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Comparison of Approaches for Urban Functional Zones Classification Based on Multi-Source Geospatial Data: A Case Study in Yuzhong District, Chongqing, China

Author

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  • Kai Cao

    (Department of Geography, National University of Singapore, Singapore 117570, Singapore)

  • Hui Guo

    (Department of Geography, National University of Singapore, Singapore 117570, Singapore
    Department of Architecture, National University of Singapore, Singapore 117566, Singapore)

  • Ye Zhang

    (Department of Architecture, National University of Singapore, Singapore 117566, Singapore)

Abstract

Accurate and timely classification and monitoring of urban functional zones prove to be significant in rapidly developing cities, to better understand the real and varying urban functions of cities to support urban planning and management. Many efforts have been undertaken to identify urban functional zones using various classification approaches and multi-source geospatial datasets. The complexity of this category of classification poses tremendous challenges to these studies especially in terms of classification accuracy, but on the opposite, the rapid development of machine learning technologies provides us with new opportunities. In this study, a set of commonly used urban functional zones classification approaches, including Multinomial Logistic Regression, K-Nearest Neighbors, Decision Tree, Support Vector Machine (SVM), and Random Forest, are examined and compared with the newly developed eXtreme Gradient Boosting (XGBoost) model, using the case study of Yuzhong District, Chongqing, China. The investigation is based on multi-variate geospatial data, including night-time imagery, geotagged Weibo data, points of interest (POI) from Gaode, and Baidu Heat Map. This study is the first endeavor of implementing the XGBoost model in the field of urban functional zones classification. The results suggest that the XGBoost classification model performed the best and was able to achieve an accuracy of 88.05%, which is significantly higher than the other commonly used approaches. In addition, the integration of night-time imagery, geotagged Weibo data, POI from Gaode, and Baidu Heat Map has also demonstrated their values for the classification of urban functional zones in this case study.

Suggested Citation

  • Kai Cao & Hui Guo & Ye Zhang, 2019. "Comparison of Approaches for Urban Functional Zones Classification Based on Multi-Source Geospatial Data: A Case Study in Yuzhong District, Chongqing, China," Sustainability, MDPI, vol. 11(3), pages 1-19, January.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:3:p:660-:d:201146
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    References listed on IDEAS

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    1. Xianyuan Zhan & Satish Ukkusuri & Feng Zhu, 2014. "Inferring Urban Land Use Using Large-Scale Social Media Check-in Data," Networks and Spatial Economics, Springer, vol. 14(3), pages 647-667, December.
    2. Yu Liu & Xi Liu & Song Gao & Li Gong & Chaogui Kang & Ye Zhi & Guanghua Chi & Li Shi, 2015. "Social Sensing: A New Approach to Understanding Our Socioeconomic Environments," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 105(3), pages 512-530, May.
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    Citations

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    Cited by:

    1. Xiyuan Ren & ChengHe Guan & De Wang & Junyan Yang & Bo Zhang & Michael Keith, 2022. "Exploring land use functional variance using mobile phone derived human activity data in Shanghai," Environment and Planning B, , vol. 49(9), pages 2531-2547, November.
    2. Guowei Luo & Jiayuan Ye & Jinfeng Wang & Yi Wei, 2023. "Urban Functional Zone Classification Based on POI Data and Machine Learning," Sustainability, MDPI, vol. 15(5), pages 1-18, March.
    3. Xin Yang & Shuaishuai Bo & Zhaojie Zhang, 2023. "Classifying Urban Functional Zones Based on Modeling POIs by Deepwalk," Sustainability, MDPI, vol. 15(10), pages 1-13, May.
    4. Tai Zhang & Bin Wang & Yisong Ge & Chengzhi Li, 2022. "Research on Green Space Service Space Based on Crowd Aggregation and Activity Characteristics under Big Data—Take Tacheng City as an Example," IJERPH, MDPI, vol. 19(22), pages 1-15, November.
    5. Jiyun Lee & Donghyun Kim & Jina Park, 2022. "A Machine Learning and Computer Vision Study of the Environmental Characteristics of Streetscapes That Affect Pedestrian Satisfaction," Sustainability, MDPI, vol. 14(9), pages 1-21, May.
    6. Zonghao Hou & Juan Zhang & Mingyuan Zhang & Gang Li, 2023. "Hospital-system functionality quantification based on supply–demand relationship under earthquake," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(1), pages 213-234, March.
    7. 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.
    8. Jiawang Zhang & Jianguo Wang & Jingmei Tao & Siqi Tang & Wutao Zhao, 2022. "Integrated Zoning Protection of Urban Remains from Perspective of Sustainable Development—A Case Study of Changchun," Sustainability, MDPI, vol. 14(10), pages 1-20, May.

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