Multi-Source Geospatial Data-Driven Approach to Identifying Existing Land Stock in Shenzhen

China's urban development is transitioning from an extensive expansion model to an intensive improvement approach. Promoting urban renewal through stock land resources has become a key focus in contemporary urban spatial governance. As cities enter the stock development era, accurately identifying the quantity and spatial distribution of inefficient stock land is crucial for improving land use efficiency. However, existing identification methods for inefficient existing space face challenges including limited data sources, over-reliance on expert experience and insufficient identification accuracy. These factors impede the realization of large-scale, high-precision, and universally applicable identification frameworks. This study proposes an identification method integrating multi-source big data, including land surveys, socioeconomic data, spatiotemporal trajectories, and air quality metrics. Using Shenzhen as a case study, we developed an index system with three dimensions (social, economic, and ecological) and eight indicators. The entropy weight method determined indicator weights, while mean-standardization and Moran’s I index characterized spatial distribution. The study identified a total of 65.37 km² of inefficient existing land in Shenzhen, accounting for approximately 7% of the city's construction land in 2019. Clusters mainly appear in the northwestern part of the city, including administrative boundary zones and urban fringe areas. Longgang District (21.11 km²) and Baoan District (12.57 km²) collectively contribute 51.5% of the inefficient land. Shenzhen's inefficient existing land exhibits an "edge aggregation and corridor extension" spatial pattern, and its formation is subject to the compound influence of historical development patterns, ecological control policies, and urban spatial structure. This study provides a scientific basis for urban renewal and inefficient land redevelopment through the integration of multidimensional big data and spatial statistical analysis. The proposed framework offers replicable technical support for sustainable urban governance and precision regeneration strategies in high-density Chinese cities.