TABLE 1.
Food environment measures: data sources, definitions, and coverage
| Census tract | “Classic” network buffer | “Sausage” network buffer | |
|---|---|---|---|
| Neighborhood definition | Administrative | Administrative | Person-based1 |
| Input point | N/A | Population-weighted centroid of census tract | Residential address |
| Size of geographic unit | Fixed | 1-mile (1.6 km) buffer2 2-mile (3.2 km) buffer 6-mile (10 km) buffer 10-mile (16 km) buffer |
1-km buffer 5-km buffer 8-km buffer 16-km buffer |
| Geographic measures | Density (count per square kilometer) Percentage (count per total food stores or restaurants) |
Density (count per square kilometer) Percentage (count per total food stores or restaurants) |
Density (count per square kilometer) Percentage (count per total food stores or restaurants) |
| Geographic coverage | Contiguous U.S. (72,538 (99.3%3) CTs) | Contiguous US (72,538 (99.3%3) CTs) | REGARDS participants’ home addresses (13,914 (19%3) CTs) |
| Geoprocessing – polygon construction | Columbia University4 | New York University5 | University of Michigan6 |
| Geoprocessing – spatial join | Columbia University4 | Drexel University5 | Columbia University4 |
NOTE: All measures use NETS establishment data to define the supermarket and fast food restaurant exposure measures.
A person-based neighborhood definition refers to a neighborhood as centered around a person-specific location (e.g., home) and across a distance that aims to capture the local environment as experienced or imagined by a participant.(35) The University of Michigan geocoded REGARDS participants to their residential addresses.
1-mile “classic” network buffer was constructed using the ‘walking’ travel mode; the remaining network buffers used the ‘driving’ travel mode.
Denominator is continental U.S., which includes the 50 states and the District of Columbia.
The Built Environment and Health (BEH) Working Group at Columbia University buffered REGARDS participants’ residential addresses by 1- and 5-km (straight-line distance) and geoprocessed NETS establishment data within census tract and “sausage” network buffer boundaries. Before geoprocessing, areas of hydrography were removed to accurately calculate land area. Aggregate counts for “sausage” network buffers exclude establishments with low geocoding quality (i.e. street name, zip code, city centroid ~ 8%).
The Department of Population Health at New York University Grossman School of Medicine buffered population-weighted centroids of census tracts where REGARDS participants lived at baseline by 1-, 2-, 6-, and 10-mile (street network distance). The “classic” network buffer was created using ESRI’s ArcGIS StreetMap Premium 2019 for the street network data and the “generalized” polygon option and default settings (with default trim and standard precision) in ArcGIS Pro 2.4.2. The Diabetes LEAD Network Data Coordinating Center at Data Drexel University geoprocessed NETS establishment data within “classic” network buffer.
The University of Michigan buffered REGARDS participants’ residential addresses by 1-, 5-, 8-, and 16-km (street network distance using a 150-meter radius from the street centerline. The “sausage” network buffer was created using ESRI’s ArcGIS StreetMap Premium 2017 (North America HERE Release 3) for the street network data and following these buffer specifications in ArcGIS Pro 2.1 or later.(35)