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Human Dynamics Research in Smart and Connected Communities pp 181–201Cite as

Grouping People in Cities: From Space-Time to Place-Time Based Profiling

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Part of the book series: Human Dynamics in Smart Cities ((HDSC))

Abstract

With the widespread use of new geospatial technologies, huge amounts of human dynamics data have been collected. Space-time activity patterns drawn from these detailed mobility datasets reflect personal interests of individuals. Applying data mining methods to analyse these space time patterns has the potential to help us identify who specific people are. Here we expand the concept ‘where, when and how long you stay is who are’ to ‘what place, when and how long you stay is who you are’, to emphases the importance of ‘place’ in understanding human dynamics. We develop a method to integrate the semantic meaning and importance of places into the analysis by making use of Points of Interest (POIs) in the city. An individual’s profile is then built as a summary of the person’s time budget allocated in different semantic places. Based on these activity profiles, groups of people with similar patterns can be uncovered. The evolution from Space-Time to Place-Time enables analysis of a large population with much higher heterogeneity and dynamism in a large city-scale area. A case study with police foot patrol data demonstrates its practical usefulness.

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Acknowledgements

This work is part of the project—Crime, Policing and Citizenship (CPC): Space-Time Interactions of Dynamic Networks (www.ucl.ac.uk/cpc), supported by the UK Engineering and Physical Sciences Research Council (EP/J004197/1). The data provided by Metropolitan Police Service (London) is highly appreciated. The second author’s Ph.D. research is funded by the China Scholarship Council (CSC). The CSC is a non-profit institution with legal person status affiliated with the Ministry of Education in China.

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Authors and Affiliations

  1. SpaceTimeLab for Big Data Analytics, Department of Civil, Environmental & Geomatic Engineering, University College London, Gower Street, London, WC1E 6BT, UK

    Tao Cheng & Jianan Shen

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  1. Tao Cheng
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  2. Jianan Shen
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Correspondence to Tao Cheng .

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Editors and Affiliations

  1. Department of Geography, University of Tennessee, Knoxville, Tennessee, USA

    Shih-Lung Shaw

  2. Department of Geography, Ohio State University, Columbus, Ohio, USA

    Daniel Sui

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Cheng, T., Shen, J. (2018). Grouping People in Cities: From Space-Time to Place-Time Based Profiling. In: Shaw, SL., Sui, D. (eds) Human Dynamics Research in Smart and Connected Communities. Human Dynamics in Smart Cities. Springer, Cham. https://doi.org/10.1007/978-3-319-73247-3_10

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  • DOI: https://doi.org/10.1007/978-3-319-73247-3_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73246-6

  • Online ISBN: 978-3-319-73247-3

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