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Identification of multi-attribute functional urban areas under a perspective of community detection: A case study

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  • Liu, Xin
  • Jiao, Pengfei
  • Yuan, Ning
  • Wang, Wenjun

Abstract

Identifying functional urban areas is a significant research of considerable interest in many important fields such as city planning and facility location problem. Traditionally, we identify the function of urban areas from the macro-level perspective. With the availability of human digital footprints, investigation of functional urban areas from a micro-level perspective becomes possible. In this paper, we identified the functional urban areas of a metropolitan city in China by some metrics of community detection based on the social network of mobile phone users. The result shows that there are close relations between urban area and individual communication network, which can help us identify the function of areas more conveniently.

Suggested Citation

  • Liu, Xin & Jiao, Pengfei & Yuan, Ning & Wang, Wenjun, 2016. "Identification of multi-attribute functional urban areas under a perspective of community detection: A case study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 827-836.
    • Handle: RePEc:eee:phsmap:v:462:y:2016:i:c:p:827-836
    DOI: 10.1016/j.physa.2016.06.106
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    References listed on IDEAS

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    Cited by:

    1. Yuewen Yang & Dongyan Wang & Zhuoran Yan & Shuwen Zhang, 2021. "Delineating Urban Functional Zones Using U-Net Deep Learning: Case Study of Kuancheng District, Changchun, China," Land, MDPI, vol. 10(11), pages 1-21, November.
    2. Yunfeng Hu & Yueqi Han, 2019. "Identification of Urban Functional Areas Based on POI Data: A Case Study of the Guangzhou Economic and Technological Development Zone," Sustainability, MDPI, vol. 11(5), pages 1-15, March.

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