Evaluating Urban Vitality of Street Blocks Based on Multi-Source Geographic Big Data: A Case Study of Shenzhen

Ziyu Wang, Nan Xia (), Xin Zhao, Xing Gao, Sudan Zhuang and Manchun Li
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Ziyu Wang: Jiangsu Provincial Key Laboratory of Geographic Information Technology, School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
Nan Xia: Jiangsu Provincial Key Laboratory of Geographic Information Technology, School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
Xin Zhao: Jiangsu Provincial Key Laboratory of Geographic Information Technology, School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
Xing Gao: Jiangsu Provincial Key Laboratory of Geographic Information Technology, School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
Sudan Zhuang: Jiangsu Provincial Key Laboratory of Geographic Information Technology, School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
Manchun Li: Jiangsu Provincial Key Laboratory of Geographic Information Technology, School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China

IJERPH, 2023, vol. 20, issue 5, 1-20

Abstract: Urban vitality is the comprehensive form of regional development quality, sustainability, and attractiveness. Urban vitality of various regions within the cities has difference, and the quantitative evaluation of urban vitality within the cities can help guide to future city constructions. Evaluation of urban vitality needs the combination of multi-source data. Existing studies have developed index method and estimation models mainly based on geographic big data to evaluate urban vitality. This study aims to combine remote sensing data with geographic big data to evaluate urban vitality of Shenzhen at street block scale and build the estimation model by random forest method. Indexes and random forest model were built, and some further analyses were conducted. The results were: (1) urban vitality in Shenzhen was high in the coastal areas, business areas, and new towns; (2) compared to indexes, the estimation model had advantages of more accurate results, combination of various data, and the ability to analyze feature contributions; and (3) taxi trajectory, nighttime light, and housing rental data had the strongest influence on urban vitality.

Keywords: urban vitality evaluations; machine learning; random forest; open-source data (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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