Uncertainty Analysis for a Social Vulnerability Index

Investigating the effects of spatial scales on social vulnerability index: A hybrid uncertainty and sensitivity analysis approach combined with remote sensing land cover data

Investigating the effects of spatial scales on the uncertainty and sensitivity analysis of the social vulnerability index (SoVI) model output is critical, especially for spatial scales finer than the census block group or census block. This study applied the intelligent dasymetric mapping approach to spatially disaggregate the census tract scale SoVI model into a 300-m grids resolution SoVI map in Davidson County, Nashville. Then, uncertainty analysis and variance-based global sensitivity analysis were conducted on two scales of SoVI models: (a) census tract scale; (b) 300-m grids scale. Uncertainty analysis results indicate that the SoVI model has better confidence in identifying places with a higher socially vulnerable status, no matter the spatial scales in which the SoVI is constructed. However, the spatial scale of SoVI does affect the sensitivity analysis results. The sensitivity analysis suggests that for census tract scale SoVI, the indicator transformation and weighting scheme are the two major uncertainty contributors in the SoVI index modeling stages. While for finer spatial scales like the 300-m grid's resolution, the weighting scheme becomes the uttermost dominant uncertainty contributor, absorbing uncertainty contributions from indicator transformation.

Reconciling and contextualising multi-dimensional aspects for consolidated water security index: A synthesis

The present paper aims to review and develop a Consolidated Water Security Index (CWSI) as a tool to evaluate water security status within river basins by considering five key dimensions, namely (i) water supply and sanitation, (ii) water demand and socio-economic, (iii) water ecosystem and environment, (iv) water-related disaster and (v) water governance. This index is a holistic assessment since it aims to capture the interconnected and complex nature of water-related issues by considering multiple dimensional aspects which helps stakeholders and policymakers to understand the overall status of water security. This framework uses the Analytic Hierarchy Process (AHP), involving pairwise comparison, normalisation and weighting. Then, a CWSI will be calculated using the Linear Aggregation method. The robustness of this consolidated index is validated using sensitivity analysis by modifying the weight in the linear aggregation formula. By following each step cautiously, a CWSI can be constructed and interpreted correctly, thus, becoming a powerful tool for conveying complex information to the different stakeholders and assessing the status of water security with river basins. The CWSI allows decision-makers to prioritise areas that are most at risk and facilitate sustainable planning and management of water resources.

Geospatial Analysis of Social Vulnerability, Race, and Firearm Violence in Chicago

INTRODUCTION

Urban firearm violence (UFV) is associated with inequities rooted in structural racism and socioeconomic disparities. Social vulnerability index (SVI) is a composite measure that encompasses both. We sought to understand the relationship between SVI and the incidence of UFV in Chicago using geospatial analysis for the first time.

MATERIALS AND METHODS

Firearm assaults in Chicago 2001-2019 were obtained from the Trace. Locations of incidents were geocoded using ArcGIS and overlaid with census tract vector files. These data were linked to 2018 SVI measures obtained from the Center for Disease Control and Prevention. Shooting rates were calculated by tabulating the total number of shootings per capita in each census tract. We used Poisson regression with robust error variance to estimate the incident rate of UFV in different levels of social vulnerability and Local Moran's I to evaluate spatial autocorrelation.

RESULTS

In total, 642 census tracts were analyzed. The median shooting rate was 2.6 per 1000 people (interquartile 0.77, 7.0). When compared to those census tracts with very low SVI, census tracts with low SVI had a 1.7-time increased incident rate of shootings (incidence rate ratio [IRR] 1.74, 95% CI 1.08, 2.81), tracts with moderate SVI had a 3.1-time increased incident rate (IRR 3.07, 95% CI 2.31, 4.10), and tracts with high SVI had a 7-time increased incident rate (IRR 7.03, 95% CI 5.45, 9.07).

CONCLUSIONS

In Chicago, social vulnerability has a significant association with rates of firearm violence, providing a focus point for policy intervention to address high rates of interpersonal violence in similar cities.

Advancing the community health vulnerability index for wildland fire smoke exposure

Wildland fire smoke risks are not uniformly distributed across people and places, and the most vulnerable communities are often disproportionately impacted. This study develops a county level community health vulnerability index (CHVI) for the Contiguous United States (CONUS) using three major vulnerability components: adaptive capacity, sensitivity, and exposure at the national and regional level. We first calculated sensitivity and adaptive capacity sub-indices using nine sensitivity and twenty adaptive capacity variables. These sub-indices were then combined with an exposure sub-index, which is based on the Community Multiscale Air Quality data (2008-2018), to develop CHVI. Finally, we conducted several analyses with the derived indices to: 1) explore associations between the level of fine particulate matter from wildland fires (fire-PM2.5) and the sub-indices/CHVI; 2) measure the impact of fire-PM2.5 on the increase in the annual number of days with 12-35 μg/m3 (moderate) and >35 μg/m3 (at or above unhealthy for sensitive groups) based on the US EPA Air Quality Index categories, and 3) calculate population size in different deciles of the sub-indices/CHVI. This study has three main findings. First, we showed that the counties with higher daily fire-PM2.5 concentration tend to have lower adaptive capacity and higher sensitivity and vulnerability. Relatedly, the counties at high risk tended to experience a greater increase in the annual number of days with 12-35 μg/m3 and >35 μg/m3 than their counterparts. Second, we found that 16.1, 12.0, and 17.6 million people out of 332 million in CONUS reside in the counties in the lowest adaptive capacity decile, highest sensitivity decile, and highest vulnerability decile, respectively. Third, we identified that the US Northwest, California, and Southern regions tended to have higher vulnerability than others. Accurately identifying a community's vulnerability to wildfire smoke can help individuals, researchers, and policymakers better understand, prepare for, and respond to future wildland fire events.

Social vulnerability of the people exposed to wildfires in U.S. West Coast states

Understanding of the vulnerability of populations exposed to wildfires is limited. We used an index from the U.S. Centers for Disease Control and Prevention to assess the social vulnerability of populations exposed to wildfire from 2000-2021 in California, Oregon, and Washington, which accounted for 90% of exposures in the western United States. The number of people exposed to fire from 2000-2010 to 2011-2021 increased substantially, with the largest increase, nearly 250%, for people with high social vulnerability. In Oregon and Washington, a higher percentage of exposed people were highly vulnerable (>40%) than in California (~8%). Increased social vulnerability of populations in burned areas was the primary contributor to increased exposure of the highly vulnerable in California, whereas encroachment of wildfires on vulnerable populations was the primary contributor in Oregon and Washington. Our results emphasize the importance of integrating the vulnerability of at-risk populations in wildfire mitigation and adaptation plans.

Impacts of household vulnerability on hurricane logistics evacuation under COVID-19: The case of U.S. Hampton Roads

Historical data suggest that when a severe tropical storm or hurricane impacts a community, the vulnerable segment of the population suffers the most severe consequences. With an increased aging population, it is crucial to understand how vulnerability alters evacuation behavior. Emergent variables such as fear of COVID-19 require additional exploration. People afraid of COVID-19 exposure may refuse to evacuate, exposing themselves unnecessarily. Differentiation is critical to evacuation logistics since it is needed to determine what proportion would stay in a local shelter, public or other, rather than evacuating or staying in their home and guide the logistics resource allocation process. This research uses data from a web and phone survey conducted in the Hampton Roads area of U.S. Virginia, with 2,200 valid responses to analyze the influence of social and demographic vulnerability factors and risk perception on evacuation decisions. This research contributes to the existing literature by developing a multinomial order logit model based on vulnerability factors and intended evacuation decisions, including staying at home, looking for a shelter, or leaving the Hampton Roads area. Findings show that race and risk perception are the variables that influence the decision-making process the most. Fear of COVID-19 transmission is also associated with an increased likelihood of leaving homes during evacuation. The variations in findings from previous studies are discussed regarding their implications for logistics emergency managers.

Design of an energy vulnerability index - spatial and temporal analysis: case of study Colombia

Climate change might affect energy production and therefore the energy security of a country or region. This situation may impact renewable energy sources such as hydro power, leading to consequences on energy transition strategies. This might be critical in sensitive regions to climate change, one of them being the Caribe and northern South America. Since there are numerous energy systems based on sensitive technologies worldwide, it is necessary to introduce techniques to analyze the effects of climate change on different possible energy transition paths. The goal of this study is to develop and assess a method to analyze one of the most critical effects faced by climate change for societies worldwide: the sensitivity of the energy systems to climate change. This is especially critical in developing countries, in locations where temperatures will strongly increase in the following years. To assess this effect, this study proposes a vulnerability index (VI) to evaluate the vulnerability of an on-grid electricity system to climate change at the national and regional scales. This index was assessed using a Monte-Carlo method for uncertainty. The case of Colombia, a country with a system based on hydropower (> 70%) is used to illustrate the method. VI is based on variables related to climate change, the energy matrix, and vulnerability. Results show that the regions with the larger vulnerability correspond to the more energy-demanding ones. The VI for these regions is greater than 50% of the maximum possible vulnerability; meanwhile, the vulnerability of the whole country was estimated as 43%.

Knowledge Domain and Emerging Trends of Social Vulnerability Research: A Bibliometric Analysis (1991–2021)

Carrying out social vulnerability research has become an important way to understand the sustainable development of resources, the environment, populations, and societies. Clarifying the research context and development trend of social vulnerability is of great significance to the follow-up theoretical research on and practical exploration of sustainable social development. With the help of a CiteSpace knowledge map analysis, this study reveals the research hotspots and their evolution in different periods and puts forward the key problems in and future direction of social vulnerability research in the future. This study found that the number of articles on social vulnerability research showed a steady upward trend and that the research experienced roughly three stages: an embryonic stage, a development stage, and a stable stage. The United States, Brazil, the United Kingdom, and China accounted for the majority of the research, but the intensity of cooperation between them is still weak. Vulnerability assessment and risk assessment in the context of policy and environmental change are hot topics in the current research. In the future, it is necessary to focus on the comprehensive research on the integrated and cross-scale research on social vulnerability, research on its occurrence and evolution, and on the dynamic monitoring as well as optimal regulation of social vulnerability under multiple pressures.

Comparison of National Vulnerability Indices Used by the Centers for Disease Control and Prevention for the COVID-19 Response

OBJECTIVE

Vulnerability indices use quantitative indicators and geospatial data to examine the level of vulnerability to morbidity in a community. The Centers for Disease Control and Prevention (CDC) uses 3 indices for the COVID-19 response: the CDC Social Vulnerability Index (CDC-SVI), the US COVID-19 Community Vulnerability Index (CCVI), and the Pandemic Vulnerability Index (PVI). The objective of this review was to describe these tools and explain the similarities and differences between them.

METHODS

We described the 3 indices, outlined the underlying data sources and metrics for each, and discussed their use by CDC for the COVID-19 response. We compared the percentile score for each county for each index by calculating Spearman correlation coefficients (Spearman ρ).

RESULTS

These indices have some, but not all, component metrics in common. The CDC-SVI is a validated metric that estimates social vulnerability, which comprises the underlying population-level characteristics that influence differences in health risk among communities. To address risk specific to the COVID-19 pandemic, the CCVI and PVI build on the CDC-SVI and include additional variables. The 3 indices were highly correlated. Spearman ρ for comparisons between the CDC-SVI score and the CCVI and between the CCVI and the PVI score was 0.83. Spearman ρ for the comparison between the CDC-SVI score and PVI score was 0.73.

CONCLUSION

The indices can empower local and state public health officials with additional information to focus resources and interventions on disproportionately affected populations to combat the ongoing pandemic and plan for future pandemics.

Assessing urban resilience to public health disaster using the rough analytic hierarchy process method: A regional study in China

In the context of frequent occurrences of disasters worldwide, disaster-coping capability is imperative for risk reduction and contemporary emergency management. The global COVID-19 pandemic since 2020 has further highlighted the significance of resilience construction at different geographical scales. Overall, the conceptual cognition of resilience in disaster management covers multiple elements and has diverse yielding on regional assessment. This study assesses the local resilience to the public health disaster in the prefecture-level cities, focusing on two dimensions consisting of vulnerability and capability in the targeted provincial region of Jiangsu in China. To this end, based on the vulnerability-capability framework, the Rough Analytic Hierarchy Process (Rough AHP) method was applied to the resilience assessment. Drawing upon the criteria derived from literature, the criteria weights were determined with the RAHP method and we assessed urban resilience with census data. In addition, the hierarchical factors contributing to urban resilience were determined using robustness analysis. This research provides constructive ideas for regional disaster reduction and contributes to the government's capability to improve urban resilience.

Weighting mechanics and the spatial pattern of composite metrics of heat vulnerability in Atlanta, Georgia, USA

This study constructs two biophysical metrics; one based on Land Surface Temperatures (LST) and an integrated spectral index. The latter is an aggregate of Normalized Difference Vegetation Index (NDVI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI). The goal is to determine how disparate weighting techniques, data transformation approaches, and spatial visualization pathways influence the computation of composite heat metrics. Using composite images made of aggregated images from late May to Early September within Google Earth Engine, we generated four composites by combining biophysical metrics with SoVI using equal and Eigen-based weightings informed by Principal Component Analysis (PCA). We compared equal interval classification, global and local Moran's as pathways for spatial visualization of hotspots. We utilized several data transformation techniques in a Geographic Information System (GIS), including rescaling, reclassification, zonal statistics, and spatial weighting. Mann Kendall and Sen's Slope detected and quantified monotonic trends in each spectral index. The results show that the LST biophysical metric and its composites indicate increased heat susceptibility over time, with disproportionately exposed core metro counties. The integrated spectral index and its proxies showed reduced vulnerability hence not a good proxy for LST. At the same time, the Mann Kendall and Sen's Slope found persistent increases in NDVI and NDWI and decreases in NDBI and NDBaI. However, opposite trends were evident in core city counties. The LST-based composites and spectral indices-based composites varied in the spatial-temporal distribution of hotspots. Disparate weighting mechanics, data transformation techniques, and visualization alternatives influence the magnitude and spatial-temporal distribution of heat hotspots.

Targeted implementation of cool roofs for equitable urban adaptation to extreme heat

Cities are facing the twin pressures of greenhouse gas driven climatic warming and locally induced urban heating. These pressures are threatening populations that are sensitive to extreme heat due to sociodemographic factors including economic means. Heat-reducing infrastructure adaptation measures such as reflective "cool" materials can reduce urban temperatures. Here we examine the needs-based equity implications associated with heat-reducing cool roofing in Maricopa County, Arizona through application of high-resolution urban-atmospheric simulations. We simulate heatwave conditions and evaluate the air temperature reduction arising from uniform cool roof implementation (i.e., the entire urbanized county), and contrast results against simulated cooling impacts of needs-based targeted cool roof implementation in sociodemographically heat sensitive areas. We find that installing cool roofs uniformly, rather than in a targeted fashion, provides on average 0.66 °C reduction in the highest heat sensitivity area and 0.39 °C temperature reduction in the lowest heat sensitivity area due in part to a higher roof area density in the heat sensitive area. Targeting cool roof implementation yields 0.45 °C cooling in the most sensitive areas compared to 0.22 °C cooling in the least sensitive areas, meaning that needs-based targeted cool roofs in high sensitivity areas provide more relief than cool roofs targeted at low sensitivity areas, thus providing more cooling where it is most needed. Needs-based targeted implementation has the dual benefits of concurrently producing more than twice as much cooling and reducing heat exposure for the largest absolute number of individuals in the densely populated, highly heat sensitive areas. Targeting cool roof implementation to high heat sensitivity areas, however, does not achieve thermally equal temperatures in Maricopa County because the high sensitivity areas were substantially warmer than low sensitivity areas prior to implementation. This study illustrates the utility of a new "Targeted Urban Heat Adaptation" (TUHA) framework to assess needs-based equity implications of heat-reducing strategies and underscores its importance by examining the impacts of cooling interventions across sociodemographically heterogeneous urban environments.

Farmers’ Distress Index: An Approach for an Action Plan to Reduce Vulnerability in the Drylands of India

Farmer distress is a widely recognized problem in India induced by multiple causes ranging from climate variability to price volatility and the low risk-bearing ability of farmers. Tracking farmers’ distress in a localized context is a prerequisite for timely action to provide sustainable livelihood options. Therefore, a field survey was conducted with 640 dryland farmers of 10 sub-district units from two states in India with the aim to identify the major indicators based on seven dimensions of distress and to construct a multidimensional Farmers’ Distress Index (FDI) at the farmer and sub-district levels. The FDI was built with seven dimensions of distress: exposure to risk, adaptive capacity, sensitivity, mitigation and adaptation strategies, triggers, psychological factors, and impacts. The study developed a broad-based FDI which can be used as a planning tool that can address the causes of farmers’ distress and also evolve measures to tackle those causes. Based on the result, the study recommends a location-specific distress management package based on various dimensions of the FDI. The paper also suggests an upscaling strategy to identify and prioritize the highly distressed farmers as well as sub-district geographical units by tracking a few sets of variables.

A GIS-Based Approach for Flood Risk Zoning by Combining Social Vulnerability and Flood Susceptibility: A Case Study of Nanjing, China

The identification of vulnerable people and places to flood is crucial for effective disaster risk management. Here, we combine flood hazard and social vulnerability index to capture the potential risk of flood. In this paper, Nanjing was taken as the case study to explore the spatial pattern of social vulnerability towards flood at the community scale by developing an index system. Based on the flood risk results of ArcSWAT, the risk of flood disaster in Nanjing was evaluated. The results show the following. (1) Social vulnerability exhibits a central–peripheral pattern in general, which means that the social vulnerability degree is high in the central city and decreases gradually to the suburbs. (2) The susceptibility to flood disaster has a similar circle-layer pattern that is the highest in the urban centre, lower in the exurban areas, and the lowest in the suburb areas. (3) By using the GIS-based zoning approach, communities are classified into four types by comprehensively considering their flood susceptibility and social vulnerability. The spatial pattern is explained, and policy recommendation for reducing flood risk is provided for each type of community. The research has important reference significance for identifying the spatial pattern of social vulnerability to flood and then formulating targeted adaptation countermeasures.

The superspreading places of COVID-19 and the associated built-environment and socio-demographic features: A study using a spatial network framework and individual-level activity data

Previous studies observed that most COVID-19 infections were transmitted by a few individuals at a few high-risk places (e.g., bars or social gathering venues). These individuals, often called superspreaders, transmit the virus to an unexpectedly large number of people. Further, a small number of superspreading places (SSPs) where this occurred account for a large number of COVID-19 transmissions. In this study, we propose a spatial network framework for identifying the SSPs that disproportionately spread COVID-19. Using individual-level activity data of the confirmed cases in Hong Kong, we first identify the high-risk places in the first four COVID-19 waves using the space-time kernel density method (STKDE). Then, we identify the SSPs among these high-risk places by constructing spatial networks that integrate the flow intensity of the confirmed cases. We also examine what built-environment and socio-demographic features would make a high-risk place to more likely become an SSP in different waves of COVID-19 by using regression models. The results indicate that some places had very high transmission risk and suffered from repeated COVID-19 outbreaks over the four waves, and some of these high-risk places were SSPs where most (about 80%) of the COVID-19 transmission occurred due to their intense spatial interactions with other places. Further, we find that high-risk places with dense urban renewal buildings and high median monthly household rent-to-income ratio have higher odds of being SSPs. The results also imply that the associations between built-environment and socio-demographic features with the high-risk places and SSPs are dynamic over time. The implications for better policymaking during the COVID-19 pandemic are discussed.

Socioeconomic Risks and Their Impacts on Ecological River Health in South Korea: An Application of the Analytic Hierarchy Process

It is imperative to develop a methodology to identify river impairment sources, particularly the relative impact of socioeconomic sources, to enhance the efficiency of various river restoration schemes and policies and to have an internal diagnosis system in place. This study, therefore, aims to identify and analyze the relative importance of the socioeconomic factors affecting river ecosystem impairment in South Korea. To achieve this goal, we applied the Analytical Hierarchy Process (AHP) to evaluate expert judgement of the relative importance of different socioeconomic factors influencing river ecosystem impairment. Based on a list of socioeconomic factors influencing stream health, an AHP questionnaire was prepared and administered to experts in aquatic ecology. Our analysis reveals that secondary industries form the most significant source of stream ecosystem impairment. Moreover, the most critical socioeconomic factors affecting stream impairment are direct inflow pollution, policy implementation, and industrial wastewater. The results also suggest that the AHP is a rapid and robust approach to assessing the relative importance of different socioeconomic factors that affect river ecosystem health. The results can be used to assist decision makers in focusing on actions to improve river ecosystem health.

How to construct and validate an Integrated Socio-Economic Vulnerability Index: Implementation at regional scale in urban areas prone to flash flooding

Flash flooding is the natural hazard provoking the largest number of casualties, so adequately characterizing vulnerability is key to improve flood risk analysis and management. Developing composite indices is the most widely used methodology in vulnerability analysis. However, very few studies have so far assessed vulnerability in urban areas prone to flash flooding and the resulting research presents two main drawbacks: i) a fragmented approach is often pursued, i.e. without jointly considering the vulnerability components (exposure, sensitivity and resilience) and the two most influential dimensions in urban environments (social and economic); and ii) vulnerability indices are not usually validated because an ancillary dataset is not generally available and flash flooding events do not happen simultaneously in all urban areas of a particular region. Considering the above gaps, this paper describes the construction of an Integrated Socio-Economic Vulnerability Index (ISEVI) at the regional scale, which considers all vulnerability components and social and economic dimensions. ISEVI was subsequently validated through an uncertainty and sensitivity analysis using the Monte Carlo method. Further, regional spatial patterns of vulnerability were identified implementing a Latent Class Cluster Analysis. Uncertainty analysis reveals the high stability of vulnerability categories of the ISEVI and sensitivity analysis shows that the type and the conservation state of buildings are the vulnerability factors that cause a greater variability in ISEVI scores. The method deployed here may allow specific strategies for vulnerability reduction to be developed based on disaggregating the validated ISEVI into dimensions and components and using the regional spatial patterns characterized.

Risk screening methods for extreme heat: Implications for equity-oriented adaptation

Morbidity and mortality impacts of extreme heat amplified by climate change will be unequally distributed among communities given pre-existing differences in socioeconomic, health, and environmental conditions. Many governments are interested in adaptation policies that target those especially vulnerable to the risks, but there are important questions about how to effectively identify and support communities most in need of heat adaptations. Here, we use an equity-oriented adaptation program from the state of California as a case study to evaluate the implications of the currently used environmental justice index (CalEnviroScreen 3.0) for the identification of socially vulnerable communities with climate change adaptation needs. As CalEnviroScreen is geared towards air and water pollution, we assess how community heat risks and adaptation needs would be evaluated differently under two more adaptation-relevant vulnerability indices: the Social Vulnerability Index and the Heat-Health Action Index. Our analysis considers communities at the census tract scale, as well as the patterns emerging at the regional scale. Using the current index, the state designates 25% of its census tracts as “disadvantaged” communities eligible for special adaptation funds. However, an additional 12.6% of the state’s communities could be considered vulnerable if the two other indices were considered instead. Only 13.4% of communities are vulnerable across all three vulnerability indices studied. Choice of vulnerability index shapes statewide trends in extreme heat risk and is linked to a community’s likelihood of receiving heat-related California Climate Investments (CCI) projects. Tracts that are vulnerable under the current pollution-focused index, but not under the heat-health specific index, received four times the number of heat-related interventions as tracts vulnerable under the reverse scenario. This study demonstrates important nuances relevant to implementing equity-oriented adaptation and explores the challenges, trade-offs, and opportunities in quantifying vulnerability.

Using Disaster Outcomes to Validate Components of Social Vulnerability to Floods: Flood Deaths and Property Damage across the USA

Social vulnerability indicators seek to identify populations susceptible to hazards based on aggregated sociodemographic data. Vulnerability indices are rarely validated with disaster outcome data at broad spatial scales, making it difficult to develop effective national scale strategies to mitigate loss for vulnerable populations. This paper validates social vulnerability indicators using two flood outcomes: death and damage. Regression models identify sociodemographic factors associated with variation in outcomes from 11,629 non-coastal flood events in the USA (2008–2012), controlling for flood intensity using stream gauge data. We compare models with (i) socioeconomic variables, (ii) the composite social vulnerability index (SoVI), and (iii) flood intensity variables only. The SoVI explains a larger portion of the variance in death (AIC = 2829) and damage (R2 = 0.125) than flood intensity alone (death—AIC = 2894; damage—R2 = 0.089), and models with individual sociodemographic factors perform best (death—AIC = 2696; damage—R2 = 0.229). Socioeconomic variables correlated with death (rural counties with a high proportion of elderly and young) differ from those related to property damage (rural counties with high percentage of Black, Hispanic and Native American populations below the poverty line). Results confirm that social vulnerability influences death and damage from floods in the USA. Model results indicate that social vulnerability models related to specific hazards and outcomes perform better than generic social vulnerability indices (e.g., SoVI) in predicting non-coastal flood death and damage. Hazard- and outcome-specific indices could be used to better direct efforts to ameliorate flood death and damage towards the people and places that need it most. Future validation studies should examine other flood outcomes, such as evacuation, migration and health, across scales.

People and the City: Urban Fragility and the Real Estate-Scape in a Neighborhood of Catania, Italy

Urban/social fragility is the main focus of most studies on civil economy involving the commitment of politics in the prospect of integrating and somehow guiding an ordered development of and ordered communities. The contemporary city is strongly influenced by the incommunicability between the social system and environment, the latter more and more, including urban and societal components. This study tries to outline a comparative social-urban profile of Picanello, a popular central neighborhood of Catania, in Sicily, Italy, characterized by the combination of different urban and social life-quality levels, thus expressing a heterogeneous vulnerability/resilience profile. The analysis is placed in the urban planning context and aims to: (1) Denotative a pattern that considers the different fragility/resilience descriptive indices; and (2) connotative a pattern of the human and urban dimensions of the social capital asset. This analysis was performed by implementing a multidimensional pattern allowing us to place the neighborhood in a ranking of the neighborhoods of Catania, thus highlighting strength and weakness under different respects. Furthermore, the monetary measurements of this vulnerability/resilience profile, was carried by means of the structured observation of the real estate market. Fuzzy k-medoids cluster analyses have been comparatively performed—showing and mapping the relationships between urban value density and real estate market prices tensions.

Spatial exploration of the CDC's Social Vulnerability Index and heat-related health outcomes in Georgia

Heat-related illness, an environmental exposure-related outcome commonly treated in U.S. hospital emergency departments (ED), is likely to rise with increased incidence of heat events related to climate change. Few studies demonstrate the spatial and statistical relationship of social vulnerability and heat-related health outcomes. We explore relationships of Georgia county-level heat-related ED visits and mortality rates (2002-2008), with CDC's Social Vulnerability Index (CDC SVI). Bivariate Moran's I analysis revealed significant clustering of high SVI rank and high heat-related ED visit rates (0.211, p < 0.001) and high smoothed mortality rates (0.210, p < 0.001). Regression revealed that for each 10% increase in SVI ranking, ED visit rates significantly increased by a factor of 1.18 (95% CI = 1.17-1.19), and mortality rates significantly increased by a factor of 1.31 (95% CI = 1.16-1.47). CDC SVI values are spatially linked and significantly associated with heat-related ED visit, and mortality rates in Georgia.

Development and application of a Socioeconomic Vulnerability Indicator Framework (SVIF) for Local Climate Change Adaptation in Taiwan

This paper outlines the development of a socioeconomic vulnerability indicator framework (SVIF) which was designed to provide a tool to inform bespoke local adaptation actions along the Taiwanese coast. The framework incorporates a range of diverse indicators, from ones that are related to demographic characteristics to others that represent economic and infrastructure features. As such, the framework encapsulates multiple and complex dimensions of socio-economic vulnerability rather than deriving a less nuanced single index; this is an approach that, whilst more commonly employed elsewhere, may mask critical features of socioeconomic vulnerability at local levels. The paper describes the piloting of the SVIF as it quantifies and visually summarizes the susceptibility and resilience of four townships (Mailiao, Kauho, Linbian and Jiadong) along the exposed coast of Southwest Taiwan. The paper demonstrates the SVIF’s potential in characterizing specific aspects of socio-economic vulnerability that local decision-makers could use to tailor local adaptation. The SVIF was successful in differentiating between the four local areas, highlighting clear differences between urban and rural townships. With further development by using a more participative approach and expanding its application to wider geographical contexts both in Taiwan and further afield, the authors are confident that the SVIF has the potential to provide a useful tool for local adaptation.

An Index-Based Assessment of Perceived Climate Risk and Vulnerability for the Urban Cluster in the Yangtze River Delta Region of China

This paper proposes an index-based assessment tool to consolidate diverse opinions of various stakeholders on their assessments of sector-specific risks posed by climate change, and to aggregate these opinions into intuitive and comparable graphs. This tool enables cities to measure and monitor the multiple factors that contribute to their resilience towards climate risk and hazard in the long term. We applied this tool to five key infrastructure sectors in six representative cities in the Yangtze River Delta region. The graphs generated provide for the first time first-hand insights into the aggregative understanding of various stakeholders with regard to the current and future climate risk in their concerned sectors and cities. Our results indicate that a high level of exposure is not automatically associated with a high level of vulnerability across our selected cities. While all cities need to make efforts to reduce their vulnerability towards climate hazards, those characterized by “lower level of exposure but higher level of vulnerability” need to make more urgent and much greater efforts.

Housing Vulnerability and Property Prices: Spatial Analyses in the Turin Real Estate Market

In the literature, several vulnerability/resilience indicators and indexes are based and assessed by taking into account and combining different dimensions. Housing vulnerability is one of these dimensions and is strictly related to the buildings’ physical features and to the socio-economic condition of their occupants. This research aims to study housing vulnerability in relation to the real estate market by identifying possible indicators and spatially analyzing their influence on property prices. Assuming the city of Turin and its territorial segmentation as a case study, spatial analyses were performed to take into account the presence of spatial dependence and to identify the variables that significantly influence the process of property price determination. The results of this study highlighted the fact that two housing vulnerability indicators, representative of fragile buildings’ physical features, were spatially correlated with property prices and had a significant and negative influence on them. In addition, their comparison with two social vulnerability indicators demonstrated that the presence of economical buildings and council houses was spatially correlated with the presence of people with a low education level. The results of the spatial regression model also confirmed that one of the social vulnerability indicators had the highest and most negative explanatory power in the property price determination process.

Risk analysis of technological hazards: Simulation of scenarios and application of a local vulnerability index

The potential impact of a technological accident can be assessed by risk estimation. Taking this into account, the latent or potential condition can be warned and mitigated. In this work we propose a methodology to estimate risk of technological hazards, focused on two components. The first one is the processing of meteorological databases to define the most probably and conservative scenario of study, and the second one, is the application of a local social vulnerability index to classify the population. In this case of study, the risk was estimated for a hypothetical release of liquefied ammonia in a meat-packing industry in the city of La Plata, Argentina. The method consists in integrating the simulated toxic threat zone with ALOHA software, and the layer of sociodemographic classification of the affected population. The results show the areas associated with higher risks of exposure to ammonia, which are worth being addressed for the prevention of disasters in the region. Advantageously, this systemic approach is methodologically flexible as it provides the possibility of being applied in various scenarios based on the available information of both, the exposed population and its meteorology. Furthermore, this methodology optimizes the processing of the input data and its calculation.

Is a clean river fun for all? Recognizing social vulnerability in watershed planning

Watershed planning can lead to policy innovation and action toward environmental protection. However, groups often suffer from low engagement with communities that experience disparate impacts from flooding and water pollution. This can limit the capacity of watershed efforts to dismantle pernicious forms of social inequality. As a result, the benefits of environmental changes often flow to more empowered residents, short-changing the power of watershed-based planning as a tool to transform ecological, economic, and social relationships. The objectives of this paper are to assess whether the worldview of watershed planning actors are sufficiently attuned to local patterns of social vulnerability and whether locally significant patterns of social vulnerability can be adequately differentiated using conventional data sources. Drawing from 35 in-depth interviews with watershed planners and community stakeholders in the Milwaukee River Basin (WI, USA), we identify five unique definitions of social vulnerability. Watershed planners in our sample articulate a narrower range of social vulnerability definitions than other participants. All five definitions emphasize spatial and demographic characteristics consistent with existing ways of measuring social vulnerability. However, existing measures do not adequately differentiate among the spatio-temporal dynamics used to distinguish definitions. In response, we develop two new social vulnerability measures. The combination of interviews and demographic analyses in this study provides an assessment technique that can help watershed planners (a) understand the limits of their own conceptualization of social vulnerability and (b) acknowledge the importance of place-based vulnerabilities that may otherwise be obscured. We conclude by discussing how our methods can be a useful tool for identifying opportunities to disrupt social vulnerability in a watershed by evaluating how issue frames, outreach messages, and engagement tactics. The approach allows watershed planners to shift their own culture in order to consider socially vulnerable populations comprehensively.

Hotspots and social background of urban traffic crashes: A case study in Cluj-Napoca (Romania)

Mobility practices have changed dramatically in Romanian towns over the last 25 years, following the collapse of socialist mobility restrictions. Urban areas like Cluj-Napoca are facing both increasing immigration and car mobility, and therefore increasing levels of road traffic crashes. The analysis of traffic crashes is one of the most important elements for improving the road safety policy. This paper is divided in two parts. In the first one, the authors focus on identifying the traffic crash hotspots along the street network, while in the second part they discuss the social background of road traffic crash occurrence. The first step in analyzing traffic crashes is to determine crash hotspots. A four-year record (2010-2013) provided by the Traffic Department of the General Inspectorate of Romanian Police (GIRPTD) was used. As a method of hotspot determination, the Kernel Density Estimation tool was employed, in the frame of the spatial analysis along network (SANET). The outcome was the hotspot map of traffic crashes in Cluj-Napoca. The results have revealed 4 categories of street segments: not-dangerous, low-dangerous, medium-dangerous and high-dangerous. Based on this classification, at least 4 dangerous areas were identified, located at the city entrances-exits (in the West, North-West and East) and the city center (the most dangerous zone). The second part of the paper focuses on social groups involved in car crashes. The following are considered: age, gender and blood alcohol concentration of the person (driver or pedestrian) found guilty for every individual crash.

Vulnerability assessments, identity and spatial scale challenges in disaster-risk reduction

Current approaches to vulnerability assessment for disaster-risk reduction (DRR) commonly apply generalised, a priori determinants of vulnerability to particular hazards in particular places. Although they may allow for policy-level legibility at high levels of spatial scale, these approaches suffer from attribution problems that become more acute as the level of analysis is localised and the population under investigation experiences greater vulnerability. In this article, we locate the source of this problem in a spatial scale mismatch between the essentialist framings of identity behind these generalised determinants of vulnerability and the intersectional, situational character of identity in the places where DRR interventions are designed and implemented. Using the Livelihoods as Intimate Government (LIG) approach to identify and understand different vulnerabilities to flooding in a community in southern Zambia, we empirically demonstrate how essentialist framings of identity produce this mismatch. Further, we illustrate a means of operationalising intersectional, situational framings of identity to achieve greater and more productive understandings of hazard vulnerability than available through the application of general determinants of vulnerability to specific places and cases.

A Heat Vulnerability Index and Adaptation Solutions for Pittsburgh, Pennsylvania

With increasing evidence of global warming, many cities have focused attention on response plans to address their populations' vulnerabilities. Despite expected increased frequency and intensity of heat waves, the health impacts of such events in urban areas can be minimized with careful policy and economic investments. We focus on Pittsburgh, Pennsylvania and ask two questions. First, what are the top factors contributing to heat vulnerability and how do these characteristics manifest geospatially throughout Pittsburgh? Second, assuming the City wishes to deploy additional cooling centers, what placement will optimally address the vulnerability of the at risk populations? We use national census data, ArcGIS geospatial modeling, and statistical analysis to determine a range of heat vulnerability indices and optimal cooling center placement. We find that while different studies use different data and statistical calculations, all methods tested locate additional cooling centers at the confluence of the three rivers (Downtown), the northeast side of Pittsburgh (Shadyside/Highland Park), and the southeast side of Pittsburgh (Squirrel Hill). This suggests that for Pittsburgh, a researcher could apply the same factor analysis procedure to compare data sets for different locations and times; factor analyses for heat vulnerability are more robust than previously thought.

Predicting hospitalization for heat-related illness at the census-tract level: accuracy of a generic heat vulnerability index in Phoenix, Arizona (USA)

BACKGROUND

Vulnerability mapping based on vulnerability indices is a pragmatic approach for highlighting the areas in a city where people are at the greatest risk of harm from heat, but the manner in which vulnerability is conceptualized influences the results.

OBJECTIVES

We tested a generic national heat-vulnerability index, based on a 10-variable indicator framework, using data on heat-related hospitalizations in Phoenix, Arizona. We also identified potential local risk factors not included in the generic indicators.

METHODS

To evaluate the accuracy of the generic index in a city-specific context, we used factor scores, derived from a factor analysis using census tract-level characteristics, as independent variables, and heat hospitalizations (with census tracts categorized as zero-, moderate-, or high-incidence) as dependent variables in a multinomial logistic regression model. We also compared the geographical differences between a vulnerability map derived from the generic index and one derived from actual heat-related hospitalizations at the census-tract scale.

RESULTS

We found that the national-indicator framework correctly classified just over half (54%) of census tracts in Phoenix. Compared with all census tracts, high-vulnerability tracts that were misclassified by the index as zero-vulnerability tracts had higher average income and higher proportions of residents with a duration of residency < 5 years.

CONCLUSION

The generic indicators of vulnerability are useful, but they are sensitive to scale, measurement, and context. Decision makers need to consider the characteristics of their cities to determine how closely vulnerability maps based on generic indicators reflect actual risk of harm.

An approach to developing local climate change environmental public health indicators, vulnerability assessments, and projections of future impacts

Environmental public health indicators (EPHIs) are used by local, state, and federal health agencies to track the status of environmental hazards; exposure to those hazards; health effects of exposure; and public health interventions designed to reduce or prevent the hazard, exposure, or resulting health effect. Climate and health EPHIs have been developed at the state, federal, and international levels. However, they are also needed at the local level to track variations in community vulnerability and to evaluate the effectiveness of interventions designed to enhance community resilience. This review draws on a guidance document developed by the U.S. Council of State and Territorial Epidemiologists' State Environmental Health Indicators Collaborative climate change working group to present a three-tiered approach to develop local climate change EPHIs. Local climate change EPHIs can assist local health departments (LHDs) in implementing key steps of the 10 essential public health services and the U.S. Centers for Disease Control and Prevention's Building Resilience Against Climate Effects framework. They also allow LHDs to incorporate climate-related trends into the larger health department planning process and can be used to perform vulnerability assessments which can be leveraged to ensure that interventions designed to address climate change do not exacerbate existing health disparities.

Spatial-explicit modeling of social vulnerability to malaria in East Africa

Background

Despite efforts in eradication and control, malaria remains a global challenge, particularly affecting vulnerable groups. Despite the recession in malaria cases, previously malaria free areas are increasingly confronted with epidemics as a result of changing environmental and socioeconomic conditions. Next to modeling transmission intensities and probabilities, integrated spatial methods targeting the complex interplay of factors that contribute to social vulnerability are required to effectively reduce malaria burden. We propose an integrative method for mapping relative levels of social vulnerability in a spatially explicit manner to support the identification of intervention measures.

Methods

Based on a literature review, a holistic risk and vulnerability framework has been developed to guide the assessment of social vulnerability to water-related vector-borne diseases (VBDs) in the context of changing environmental and societal conditions. Building on the framework, this paper applies spatially explicit modeling for delineating homogeneous regions of social vulnerability to malaria in eastern Africa, while taking into account expert knowledge for weighting the single vulnerability indicators. To assess the influence of the selected indicators on the final index a local sensitivity analysis is carried out.

Results

Results indicate that high levels of malaria vulnerability are concentrated in the highlands, where immunity within the population is currently low. Additionally, regions with a lack of access to education and health services aggravate vulnerability. Lower values can be found in regions with relatively low poverty, low population pressure, low conflict density and reduced contributions from the biological susceptibility domain. Overall, the factors characterizing vulnerability vary spatially in the region. The vulnerability index reveals a high level of robustness in regard to the final choice of input datasets, with the exception of the immunity indicator which has a marked impact on the composite vulnerability index.

Conclusions

We introduce a conceptual framework for modeling risk and vulnerability to VBDs. Drawing on the framework we modeled social vulnerability to malaria in the context of global change using a spatially explicit approach. The results provide decision makers with place-specific options for targeting interventions that aim at reducing the burden of the disease amongst the different vulnerable population groups.

Studying Displacement After a Disaster Using Large Scale Survey Methods: Sumatra After the 2004 Tsunami

Understanding of human vulnerability to environmental change has advanced in recent years, but measuring vulnerability and interpreting mobility across many sites differentially affected by change remains a significant challenge. Drawing on longitudinal data collected on the same respondents who were living in coastal areas of Indonesia before the 2004 Indian Ocean tsunami and were re-interviewed after the tsunami, this paper illustrates how the combination of population-based survey methods, satellite imagery and multivariate statistical analyses has the potential to provide new insights into vulnerability, mobility and impacts of major disasters on population well-being. The data are used to map and analyze vulnerability to post-tsunami displacement across the provinces of Aceh and North Sumatra and to compare patterns of migration after the tsunami between damaged areas and areas not directly affected by the tsunami. The comparison reveals that migration after a disaster is less selective overall than migration in other contexts. Gender and age, for example, are strong predictors of moving from undamaged areas but are not related to displacement in areas experiencing damage. In our analyses traditional predictors of vulnerability do not always operate in expected directions. Low levels of socioeconomic status and education were not predictive of moving after the tsunami, although for those who did move, they were predictive of displacement to a camp rather than a private home. This survey-based approach, though not without difficulties, is broadly applicable to many topics in human-environment research, and potentially opens the door to rigorous testing of new hypotheses in this literature.