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Space-Time Analytics of Tracks for the Understanding of Patterns of Life

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Space-Time Integration in Geography and GIScience

Abstract

Location-aware devices have enabled the recording of personal whereabouts at fine spatial and temporal resolutions. These temporal sequences of personal locations provide unprecedented opportunities to explore patterns of life through space-time analytics of movement and stops of individuals. At a disaggregated level, patterns of life reveal the activities and places as well as the development of routines for individuals. At an aggregate level, patterns of life suggest potential social networks and social hot spots for interactions. Moreover, the concept of “neighborhood” can become personalized and dynamic with space-time analytics to identify the spatial extent to which an individual operates and how the extent varies with temporal granularity. This chapter starts with an overview of space-time track analysis. While time geography has proven useful for analysis of space-time paths and space-time constraints on human activities, its scalability to large data sets is questionable. This chapter provides a conceptual framework and methodology for conducting space-time analysis with a massive number of space-time tracks including over a million points of moves and stops over the course of a year. The examples demonstrate the usefulness of the proposed conceptual framework and methodology to distill complex patterns of life at both disaggregate and aggregate levels that can lead to research opportunities for space-time integration in GIScience for an improved understanding of geography.

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Notes

  1. 1.

    The issue is referred to as Geohash faultlines. Algorithms have been developed to address the faultline issues in spatial search. One algorithm with its source code is available at http://code.google.com/p/geohash-fcdemo/

  2. 2.

    The issue can be solved with the fault line algorithm. Its source code is available at http://code.google.com/p/geohash-fcdemo/

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Acknowledgment

The research was in part supported by Award #2010-DE-BX-K005, awarded by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice. The opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect those of the Department of Justice. NIJ defines publications as any planned, written, visual or sound material substantively based on the project, formally prepared by the award recipient for dissemination to the public.

Author information

Authors and Affiliations

  1. Geospatial Information Science, University of Texas at Dallas, Richardson, TX, USA

    May Yuan

  2. Geography, San Diego State University, San Diego, CA, USA

    Atsushi Nara

Authors
  1. May Yuan
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  2. Atsushi Nara
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Corresponding author

Correspondence to May Yuan .

Editor information

Editors and Affiliations

  1. Department of Geography and Geographic Information Science, University of Illinois, Urbana-Champaign, Illinois, USA

    Mei-Po Kwan

  2. Association of American Geographers, Washington, District of Columbia, USA

    Douglas Richardson

  3. Department of Geography, Hong Kong Baptist University, Hong Kong, China

    Donggen Wang

  4. Institute of Geographical Sciences and Natural Resources, Chinese Academy of Sciences, Beijing, China

    Chenghu Zhou

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Yuan, M., Nara, A. (2015). Space-Time Analytics of Tracks for the Understanding of Patterns of Life. In: Kwan, MP., Richardson, D., Wang, D., Zhou, C. (eds) Space-Time Integration in Geography and GIScience. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9205-9_20

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