Urban Green Space Supply-Demand Mismatches in Nanjing, 2022

2022 data quantifying urban green space supply, demand, and spatial mismatches for traffic analysis zones in Nanjing's central urban area. The dataset was created by Lijuan Sun and uses an explainable machine learning model (XGBoost–SHAP) to identify urban morphological drivers. It was last updated on March 18, 2026.

Use Cases

• Analyzing spatial mismatches between green space supply and demand based on traffic analysis zone data.
• Identifying urban morphological drivers of green inequities using the XGBoost–SHAP model framework.
• Comparing green space deficits in urban cores versus surpluses in peripheral areas.
• Evaluating the impact of urban compactness, floor area ratio, and road density on supply–demand balance.
• Developing governance strategies for equity-oriented green space planning.

Strengths

• Quantifies supply-demand mismatches for 251 traffic analysis zones (48 deficits, 203 surpluses).
• Focuses on a specific case study (central urban area of Nanjing) for 2022.
• Integrates an explainable machine learning model (XGBoost–SHAP) to identify drivers.
• File size is 5.5 KB, indicating a compact, focused dataset.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data is limited to a single city (Nanjing) and year (2022), reducing generalizability.

Provenance

Source

figshare, authored by Lijuan Sun.

Collection Method

Developed a framework to quantify UGS supply, demand, and mismatches, then applied XGBoost–SHAP.

Time Range

2022

Freshness

Last updated 2026-03-18 17:24:43; freshness should be verified.

Geography

Central urban area of Nanjing.