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Multi-Scale Recursive Identification of Urban Functional Areas Based on Multi-Source Data

Author

Listed:
  • Ting Liu

    (College Surveying & Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China)

  • Gang Cheng

    (College Surveying & Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China)

  • Jie Yang

    (College Surveying & Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China)

Abstract

The study of urban functional area identification is of great significance for urban function cognition, spatial planning, and economic development. In the identification of urban functional areas, most studies considered only a single data source and a single division scale, the research results have problems such as low update frequency or incomplete information in a single data set, and overfitting or underfitting in a single spatial resolution. Aiming at the above problems, this paper proposes a multi-scale recursive recognition method based on interactive validation for urban functional areas using taxi trajectory data and point of interest (POI) data as the main data sources. First, the dynamic time warping (DTW) algorithm generates a time series similarity matrix, a CA-RFM model combining the clustering algorithm and random forest model is constructed. The model extracts significant feature regions as inputs through a K-medoid clustering algorithm, which are imported into the random forest model for urban functional zone (UFZ) identification. Then, to overcome the shortcomings of a single scale in expressing urban structural characteristics, a recursive model of different levels of urban road networks is established to classify multi-scale functional areas. Finally, cross-validation using the CA-RFM model and POI quantitative identification method obtains the final identification results of urban functional areas. This paper selects Shenzhen as the study area, the results show that the combination of clustering algorithm and random forest model greatly reduces the error of manual selection of training samples. In addition, the study demonstrates the superiority of the proposed method in two aspects, namely, faster delineation and improved accuracy in urban functional area identification.

Suggested Citation

  • Ting Liu & Gang Cheng & Jie Yang, 2023. "Multi-Scale Recursive Identification of Urban Functional Areas Based on Multi-Source Data," Sustainability, MDPI, vol. 15(18), pages 1-24, September.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13870-:d:1242396
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    References listed on IDEAS

    as
    1. Qingke Gao & Jianhong Fu & Yang Yu & Xuehua Tang, 2019. "Identification of urban regions’ functions in Chengdu, China, based on vehicle trajectory data," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-17, April.
    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.
    3. Bosiu E. Lefulebe & Adriaan Van der Walt & Sifiso Xulu, 2022. "Fine-Scale Classification of Urban Land Use and Land Cover with PlanetScope Imagery and Machine Learning Strategies in the City of Cape Town, South Africa," Sustainability, MDPI, vol. 14(15), pages 1-16, July.
    4. Yuan Meng & Dongyang Hou & Hanfa Xing, 2017. "Rapid Detection of Land Cover Changes Using Crowdsourced Geographic Information: A Case Study of Beijing, China," Sustainability, MDPI, vol. 9(9), pages 1-16, August.
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