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I came to know rivers throughout the United States, ranging from the Canning in Alaska to the Everglades in Florida, from the wilderness Colorado River to the artificial Los Angeles River. Like a r...

The concept of the Anthropocene provides the reflexive rubric for examining human alteration of the Earth’s basic natural systems and, in turn, how society responds to and adapts to such changes in...

This study investigates evacuation behaviors associated with Hurricane Matthew in October of 2016. It assesses factors influencing evacuation decisions and evacuation departure times for Florida, Georgia, and South Carolina from an online survey of respondents. Approximately 62% of the Florida sample, 77% of the Georgia sample, and 67% of the South Carolina sample evacuated. Logistic regression analysis of the departures in the overall time period identified variability in evacuation timing, primarily dependent on prior experience, receipt of an evacuation order, and talking with others about the evacuation order. However, using four logistic regressions to analyze differences in departure times by day shows that the only significant variable across the three main days of evacuation was our proxy variable for evacuation-order times. Depending on the day, other variables of interest include number of household vehicles, previous hurricane experience, and receipt of an evacuation order. Descriptive results show that many variables are considered in the decision to evacuate, but results from subsequent analyses, and respondents’ comments about their experiences, highlight that evacuation orders are the primary triggering variable for when residents left.

With rapid urbanization and increasing climate risks, enhancing the resilience of urban systems has never been more important. Despite the availability of massive datasets of human behavior (e.g., mobile phone data, satellite imagery), studies on disaster resilience have been limited to using static measures as proxies for resilience. However, static metrics have significant drawbacks such as their inability to capture the effects of compounding and accumulating disaster shocks; dynamic interdependencies of social, economic, and infrastructure systems; and critical transitions and regime shifts, which are essential components of the complex disaster resilience process. In this article, we argue that the disaster resilience literature needs to take the opportunities of big data and move toward a different research direction, which is to develop data-driven, dynamical complex systems models of disaster resilience. Data-driven complex systems modeling approaches could overcome the drawbacks of static measures and allow us to quantitatively model the dynamic recovery trajectories and intrinsic resilience characteristics of communities in a generic manner by leveraging large-scale and granular observations. This approach brings a paradigm shift in modeling the disaster resilience process and its linkage with the recovery process, paving the way to answering important questions for policy applications via counterfactual analysis and simulations.

In this special issue of Environment, we focus on what sustainability might look like in a post-COVID-19 era. With topics ranging from the nexus of zoonotic infectious diseases and nature, to how t...

The nexus of natural hazards, climate change, and community resilience poses both conceptual and methodological challenges. One key consideration is the underlying notion of dynamic change or trans...

Photo courtesy of Barb RichmanGilbert F. White (1911–2006) is among the most distinguished and honored American geographers of the 20th century. Among his many contributions, he stimulated efforts ...

This paper examines the spatial and temporal trends in county-level COVID-19 cases and fatalities in the United States during the first year of the pandemic (January 2020–January 2021). Statistical and geospatial analyses highlight greater impacts in the Great Plains, Southwestern and Southern regions based on cases and fatalities per 100,000 population. Significant case and fatality spatial clusters were most prevalent between November 2020 and January 2021. Distinct urban–rural differences in COVID-19 experiences uncovered higher rural cases and fatalities per 100,000 population and fewer government mitigation actions enacted in rural counties. High levels of social vulnerability and the absence of mitigation policies were significantly associated with higher fatalities, while existing community resilience had more influential spatial explanatory power. Using differences in percentage unemployment changes between 2019 and 2020 as a proxy for pre-emergent recovery revealed urban counties were hit harder in the early months of the pandemic, corresponding with imposed government mitigation policies. This longitudinal, place-based study confirms some early urban–rural patterns initially observed in the pandemic, as well as the disparate COVID-19 experiences among socially vulnerable populations. The results are critical in identifying geographic disparities in COVID-19 exposures and outcomes and providing the evidentiary basis for targeting pandemic recovery.

We develop and study a quantitative, interdisciplinary strategy for conducting statistical risk analyses within the 'benchmark risk' paradigm of contemporary risk assessment when potential autocorrelation exists among sample units. We use the methodology to explore information on vulnerability to natural hazards across 3108 counties in the conterminous 48 US states, applying a place-based resilience index to an existing knowledgebase of hazardous incidents and related human casualties. An extension of a centered autologistic regression model is applied to relate local, county-level vulnerability to hazardous outcomes. Adjustments for autocorrelation embedded in the resiliency information are applied via a novel, non-spatial neighborhood structure. Statistical risk-benchmarking techniques are then incorporated into the modeling framework, wherein levels of high and low vulnerability to hazards are identified.

As improved data availability and disaster resilience knowledge help progress community resilience quantification schemes, spatial refinements of the associated empirical methods become increasingly crucial. Most existing empirically based indicators in the U.S. use county-level data, while qualitatively based schemes are more locally focused. The process of replicating resilience indices at a sub-county level includes a comprehensive study of existing databases, an evaluation of their conceptual relevance in the framework of resilience capitals, and finally, an analysis of the statistical significance and internal consistency of the developed metrics. Using the U.S. Gulf Coast region as a test case, this paper demonstrates the construction of a census tract-level resilience index based on BRIC (Baseline Resilience Indicators for Communities), called TBRIC. The final TBRIC construct gathers 65 variables into six resilience capitals: social, economic, community, institutional, infrastructural, and environmental. The statistical results of tract- and county-level BRIC comparisons highlight levels of divergence and convergence between the two measurement schemes and find higher reliability for the fine-scale results.

Walter W. Piegorsch, Rachel R. McCaster and Susan L. Cutter explain how the tools of data science can help quantify the risks and vulnerabilities to hazards in the places where we live and work

Climate change migration is increasing and necessitates a re-examination of resettlement planning and processes. Although evidence-based selection of host places would improve climate change resettlement outcomes, few methods for the selection of host communities exist. Consequently, the information base on which most resettlement programs select a host place is often inadequate. This article proposes an empirical methodology to assess resettlement capacity. The methodology uses a hierarchical aggregation approach, where resettlement capacity indicator values are aggregated first into sub-dimension resettlement capacity scores, then further into dimension resettlement capacity scores, and finally into an overall resettlement capacity index. The aggregation allows for the calculation of the relative importance of the different sub-dimensions and the two primary dimensions – assets and conditions. Using 75 indicators and a hierarchical min–max additive approach based on a five-kilometer grid, we create an overall resettlement capacity index for Ethiopia, with and without normalizing the relevant indicators for population size. The results show significant spatial variation in resettlement capacity, and a clear difference between using the population size normalized and non-normalized indicators, particularly regarding places with very low population density. High resettlement capacity sites are scattered in central, southcentral, and northern Ethiopia, and they also occur in small clusters along southern and northwestern borders. Moderate resettlement capacity sites occur more generally all over Ethiopia. Sites with low resettlement capacity cluster in southeastern and western parts of the country. Compared to the low and moderate resettlement capacity sites, those with high resettlement capacity are endowed with human, physical, and financial capital infrastructures. In all three capacity groups, assets contribute significantly less to resettlement capacity than conditions. Sites that are prone to natural hazards, both currently and in the future, are concentrated in the western and northern tips of the country. The calculated resettlement capacity indices are robust to potential missing indicators and change in the unit of analysis. The findings of the study can be used to identify areas for more comprehensive, localized analyses to determine their suitability for resettlement.

The resilience concept has become more significant in the past decade as a means for understanding how cities prepare and plan for, absorb, recover from, and more successfully adapt to adverse events. Definitional differences—resilience as an outcome or end-point versus resilience as a process of building capacity—dominate the literature. Lagging behind are efforts to systematically measure resilience to produce a baseline and subsequent monitoring, in order to gauge what, where, and how intervention or mitigation strategies would strengthen or weaken urban resilience. The chapter reviews research and practitioner attempts to develop urban informatics for resilience and provides selected case studies of cities as exemplars.

Evacuations are the most effective protective strategy adopted to minimize the deadly threat of an incoming hurricane. The study of evacuations has a long history in the United States, and ...

How should we characterize catastrophic events like the 2011 Tohoku earthquake in Japan, or Hurricane Harvey in Houston, or Hurricanes Irma/Maria in Puerto Rico? Are these singular events? Multihaz...

The allure of disaster resilience studies continues to garner interest by policy makers and academics alike. While there are advances in assessing communities’ resilience to natural hazards at different scales, monitoring changes in resilience lags behind. This paper updates the 2010 Baseline Resilience Index for Communities (BRIC) using the six different domains of disaster resilience. The purpose is to test for significant spatial and temporal change in county index values by providing a comparative assessment of increased or decreased resilience over a five-year period across the U.S. The significance of monitoring change is to empirically demonstrate the dynamic nature of resilience and the causal mechanisms that lead to increasing or decreasing resilience in places. Such evidence sets the stage for implementing intervention policies or programs designed to enhance disaster resilience. The national distribution of BRIC index values in 2015 is generally similar to the 2010 BRIC distribution, bu...

Summary We develop a quantitative methodology to characterize vulnerability among 132 US urban centres (‘cities’) to terrorist events, applying a place-based vulnerability index to a database of terrorist incidents and related human casualties. A centred autologistic regression model is employed to relate urban vulnerability to terrorist outcomes and also to adjust for auto-correlation in the geospatial data. Risk analytic ‘benchmark’ techniques are then incorporated in the modelling framework, wherein levels of high and low urban vulnerability to terrorism are identified. This new translational adaptation of the risk benchmark approach, including its ability to account for geospatial auto-correlation, is seen to operate quite flexibly in this sociogeographic setting.

In the last decade, with the increment of the occurrence of extreme meteorological events in Colombia, the disaster management field has grown aided by political intervention and investment. However, the country currently lacks a comprehensive integrated place-based assessment of social vulnerability that covers the entire territory. In consequence, the present study employed the SoVI® method to understand the pre-existing social vulnerability throughout the territory as a first step in national disaster risk reduction and climate change adaptation planning. The social vulnerability assessment presented in this study is based on the Social Vulnerability Index (SoVI®) developed for the United States of America (USA). The Colombian index was constructed using data from the 2005 census provided by the National Administrative Department of Statistics (DANE) and the 2009 data of the Selecting Beneficiaries of Social Programs (SISBEN). The analysis included the municipalities with available information of their urban and rural areas (1118). The results show seven factors that explain 64% of the variance. The values range from +17.85 (High social vulnerability) to −4.85 (low social vulnerability). In general, the most vulnerable municipalities are located in the Pacific and Amazon regions, the southern areas of the Orinoco and the northern part of the Caribbean. The application of SoVI® for Colombia presented in this paper indicates a regionalization of vulnerability within the territory between areas of industrial and economic growth (urban), and areas of poor socio-economic development that may also be marked by the presence of political conflict and production of illegal crops (rural).

Resilience measurement continues to be a meeting ground between policy makers and academics. However, there are inherent limitations in measuring disaster resilience. For example, resilience indicators produced by FEMA and one produced by an independent academic group (BRIC) measure community resilience by defining and quantifying community resilience at a national level, but they each have a different conceptual model of the resilience concept. The FEMA approach focuses on measuring resilience capacity based on preparedness capabilities embodied in the National Preparedness Goals at state and county scales. BRIC examines community (spatially defined as county) components (or capitals) that influence resilience and provides a baseline of pre-existing resilience in places to enable periodic updates to measure resilience improvements. Using these two approaches as examples, this paper examines the differences and similarities in these two approaches in terms of the conceptual framing, data resolution, and representation and the resultant statistical and spatial differences in outcomes. Users of resilience measurement tools need to be keenly aware of the conceptual framing, input data, and geographic scale of any schema before implementation as these parameters can and do make a difference in the outcome even when they claim to be measuring the same concept.

Twenty years ago this journal published an article examining the disproportionate burden of global environmental changes on women and children, highlighting the inequalities in capabilities and opportunities for coping with risks. This retrospective re-examines selected global social and environmental transformations that have increased gendered vulnerability illustrating that over the past two decades, inequalities persist and in some instances have worsened. The social transformations underway—especially widening wealth gaps, large-scale population movements, gendered violence–exacerbate the environmental burdens on women and children even more. Changes in food security, water availability, and air quality continue to produce adverse effects on women and children reducing their ability to cope with everyday risk let alone respond and recover from disasters. Women and children continue to bear the brunt of adverse impacts of disasters and environmental change however we don't know the magnitude or geographic extent of the burden because there is no legacy of data reliability to consistently document these injustices. What gets measured gets done, but in the case of women and children they are often the undocumented victims further reproducing the notion of the forgotten casualties.

Big data, that is, data that are byproducts of our lives rather than designed for research purposes, are the newest of the information highway innovations. One of the important challenges to social and behavioral science data collection, curation, and dissemination for the foreseeable future is to link diverse forms of data in a way that is cumulative, representative, meaningful, and accessible to a broad range of researchers. It is critical to explore the new questions these data can address and to develop new methods to address them, including linking persons and information about them and their environments across different data platforms while maintaining confidentiality and privacy. Linking a broad array of information—from administrative data (local and state and regional), to social media (Twitter, Facebook), to census and other surveys, to ethnographic data, and data from experiments such as randomized controlled trials—to address how humans and their communities make decisions is challenging. This issue was addressed by papers presented at a conference on New Data Linkages convened by the Social Observatories Coordinating Network in 2016; those articles are brought together in this volume.

Food security is a complex and intertwined problem of reliability, quantity, and affordability of nutritious food, including the costs of production. It is a problem in developing and developed nat...

Despite major advancements in knowledge on disaster risks and disasters caused by natural hazards, the number and severity of disasters are increasing. Convolving natural, engineering, social and behavioral sciences and practices with policymaking should significantly reduce disaster risks caused by natural hazards. To this end, a fundamental change in scientific approaches to disaster risk reduction is needed by shifting the current emphasis on individual hazard and risk assessment dominant in the geoscientific community to a transdisciplinary system analysis with action-oriented research on disaster risk reduction co-produced with multiple stakeholders, including policymakers. This paradigm shift will allow for acquisition of policy-relevant knowledge and its immediate application to evidence-based policy and decision making for disaster risk reduction. The need for the paradigm shift is more critical now than ever before because of the increasing vulnerability and exposure of society to disaster risk and the need for cross-cutting actions in policy and practice related to climate change and sustainability.

The concept of disaster resilience has gained attention in political spheres and news outlets over the past few years, yet relatively few empirical measures of the concept exist. Furthermore, resea...

During the past decade, the number of earthquakes has increased dramatically in the state of Oklahoma, largely attributed to induced seismicity from wastewater injections (hydraulic fracturing). The increased seismic disaster risk in Oklahoma has captured public attention and necessitated actions by decision makers to mediate the consequences. Geospatial modeling to identify the populations exposed to higher levels of potential risk can help prioritize locations for mitigation actions based on the underlying social vulnerability of residents. In this paper, we explore a method for integrating the spatial distribution of seismic risk (hazard exposure) with social vulnerability (hazard impact). Loss scenarios, social vulnerability metrics, and potential physical damage are combined in a geographic information system to identify the spatial vulnerability of an exposed population to the increased seismic risk, and the locations for targeting mitigation actions — areas with the greatest exposure and vulnerability. The results of induced seismicity earthquake scenarios show disproportionately higher losses for places with more minority populations (primarily African-American) and more renters when compared to the non-induced scenarios, suggesting a potential environmental justice concern.

Hurricanes are one of the most common natural hazards in the United States. To reduce fatalities and economic losses, coastal states and counties take protective actions, including sheltering in place and evacuation away from the coast. Not everyone adheres to hurricane evacuation warnings or orders. In reality, evacuation rates are far less than 100 percent and are estimated using posthurricane questionnaire surveys to residents in the affected area. To overcome limitations of traditional data collection methods that are costly in time and resources, an increasing number of natural hazards studies have used social media data as a data source. To better understand social media users’ evacuation behaviors, this article investigates whether activity space, social network, and long-term sentiment trends are associated with individuals’ evacuation decisions by measuring and comparing Twitter users’ evacuation decisions during Hurricane Matthew in 2016. We find that (1) evacuated people have larger long-term activity spaces than nonevacuated people, (2) people in the same social network tend to make the same evacuation decision, and (3) evacuated people have smaller long-term sentimental variances than nonevacuated people. These results are consistent with previous studies based on questionnaire and survey data and thus provide researchers a new method to study human behavior during disasters. Key Words: big data, disaster management, evacuation, hurricane, social media.