how does the 'who' of plumbing poverty change through space?

Here are the results of the PUMA household regression analyses. As you can see, the regression results are spatially varying and blanket statements based on national data do not capture the full picture.

who where

For example, recall that nationally, Indigenous households are 3.7 times more likely to lack complete plumbing than households that are not native or black. However, as you can see on the map below there are areas in orange where living in an indigenous household increases the odds of having complete plumbing. And there are areas in indigo where living in a native household increases the odds of having incomplete plumbing more than 3.7 times. There are also numerous areas – in off-white – where living in an indigenous household is an insignificant factor.


Here I want to highlight how race and space interact. On these maps (below), a hot spot – in red – is an area where the local odds ratios are higher than the global patterns and the difference is too great to be the result of random chance. Cold spots (in blue) are areas where the local odds ratios are significantly lower than one would expect given random chance. These results are really important because they suggest that there is a social geography of plumbing inequality. Race undeniable matters in identifying households without complete plumbing across the entire United States as the national logistic model suggested however those patterns are stronger in particular areas of the country and insignificant in other areas.

race and space.png

The promise of universal public infrastructure in the United States is an incomplete promise that is not socially or spatially random. We need rigorous geospatial methodologies drawing on large datasets to see that. More work needs to be done to understand further how this has come to be, what it means for the daily lives and health of households without complete plumbing, and what might be done to fix the issue.