Locating Spatial Variation in the Association Between Wildland Fire Risk and Social Vulnerability Across Six Southern States

Poudyal, Neelam C.; Johnson-Gaither, Cassandra; Goodrick, Scott L.; Bowker, J.M.; Gan, Jianbang

doi:10.1007/s00267-011-9796-z

Cited by 50 publications

(29 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Social vulnerability assessments typically compare the mean value of selected metrics for a broad range of populations across a geographic area (e.g., a county or census block) (Gaither et al 2015;Krietler et al 2015). Assumptions about what sociodemographic characteristics make people more or less vulnerable to a given disturbance are based on lessons from previous hazard research (Ojerio et al 2011;Poudyal et al 2012). Characteristics used in social vulnerability analysis often are summed (predominantly with equal weights) as a composite index to assess overall vulnerability to a hazard.…”

Section: Vulnerability To Wildfirementioning

confidence: 99%

Evaluating the Characteristics of Social Vulnerability to Wildfire: Demographics, Perceptions, and Parcel Characteristics

Paveglio

Prato

Edgeley

et al. 2016

Environmental Management

View full text Add to dashboard Cite

A large body of research focuses on identifying patterns of human populations most at risk from hazards and the factors that help explain performance of mitigations that can help reduce that risk. One common concept in such studies is social vulnerability-human populations' potential exposure to, sensitivity from and ability to reduce negative impacts from a hazard. While there is growing interest in social vulnerability for wildfire, few studies have critically evaluated the characteristics that scholars often indicate influence social vulnerability to that hazard. This research utilizes surveys, wildfire simulations, and GIS data to test the relationships between select demographic, perceptual and parcel characteristics of property owners against empirically simulated metrics for wildfire exposure or wildfire-related damages and their performance of mitigation actions. Our results from Flathead County, MT, USA, suggest that parcel characteristics such as property value, building value, and the year structures were built explaining a significant amount of the variance in elements of social vulnerability. Demographic characteristics commonly used in social vulnerability analysis did not have significant relationships with measures of wildfire exposure or vulnerability. Part-time or full-time residency, age, perceived property risk, and year of development were among the few significant determinants of residents' performance of fuel reduction mitigations, although the significance of these factors varied across the levels of fuel reduction performed by homeowners. We use these and other results to argue for a renewed focus on the finer-scale characteristics that expose some populations to wildfire risk more than others.

show abstract

Section: Vulnerability To Wildfirementioning

confidence: 99%

Evaluating the Characteristics of Social Vulnerability to Wildfire: Demographics, Perceptions, and Parcel Characteristics

Paveglio

Prato

Edgeley

et al. 2016

Environmental Management

View full text Add to dashboard Cite

show abstract

“…Based on prior work involving social vulnerability and wildfire risk [17,19], we created a social vulnerability index comprised of eight socio-demographic variables from the 2010 U.S. Census of Population and Housing and the 2006-2010 American Community Survey [32,33]. Variables from the 2010 decadal census include the proportion of population at the CBG scale: greater than 65 years old; less than 15 years old; American Indian/Alaskan Native; African American; Hispanic; and renters.…”

Section: Social Vulnerabilitymentioning

confidence: 99%

“…This topic is also of considerable concern in the southeastern U.S. given the proximity of human populations in rural and Wildland Urban Interface areas to forests and other wooded areas and the higher likelihood of wildfire and prescribed fire activities on those lands [16][17][18][19]. Indeed, there have been substantial increases in the number of people relocating to the Wildland Urban Interface across the South over the past 30 years [16,[20][21][22].…”

mentioning

confidence: 99%

An Exploratory Spatial Analysis of Social Vulnerability and Smoke Plume Dispersion in the U.S. South

Gaither

Goodrick

Murphy

et al. 2015

Forests

Self Cite

View full text Add to dashboard Cite

This study explores the spatial association between social vulnerability and smoke plume dispersion at the census block group level for the 13 southern states in the USDA Forest Service's Region 8. Using environmental justice as a conceptual basis, we use Exploratory Spatial Data Analysis to identify clusters or "hot spots" for the incidence of both higher than average socially marginal populations and plume dispersion. The larger health disparities and environmental justice literature suggests that lower income and minority populations in the U.S. face greater exposure than middle/upper income, non-minority populations to environmental pollutants; however, we are aware of only a few studies examining this relationship in the context of population exposure to wildfires or prescribed fires in the U.S. South, despite the high occurrence of wildfires in the region. Analyses were conducted across five ecoregions in the South and for winter and spring/summer seasons. Results by ecoregion show significant spatial clustering of high social vulnerability block groups in the vicinity of block groups with a high number of smoke plumes (i.e., "hot spots"). Overall, however, socially vulnerable communities are not exposed to more smoke than non-socially vulnerable communities. Data limitations and suggestions for further research are discussed. OPEN ACCESSForests 2015, 6 1398

show abstract

“…Although interest in accounting for regional variations in wildfire occurrence factors has been shown recently in some studies Carmo et al, 2011;GonzalezOlabarria et al, 2011;Padilla and Vega-García, 2011;Nunes, 2012), except for Koutsias et al (2005Koutsias et al ( , 2010, this has only begun to be addressed very recently by using local geographically weighted regression (Tulbure et al 2011;Poudyal et al, 2012;Avila-Flores et al, 2010;Sá et al, 2011;Rodrigues and De la Riva, 2012). In our study, similar to those of Koutsias et al (2010) and Sá et al (2011), GWR is considered as a complement to the "global" regression modelling approach, with which it is compared in order to better understand particular processes at the regional scale, but at the same time recognizing its own local characteristics and patterns (Fotheringham et al, , 1997(Fotheringham et al, , 2002.…”

Section: Introductionmentioning

confidence: 99%

Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression

Fernández¹,

Chuvieco

Koutsias³

2013

Nat. Hazards Earth Syst. Sci.

130

View full text Add to dashboard Cite

Humans are responsible for most forest fires in Europe, but anthropogenic factors behind these events are still poorly understood. We tried to identify the driving factors of human-caused fire occurrence in Spain by applying two different statistical approaches. Firstly, assuming stationary processes for the whole country, we created models based on multiple linear regression and binary logistic regression to find factors associated with fire density and fire presence, respectively. Secondly, we used geographically weighted regression (GWR) to better understand and explore the local and regional variations of those factors behind human-caused fire occurrence. The number of human-caused fires occurring within a 25-yr period (1983–2007) was computed for each of the 7638 Spanish mainland municipalities, creating a binary variable (fire/no fire) to develop logistic models, and a continuous variable (fire density) to build standard linear regression models. A total of 383 657 fires were registered in the study dataset. The binary logistic model, which estimates the probability of having/not having a fire, successfully classified 76.4% of the total observations, while the ordinary least squares (OLS) regression model explained 53% of the variation of the fire density patterns (adjusted R2 = 0.53). Both approaches confirmed, in addition to forest and climatic variables, the importance of variables related with agrarian activities, land abandonment, rural population exodus and developmental processes as underlying factors of fire occurrence. For the GWR approach, the explanatory power of the GW linear model for fire density using an adaptive bandwidth increased from 53% to 67%, while for the GW logistic model the correctly classified observations improved only slightly, from 76.4% to 78.4%, but significantly according to the corrected Akaike Information Criterion (AICc), from 3451.19 to 3321.19. The results from GWR indicated a significant spatial variation in the local parameter estimates for all the variables and an important reduction of the autocorrelation in the residuals of the GW linear model. Despite the fitting improvement of local models, GW regression, more than an alternative to "global" or traditional regression modelling, seems to be a valuable complement to explore the non-stationary relationships between the response variable and the explanatory variables. The synergy of global and local modelling provides insights into fire management and policy and helps further our understanding of the fire problem over large areas while at the same time recognizing its local character

show abstract

Locating Spatial Variation in the Association Between Wildland Fire Risk and Social Vulnerability Across Six Southern States

Cited by 50 publications

References 18 publications

Evaluating the Characteristics of Social Vulnerability to Wildfire: Demographics, Perceptions, and Parcel Characteristics

Evaluating the Characteristics of Social Vulnerability to Wildfire: Demographics, Perceptions, and Parcel Characteristics

An Exploratory Spatial Analysis of Social Vulnerability and Smoke Plume Dispersion in the U.S. South

Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression

Contact Info

Product

Resources

About