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Profiling Residents’ Mobility with Grid-Aggregated Mobile Phone Trace Data Using Chengdu as the Case

Author

Listed:
  • Xuesong Gao

    (College of Resources, Sichuan Agricultural University, Chengdu 611130, China)

  • Hui Wang

    (School of Architecture, Tsinghua University, Beijing 100084, China)

  • Lun Liu

    (School of Government, Peking University, Beijing 100871, China)

Abstract

People’s movement trace harvested from mobile phone signals has become an important new data source for studying human behavior and related socioeconomic topics in social science. With growing concern about privacy leakage of big data, mobile phone data holders now tend to provide aggregate-level mobility data instead of individual-level data. However, most algorithms for measuring mobility are based on individual-level data—how the existing mobility algorithms can be properly transformed to apply on aggregate-level data remains undiscussed. This paper explores the transformation of individual data-based mobility metrics to fit with grid-aggregate data. Fifteen candidate metrics measuring five indicators of mobility are proposed and the most suitable one for each indicator is selected. Future research about aggregate-level mobility data may refer to our analysis to assist in the selection of suitable mobility metrics.

Suggested Citation

  • Xuesong Gao & Hui Wang & Lun Liu, 2021. "Profiling Residents’ Mobility with Grid-Aggregated Mobile Phone Trace Data Using Chengdu as the Case," Sustainability, MDPI, vol. 13(24), pages 1-13, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13713-:d:700596
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    References listed on IDEAS

    as
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