Multi-level Spatiotemporal Dataset of Land Transfer in China (2000 to 2024)

In China's hierarchical land-governance system, the central government establishes the regulatory framework, while provincial and municipal authorities adapt the implementation according to local fiscal capacity, development priorities, and spatial constraints. However, empirical studies have long relied on fragmented or city-specific samples, and there is still no unified dataset that captures the institutional evolution of land transfer across multiple administrative levels over a long time span. To bridge this gap, this study constructs a multi-level spatiotemporal dataset of land transfer in China from 2000 to 2024 at the national, provincial, and city levels.The dataset integrates seven core indicators under a standardized statistical specification: land supply area, transaction price, gross floor area (GFA), payment amount, floor area ratio (FAR), building density, and greenery rate. Data are sourced from official land-market disclosures and verified administrative transaction records, and undergo multi-stage quality control including cross-source reconciliation, unit harmonization, and outlier screening to ensure temporal consistency and cross-regional comparability. The dataset supports both macro-level trajectory tracing and subnational spatial heterogeneity analysis, and is accompanied by a series of visualization outputs depicting long-term land supply dynamics, regional structural divergence, and spatial clustering patterns. This dataset fills a foundational empirical gap for research on China's land-governance mechanism, land finance, urban spatial form, and the institutional transition toward high-quality development.