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

A provincial-level dataset for China in 2020, used to model determinants of life expectancy. The data was analyzed by Ke Hu using multiple geospatial regression methods and Geodetector. It was last updated on the figshare platform on 2026-05-07.

Use Cases

• Modeling the relationship between education levels and life expectancy based on the identified fundamental determinant.
• Analyzing the spatial clustering of health outcomes based on the reported Global Moran's I statistic.
• Investigating the synergistic effects of economic development and education on longevity based on the interaction analysis.
• Comparing the performance of different geospatial regression models (MLR, SLM, GWR, MGWR) for public health data.

Strengths

• Dataset is based on a multi-method analytical approach, including spatial lag and geographically weighted regression models.
• Analysis identifies specific determinants with statistical significance, such as education level (PD=0.850) and economic development (PD=0.661).
• Spatial analysis reveals a pronounced east-west gradient with significant clustering (Global Moran’s I=0.5339, p<0.001).

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 very small (34.0 KB), indicating limited scope or a summary table rather than raw underlying data.

Provenance

Source

Ke Hu via figshare.

Collection Method

Provincial-level data analyzed using multiple linear regression, spatial lag model, geographically weighted regression, multiscale geographically weighted regression, and Geodetector.

Time Range

2020

Freshness

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

Geography

China, provincial-level.