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The Geography of Cultural Ties and Human Mobility: Big Data in Urban Contexts

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  • Wenjie Wu
  • Jianghao Wang
  • Tianshi Dai

Abstract

A largely unexplored big data application in urban contexts is how cultural ties affect human mobility patterns. This article explores China's intercity human mobility patterns from social media data to contribute to our understanding of this question. Exposure to human mobility patterns is measured by big data computational strategy for identifying hundreds of millions of individuals' space–time footprint trajectories. Linguistic data are coded as a proxy for cultural ties from a unique geographically coded atlas of dialect distributions. We find that cultural ties are associated with human mobility flows between city pairs, contingent on commuting costs and geographical distances. Such effects are not distributed evenly over time and space, however. These findings present useful insights in support of the cultural mechanism that can account for the rise, decline, and dynamics of human mobility between regions.

Suggested Citation

  • Wenjie Wu & Jianghao Wang & Tianshi Dai, 2016. "The Geography of Cultural Ties and Human Mobility: Big Data in Urban Contexts," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 106(3), pages 612-630, May.
    • Handle: RePEc:taf:raagxx:v:106:y:2016:i:3:p:612-630
    DOI: 10.1080/00045608.2015.1121804
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    Cited by:

    1. Li, Tao & Wang, Jiaoe & Huang, Jie & Yang, Wenyue & Chen, Zhuo, 2021. "Exploring the dynamic impacts of COVID-19 on intercity travel in China," Journal of Transport Geography, Elsevier, vol. 95(C).
    2. Wu, Wenjie & Wang, Jianghao & Li, Chengyu & Wang, Mark, 2016. "The geography of city liveliness and consumption: evidence from location-based big data," LSE Research Online Documents on Economics 83642, London School of Economics and Political Science, LSE Library.
    3. Chengyu Li & Mark Wang & Jianghao Wang & Wenjie Wu, 2016. "The Geography of City Liveliness and Land Use Configurations: Evidence from Location-Based Big Data in Beijing," SERC Discussion Papers 0201, Centre for Economic Performance, LSE.
    4. Pablo Martí & Leticia Serrano-Estrada & Mohsen Aboutorabi, 2021. "Culturally Diverse Street-Level Urban Activities through the Lens of Digital Footprints," Sustainability, MDPI, vol. 13(20), pages 1-22, October.
    5. Yaojin Zhou & Yao Shen & Xuexi Yang & Zhifang Wang & Liyan Xu, 2021. "Where to Revitalize, and How? A Rural Typology Zoning for China," Land, MDPI, vol. 10(12), pages 1-21, December.
    6. Shenzhen Tian & Bing Yang & Zhangli Liu & Xueming Li & Wei Zhang, 2022. "Coupling Coordination of Urban Pseudo and Reality Human Settlements," Land, MDPI, vol. 11(3), pages 1-21, March.
    7. Luo, Shuli & He, Sylvia Y., 2021. "Understanding gender difference in perceptions toward transit services across space and time: A social media mining approach," Transport Policy, Elsevier, vol. 111(C), pages 63-73.
    8. Shenzhen Tian & Xueming Li & Jun Yang & Hui Wang & Jianke Guo, 2023. "Spatiotemporal evolution of pseudo human settlements: case study of 36 cities in the three provinces of Northeast China from 2011 to 2018," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(2), pages 1742-1772, February.
    9. Jie Huang & David Levinson & Jiaoe Wang & Haitao Jin, 2019. "Job-worker spatial dynamics in Beijing: Insights from Smart Card Data," Working Papers 2019-01, University of Minnesota: Nexus Research Group.
    10. Xiaorong Jiang & Wei Wei & Shenglan Wang & Tao Zhang & Chengpeng Lu, 2021. "Effects of COVID-19 on Urban Population Flow in China," IJERPH, MDPI, vol. 18(4), pages 1-14, February.

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