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Another Tale of Two Cities: Understanding Human Activity Space Using Actively Tracked Cellphone Location Data

Author

Listed:
  • Yang Xu
  • Shih-Lung Shaw
  • Ziliang Zhao
  • Ling Yin
  • Feng Lu
  • Jie Chen
  • Zhixiang Fang
  • Qingquan Li

Abstract

Activity space is an important concept in geography. Recent advancements of location-aware technologies have generated many useful spatiotemporal data sets for studying human activity space for large populations. In this article, we use two actively tracked cellphone location data sets that cover a weekday to characterize people's use of space in Shanghai and Shenzhen, China. We introduce three mobility indicators (daily activity range, number of activity anchor points, and frequency of movements) to represent the major determinants of individual activity space. By applying association rules in data mining, we analyze how these indicators of an individual's activity space can be combined with each other to gain insights of mobility patterns in these two cities. We further examine spatiotemporal variations of aggregate mobility patterns in these two cities. Our results reveal some distinctive characteristics of human activity space in these two cities: (1) A high percentage of people in Shenzhen have a relatively short daily activity range, whereas people in Shanghai exhibit a variety of daily activity ranges; (2) people with more than one activity anchor point tend to travel further but less frequently in Shanghai than in Shenzhen; (3) Shenzhen shows a significant north–south contrast of activity space that reflects its urban structure; and (4) travel distance in both cities is shorter around noon than in regular work hours, and a large percentage of movements around noon are associated with individual home locations. This study indicates the benefits of analyzing actively tracked cellphone location data for gaining insights of human activity space in different cities.

Suggested Citation

  • Yang Xu & Shih-Lung Shaw & Ziliang Zhao & Ling Yin & Feng Lu & Jie Chen & Zhixiang Fang & Qingquan Li, 2016. "Another Tale of Two Cities: Understanding Human Activity Space Using Actively Tracked Cellphone Location Data," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 106(2), pages 489-502, March.
    • Handle: RePEc:taf:raagxx:v:106:y:2016:i:2:p:489-502
    DOI: 10.1080/00045608.2015.1120147
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    Citations

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    Cited by:

    1. Yaxi Gong & Xiang Ji & Yuan Zhang & Shanshan Cheng, 2023. "Spatial Vitality Evaluation and Coupling Regulation Mechanism of a Complex Ecosystem in Lixiahe Plain Based on Multi-Source Data," Sustainability, MDPI, vol. 15(3), pages 1-32, January.
    2. Liu, Lun & Gao, Xuesong & Zhuang, Jiexin & Wu, Wen & Yang, Bo & Cheng, Wei & Xiao, Pengfei & Yao, Xingzhu & Deng, Ouping, 2020. "Evaluating the lifestyle impact of China’s rural housing land consolidation with locational big data: A study of Chengdu," Land Use Policy, Elsevier, vol. 96(C).
    3. Olga Semenova & Julia Apalkova & Marina Butovskaya, 2021. "Sex Differences in Spatial Activity and Anxiety Levels in the COVID-19 Pandemic from Evolutionary Perspective," Sustainability, MDPI, vol. 13(3), pages 1-18, January.
    4. Zhang, Xiaohu & Xu, Yang & Tu, Wei & Ratti, Carlo, 2018. "Do different datasets tell the same story about urban mobility — A comparative study of public transit and taxi usage," Journal of Transport Geography, Elsevier, vol. 70(C), pages 78-90.
    5. Yang, Xiong & Zhuge, Chengxiang & Shao, Chunfu & Huang, Yuantan & Hayse Chiwing G. Tang, Justin & Sun, Mingdong & Wang, Pinxi & Wang, Shiqi, 2022. "Characterizing mobility patterns of private electric vehicle users with trajectory data," Applied Energy, Elsevier, vol. 321(C).
    6. Ling Yin & Jie Chen & Hao Zhang & Zhile Yang & Qiao Wan & Li Ning & Jinxing Hu & Qi Yu, 2020. "Improving emergency evacuation planning with mobile phone location data," Environment and Planning B, , vol. 47(6), pages 964-980, July.
    7. Zhang, Shanqi & Yang, Yu & Zhen, Feng & Lobsang, Tashi & Li, Zhixuan, 2021. "Understanding the travel behaviors and activity patterns of the vulnerable population using smart card data: An activity space-based approach," Journal of Transport Geography, Elsevier, vol. 90(C).
    8. Wenlai Wang & Tao Pei & Jie Chen & Ci Song & Xi Wang & Hua Shu & Ting Ma & Yunyan Du, 2019. "Population Distributions of Age Groups and Their Influencing Factors Based on Mobile Phone Location Data: A Case Study of Beijing, China," Sustainability, MDPI, vol. 11(24), pages 1-19, December.
    9. Tao, Sui & He, Sylvia Y. & Kwan, Mei-Po & Luo, Shuli, 2020. "Does low income translate into lower mobility? An investigation of activity space in Hong Kong between 2002 and 2011," Journal of Transport Geography, Elsevier, vol. 82(C).
    10. Zhou, Yang & Thill, Jean-Claude & Xu, Yang & Fang, Zhixiang, 2021. "Variability in individual home-work activity patterns," Journal of Transport Geography, Elsevier, vol. 90(C).
    11. Fangye Du & Jiaoe Wang & Liang Mao & Jian Kang, 2024. "Daily rhythm of urban space usage: insights from the nexus of urban functions and human mobility," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    12. Yang, Xiping & Fang, Zhixiang & Xu, Yang & Yin, Ling & Li, Junyi & Lu, Shiwei, 2019. "Spatial heterogeneity in spatial interaction of human movements—Insights from large-scale mobile positioning data," Journal of Transport Geography, Elsevier, vol. 78(C), pages 29-40.

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