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Exploring Spatial-Temporal Patterns of Urban Human Mobility Hotspots

Author

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  • Xiping Yang

    (State Key Laboratory of Information Engineering in Surveying, Mapping, Remote and Sensing, Wuhan University, Wuhan 430079, China)

  • Zhiyuan Zhao

    (State Key Laboratory of Information Engineering in Surveying, Mapping, Remote and Sensing, Wuhan University, Wuhan 430079, China)

  • Shiwei Lu

    (State Key Laboratory of Information Engineering in Surveying, Mapping, Remote and Sensing, Wuhan University, Wuhan 430079, China)

Abstract

Understanding human mobility patterns provides us with knowledge about human mobility in an urban context, which plays a critical role in urban planning, traffic management and the spread of disease. Recently, the availability of large-scale human-sensing datasets enables us to analyze human mobility patterns and the relationships between humans and their living environments on an unprecedented spatial and temporal scale to improve decision-making regarding the quality of life of citizens. This study aims to characterize the urban spatial-temporal dynamic from the perspective of human mobility hotspots by using mobile phone location data. We propose a workflow to identify human convergent and dispersive hotspots that represent the status of human mobility in local areas and group these hotspots into different classes according to clustering their temporal signatures. To illustrate our proposed approach, a case study of Shenzhen, China, has been conducted. Six typical spatial-temporal patterns in the city are identified and discussed by combining the spatial distribution of these identified patterns with urban functional areas. The findings enable us to understand the human dynamics in a different area of the city, which can serve as a reference for urban planning and traffic management.

Suggested Citation

  • Xiping Yang & Zhiyuan Zhao & Shiwei Lu, 2016. "Exploring Spatial-Temporal Patterns of Urban Human Mobility Hotspots," Sustainability, MDPI, vol. 8(7), pages 1-18, July.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:7:p:674-:d:74101
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    4. Xiping Yang & Zhixiang Fang & Ling Yin & Junyi Li & Yang Zhou & Shiwei Lu, 2018. "Understanding the Spatial Structure of Urban Commuting Using Mobile Phone Location Data: A Case Study of Shenzhen, China," Sustainability, MDPI, vol. 10(5), pages 1-14, May.
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    6. Yun Wang & Faiz Currim & Sudha Ram, 2022. "Deep Learning of Spatiotemporal Patterns for Urban Mobility Prediction Using Big Data," Information Systems Research, INFORMS, vol. 33(2), pages 579-598, June.
    7. Zuoxian Gan & Min Yang & Tao Feng & Harry Timmermans, 2020. "Understanding urban mobility patterns from a spatiotemporal perspective: daily ridership profiles of metro stations," Transportation, Springer, vol. 47(1), pages 315-336, February.
    8. Satomi Kimijima & Masahiko Nagai, 2017. "Human Mobility Analysis for Extracting Local Interactions under Rapid Socio-Economic Transformation in Dawei, Myanmar," Sustainability, MDPI, vol. 9(9), pages 1-14, September.
    9. Mateusz Ciski & Krzysztof Rząsa & Marek Ogryzek, 2019. "Use of GIS Tools in Sustainable Heritage Management—The Importance of Data Generalization in Spatial Modeling," Sustainability, MDPI, vol. 11(20), pages 1-21, October.
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