Learning Urban Community Structures: A Collective Embedding Perspective with Periodic Spatial-Temporal Mobility Graphs
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.
P. Wang et al., "Learning Urban Community Structures: A Collective Embedding Perspective with Periodic Spatial-Temporal Mobility Graphs," ACM Transactions on Intelligent Systems and Technology, vol. 9, no. 6, Association for Computing Machinery (ACM), Nov 2018.
The definitive version is available at https://doi.org/10.1145/3209686
Intelligent Systems Center
Keywords and Phrases
Graphic methods; Housing; Aggregation methods; Collective embedding; Community structures; Dynamic structure; Mobility graphs; Social interactions; Static structures; Urban communities; Periodic structures; Collective embedding; Periodic mobility graphs
International Standard Serial Number (ISSN)
Article - Journal
© 2018 Association for Computing Machinery (ACM), All rights reserved.
01 Nov 2018
This research was partially supported by the National Science Foundation (NSF) via grant number 1755946, by the University of Missouri Research Board (UMRB) via proposal number 4991, and by the Natural Science Foundation of China (NSFC) via grant numbers 71701007 and 61773199.