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Ranking locations in a city via the collective home-work relations in human mobility data

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  • He, Yifan
  • Zhao, Chen
  • Zeng, An

Abstract

Human mobility has long been a hot topic of research in complex systems. An intra-urban network treats a specific set of locations within a city as nodes and the various connections between locations at different spatial scales as edges. Human mobility is one of the direct responses to the connection of locations. Thus, human mobility data can be used to study the importance of locations in intra-urban networks, which has practical applications in urban layout. However, the datasets used in existing studies have shortcomings such as low temporal resolution and narrow range of covered users. Therefore, these kind of datasets only capture partial human mobility behavior. In this paper, a 4G communication data of 1.7million users in Shijiazhuang, a city in northern China, is used to investigate the mobility patterns of users. Our dataset covers a large proportion of the population in the city. We can also identify the location of a user to a high frequency of every second. Then two kinds of directed weighted networks, which consist of links separately from home to workplace and from home to weekend-stay, are respectively constructed based on the users’ mobility record. By analyzing the statistical properties of these networks and their skeleton networks, we find that although most users leave home to work and spend weekends, they prefer to live and work within the same district. To identify important locations in the city, we use the weighted PageRank algorithm to rank nodes in the intra-urban network. We find that the locations in the central area ranked highest, but the ranking is still different from the simple ranking based on population. Finally, using housing prices in the entire urban area of the city, and locations on Zhongshan road, a traditional commercial road in the city, as well as locations on the first ring area, which is the most densely populated area in the city, we verify that in identifying essential locations, PageRank is more effective than only using in-strength, which represents the frequency of a location is visited.

Suggested Citation

  • He, Yifan & Zhao, Chen & Zeng, An, 2022. "Ranking locations in a city via the collective home-work relations in human mobility data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    • Handle: RePEc:eee:phsmap:v:608:y:2022:i:p1:s037843712200841x
    DOI: 10.1016/j.physa.2022.128283
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    References listed on IDEAS

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