Disaster Risk Analysis for the Food Access Issues and its Relevance to Health Effects using Unsupervised Clustering Techniques for Spatial Variability.

Abstract:

Food Access is a critical factor for human survival. Equal distribution of food outlets supports a healthy and active life in the communities. While unequal distribution may show a negative impact on people's health and higher numbers in diabetes and other health conditions. Analyzing food distribution is a multi-variate problem as it depends on various factors of influence ranging from income to demography. Analyzing the food distribution with the relation to income, transportation access, walkable distance, and poverty result in the food access area. These food areas are categorized in to food desert, food swamp, and food forest and have specific criteria regarding its food distribution in terms of healthy or unhealthy food. More crucial and infrequent factors would affect food access such as a natural disaster and pandemic, where people have limited access and time to purchase food and emergency supplies. In case of a disaster, accessing food stores would be limited by the disruption of electric-grid and transportation systems. It also affects availability of short-term food outlets such as farmers' markets, and food stands. The pandemic disaster happens in a large geographical area or worldwide for longer period of time, unlike natural disasters in very short time frames. In the case of a pandemic, the food access would be limited by a stay-at-home order, curfew, and social distance rule. At the same time, human traffic in public places such as food access areas would increase the chance of infection.

In this scenario, food outlets are considered as high risk for disease spread. CoVID-19 or SARS-CoV-2 consider one of the biggest pandemics in our modern time. It affected people health, education, employment, economy, tourism, and health and transportation systems. These affects will take a long time to recover, and revive the humans’ life back to normal. However, this pandemic is not the first and will not be the last, only the frequency of these pandemics might increase, as the influenza mutates every cold season to form a new strain. To address this issue, we study the inter-relation of various socio-economic factors that would help all humans, to better prepare with the next pandemic. One of these critical factors is the food access and food distribution, as its impact on population density located near food outlets would increase infected case numbers. We will analyze the cluster of covid-19 cases and investigate them with the clusters of supermarkets and restaurants. While analyzing the restaurants, we consider the regulation of only pick up orders from restaurants, at the beginning of the pandemic. This study will produce the spatial extent of Covid-19 cases in relation to food outlets by using the spatial analysis method of geographic information systems. The method consists of clustering techniques, and mapping the clusters of food outlets and the infected cases. Post-mapping, we analyze these clusters’ proximity for any spatial variability, correlations between them, and their causal relationships.