The spatiotemporal evolution pattern of urban resilience in the Yangtze River Delta urban agglomeration based on TOPSIS-PSO-ELM. (December 2022)

• Record Type: Journal Article
• Authors: Chenhong, Xia; Guofang, Zhai
• Is Part Of: Sustainable cities and society. Volume 87(2022)
• Publication Date: 2022-12
• Subjects: Urban resilience -- TOPSIS -- Particle swarm optimization (PSO) -- Extreme learning machine (ELM) -- Spatiotemporal evolution -- Yangtze River Delta urban agglomeration (YRDUA) -- Sustainable development; PSO Particle swarm optimization -- ELM Extreme learning machine -- YRDUA Yangtze River Delta urban agglomeration; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2022.104223
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: The urban resilience evaluation model based on TOPSIS-PSO-ELM is proposed. By using geospatial analysis, the spatial trend and driving force of urban resilience are revealed. A multi-dimensional evaluation framework of urban resilience is constructed. It reveals the urban resilience of the largest urban agglomeration in China. Abstract: Urban resilience, a methodology that can quantify the healthy operation of cities, has theoretical and practical significance for clarifying urban development rules and improving sustainable urban development. Using 49 cities in the Yangtze River Delta urban agglomeration as a research object, we employed the TOPSIS method, particle swarm optimization (PSO), and extreme learning machine (ELM) to measure urban resilience from 2010 to 2020. We used the mainstream exploratory spatial data analysis, geostatistical trend line analysis and geographical detectors to analyze urban resilience's temporal and spatial evolution characteristics and its influencing factors. The results in the Yangtze River Delta urban agglomeration were as follows: (1) The resilience level of cities rose, and their rank changed significantly. (2) There was an apparent spatial imbalance in the development of urban resilience. The "peak" centers of resilience were mainly distributed in first-tier developed cities such as Shanghai, Nanjing, and Hangzhou, while the "valley" centers were mainly distributed in the northwestern marginal cities such as Huainan, Huaibei, and Bozhou. (3) The spatial agglomeration of the urban resilience index was significant, with prominent H-H agglomeration in eastern coastal areas and apparent L-L clustering in the northern Huaibei plain and northern Jiangsu plain. (4) The leading driving factors that influenced the spatial differentiation of urban resilience were the per capita deposit balance of financial institutions; the local financial revenue; the foreign capital actually used; the average wage of fully employed staff and workers; the number of hospital beds; the public library collection per 100 persons; the electricity consumption of the whole society; the total gas supply; the number of mobile phone users at the end of each year; and the number of internet broadband users.