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Exploring Urban Spatial Feature with Dasymetric Mapping Based on Mobile Phone Data and LUR-2SFCAe Method

Author

Listed:
  • Lingbo Liu

    (Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Zhenghong Peng

    (Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Hao Wu

    (Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Hongzan Jiao

    (Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Yang Yu

    (Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China)

Abstract

Dasymetric mapping of high-resolution population facilitates the exploration of urban spatial feature. While most relevant studies are still challenged by weak spatial heterogeneity of ancillary data and quality of traditional census data, usually outdated, costly and inaccurate, this paper focuses on mobile phone data, which can be real-time and precise, and also strengthens spatial heterogeneity by its massive mobile phone base stations. However, user population recorded by mobile phone base stations have no fixed spatial boundary, and base stations often disperse in extremely uneven spatial distribution, this study defines a distance-decay supply–demand relation between mobile phone user population of gridded base station and its surrounding land patches, and outlines a dasymetric mapping method integrating two-step floating catchment area method (2SFCAe) and land use regression (LUR). The results indicate that LUR-2SFCAe method shows a high fitness of regression, provides population mapping at a finer scale and helps identify urban centrality and employment subcenters with detailed worktime and non-worktime populations. The work involving studies of dasymetric mapping based on LUR-2SFCAe method and mobile phone data proves to be encouraging, sheds light on the relationship between mobile phone users and nearby land use, brings about an integrated exploration of 2SFCAe in LUR with distance-decay effect and enhances spatial heterogeneity.

Suggested Citation

  • Lingbo Liu & Zhenghong Peng & Hao Wu & Hongzan Jiao & Yang Yu, 2018. "Exploring Urban Spatial Feature with Dasymetric Mapping Based on Mobile Phone Data and LUR-2SFCAe Method," Sustainability, MDPI, vol. 10(7), pages 1-15, July.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:7:p:2432-:d:157536
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    References listed on IDEAS

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    1. Myung-Jin Jun & Simon Choi & Frank Wen & Ki-Hyun Kwon, 2018. "Effects of urban spatial structure on level of excess commutes: A comparison between Seoul and Los Angeles," Urban Studies, Urban Studies Journal Limited, vol. 55(1), pages 195-211, January.
    2. Hao Wu & Lingbo Liu & Yang Yu & Zhenghong Peng, 2018. "Evaluation and Planning of Urban Green Space Distribution Based on Mobile Phone Data and Two-Step Floating Catchment Area Method," Sustainability, MDPI, vol. 10(1), pages 1-11, January.
    3. Jakub Novak & Rein Ahas & Anto Aasa & Siiri Silm, 2013. "Application of mobile phone location data in mapping of commuting patterns and functional regionalization: a pilot study of Estonia," Journal of Maps, Taylor & Francis Journals, vol. 9(1), pages 10-15, March.
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    Cited by:

    1. Shaojun Liu & Yao Long & Ling Zhang & Hao Liu, 2021. "Quantifying and Characterizing Urban Leisure Activities by Merging Multiple Sensing Big Data: A Case Study of Nanjing, China," Land, MDPI, vol. 10(11), pages 1-20, November.
    2. Areum Jo & Sang-Kyeong Lee & Jaecheol Kim, 2020. "Gender Gaps in the Use of Urban Space in Seoul: Analyzing Spatial Patterns of Temporary Populations Using Mobile Phone Data," Sustainability, MDPI, vol. 12(16), pages 1-22, August.
    3. Lingbo Liu & Zhenghong Peng & Hao Wu & Hongzan Jiao & Yang Yu & Jie Zhao, 2018. "Fast Identification of Urban Sprawl Based on K-Means Clustering with Population Density and Local Spatial Entropy," Sustainability, MDPI, vol. 10(8), pages 1-16, July.
    4. Yanyan Chen & Hanqiang Qian & Yang Wang, 2020. "Analysis of Beijing’s Working Population Based on Geographically Weighted Regression Model," Sustainability, MDPI, vol. 12(12), pages 1-16, June.
    5. Dian Shao & Weiting Xiong, 2022. "Does High Spatial Density Imply High Population Density? Spatial Mechanism of Population Density Distribution Based on Population–Space Imbalance," Sustainability, MDPI, vol. 14(10), pages 1-22, May.

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