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Identification of Urban Functional Areas and Their Mixing Degree Using Point of Interest Analyses

Author

Listed:
  • Ya Li

    (School of Geography and Tourism, Chongqing Key Laboratory of GIS Application, Chongqing Normal University, Chongqing 401331, China
    These authors contributed equally to this work.)

  • Chunxia Liu

    (School of Geography and Tourism, Chongqing Key Laboratory of GIS Application, Chongqing Normal University, Chongqing 401331, China
    These authors contributed equally to this work.)

  • Yuechen Li

    (Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China)

Abstract

With the rise of smart cities and geographic big-data applications, the refined identification of urban functional areas is of great significance for decision-makers to formulate scientific and reasonable urban planning. In this paper, a random forest algorithm was adopted to analyze Point of Interest (POI) data, with the aim of identifying the functional zoning of Chongqing’s central urban area and to quantify the functional mixing degree by combining POI data with Open Street Map (OSM) road networks. The main conclusions include: (1) Due to the topography and previous urban planning strategies, the central urban area of Chongqing has a significant cluster development that radiates outward from the center of each district. Mixed functional areas account for about 40% of the total area, excluding non-functional areas. The land-use intensity of the central urban area is significant. (2) The mixing degree of the inner ring is generally high, while the aggregation characteristics of the outer ring are weaker. The functions of catering and transportation are dispersed and are mutually exclusive from other functions. (3) The identification of residential service and green spaces and squares was the best, while the identification of catering service areas was slightly less accurate. The overall identification accuracy of the single-function areas was 82%. The results of functional zoning provide valuable information for understanding the downtown area of Chongqing and represent a new method for the study of urban structures in the future.

Suggested Citation

  • Ya Li & Chunxia Liu & Yuechen Li, 2022. "Identification of Urban Functional Areas and Their Mixing Degree Using Point of Interest Analyses," Land, MDPI, vol. 11(7), pages 1-17, June.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:7:p:996-:d:852763
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    References listed on IDEAS

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