Driving forces and prediction of urban open spaces morphology: The case of Shanghai, China using geodetector and CA-Markov model

Urban open spaces offer both environmental and social benefits. However, comprehensive studies that integrate both quantitative and qualitative evaluations of the factors driving change in these spaces and their long-term predictions are lacking. Most existing studies concentrate on land-use development rather than conducting empirical research specific to urban open spaces in Shanghai. This study addresses this gap by employing a geographic detector (geodetector) to analyze the influence of various driving factors on open-space changes. These factors were then used as weight values in a multicriteria CA-Markov model to simulate and predict change in Shanghai's urban open spaces by 2050. The advantage of analyzing driving forces lies in their ability to capture the multifactor synergy influencing change in urban open spaces, aligning with the aim of this study to quantitatively evaluate the interaction between natural, climatic, and socioeconomic factors. Additionally, semi-structured interviews were conducted with 10 policymakers and planners to assess the reliability of the quantitative predictions. The results indicate that socioeconomic factors are the primary drivers of change in urban open spaces. Specifically, the interaction between the normalized difference vegetation index (NDVI) and population density (PD) emerged as the most influential variables. For prediction outcomes, the unconstrained scenario predicts a decrease in open-space area from 5610.94 km2 in 2020 to 5124.36 km2 in 2050. The planning intervention scenario anticipates minimal changes in Shanghai's total urban open-space area with almost no floating changes. However, the economic development scenario predicts a rapid decline in open spaces. Experts and planners evaluated these three scenarios and confirmed the reliability and accuracy of the prediction models. The methods and findings of this study can support zoning planning for urban open-space systems in other cities and regions.

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Publication Details

Journal
Ecological Informatics
Published
2024-08-11
DOI
https://doi.org/10.1016/j.ecoinf.2024.102763
Citations
16
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
4.52
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article

Driving forces and prediction of urban open spaces morphology: The case of Shanghai, China using geodetector and CA-Markov model

Gabriel Hoh Teck Ling, Yaoyao Zhu
16 citations
Ecological Informatics
Land Use and Ecosystem Services
4.52
article

Driving forces and prediction of urban open spaces morphology: The case of Shanghai, China using geodetector and CA-Markov model

Gabriel Hoh Teck Ling, Yaoyao Zhu
article en
16 citations

Abstract

Urban open spaces offer both environmental and social benefits. However, comprehensive studies that integrate both quantitative and qualitative evaluations of the factors driving change in these spaces and their long-term predictions are lacking. Most existing studies concentrate on land-use development rather than conducting empirical research specific to urban open spaces in Shanghai. This study addresses this gap by employing a geographic detector (geodetector) to analyze the influence of various driving factors on open-space changes. These factors were then used as weight values in a multicriteria CA-Markov model to simulate and predict change in Shanghai's urban open spaces by 2050. The advantage of analyzing driving forces lies in their ability to capture the multifactor synergy influencing change in urban open spaces, aligning with the aim of this study to quantitatively evaluate the interaction between natural, climatic, and socioeconomic factors. Additionally, semi-structured interviews were conducted with 10 policymakers and planners to assess the reliability of the quantitative predictions. The results indicate that socioeconomic factors are the primary drivers of change in urban open spaces. Specifically, the interaction between the normalized difference vegetation index (NDVI) and population density (PD) emerged as the most influential variables. For prediction outcomes, the unconstrained scenario predicts a decrease in open-space area from 5610.94 km2 in 2020 to 5124.36 km2 in 2050. The planning intervention scenario anticipates minimal changes in Shanghai's total urban open-space area with almost no floating changes. However, the economic development scenario predicts a rapid decline in open spaces. Experts and planners evaluated these three scenarios and confirmed the reliability and accuracy of the prediction models. The methods and findings of this study can support zoning planning for urban open-space systems in other cities and regions.

Ecological InformaticsVol. 82
University of Technology Malaysia (MY), Anhui University of Technology (CN)
Sustainable cities and communities
Openalex Percentile: Top 5%
Land Use and Ecosystem Services
4.52
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