Socioeconomic and environmental factors of poverty in China using geographically weighted random forest regression model.

ABSTRACT: Correlations between socioeconomic factors and poverty in regression models do not reflect actual relationships, especially when data exhibit patterns of spatial heterogeneity. Spatial regression models can estimate the relationships between socioeconomic factors and poverty in defined geographical areas, explaining the imbalanced distribution of poverty, but the relationships between these factors and poverty are not always linear however, and conventional simple linear local regression models do not accurately capture these nonlinear relationships. To fill this gap, we used a local regression method, geographically weighted random forest regression (GW-RFR), that integrates a spatial weight matrix (SWM) and random forest (RF). The GW-RFR evaluates the spatial variations in the nonlinear

SUBMITTER: Luo Y 

PROVIDER: S-EPMC8754530 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature