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

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 (GDPS), 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 GDPS 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 R2 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.