Part 4 case study
A factor-analysis and clustering case using ZIP-level health prevalence measures. Demographics are held out of the factor model, then used to interpret what the factors mean.
PCA on the health variables says the first two dimensions carry most of the variation. A rotated factor model turns that compression into two readable axes: overall health burden and older-age chronic conditions.
Income, college share, deprivation, and age are not used to build the factors. Their correlations with the factor scores explain why the map reads as socioeconomic burden crossed with an age/chronic-care dimension.