们将研究区域划分为不相交的区域,并将每个分区区域作为节点,将它们之间的连接作为由移动性流加权的链路来构建网络。在网络上实现了社区检测算法以发现空间移动模式,并采用基于logistic回归方法的多类分类方法对以poi为特征的空间社区进行分类。以上海和北京的出租车系统为例,我们基于区域间的移动强度来检测空间社区。然后我们研究了它们与泊松的相关性。研究发现,社区的模块化程度与POI呈线性相关;尤其是政府、酒店和交通设施对产生流动模式最为重要。这项研究可以为从POI的角度理解空间迁移模式提供有价值的见解。

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