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Learning Urban Community Structures: A Collective Embedding Perspective with Periodic Spatial-temporal Mobility Graphs

Published: 15 November 2018 Publication History

Abstract

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.

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      cover image ACM Transactions on Intelligent Systems and Technology
      ACM Transactions on Intelligent Systems and Technology  Volume 9, Issue 6
      Regular Papers
      November 2018
      290 pages
      ISSN:2157-6904
      EISSN:2157-6912
      DOI:10.1145/3289398
      Issue’s Table of Contents
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      Publication History

      Published: 15 November 2018
      Accepted: 01 April 2018
      Revised: 01 March 2018
      Received: 01 December 2017
      Published in TIST Volume 9, Issue 6

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      Author Tags

      1. Urban communities
      2. collective embedding
      3. community structure
      4. periodic mobility graphs

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