Figure 6.

(a) Permutation-based feature importance from global random forest, (b,c) partial dependency profiles of the first four important variables of global random forest model, and (f–l) spatial variation of local feature importance (%incMSE) of obesity, physical inactivity, access to exercise, food environment index, poverty, and education in geographically weighted random forest regression models. Higher values imply increased importance. The random forest model was trained with 5 years of mean data (2013–2017) of 3108 counties. Maps were created in the R (version 4.0.0) Statistical Computing Environment³⁹.