Developing Human-Centered Urban Digital Twins for Community Infrastructure Resilience: A Research Agenda.

Urban digital twins (UDTs) have been identified as a potential technology to achieve digital transformative positive urban change through landscape architecture and urban planning. However, how this new technology will influence community resilience and adaptation planning is currently unclear. This article: (1) offers a scoping review of existing studies constructing UDTs, (2) identifies challenges and opportunities of UDT technologies for community adaptation planning, and (3) develops a conceptual framework of UDTs for community infrastructure resilience. This article highlights the need for integrating multi-agent interactions, artificial intelligence, and coupled natural-physical-social systems into a human-centered UDTs framework to improve community infrastructure resilience.

Network Science-based Urban Forecast Dashboard.

The urban environment is a highly dynamic and complex system. Urban dynamics in this complex system is largely reflected by the movement of people to and from Places of Interest (POIs) in the urban area. To better understand and plan for the city's various scenarios, there is a need to forecast urban dynamic conditions in terms of the possible movements of people across POIs. However, such predictions are not easy because an interdependent and living system is hard to forecast. In addition, the commuting and shopping of individuals in urban environments will show distinct patterns at various stages of disasters as compared to normal situations. This paper presents a network science-based urban forecast dashboard, in order to monitor urban events and identify the interdependencies that characterize urban dynamics. Behind the dashboard is a deep learning model that incorporates the network dynamics between POIs. The dashboard powers the prediction of urban dynamics from a network science perspective. This research calls for a unified framework to model the flow and network in the city. The dashboard visualizes how network science and urban science can mutually benefit from each other.

Determinants of Hurricane Evacuation from a Large Representative Sample of the U.S. Gulf Coast.

Exposure to natural disasters like hurricanes negatively impacts the mental and physical health of populations, and evacuation is an important step taken to prevent these adverse health events. This study uses data from a large representative sample of U.S. Gulf Coast residents to explore the determinants of hurricane evacuation. In December 2017, data were collected from 3030 residents of the U.S. Gulf Coast, including Texas, Louisiana, Mississippi, Alabama, and Florida-2557 of whom reported being impacted during the 2017 hurricane season. Bivariate analyses were conducted using prevalence differences and tested for statistical significance with chi-square tests. Multivariable logistic regression models were fitted to identify factors associated with hurricane evacuation. One-third of the respondents (919 of 2557; 35.9%) evacuated from a hurricane that impacted the U.S. Gulf Coast in 2017. The determinants of hurricane evacuation in this population were: residing in a mobile home, higher perception of storm surge risk, higher perception of wind risk, self-sufficiency, carrying flood insurance, and reliance on media and family for evacuation decisions. These findings may be relevant for reducing the adverse health effects of hurricanes by improving emergency planning and evacuation in this highly vulnerable region.