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Urban Vitality Area Identification and Pattern Analysis from the Perspective of Time and Space Fusion

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  • Shaojun Liu

    (Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
    State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China)

  • Ling Zhang

    (Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
    State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China)

  • Yi Long

    (Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
    State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China)

Abstract

Urban vitality provides an important basis for evaluating urban development and spatial balance. In the era of big data, the quantitative analysis of urban vitality has become a research hotspot in the field of urban sustainability and planning research. However, time variation characteristics are often neglected, which leads to one-sidedness in the pattern analysis of urban vitality. In this paper, a method for extracting vitality areas and integrating spatiotemporal features clustering is proposed. The method is used to divide urban space into multiple vitality areas scientifically. The spatial and temporal distribution patterns of urban vitality areas are found, and the driving factors of various vitality patterns are analyzed by combining points of interest (POI)-based land use characteristics. To illustrate this method, this paper takes Nanjing city as an example. One week’s worth of mobile phone data indicated that Nanjing has 10 and 8 vitality areas on weekdays and weekends, respectively. The spatial and temporal distribution patterns of the vitality areas and their correlation with land use were analyzed, which proved that POI density and entropy have strong correlations with urban vitality.

Suggested Citation

  • Shaojun Liu & Ling Zhang & Yi Long, 2019. "Urban Vitality Area Identification and Pattern Analysis from the Perspective of Time and Space Fusion," Sustainability, MDPI, vol. 11(15), pages 1-27, July.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:15:p:4032-:d:251739
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    References listed on IDEAS

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    11. Fengshuo Sun & Enxu Wang, 2025. "Unveiling the Spatial Heterogeneity of Urban Vitality Using Machine Learning Methods: A Case Study of Tianjin, China," Land, MDPI, vol. 14(7), pages 1-25, June.
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    13. Xuanxuan Xia & Kexin Lin & Yang Ding & Xianlei Dong & Huijun Sun & Beibei Hu, 2020. "Research on the Coupling Coordination Relationships between Urban Function Mixing Degree and Urbanization Development Level Based on Information Entropy," IJERPH, MDPI, vol. 18(1), pages 1-24, December.
    14. 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.
    15. Yihao Jiang & Zhaojin Chen & Pingjun Sun, 2022. "Urban Shrinkage and Urban Vitality Correlation Research in the Three Northeastern Provinces of China," IJERPH, MDPI, vol. 19(17), pages 1-22, August.
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