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1. Large-scale diet tracking data reveal disparate associations between food environment and diet

   https://doi.org/10.1038/s41467-021-27522-y  

   Althoff, Tim; Nilforoshan, Hamed; Hua, Jenna; Leskovec, Jure (December 2022, Nature Communications)

   Abstract An unhealthy diet is a major risk factor for chronic diseases including cardiovascular disease, type 2 diabetes, and cancer 1–4 . Limited access to healthy food options may contribute to unhealthy diets 5,6 . Studying diets is challenging, typically restricted to small sample sizes, single locations, and non-uniform design across studies, and has led to mixed results on the impact of the food environment 7–23 . Here we leverage smartphones to track diet health, operationalized through the self-reported consumption of fresh fruits and vegetables, fast food and soda, as well as body-mass index status in a country-wide observational study of 1,164,926 U.S. participants (MyFitnessPal app users) and 2.3 billion food entries to study the independent contributions of fast food and grocery store access, income and education to diet health outcomes. This study constitutes the largest nationwide study examining the relationship between the food environment and diet to date. We find that higher access to grocery stores, lower access to fast food, higher income and college education are independently associated with higher consumption of fresh fruits and vegetables, lower consumption of fast food and soda, and lower likelihood of being affected by overweight and obesity. However, these associations vary significantly across zip codes with predominantly Black, Hispanic or white populations. For instance, high grocery store access has a significantly larger association with higher fruit and vegetable consumption in zip codes with predominantly Hispanic populations (7.4% difference) and Black populations (10.2% difference) in contrast to zip codes with predominantly white populations (1.7% difference). Policy targeted at improving food access, income and education may increase healthy eating, but intervention allocation may need to be optimized for specific subpopulations and locations. 

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2. Characterizing equitable access to grocery stores during disasters using location-based data

   https://doi.org/10.1038/s41598-022-23532-y  

   Esmalian, Amir; Coleman, Natalie; Yuan, Faxi; Xiao, Xin; Mostafavi, Ali (December 2022, Scientific Reports)

   Abstract Natural hazards cause disruptions in access to critical facilities, such as grocery stores, impeding residents’ ability to prepare for and cope with hardships during the disaster and recovery; however, disrupted access to critical facilities is not equal for all residents of a community. In this study, we examine disparate access to grocery stores in the context of the 2017 Hurricane Harvey in Harris County, Texas. We utilized high-resolution location-based datasets in implementing spatial network analysis and dynamic clustering techniques to uncover the overall disparate access to grocery stores for socially vulnerable populations during different phases of the disaster. Three access indicators are examined using network-centric measures: number of unique stores visited, average trip time to stores, and average distance to stores. These access indicators help us capture three dimensions of access: redundancy , rapidity , and proximity . The findings show the insufficiency of focusing merely on the distributional factors, such as location in a food desert and number of facilities, to capture the disparities in access, especially during the preparation and impact/short-term recovery periods. Furthermore, the characterization of access by considering combinations of access indicators reveals that flooding disproportionally affects socially vulnerable populations. High-income areas have better access during the preparation period as they are able to visit a greater number of stores and commute farther distances to obtain supplies. The conclusions of this study have important implications for urban development (facility distribution), emergency management, and resource allocation by identifying areas most vulnerable to disproportionate access impacts using more equity-focused and data-driven approaches. 

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3. An integrated approach to analyze equitable access to food stores under disasters from human mobility patterns

   https://doi.org/10.1111/risa.16873  

   Wei, Zhiyuan; Mukherjee, Sayanti (July 2024, Risk Analysis)

   Abstract Limited access to food stores is often linked to higher health risks and lower community resilience. Socially vulnerable populations experience persistent disparities in equitable food store access. However, little research has been done to examine how people's access to food stores is affected by natural disasters. Previous studies mainly focus on examining potential access using the travel distance to the nearest food store, which often falls short of capturing the actual access of people. Therefore, to fill this gap, this paper incorporates human mobility patterns into the measure of actual access, leveraging large‐scale mobile phone data. Specifically, we propose a novel enhanced two‐step floating catchment area method with travel preferences (E2SFCA‐TP) to measure accessibility, which extends the traditional E2SFCA model by integrating actual human mobility behaviors. We then analyze people's actual access to grocery and convenience stores across both space and time under the devastating winter storm Uri in Harris County, Texas. Our results highlight the value of using human mobility patterns to better reflect people's actual access behaviors. The proposed E2SFCA‐TP measure is more capable of capturing mobility variations in people's access, compared with the traditional E2SFCA measure. This paper provides insights into food store access across space and time, which could aid decision making in resource allocation to enhance accessibility and mitigate the risk of food insecurity in underserved areas. 

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4. E-Commence: Visualizing a Growing Future of Tackling the Food Deserts Problem

   https://doi.org/10.46254/EU6.20230040  

   Washington, Henry; Jiang, Steven; Davis, Lauren; Kim, Hyun Nam (July 2023, IEOM Society International)

   Food insecurity is a problem that should not be overlooked in America. In 2022, about 1.3 billion people were food insecure, an increase of 10% from the previous year. Food deserts, an area with low income and low access to nutritious foods, can be characterized by 5A’s: availability, accessibility, accommodation, affordability, and acceptability. As more investments go into building infrastructure for population growth, the local food industry must expand for both current and new residents. One strategy to combat food deserts is to use the Internet and e-commerce like online food services can contribute to solving food insecurity. From Generation X to Millennials to Generation Z, the adoption of the Internet is rapid. However, these services have additional costs for the consumer. The goal of this research was to examine the current e-commerce solutions to food deserts in a local area and review the literature on how e-commerce can alleviate food deserts. Data were collected from published government and company sites. Results reveal Internet accessibility continues to be an issue for reliable e-commerce use. E-commerce can alleviate food deserts by reducing the cost for both consumers and businesses, accommodating various groups of people, and focusing on rural areas. 

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5. Exploring Store Visit Changes During the COVID-19 Pandemic Using Mobile Phone Location Data

   https://doi.org/10.1007/978-3-030-72808-3_13  

   Liang, Yunlei; McNair, Kyle W; Gao, Song; Göçmen, Aslıgül (January 2021, Mapping COVID-19 in Space and Time: Understanding the Spatial and Temporal Dynamics of a Global Pandemic)

   Shaw, Shih-Lung; Sui, Daniel (Ed.)

   When the World Health Organization (WHO) announced the pandemic of COVID-19, people around the globe scattered to stores for groceries, supplies, and other miscellaneous items in preparation for quarantine. The dynamics of retail visits changed dramatically due to the pandemic outbreak. The study intends to analyze how the store visit patterns have changed due to the lockdown policies during the COVID-19 pandemic. Using mobile phone location data, we build a time-aware Huff model to estimate and compare the visiting probability of different brands of stores over different time periods. We are able to identify certain retail and grocery stores that have more or fewer visits due to the pandemic outbreak, and we detect whether there are any trends in visiting certain retail establishments (e.g., department stores, grocery stores, fast-food restaurants, and cafes) and how the visiting patterns have adjusted with lockdowns. We also make comparisons among brands across three highly populated U.S. cities to identify potential regional variability. It has been found that people in large metropolitan areas with a well-developed transit system tend to show less sensitivity to long-distance visits. In addition, Target, which is a department store, is found to be more negatively affected by longer-distance trips than other grocery stores after the lockdown. The findings can be further applied to support policymaking related to public health, urban planning, transportation, and business in post-pandemic cities. 

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