For an approaching disaster, the tracking of time-sensitive critical information such as hurricane evacuation notices is challenging in the United States. These notices are issued and distributed rapidly by numerous local authorities that may spread across multiple states. They often undergo frequent updates and are distributed through diverse online portals lacking standard formats. In this study, we developed an approach to timely detect and track the locally issued hurricane evacuation notices. The text data were collected mainly with a spatially targeted web scraping method. They were manually labeled and then classified using natural language processing techniques with deep learning models. The classification of mandatory evacuation notices achieved a high accuracy (recall = 96%). We used Hurricane Ian (2022) to illustrate how real-time evacuation notices extracted from local government sources could be redistributed with a Web GIS system. Our method applied to future hurricanes provides live data for situation awareness to higher-level government agencies and news media. The archived data helps scholars to study government responses toward weather warnings and individual behaviors influenced by evacuation history. The framework may be applied to other types of disasters for rapid and targeted retrieval, classification, redistribution, and archiving of real-time government orders and notifications.

Amidst the COVID-19 pandemic, travel restrictions have emerged as crucial interventions for mitigating the spread of the virus. In this study, we enhance the predictive capabilities of our model, Sequence-to-Sequence Epidemic Attention Network (S2SEA-Net), by incorporating an attention module, allowing us to assess the impact of distinct classes of travel distances on epidemic dynamics. Furthermore, our model provides forecasts for new confirmed cases and deaths. To achieve this, we leverage daily data on population movement across various travel distance categories, coupled with county-level epidemic data in the United States. Our findings illuminate a compelling relationship between the volume of travelers at different distance ranges and the trajectories of COVID-19. Notably, a discernible spatial pattern emerges with respect to these travel distance categories on a national scale. We unveil the geographical variations in the influence of population movement at different travel distances on the dynamics of epidemic spread. This will contribute to the formulation of strategies for future epidemic prevention and public health policies.

Walmart employees will start delivering groceries right to a customer's refrigerator in three cities this fall. Starting this fall, Walmart customers can not only buy groceries online, they can then have them dropped off right in their kitchen. More than 1 million shoppers in Kansas City, Mo., Vero Beach, Fl, and Pittsburgh, Pa. will be able to use Walmart's new "InHome'' service, the latest volley in the delivery wars being waged by retailers racing to woo customers with convenience and speed. Walmart employees will first pick the produce or other household items, ordered by a shopper online. They will then deliver food items into the customer's refrigerator, using smart technology that enables the homeowner to let them in and watch what they do while they're there "Once we learned how to do pickup well, we knew it would unlock the ability to deliver,'' Doug McMillon, Walmart's president and CEO, said in a statement.

Perhaps you'll see this one coming like an angry regulator pacing up to the door. But if you wonder what federal mandates, a motorcycle, a baby boomer hairdresser and a blown business opportunity might have in common, read on. Our story of regulatory woe on the go begins with 60-year-old Geri Michael of Vero Beach, Florida. The hairdresser decided it was high time to treat herself to a Honda motorcycle. Her checklist was looking pretty good, too.