PM2.5 Spatial Patterns Across China from 2000 to 2023

Xueyuan Zhang's dataset supports the analysis of PM2.5 spatial patterns across China from 2000 to 2023. It includes processed model input data, geocomplexity results, spatial discretization outputs, and local indicator of stratified power (LISP) results. The explanatory variables include temperature, precipitation, digital elevation model, normalized difference vegetation index, nighttime light data, industrialization level, energy consumption, wind speed, and boundary layer height.

Use Cases

• Model PM2.5 concentrations based on explanatory variables like temperature, precipitation, and industrialization level.
• Analyze spatial patterns of air pollution using geocomplexity calculation and spatial discretization outputs.
• Investigate local determinants of PM2.5 using local indicator of stratified power (LISP) results.
• Reproduce analyses from the associated research article using the provided processed data and code.

Strengths

• Includes processed data for a 23-year time range (2000-2023).
• Contains multiple explanatory variables such as temperature, precipitation, and nighttime light data.
• Provides accompanying code for geocomplexity calculation, spatial discretization, and LISP-based analysis.
• Released under a CC-BY-4.0 license, supporting open data reuse.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is packaged as a ZIP file; data structure requires manual inspection after download.

Provenance

Source

figshare

Collection Method

Processed model input data and analysis outputs.

Time Range

2000 to 2023

Freshness

Last updated 2026-04-27 07:49:03; freshness should be verified.

Geography

China