Overview

From the United States Department of Agriculture's Economic Research Service, the dataset contains information about US county's ability to access supermarkets, supercenters, grocery stores, or other sources of healthy and affordable food. Most measures of how individuals and neighborhoods are able to access food are based on the following indicators: - Accessibility to sources of healthy food, as measured by distance to a store or by the number of stores in an area. - Individual-level resources that may affect accessibility, such as family income or vehicle availability. - Neighborhood-level indicators of resources, such as the average income of the neighborhood and the availability of public transportation.

http://www.ers.usda.gov/data-products/food-access-research-atlas.aspx

Explore Structure




Index Type Example Value
0 dict { }
... ... ...
Key Type Example Value Comment
"1/2 and 10 Miles" float 0.5
"1 and 20 Miles" float 0.166666666667
Key Type Example Value Comment
"Total Housing Units" int 9990
"Urban Housing Percentage" float 0.166666666667 Percentage is number of Census Tracts classified as urban divided by total tracts for the county
"Residing in Group Quarters" float 901.0 Group Quarters are dormitories, military bases, assisted living or skilled nursing facilities, and other large institutions.
"Rural Housing Percentage" float 0.833333333333 Percentage is number of Census Tracts classified as rural divided by total tracts for the county
Key Type Example Value Comment
"20 Miles" float 0.0
"1/2 Mile" float 3960.8077309
"1 Mile" float 3409.21234164
"10 Miles" float 984.86043463
Key Type Example Value Comment
"20 Miles" float 0.0
"1/2 Mile" float 5558.40555915
"1 Mile" float 4961.14084519
"10 Miles" float 1114.80423993
Key Type Example Value Comment
"20 Miles" float 0.0
"1/2 Mile" float 10638.2083161
"1 Mile" float 9451.794654
"10 Miles" float 2422.05604577
Key Type Example Value Comment
"Seniors" dict { } Age 65+
"Low Income People" dict { } Low income is defined as annual family income at or below 200 percent of the Federal poverty threshold for family size.
"Children" dict { } Age 0-17
"People" dict { }
Key Type Example Value Comment
"1/2 and 10 Miles" float 0.5
"1 and 20 Miles" float 0.166666666667
Key Type Example Value Comment
"20 Miles" float 0.0
"1/2 Mile" float 24360.3322494
"1 Mile" float 21510.194817
"10 Miles" float 5176.76934843
Key Type Example Value Comment
"20 Miles" float 0.0
"1/2 Mile" float 708.347212727
"1 Mile" float 604.389670134
"10 Miles" float 167.470885381
Key Type Example Value Comment
"Low Access Numbers" dict { } Numbers are summation of the Census Tracts for each county, distances refer to distance that an individual has to travel to access supermarkets, supercenters, grocery stores, or other sources of healthy and affordable food.
"Vehicle Access" dict { } Housing units without vehicle access and low access as specified distance
"Housing Data" dict { }
"County" str "Abbeville"
[Preview ]
"Low Access Percents" dict { } Percents are number of Census Tracts with listed classification divided by total tracts for the county
"State" str "SC"
[Preview ]
"Population" int 25417
Value Count
"Washington" 31
"Jefferson" 26
"Franklin" 25
"Lincoln" 24
"Jackson" 24
"Madison" 20
"Clay" 18
"Union" 18
"Montgomery" 18
"Marion" 17
"Monroe" 17
"Wayne" 16
"Grant" 15
"Warren" 14
"Greene" 14
"Carroll" 13
"Lake" 12
"Douglas" 12
"Polk" 12
"Clark" 12
"Marshall" 12
"Johnson" 12
"Lee" 12
"Adams" 12
"Lawrence" 11
"Morgan" 11
"Scott" 11
"Fayette" 11
"Crawford" 11
"Calhoun" 11
"Logan" 10
"Perry" 10
"Pike" 10
"Hancock" 10
"Hamilton" 10
"Henry" 10
"Knox" 9
"Cass" 9
"Brown" 9
"Shelby" 9
"Putnam" 9
"Benton" 9
"Clinton" 9
"Jasper" 8
"Fulton" 8
"Cumberland" 8
"Mercer" 8
"Boone" 8
"Webster" 8
"Orange" 8
"Columbia" 8
"Randolph" 8
"Harrison" 8
"Butler" 8
"Cherokee" 8
"Howard" 7
"Taylor" 7
"Pulaski" 7
"Richland" 7
"Lewis" 7
"Mason" 6
"Livingston" 6
"Sullivan" 6
"Lafayette" 6
"Newton" 6
"Floyd" 6
"Delaware" 6
"Hardin" 6
"Martin" 6
"Macon" 6
"Custer" 6
"Garfield" 6
"Jones" 6
"DeKalb" 6
"Saline" 5
"Houston" 5
"Mitchell" 5
"York" 5
"Decatur" 5
"Carter" 5
"Henderson" 5
"Pierce" 5
"Clarke" 5
"Allen" 5
"Kent" 5
"Dallas" 5
"White" 5
"Essex" 5
"Sheridan" 5
"Caldwell" 5
"Campbell" 5
"Anderson" 5
"Lyon" 5
"St. Clair" 4
"Van Buren" 4
"Camden" 4
"Liberty" 4
"Phillips" 4
"Smith" 4
"Wilson" 4
... ...
Value Count
"TX" 254
"GA" 159
"VA" 134
"KY" 120
"MO" 115
"KS" 105
"IL" 102
"NC" 100
"IA" 99
"TN" 95
"NE" 93
"IN" 92
"OH" 88
"MN" 87
"MI" 83
"MS" 82
"OK" 77
"AR" 75
"WI" 72
"AL" 67
"PA" 67
"FL" 67
"SD" 66
"CO" 64
"LA" 64
"NY" 62
"CA" 58
"MT" 56
"WV" 55
"ND" 53
"SC" 46
"ID" 44
"WA" 39
"OR" 36
"NM" 33
"UT" 29
"AK" 29
"MD" 24
"WY" 23
"NJ" 21
"NV" 17
"ME" 16
"AZ" 15
"MA" 14
"VT" 14
"NH" 10
"CT" 8
"RI" 5
"HI" 5
"DE" 3
"DC" 1
Key Type Example Value Comment
"Low Income and Low Access" dict { } Low income is defined as annual family income at or below 200 percent of the Federal poverty threshold for family size.
"Low Access Only" dict { }

Downloads

Download all of the following files.

Usage

This library has 1 function you can use.
import food_access
list_of_record = food_access.get_records()
Additionally, some of the functions can return a sample of the Big Data using an extra argument. If you use this sampled Big Data, it may be much faster. When you are sure your code is correct, you can remove the argument to use the full dataset.
import food_access
# These may be slow!
list_of_record = food_access.get_records(test=True)

Documentation

 food_access.get_records(test=False)

Returns food access records from the dataset.