Application of Clustering Analysis for Investigation of Food Accessibility

TL;DR

This paper uses clustering analysis, specifically Gaussian Mixture Models, to identify food assistance deserts in Ohio, aiming to improve food access by targeting underserved regions based on demographic and geographic data.

Contribution

It applies GMM clustering to analyze food assistance network characteristics and identify underserved areas, providing a novel approach to addressing food insecurity.

Findings

Identification of food assistance deserts in Ohio

Insights into demographic and geographic factors affecting food access

Recommendations for targeted resource allocation

Abstract

Access to food assistance programs such as food pantries and food banks needs focus in order to mitigate food insecurity. Accessibility to the food assistance programs is impacted by demographics of the population and geography of the location. It hence becomes imperative to define and identify food assistance deserts (Under-served areas) within a given region to find out the ways to improve the accessibility of food. Food banks, the supplier of food to the food agencies serving the people, can manage its resources more efficiently by targeting the food assistance deserts and increase the food supply in those regions. This paper will examine the characteristics and structure of the food assistance network in the region of Ohio by presenting the possible reasons of food insecurity in this region and identify areas wherein food agencies are needed or may not be needed. Gaussian Mixture Model (GMM) clustering technique is employed to identify the possible reasons and address this problem of food accessibility.