Evaluating Urban Housing Contradictions Through Multisource Data Fusion: a Case Study of Spatiotemporal Mismatch Analysis in Shenzhen with the HCEWI Model

The rapid urbanization and structural imbalances in Chinese megacities have exacerbated the housing supplydemand mismatch, creating an urgent need for fine-scale diagnostic tools. This study addresses this critical gap by developing the Housing Contradiction Evaluation Weighted Index (HCEWI) model, making three key contributions to high-resolution housing monitoring. First, we establish a tripartite theoretical framework integrating dynamic population pressure (PPI), housing supply potential (HSI), and functional diversity (HHI). The PPI innovatively combines mobile signaling data with principal component analysis to capture real-time commuting patterns, while the HSI introduces a novel dual-criteria system based on Local Climate Zones (LCZ), weighted by building density and residential function ratio. Second, we develop a spatiotemporal coupling architecture featuring an entropy-weighted dynamic integration mechanism with self-correcting modules, demonstrating robust performance against data noise. Third, our 25-month longitudinal analysis in Shenzhen reveals significant findings, including persistent bipolar clustering patterns, contrasting volatility between peripheral and core areas, and seasonal policy responsiveness. Methodologically, we advance urban diagnostics through 500-meter grid monthly monitoring and process-oriented temporal operators that reveal “tentacle-like” spatial restructuring along transit corridors. Our findings provide a replicable framework for precision housing governance and demonstrate the transformative potential of mobile signaling data in implementing China’s “city-specific policy” approach. We further propose targeted intervention strategies, including balance regulation for high-contradiction zones, Transit-Oriented Development (TOD) activation for low-contradiction clusters, and dynamic land conversion mechanisms for transitional areas.