Identifying Inefficient Urban Land Stock in High-Density Cities: A Multi-Source Geospatial Framework

China’s urban development is shifting from extensive expansion to stock-oriented renewal, making the precise identification of inefficient land stock essential for sustainable spatial governance. To overcome the limitations of single-source data and coarse delineation, we propose a multi-source geospatial framework integrating land-use surveys, socioeconomic statistics, spatiotemporal mobility trajectories, and ecological indicators. Using Shenzhen as a case study, we construct an eight-indicator system across social, economic, and ecological dimensions and apply entropy-based objective weighting to support GIS-based weighted overlay evaluation. The results identify 65.37 km2 of inefficient land in 2019, accounting for approximately 7% of Shenzhen’s construction land, with a distinctive “edge aggregation and corridor extension” pattern concentrated along urban–rural fringes and administrative boundaries. Inefficient land is highly uneven across districts, with Longgang (21.11 km2) and Bao’an (12.57 km2) contributing 51.5% of the total and exhibiting statistically spatial clustering (p < 0.01). The observed configuration reflects path-dependent historical development and policy–ecology constraints, including the interaction between ecological control boundaries and peripheral expansion. Overall, by integrating multi-source spatiotemporal big data within a multi-dimensional evaluation framework, the framework offers an objective and transferable approach for diagnosing inefficient land stock and informing targeted urban renewal strategies in high-density cities worldwide.