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Hurricanes lead to substantial infrastructure system damages, such as roadway closures and power outages, in the US annually, especially in states like Florida. As such, this paper aimed to assess the impacts of Hurricane Hermine (2016) and Hurricane Michael (2018) on the City of Tallahassee, the capital of Florida, via exploratory spatial and statistical analyses on power outages and roadway closures. First, a geographical information systems (GIS)-based spatial analysis was conducted to explore the power outages and roadway closure patterns in the city including kernel density estimation (KDE) and density ratio difference (DRD) methods. In order to provide a more detailed assessment on which population segments were more affected, a second step included a statistical analysis to identify the relationships between demographic- and socioeconomic-related variables and the magnitude of power outages and roadway closures caused by these hurricanes. The results indicate that the high-risk locations for roadway closures showed different patterns, whereas power outages seemed to have similar spatial patterns for the hurricanes. The findings of this study can provide useful insights and information for city officials to identify the most vulnerable regions which are under the risk of disruption. This can lead to better infrastructure plans and policies.

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Roadway accidents claim more than 30,000 lives each year in the United States, and they continue adversely affecting people's well-being. This problem becomes even more challenging when aging populations are considered due to their vulnerability to accidents. This is especially a major concern in Florida since the accident risk is increasing proportionally to the population growth of aging Floridians. This study investigates the spatial and temporal patterns of aging people-involved accidents using geographical information systems (GIS)-based methods via a case study of three urban counties in Florida, selected based on their high aging-involved accident rates. A series of spatial analytic methods are utilized to explore accident patterns, including a network distance-based kernel density estimation method, which provides an unbiased distribution of the accidents over the local roadways. An accident density ratio measure is also developed in order to understand how accidents involving aging people occur at different locations than those of the general population. Results indicate that high risk locations for aging-involved accidents show different spatial and temporal patterns than those for other age groups. Investigating these distinct patterns at a high spatio-temporal scale can lead to better aging-focused transportation plans and policies.

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