Modifying areal interpolation techniques for analysis of data on food assistance benefits

Jerry Shannon, Francis Harvey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Analyses aimed at identifying food deserts-defined as areas with limited access to healthy food-have garnered much recent attention from the news media, policy makers, and non-profit groups. Much of this research relies on the proximity of large grocery stores as a measure of food access. These studies have been limited by poor data quality, boundary effects, and scale dependence. Drawing on data from the Supplemental Nutrition Assistance Program (SNAP, formerly known as food stamps), we suggest an alternative approach that incorporates the distribution and redemption of food assistance benefits in low-income neighborhoods. This data is publically available, but at the zip code level, limiting its usefulness for neighborhood analysis. We use a three-class areal interpolation method to develop three disaggregation techniques that increase the usability of this data. These utilize several external data sources to weight the distribution of this data, including the U.S. Census, U.S. Geological Survey satellite imagery, and existing cadastral data. Our analysis, focused on the Twin Cities metropolitan region for federal fiscal year 2010, thus allows for a more accurate depiction of how residents actually access the food system.

Original languageEnglish (US)
Title of host publicationAdvances in Spatial Data Handling
Subtitle of host publicationGeospatial Dynamics, Geosimulation and Exploratory Visualization
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages17
ISBN (Print)9783642323157
StatePublished - 2013
Event15th International Symposium on Spatial Data Handling, SDH 2012 - Bonn, Germany
Duration: Aug 22 2012Aug 24 2012

Publication series

NameAdvances in Geographic Information Science
ISSN (Print)1867-2434
ISSN (Electronic)1867-2442


Other15th International Symposium on Spatial Data Handling, SDH 2012


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