North Carolina is the Industrial Hog Operation (IHO) capital of the world – unlike traditional farming methods, these super-high density IHOs create much higher levels of waste. This waste (~ 1 billion gallons of pig feces a year) exposes an estimated 1 million nearby North Carolinians to a host of negative outcomes including potent smells that impact daily activies and ability to be outdoors; airborne particulates as feces are sprayed on nearby fields and fans blow toxic fumes from enclosures; and potential polluting of drinking water when feces lagoons leak or are flooded. My advisor, Professor Steve Wing has been working on this issue for over a decade, and in the past few years with the help of post-doc Jill Johnston. Most recently a preliminary analysis of disparate exposure to these IHOs by race was used as support for a Title 6 claim against NC DENR to the US EPA.
The original data analysis was done in multiple tools – excel for dataset prep, ArcGIS for mapping and spatial joins, and STATA for regressions. I updated the excel-based dataset prep using more robust, automated methods and was able to isolate a set of strangely coded records to follow-up on. I’m moving the three-part analysis to a push-button process in R, handling dataset prep, spatial overlays and mapping, and regressions in one fell swoop.
Recent headlines on the topic:
- Environmental Health News, 2015
- Rural America, In these times, 2015
- (including Nat McHardy’s excellent map of % enslaved in 1860 vs. anticipated siting of Industrial Hog Operations in 2014)
- National Geographic, 2014.
If you’re interested in this kind of analysis in R, you might also check StackExchange GIS for the question I posed related to the poly-poly analysis. I’ll be posting the code itself soon, for reference.