Multi-Source Geospatial Framework for Identifying Inefficient Land Stock in Shenzhen

China’s urban development is shifting from extensive expansion to intensive improvement, making the identification of inefficient stock land essential for sustainable urban renewal. Yet, existing approaches are often limited by incomplete data sources and low spatial precision. To address these issues, this study proposes a scalable framework that integrates multi-source big data, including land-use surveys, socioeconomic statistics, spatiotemporal trajectories, and ecological metrics. Using Shenzhen as a case study, we developed a multidimensional evaluation system across social, economic, and ecological dimensions, comprising eight specific indicators. Indicator weights were objectively determined using the entropy weight method, and GIS-based spatial analysis (mean-standardization and Moran’s I) was applied to characterize spatial patterns. Results identify 65.37 km² of inefficient land—about 7% of Shenzhen’s construction land in 2019—exhibiting an “edge aggregation and corridor extension” pattern, mainly distributed along urban–rural fringes and administrative boundaries. The spatial configuration is shaped by historical development, ecological constraints, and the city’s spatial structure. This study provides an objective and replicable framework for the precise identification of inefficient land, supporting data-driven urban renewal and spatial governance in high-density cities.