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The COVID-19 pandemic has exacerbated the food insecurity issue across the nation, especially for vulnerable population groups. As an important way to access food, online grocery shopping has gained substantial popularity during COVID-19. However, it is not clear how COVID-19 impacted both the in-store and online grocery shopping simultaneously and how the impacts varied among different population groups. To gain a comprehensive understanding of the impacts of COVID-19 on food access, we conducted two rounds of online survey during the initial and middle stages of COVID-19 in Maricopa County, Arizona, United States. In addition to the factors that have been examined in the literature, we examined how travel behavior, food environment, and lifestyle change affected both in-store and online shopping during COVID-19 when compared with before COVID-19. Analysis results suggest that the methods people used for grocery shopping varied with different stages of COVID-19 and among different population groups. Changes of people's daily life and travel behavior during COVID-19 are found to be associated with people's grocery shopping method selection. While people with better food environment were more likely to keep in-store grocery shopping, as the pandemic progressed, food environment became less important as a factor influencing the adoption of online grocery shopping. Food assistance program participants had a higher demand for the online grocery shopping service. However, the barriers associated with redeeming their benefits online prohibited them from becoming new online grocery shoppers during COVID-19. The study provides important insights into planning of food provision and formulating effective intervention strategies during future shocks. [ABSTRACT FROM AUTHOR]

Geoforensic science investigates the location and time of criminal occurrences by integrating multiple fields, including geography, criminology, ecology, biology, and geology. The ubiquity, durability, and spatial-temporal predictability make pollen a frequently used biomarker in geoforensic investigations to help determine the provenance of hard-to-trace items, including computers, counterfeit products, digging equipment, clothing, and undetonated explosives. The recently developed Geoforensic Interdiction (GOFIND) model links the pollen combination collected from a sample object with the probability of locations traversed by the object. Although the GOFIND model improves over the traditional single-site joint probability approach and can be used to identify multiple locations simultaneously, substantial limitations remain. In particular, GOFIND requires specifying the number of locations traversed by an object in advance—a priori knowledge that is almost impossible to obtain in real-world applications. This article aims to introduce the GOFIND + model that leverages detected and undetected pollen to establish a probabilistic relation between pollen and the corresponding species distribution in the environment. Our simulation tests using the USDA CropScape data for the state of Texas show that the GOFIND + model outperforms the GOFIND model in predictive accuracy. Further, GOFIND + does not require that users specify the number of geographical stops and sites a priori. Key Words: geoforensics, GOFIND+, pollen, spatial optimization. [ABSTRACT FROM AUTHOR]