Provincial Life Expectancy Determinants in China, 2020, with Geospatial Models

Provincial-level data for China in 2020, analyzing determinants of life expectancy using multiple geospatial modeling techniques. The dataset was created by Ke Hu and last updated on 2026-05-07. It includes socioeconomic, educational, healthcare, and environmental factors to examine spatial disparities.

Use Cases

• Identify spatial clusters of high and low life expectancy based on Global Moran's I statistic.
• Model the varying influence of education level across provinces using multiscale geographically weighted regression (MGWR).
• Analyze the synergistic effect between economic development and education level using Geodetector interaction analysis.
• Assess the explanatory power of factors like household consumption expenditure and urbanization rate on life expectancy.

Strengths

• Applies multiple analytical methods (MLR, SLM, GWR, MGWR, Geodetector) for robust spatial analysis.
• Identifies specific determinants with statistical significance, such as education level (PD=0.850) and economic development (PD=0.661).
• Reveals a pronounced east-west gradient and distinct spatial clustering patterns (Global Moran's I=0.5339).

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is small (12.2 KB), indicating limited scope, likely a summary table.

Provenance

Source

figshare

Collection Method

Provincial-level analysis incorporating datasets covering socioeconomic indicators, education, healthcare resources, and environmental factors.

Time Range

2020

Freshness

Last updated 2026-05-07 05:31:55; freshness should be verified.

Geography

China, provincial-level