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首页> 外文期刊>ACM transactions on intelligent systems >Learning Urban Community Structures: A Collective Embedding Perspective with Periodic Spatial-temporal Mobility Graphs
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Learning Urban Community Structures: A Collective Embedding Perspective with Periodic Spatial-temporal Mobility Graphs

机译：学习城市社区结构：具有周期性时空流动图的集体嵌入视角

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摘要

Learning urban community structures refers to the efforts of quantifying, summarizing, and representing an urban community's (i) static structures, e.g., Point-Of-Interests (POIs) buildings and corresponding geographic allocations, and (ii) dynamic structures, e.g., human mobility patterns among POIs. By learning the community structures, we can better quantitatively represent urban communities and understand their evolutions in the development of cities. This can help us boost commercial activities, enhance public security, foster social interactions, and, ultimately, yield livable, sustainable, and viable environments. However, due to the complex nature of urban systems, it is traditionally challenging to learn the structures of urban communities. To address this problem, in this article, we propose a collective embedding framework to learn the community structure from multiple periodic spatial-temporal graphs of human mobility. Specifically, we first exploit a probabilistic propagation-based approach to create a set of mobility graphs from periodic human mobility records. In these mobility graphs, the static POIs are regarded as vertexes, the dynamic mobility connectivities between POI pairs are regarded as edges, and the edge weights periodically evolve over time. A collective deep auto-encoder method is then developed to collaboratively learn the embeddings of POIs from multiple spatial-temporal mobility graphs. In addition, we develop a Unsupervised Graph based Weighted Aggregation method to align and aggregate the POI embeddings into the representation of the community structures. We apply the proposed embedding framework to two applications (i.e., spotting vibrant communities and predicting housing price return rates) to evaluate the performance of our proposed method. Extensive experimental results on real-world urban communities and human mobility data demonstrate the effectiveness of the proposed collective embedding framework.
机译：学习城市社区结构是指量化，概括和表示城市社区的（i）静态结构（例如，兴趣点（POI）建筑物和相应的地理分配）以及（ii）动态结构（例如，人类）的工作POI之间的移动模式。通过学习社区结构，我们可以更好地定量代表城市社区，并了解其在城市发展中的演变。这可以帮助我们促进商业活动，增强公共安全，促进社会互动，并最终创造出宜居，可持续和可行的环境。但是，由于城市系统的复杂性，传统上学习城市社区的结构具有挑战性。为了解决这个问题，在本文中，我们提出了一个集体嵌入框架，以从人类活动的多个周期性时空图学习社区结构。具体来说，我们首先利用一种基于概率传播的方法从周期性的人类活动记录创建一组活动图。在这些迁移率图中，静态POI被视为顶点，POI对之间的动态迁移率连通性被视为边缘，并且边缘权重随时间周期性地发展。然后，开发了一种集体深度自动编码器方法，以从多个时空迁移图上协作学习POI的嵌入。此外，我们开发了一种基于加权的无监督图加权聚合方法，以将POI嵌入对齐并聚合到社区结构的表示中。我们将建议的嵌入框架应用于两个应用程序（即发现充满活力的社区并预测房价回报率）以评估我们建议的方法的性能。在现实世界中的城市社区和人类流动性数据上的大量实验结果证明了所提出的集体嵌入框架的有效性。

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