Knowledge Visualizations to Inform Decision Making for Improving Food Accessibility and Reducing Obesity Rates in the United States

Raphael D. Isokpehi, Matilda O. Johnson, Bryanna Campos, Arianna Sanders, Thometta Cozart and Idethia S. Harvey
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Raphael D. Isokpehi: Center for Trans-Disciplinary Data Analytics, Department of Natural Sciences, College of Science, Engineering and Mathematics, Bethune-Cookman University, Daytona Beach, FL 32114, USA
Matilda O. Johnson: Department of Public Health and Health Equity, Petrock College of Health Sciences, Bethune-Cookman University, Daytona Beach, FL 32114, USA
Bryanna Campos: Department of Public Health and Health Equity, Petrock College of Health Sciences, Bethune-Cookman University, Daytona Beach, FL 32114, USA
Arianna Sanders: Center for Trans-Disciplinary Data Analytics, Department of Natural Sciences, College of Science, Engineering and Mathematics, Bethune-Cookman University, Daytona Beach, FL 32114, USA
Thometta Cozart: Department of Public Health and Health Equity, Petrock College of Health Sciences, Bethune-Cookman University, Daytona Beach, FL 32114, USA
Idethia S. Harvey: Transdisciplinary Center for Health Equity Research, Department of Health and Kinesiology, Texas A & M University, College Station, TX 77843, USA

IJERPH, 2020, vol. 17, issue 4, 1-27

Abstract: The aim of this article is to promote the use of knowledge visualization frameworks in the creation and transfer of complex public health knowledge. The accessibility to healthy food items is an example of complex public health knowledge. The United States Department of Agriculture Food Access Research Atlas (FARA) dataset contains 147 variables for 72,864 census tracts and includes 16 food accessibility variables with binary values (0 or 1). Using four-digit and 16-digit binary patterns, we have developed data analytical procedures to group the 72,684 U.S. census tracts into eight and forty groups respectively. This value-added FARA dataset facilitated the design and production of interactive knowledge visualizations that have a collective purpose of knowledge transfer and specific functions including new insights on food accessibility and obesity rates in the United States. The knowledge visualizations of the binary patterns could serve as an integrated explanation and prediction system to help answer why and what-if questions on food accessibility, nutritional inequality and nutrition therapy for diabetic care at varying geographic units. In conclusion, the approach of knowledge visualizations could inform coordinated multi-level decision making for improving food accessibility and reducing chronic diseases in locations defined by patterns of food access measures.

Keywords: food access; Food Access Research Atlas (FARA); food desert; knowledge visualization; obesity; visual analytics (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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