Week 3: Mapping Proximity to Target Population and Asthma Triggers

For the final week of this asthma-related project we had to do a proximity analysis to show which Alameda county hospitals were a) close to census tracts with a high proportion of black residents, the target population (I used more-than-50%-black; most of the census tracts were near South Berkeley and in Oakland), b) close to major roads (buffered by 0.1 mile), and c) close to Toxic Release Inventory (TRI) facilities (buffered by 0.5 miles). All went fairly well until I got to the point of doing Euclidean Distance for each of the inputs (hospitals, high-black-pop, TRI and roads). First I made at least 20 tries to endeavor to figure out which of the "environments" and "analysis" settings for the Euclidean Distance function did what. Eventually I discovered the distance between a mask, which could be the shape of a shapefile if needed, and the raster analysis extent, which is a rectangle. (Perhaps I was told this earlier - but if so, I had forgotten.) I also discovered that if you set the analysis extent to "extent of display", and adjust the display, the distance radii become longer or shorter. It was an instructive, if frustrating, process. I was pretty excited when I finally got it straight, but it was a long road to enlightenment.

Perhaps I shouldn't have shown this map, since it makes glaringly obvious the difference between the outline of the Alameda County boundary shapefile (the western part, where I did the analysis, is in color) and the outline of the Alameda County census tract shapefile (that grey thing sticking out at the bottom is a high-black-percent census tract that extends beyond the Alameda County boundary shapefile that I intersected with the analysis extent). However it does show the areas where the hospitals are close to a combination of high black population, major roads, and TRI facilities (closest hospitals are in the lightest yellow section). I made the black population file the most important (worth 60%) when I did the weighted overlay of the four files (hospitals (10%), TRI (15%), roads (15%) and black population) because the most important thing is for the hospitals to be close to where the target population is.