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Spatiotemporal Heterogeneity and Socioeconomic Drivers of Landscape Patterns in High-Density Communities of Wuhan

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  • Wenjun Peng

    (School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, China
    Key Laboratory of Intelligent Health Perception and Ecological Restoration of Rivers and Lakes, Ministry of Education, Hubei University of Technology, Wuhan 430068, China)

  • Dakun Dai

    (School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, China)

  • Fuqin Liu

    (School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, China
    Key Laboratory of Intelligent Health Perception and Ecological Restoration of Rivers and Lakes, Ministry of Education, Hubei University of Technology, Wuhan 430068, China)

  • Xu Wang

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

High-density communities, characterized by concentrated populations and compact built environments, often exacerbate issues such as green space fragmentation, uneven distribution, and intensified urban heat island effects. Investigating the spatiotemporal heterogeneity and evolutionary characteristics of landscape patterns driven by population density (POP), road density (RD), street-level GDP (GDP S ), and nighttime light intensity (NTL) in Wuhan’s high-density communities using a geographically weighted regression (GWR) model is essential for informing sustainable urban planning strategies. The results showed that ED, PD, and SHDI exhibit consistent annual declines averaging 1.53%, 0.97% and 0.59%, respectively, while AI increased steadily at 0.11% per year. This indicates that human intervention has surpassed natural succession and become the dominant force in shaping landscape patterns. Among them, POP and RD are the direct driving factors for landscape pattern changes, while GDP S and NTL indirectly affect landscape patterns through economic structural adjustments and land use changes, forming differentiated spatial patterns in high-density communities. In terms of relationships, the GWR model performs better than ordinary least squares regression (OLS) by adjusting R 2 and residual Moran’s I, significantly improving its explanatory power. This study demonstrates the effectiveness of the GWR model in revealing the spatiotemporal heterogeneity between socioeconomic factors and landscape patterns, providing a transferable analytical framework for high-density cities. It thereby offers empirical and methodological support for addressing regional ecological constraints and advancing sustainable urban renewal.

Suggested Citation

  • Wenjun Peng & Dakun Dai & Fuqin Liu & Xu Wang, 2025. "Spatiotemporal Heterogeneity and Socioeconomic Drivers of Landscape Patterns in High-Density Communities of Wuhan," Sustainability, MDPI, vol. 17(18), pages 1-23, September.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:18:p:8093-:d:1745195
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    References listed on IDEAS

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