RTI International released obesity data for the US at the neighborhood level in 250×250 meter grid squares. I downloaded the state files and joined them together into a single PostGIS layer.

wget 'http://synthpopviewer.rti.org/obesity/downloads/AL.zip'
7za e AL.zip

/Applications/Postgres93.app/Contents/MacOS/bin/shp2pgsql -s 3857 '/Users/sparafina/projects/obesity/AL.shp' obesity | /Applications/Postgres93.app/Contents/MacOS/bin/psql -U sparafina -d food_access

/Applications/Postgres93.app/Contents/MacOS/bin/psql -U sparafina -c 'delete from obesity' -d food_access

rm AL.s* AL.cpg AL.dbf AL.prj AL.zip

for s in {'AL','AZ','AR','CA','CO','CT','DE','DC','FL','GA','ID','IL','IN','IA','KS','KY','LA','ME','MD','MA','MI','MN','MS','MO','MT','NE','NV','NH','NJ','NM','NY','NC','ND','OH','OK','OR','PA','RI','SC','SD','TN','TX','UT','VT','VA','WA','WV','WI','WY'}
 do
 f='/Users/sparafina/projects/obesity/'$s'.shp'
 echo $f
 wget 'http://synthpopviewer.rti.org/obesity/downloads/'$s'.zip'
 7za e $s'.zip'
 /Applications/Postgres93.app/Contents/MacOS/bin/shp2pgsql -a -s 3857 $f obesity | /Applications/Postgres93.app/Contents/MacOS/bin/psql -U sparafina -d food_access
done

/Applications/Postgres93.app/Contents/MacOS/bin/psql -U sparafina -c 'create index obesity_gist on obesity using gist (geom)' -d food_access

I converted the grids into points by generating the centroids of the grid.

CREATE TABLE obesity_point AS
 SELECT popgte20, popbmige30, bmivsus, pctbmige30,      
 ST_Centroid(ST_Transform(geom, 4269)) AS geom FROM 
 obesity;

CREATE INDEX obesity_point_gist ON obesity_point USING gist(geom);

The points were then joined to Census tracts and their values were averaged by tract.

CREATE TABLE obesity_tract AS
SELECT AVG(o.popgte20) as avg_popgte20, 
 AVG(o.popbmige30) AS avg_popbmige30, 
 AVG(o.bmivsus) as avg_bmivsus, 
 AVG(o.pctbmige30) as avg_octbmige30, 
 MAX(o.popgte20) as max_popgte20, 
 MAX(o.popbmige30) AS max_popbmige30, 
 MAX(o.bmivsus) as max_bmivsus, 
 MAX(o.pctbmige30) as max_octbmige30, 
 MIN(o.popgte20) as min_popgte20, 
 MIN(o.popbmige30) AS min_popbmige30, 
 MIN(o.bmivsus) as min_bmivsus, 
 MIN(o.pctbmige30) as min_octbmige30,
 COUNT(*) as total_points, 
 t.censustract
FROM obesity_point as o, food_access_tracts as t 
WHERE ST_Intersects(o.geom, t.geom)
GROUP BY t.censustract;

The tracts with the obesity data were then joined to the food desert tracts from the USDA.

CREATE TABLE access_obesity_tract AS
SELECT 
 f.gid,
 f.statefp,
 f.countyfp,
 f.tractce,
 f.affgeoid,
 f.geoid,
 f.name,
 f.lsad,
 f.aland,
 f.awater,
 f.foodaccess_id,
 f.state,
 f.county,
 f.lilatracts_1and10,
 f.lilatracts_halfand10,
 f.lilatracts_1and20,
 f.lilatracts_vehicle,
 f.urban,
 f.rural,
 f.la1and10,
 f.lahalfand10,
 f.la1and20,
 f.latracts_half,
 f.latracts1,
 f.latracts10,
 f.latracts20,
 f.hunvflag,
 f.groupquartersflag,
 f.ohu2010,
 f.numgqtrs,
 f.pctgqtrs,
 f.lowincometracts,
 f.pop2010,
 f.uatyp10,
 f.lapophalf,
 f.lapophalfshare,
 f.lalowihalf,
 f.lalowihalfshare,
 f.lakidshalf,
 f.lakidshalfshare,
 f.laseniorshalf,
 f.laseniorshalfshare,
 f.lahunvhalf,
 f.lahunvhalfshare,
 f.lapop1,
 f.lapop1share,
 f.lalowi1,
 f.lalowi1share,
 f.lakids1,
 f.lakids1share,
 f.laseniors1,
 f.laseniors1share,
 f.lahunv1,
 f.lahunv1share,
 f.lapop10,
 f.lapop10share,
 f.lalowi10,
 f.lalowi10share,
 f.lakids10,
 f.lakids10share,
 f.laseniors10,
 f.laseniors10share,
 f.lahunv10,
 f.lahunv10share,
 f.lapop20,
 f.lapop20share,
 f.lalowi20,
 f.lalowi20share,
 f.lakids20,
 f.lakids20share,
 f.laseniors20,
 f.laseniors20share,
 f.lahunv20,
 f.lahunv20share,
 o.avg_popgte20,
 o.avg_popbmige30,
 o.avg_bmivsus,
 o.avg_octbmige30,
 o.max_popgte20,
 o.max_popbmige30,
 o.max_bmivsus,
 o.max_octbmige30,
 o.min_popgte20,
 o.min_popbmige30,
 o.min_bmivsus,
 o.min_octbmige30,
 o.total_points,
 o.censustract
FROM obesity_tract as o INNER JOIN food_access_tracts as f ON o.censustract = f.censustract;

Each PostGIS layer was export to a sql file via pg_dump. The data is available as a zip file compressed with 7zip. The archive contains the following:

• obesity_grid.sql – the original RTI data of the US
• obesity_point.sql – RTI data as points
• obesity_tract.sql – RTI data averaged into Census tracts
• access_obesity_tract.sql – obesity and food desert data by Census tract

Download the archive