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Food pantries play a growing role in supporting households facing or at risk of food insecurity in the United States. They also support emergency response and recovery following disasters and extreme weather events. Although food pantries are often placed in close proximity to communities with the highest rates of poverty and risk of food insecurity, access to these facilities can be disrupted during and after extreme events. Decreased access to food pantries following disasters can be particularly problematic as the need for these services is also likely to grow after such events. Despite the vast body of research on food retail access and food environments, relatively little research has utilized spatial analysis to understand food pantry access, particularly after extreme events. Using Geographic Information Systems (GIS), we characterize changes in access to food pantries following flooding events in Harris County, Texas-a highly populated and flood prone region with high levels of food insecurity and poverty. Specifically, our study models disruptions in road networks due to flooding and assesses the impacts of these disruptions on proximity to food pantries. The results reveal that much of Harris County sees only small increases in travel distance to food pantries due to extreme flooding, but some areas may be unable to access food pantries at all. This research highlights the potential and some of the limits of private food assistance networks to support emergency response efforts.

This paper presents a methodology to deaggregate the results of a multi-hazard damage analysis by extending the traditional multi-hazard damage analysis to consider both population characteristics and independent hazards. The methodology is applied to the joint seismic-tsunami hazard at Seaside, Oregon, considering four infrastructure systems: (1) buildings, (2) transportation network, (3) electric power network and (4) water supply network. Damages to all infrastructure systems are evaluated, and the networked infrastructures are used to inform parcel connectivity to critical facilities. US Census data and a probabilistic housing unit allocation method are implemented to assign detailed household demographic characteristics at the parcel level. Six dimensions of deaggregation are introduced: (1) spatial, (2) hazard type, (3) hazard intensity, (4) infrastructure system, (5) infrastructure component, and (6) housing unit characteristics. The damages, economic losses and risks, and connectivity to critical facilities are deaggregated across these six dimensions. The results show that deaggregated economic loss and risk plots can allow community resilience planners the ability to isolate high-risk events, as well as provide insights into the underlying driving forces. Geospatial representation of the results allows for the identification of both vulnerable buildings and areas within a community and is highlighted by the spatial pattern of parcel disconnection from critical facilities. The incorporation of population characteristics provides an understanding of how hazards disproportionately impact population subgroups and can aide in equitable resilience planning.

The data and information available at the community-scale are directly linked to the ability to make a resilience-informed decision in natural hazards. This paper develops a systematic approach to ...

This paper presents a methodology that generates and links high-resolution spatial data on households and housing units with heterogeneous characteristics (i.e., size, tenure status, occupi...

Tornadoes occur at a high frequency in the United States compared with other natural hazards but have a substantially small footprint. A single high-intensity tornado can result in high cas...

The combined effect of storm surge and wave action during severe storms in coastal regions can cause significant damage to civil infrastructures with cascading consequences to coastal commu...

This paper proposes probabilistic flow-based models for the capacity and demand of critical infrastructure considering different levels of resolution. The models are set in a probabilistic procedure that captures the impact of the damaging event on the infrastructure. The procedure predicts the reduction or loss of functionality of the infrastructure in terms of their ability to provide essential goods or services. In the aftermath of a damaging event, infrastructure change their capacities, due to the damage to the physical components, and they may not be able to satisfy pre-event demands. Furthermore, the post-event demands at the nodes of the infrastructure might change because of the human response (e.g., evacuation or relocation.) The probabilistic procedure presented in this paper integrates physical infrastructure and social systems to predict the change in demand on the infrastructure. As an example, the paper applies the proposed procedure to the modeling of the potable water network of Seaside, Oregon considering a seismic event as the damaging event. The paper shows that neglecting the interdependency between physical infrastructure and social systems may result in estimates of i) higher demands on the physical systems; ii) slower recovery; and iii) smaller impacts on society in terms of population dislocation.

For households of all income levels, and especially for those that are food insecure, food access can be threatened by natural hazards. Extreme natural hazards can disrupt critical infrastructure s...

Tornadoes occur at a high frequency in the United States compared with other natural hazards but have a substantially smaller footprint. Even a single high-intensity tornado can result in high casualty rates and associated catastrophic economic losses as well as social consequences, particularly for small to medium communities. The city of Joplin, Missouri, USA, was hit by an EF-5 tornado on May 22, 2011. The Center for Risk-Based Community Resilience Planning simulated this event for buildings and the electrical power network of Joplin in an open source computational environment called IN-CORE. The initial damage prediction utilized the tornado path, tornado fragility curves representative of a 19-archetype building dataset, and EPN datasets. The functionality of the infrastructure was linked with a computable general equilibrium (CGE) economics model that computes household income, employment, and domestic supply before and after the tornado event occurrence. Detailed demographic data was allocated to each structure to provide resilience metrics related to population impacts such as population dislocation as a function of tenure status of households. This example demonstrates how users interact with the IN-CORE computational environment.

In early October 2016, Hurricane Matthew crossed North Carolina as a Category 1 storm, with some areas receiving 0.38–0.46 m (15–18 in.) of rainfall on already saturated soil. The NIST-fund...

Medicare utilization and costs for residents of the U.S. Gulf Coast, who are highly vulnerable to natural disasters, may be impacted by their disaster exposure. To estimate differences in healthcare utilization by disaster exposure, we calculated Medicare expenditures among residents of U.S. Gulf States and compared them with expenditures among residents of other regions of the U.S. Panel models were used to calculate changes in overall Medicare expenditures, inpatient expenditures, and home health expenditures for 32,819 Medicare beneficiaries. Individual demographic characteristics were included as predictors of change in expenditures. Medicare beneficiaries with National Health Interview Survey participation were identified and Part A claims were linked. Federal Emergency Management Agency (FEMA) data was used to determine counties that experienced no, some, high, and extreme hazard exposure. FEMA data was merged with Medicare claims data to create a panel dataset from 2001 to 2007. Medicare Part A claims for the years 2001 to 2007 were merged with FEMA data related to disasters in each U.S. County. Overall Medicare costs, as well as costs for inpatient and home health care for residents of states located along the U.S. Gulf Coast (Texas, Louisiana, Mississippi, Alabama, and Florida) were compared to costs for residents of the rest of the U.S. Expenditures among residents of U.S. Gulf States decreased with increased hazard exposure. Decreases in inpatient expenditures persisted in the years following a disaster. The use of beneficiary-level data highlights the potential for natural hazards to impact health care costs. This study demonstrates the possibility that exposure to more severe disasters may limit access to health care and therefore reduce expenditures. Additional research is needed to determine if there is a substitution of services (e.g., inpatient rehabilitation for home health) in disaster-affected areas during the post-disaster period.