Regions, periods, activities: Uncovering urban dynamics via cross-modal representation learning

Chao Zhang, Keyang Zhang, Quan Yuan, Haoruo Peng, Yu Zheng, Tim Hanratty, Shaowen Wang, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the ever-increasing urbanization process, systematically modeling people’s activities in the urban space is being recognized as a crucial socioeconomic task. This task was nearly impossible years ago due to the lack of reliable data sources, yet the emergence of geo-tagged social media (GTSM) data sheds new light on it. Recently, there have been fruitful studies on discovering geographical topics from GTSM data. However, their high computational costs and strong distributional assumptions about the latent topics hinder them from fully unleashing the power of GTSM. To bridge the gap, we present CrossMap, a novel cross-modal representation learning method that uncovers urban dynamics with massive GTSM data. CrossMap first employs an accelerated mode seeking procedure to detect spatiotemporal hotspots underlying people’s activities. Those detected hotspots not only address spatiotemporal variations, but also largely alleviate the sparsity of the GTSM data. With the detected hotspots, CrossMap then jointly embeds all spatial, temporal, and textual units into the same space using two different strategies: one is reconstruction-based and the other is graph-based. Both strategies capture the correlations among the units by encoding their co-occurrence and neighborhood relationships, and learn low-dimensional representations to preserve such correlations. Our experiments demonstrate that CrossMap not only significantly outperforms state-of-the-art methods for activity recovery and classification, but also achieves much better efficiency.

Original languageEnglish (US)
Title of host publication26th International World Wide Web Conference, WWW 2017
PublisherInternational World Wide Web Conferences Steering Committee
Pages361-370
Number of pages10
ISBN (Print)9781450349130
DOIs
StatePublished - 2017
Event26th International World Wide Web Conference, WWW 2017 - Perth, Australia
Duration: Apr 3 2017Apr 7 2017

Publication series

Name26th International World Wide Web Conference, WWW 2017

Other

Other26th International World Wide Web Conference, WWW 2017
CountryAustralia
CityPerth
Period4/3/174/7/17

Keywords

  • Activity
  • Geographical topic
  • Representation learning
  • Social media
  • Spatiotemporal data
  • Twitter
  • Urban dynamics

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications

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