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2 The Black Belt is comprised of 623 counties contained in eleven states of the former Confederacy Alabama, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Texas, and Virginia. The region holds 18% of the nation's population (Allen-Smith et al., 2000). These counties are mostly adjacent although



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C J Gaither et al Forest Policy and Economics 13 2011 24 36 25
lived in the South representing 40 of total U S poverty Carl Vinson The greatest number of wildland res by region occurs in the
Institute of Government 2002 Along with high poverty concentra South National Interagency Fire Center Wildland Fire Statistics n d
tions however the South also contains areas of af uence in urban In 2007 one half of all reported wildland res in the nation occurred
metropolises such as Atlanta Georgia and wealth pockets in amenity in the thirteen states comprising the U S Forest Service s Southern
rich wildland areas The South contained six of the fastest growing Region in 2006 more than one half of all reported wildland res in
counties in the nation in terms of percentage change in population the nation were in the South and 42 of all large wildland res
from 1 April 2000 to 1 July 2009 U S Census Bureau 2009a reported were in this region Andreu and Hermansen B ez 2008
Population growth increases demand for housing and other In pre industrial times Native Americans and early European
development much of which contributes to the expanding Wildland settlers used re to reduce fuel loads The advent of agricultural and
Urban Interface or the WUI the area where structures and other industrial development during the nineteenth century resulted in
human development meet or intermingle with undeveloped wild wide spread loss of forest cover throughout the South To aid forest
land http silvis forest wisc edu projects WUI Main asp WUI regeneration in the early twentieth century re suppression
growth in turn increases the likelihood of wildland re ignition programs were implemented across the region However decades
caused by humans given the closer proximity of human dwellings of re suppression have resulted in substantial fuel buildup in
and activities to woodlands Macie and Hermansen 2002 Research Southern woodlands which contribute to an increased likelihood of
indicates that WUI expansion is driven largely by af uent migration to wildland re Fowler and Konopik 2007 Monroe 2002
peri urban areas Rodrigue 1993 Collins 2008a b In many In addition severe drought conditions over the past several years
instances then WUI settlement implies higher income strata have made some areas in the region especially susceptible to wildland
populating woodland and wildland areas 3 re In Florida for instance state re of cials reported 1847 wildland
Federal mandates for wildland re mitigation efforts prioritize res on state and private lands from January to April 2009 This
WUI communities Lynn and Gerlitz 2006 Western Governor s number represents an increase of 88 over 2008 gures for the same
Association 2002 This is justi able given the combination of period Florida Division of Forestry 2009
physical and social factors increasing population and housing The Southern Group of State Foresters 2005 report Fire in the
density contributing to higher wildland re risk in the WUI South identi es a number of factors contributing to the problem of
However less densely populated rural areas outside the WUI wildland re in the region These include the fact that there is
containing abundant vegetation may be at a comparable risk of relatively little federally owned land in the South which makes states
wildland re responsible for wildland re protection on greater than 94 of the
Importantly non WUI settlements have been found to contain region s land area Again the wildland urban interface WUI
higher percentages of lower income populations in contrast to the exacerbates wildland re threat in many areas and local re
WUI In Oregon and Washington Lynn and Gerlitz 2006 found a departments must contribute heavily to re suppression Also
higher percentage of poor people in a class of wildlands they term changing demographics in heavily forested areas makes the task of
Inhabited Wildlands as compared with the WUI As well analysis of prescribed burning harder to implement resulting in increased fuel
county level WUI data4 for the six states included in this study shows loadings in some communities
that non WUI acreage in nonmetropolitan counties5 varies positively
with percentage of population below the poverty threshold 3 Social vulnerability and wildland re risk
r 0 363 p b 0 0001 correlation between a county s WUI acreage
and percentage of population below poverty is r 0 439 p b 0 0001 Haque and Etkin 2007 write that an after the fact response to
Radeloff et al 2005 Hence those places where development is disaster emphasizing cleanup and recovery efforts has for the most
expanding into rural wildlands are less likely to be in high poverty part been replaced with a vulnerability resilience paradigm This
counties in Alabama Arkansas Florida Georgia Mississippi and perspective places as much emphasis on the social dimensions of
South Carolina disaster that is on suspected societal conditions and inequities which
Again however our interest in wildland re across these may cause some groups to be less prepared for and less able to recover
southeastern states concentrates on those socially vulnerable popula from hazard events as physical causes
tions that locate in nonmetropolitan areas outside the WUI Thus our In a review of the literature on poverty and disasters in the U S
analysis includes not just the WUI but also less densely settled high Fothergill and Peek 2004 describe disasters as a social phenome
vegetation places outside the WUI that contain long established non and cite a number of studies showing that poorer people are
socially vulnerable groups These populations are prevalent in Black more likely than other income groups to perceive greater risks from
Belt counties such as Jefferson County Mississippi and Perry County natural disasters but are less likely to respond to disaster warnings
Alabama where 37 5 and 31 7 respectively of the population is Poor people also suffer disproportionately from the physical and
classi ed as impoverished U S Census Bureau 2009b psychological impacts of disasters experience higher mortality rates
and nd it more dif cult to recover after disasters The authors
2 Wildland re risk in the South conclude that these ndings illustrate a systematic pattern of
strati cation within the United States and that disasters often
Physiographic features contribute signi cantly to wildland re risk highlight a priori disparities in social well being Fothergill and
in the South Stanturf et al 2002 Monroe 2002 Critical factors are Peek 2004 p 103
long growing seasons with frequent rainfall and wind which Cannon in Haque and Etkin 1994 makes explicit social variables
contribute to abundant vegetation This growth along with a high that contribute to social vulnerability social economic and political
frequency of lightning strikes and lack of a persistent snow layer factors These factors can either enhance or detract from a commu
increase the likelihood of wildland re nity s ability to mitigate disaster Along similar lines Cutter et al
2000 argue that socially vulnerable groups such as the elderly lower
Collins 2005 stresses that poor communities may coexist with af uent income racial minorities and women are more likely to be exposed to
populations in the WUI a larger number of hazards and or be less able to recover from
Data source Forest and Wildlife Ecology University of Wisconsin at Madison disasters e g chemical spills hurricanes wild re than wealthier
Wildland Interface Maps Statistics and GIS Download http silvis forest wisc edu
projects WUI Main asp
more able bodied individuals and communities Morrow 1999 and
As measured by the USDA s Rural Urban Continuum Codes http www ers usda Lynn and Gerlitz 2006 also posit that poor communities are less able
gov brie ng Rurality RuralUrbCon to absorb the effects of natural disasters
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26 C J Gaither et al Forest Policy and Economics 13 2011 24 36
Similar to Cutter et al 2000 Ojerio 2008 examined both analyses the CBG provides the most detailed spatial resolution
biophysical and social data to assess the vulnerability of census block publicly available
groups in Arizona to wildland re risks Results consistently showed
that census block groups comprised largely of poor non Whites
Navajo and Apache were less likely than majority white census 5 1 Wildland re susceptibility index
block groups to participate in either state sponsored grants aimed at
wildland re mitigation community wild re protection programs or We selected the Wildland Fire Susceptibility Index WFSI as our
the Firewise Community program indicator of wildland re risk The index is one of several indices
Importantly Collins 2008a critiques assumptions of risk expo produced by the Southern Wild re Risk Assessment SWRA The
sure in the First World which assume that higher income households SWRA is the rst comprehensive wildland re risk assessment of its
willingly expose themselves to risk by locating in aesthetically kind in the nation It is supported by the thirteen state forestry
pleasing yet ecologically fragile environments Marginalized groups agencies that comprise the USDA Forest Service s Southern Region in
he argues are rendered invisible in these settings Collins 2008a partnership with the USDA Forest Service USDI Fish and Wildlife
offers instead a political ecology view of risk exposure in developed Service USDI National Park Service Bureau of Indian Affairs and the
nations which makes marginalization relative He stresses that Department of Defense The WFSI measures on a scale of zero to one
socially vulnerable populations exist alongside the well heeled in the probability6 of an acre burning based on surface fuels and forest
places with high environmental risk in developed nations However conditions weather historical re sizes and historical suppression
state and market institutions local re protection and re risk effectiveness Buckley et al 2006a b
insurance act to insulate the rich from devastating loss in the event of The index includes three key components 1 probability of re
disaster by the provision of such services Marginal communities occurrence 2 re behavior and 3 re suppression effectiveness The
conversely absorb the risk avoided by the wealthy because of their rst component probability of re occurrence is comprised princi
relative inability to access these safeguards pally of Fire Occurrence Areas FOA and Weather In uence Zones
Collins 2008a focus is the contribution of institutions to the WIZ Buckley et al 2006a p 41 52 FOAs are determined by
facilitation of more af uent communities A more comprehensive look historical data pinpointing re ignition Quantitatively FOA is the
at the advantages accruing to the rich or disadvantages of the poor historical mean of ignitions calculated as the number of res per year
necessitates an examination of agency that is not just the larger per thousand acres Periods of re occurrence were not speci ed but
society shielding some sectors from harm but also the activities rather referred to generally as re history reports which we assume
initiated by the well off to insulate their properties from wild re loss were supplied by state and federal land management agencies Fire
Not only do the more af uent have better access to structural services ignition data were collected between 1997 and 2002
to mitigate re but residents act at the individual and community Weather also in uences probability of re occurrence To
level to prevent loss by engaging with mitigation programs in the incorporate this variable WIZs or weather zones were designated
communities where they live Such participation distinguishes upper for the thirteen southern states and daily weather observations for
income areas from poor and working class communities each WIZ were recorded from 1 January 1994 to 31 December 2003
Buckley et al 2006a Weather conditions were categorized into
percentiles that indicated conditions which were more or less
4 Research hypothesis conducive to re ignition low moderate high and extremely high
percentiles Various land management agencies and the National
We expect that the type of association between social vulnerability Oceanic and Atmospheric Administration supplied weather data
and wildland re risk will vary geographically cluster with hot spot The second signi cant component of WFSI is Fire Behavior rate of
clusters high social vulnerability high wildland re risk prevalent in spread ROS crown re potential and ame length ROS is simulated
less densely populated rural areas We do not suppose that a using FB3 DLL Windows software commercial software licensed by
particular type of association for instance hot spots or cold Fire Program Solutions LLC Fire Behavior attributes in turn are
spots would characterize an entire state because again socially calculated based on surface fuels canopy closure canopy character
vulnerable populations also locate in urban areas with very low istics 7 and topography aspect slope elevation Surface and canopy
wildland re risk and more af uent populations concentrate in or fuels data were obtained from crosswalks of existing datasets Fire
near high wildland re risk rural areas However we expect fewer behavior is estimated in 30 30 m cells with speci c weather
wildland re mitigation programs to exist near hot spot clusters conditions ROS is calculated for the four weather categories low
compared to low social vulnerability high wildland re risk clusters moderate high and extreme
Lastly WFSI includes Fire Suppression Effectiveness which is a
H1 Communities with high wildland re risk and high social function of Final Fire Size FFS and ROS Fire suppression effective
vulnerability hot spots are less likely than communities with high ness is the comparison of actual re sizes to a theoretical size which
wildland re risk and low social vulnerability to be engaged with assumes re spreads under stable conditions with homogenous
wildland re mitigation programs weather and fuel conditions with no suppression activity Data used
for these calculations are from states and federal agencies for the time
5 Methods period 1997 2002 The nal WFSI gure for a 30 30 m cell in a given
WIZ is the summation of the respective WFSI calculations for the four
To examine the association between wildland re risk and social weather percentile areas WFSI is available in a raster format
vulnerability in the six state region we rst identi ed indicators of To facilitate analysis at the CBG level basic statistics maximum
wildland re risk and social vulnerability at the Census Block Group mean minimum and standard deviation were calculated for all 30 m
CBG level We chose the CBG as the unit of analysis because this pixels within each CBG using the summarize zones function in the
geography approximates community groupings The U S Census ESRI s Environmental Systems Research Institute Spatial Analyst
Bureau de nes a CBG as an aggregation of blocks with blocks being
analogous to city blocks demarcated by streets in rural areas CBGs Although due to some necessary assumptions such as fuel homogeneity it is not
the true probability
can contain an extensive number of square miles and do not have 7
Data on canopy characteristics were limited by the lack of extant data and funding
street boundaries Also the CBG level approximates the spatiality at to collect primary canopy fuels data canopy ceiling height canopy base height and
which most wild res occur and for the variables included in our canopy bulk density Buckley et al 2006a p 49
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C J Gaither et al Forest Policy and Economics 13 2011 24 36 27
extension for ArcVIEW Values ranged from 0 to 0 86 mean 0 04 Census Bureau Summary File 3 sample data tables Data were
standard deviation 0 086 and median 0 005 obtained for each CBG in the six state region We downloaded total
population total African American population total population
5 2 Social vulnerability index 25 years and older both male and female population 25 and older
with varying degrees of educational attainment total population for
Concurrently we constructed an index to measure social vulnera whom poverty was determined population with income below
bility SOVUL We de ne vulnerability as marginalization character poverty total housing units total mobile home units total occupied
ized by the lack of ability to assertively navigate social systems or to housing units and total renter occupied housing units
move progressively towards higher living standards in terms of material From these frequencies percent African American percent over 25
wealth and in uence As indicated a number of researchers have found without high school diploma percent below poverty percent mobile
a range of social indicators associated with an individual household or home dweller and percent renter were calculated Percentages for
community s ability to mitigate and or recover from disasters Cutter et each indicator e g percent below poverty black etc were summed
al 2000 identi ed eleven county level factors that in uence social to produce the SOVUL value for a given census block group Values
vulnerability These have to do with personal wealth housing stock and were not standardized and all variables are assumed to carry equal
tenancy percent mobile homes in county and race ethnicity Morrow weight
1999 includes similar factors physically and mentally disabled SOVUL values ranged from 0 to 3 64 with a mean of 1 10 standard
elderly female headed households and the homeless Cutter et al deviation 0 64 and median 1 03 Values larger than the mean indicate
2003 developed a Social Vulnerability Index SoVi8 which examines high social vulnerability Zero values would be observed in the case of
how socio demographic characteristics in uence climate related CBGs with no population
hazards drought oods hurricane force winds and sea level rise in
the southeast Oxfam 2009 Wildland re hazard is not included 6 Exploratory spatial data analysis
among the environmental risks this group examines
Our SOVUL index includes percent of population below poverty 6 1 Bivariate clusters of wildland re risk and social vulnerability
percent of population 25 or older without a high school diploma
percent African American percent of housing structures that are We use the LISA statistic localized indicator of spatial association
mobile homes and percent of renter occupied housing units Each of to test the strength of association between WFSI and SOVUL and also
these variables can have a direct bearing on social vulnerability for to map these associations at the CBG level Anselin 1995 The
both individuals and communities As discussed persons or house correlation statistic indicates how observations of a variable in a given
holds below poverty and those with lower education levels typically CBG say i are associated with observations of a different variable in
have less ef cacy in obtaining services or information about adjacent CBGs or the neighborhood of the ith CBG In our case this
environmental protection Also race often gures into issues involving involves correlations between WFSI in an areal unit i and SOVUL in
services and information access White middle class neighborhoods the cluster of CBGs surrounding and including the ith CBG
and communities typically have a greater number of facilities and Neighboring CBGs or the neighborhood of the ith CBG was de ned
services compared to poorer minority areas Taylor et al 2007 based on a rst order contiguity weight matrix CBGs adjacent to the
Taylor 2000 Wolch et al 2002 ith CBG sharing a common border length or at least a vertex were
Racial status tends to correlate positively with other socio considered to be in the neighborhood The mean neighborhood value
demographic and economic indicators such as those included in our for SOVUL and WFSI includes the value for the variable in the ith CBG
index particularly poverty and education However we also believe as well as the values for all CBGs adjacent to it This was achieved by
that the descriptor African American or Black carries an additional manually editing the weight matrix les
weight beyond that of income or education This relates to both overt Bivariate LISA statistics were used in GeoDa 0 9 5 I to map four
and more subtle forms of discrimination from the larger society and different types of spatial clusters for WFSI and SOVUL at the CBG level
also to self imposed racial segregation which continues de facto racial For WFSI for example clusters include 1 High High CBGs with high
separation Mobile homes are less able to withstand natural disasters wildland re risk surrounded by CBGs with high social vulnerability
such as hurricanes because the building material is generally of lower 2 Low Low CBGs with low wildland re risk surrounded by CBGs
quality than constructed dwellings This may also be the case with re with low social vulnerability 3 Low High CBGs with low wildland
resistance as mobile structures are less likely than constructed homes re risk surrounded by CBGs with high social vulnerability 4 High
to be made of re resistant durable materials Finally renters have Low CBGs with high wildland re risk surrounded by CBGs with low
less control over building materials landscaping re insurance or social vulnerability
other safeguards against wildland re which could result in greater Again the high and low level of a given variable is de ned in
vulnerability for this group reference to its mean value for the neighborhood We de ned High
Because of overlaps between race and the other variables included High and Low Low clusters as hot spots and cold spots
in SOVUL we examined the degree of multicollinearity for the respectively where the association between two phenomena is
variables comprising the index by examining a regression model positive For the other clusters Low High and High Low the
where WFSI was the dependent variable and percent black percent associations are negative and are described as spatial outliers
below poverty percent low education percent renter and mobile Anselin 2005 LISA scores signi cant at p 0 05 or less were used
home were predictors Here we wanted to detect in ated standard to map statistically signi cant clusters Pseudo p values were
errors by looking at the variance in ation factor VIF as multi generated for LISA statistics utilizing 999 permutation criteria
collinearity is indicated by uctuating standard errors Generally VIF available in GeoDa 0 9 5 I www geodacenter asu edu
values greater than ten may indicate multicollinearity among The following equation Sunderlin et al 2008 provides the
variables VIF values for each of our predictors were below three computation of bivariate LISA based on Anselin 1995
which suggests low or moderate multicollinearity Frequencies for N
variables comprising SOVUL were downloaded from the 2000 U S Il zxi wij zyj 1
SoVi includes eight variables which explain 75 of the variance in social
vulnerability The variables are wealth age race ethnicity rural residence special where Il is the local Moran s I LISA x and y are two variables of
needs populations gender and employment Oxfam 2009 interest measured for CBG i and neighborhood j respectively
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28 C J Gaither et al Forest Policy and Economics 13 2011 24 36
Similarly zx and zy represent the standardized z scores for variables x To make the interpretation easier and more meaningful cluster
and y respectively The term wij is the weight matrix that de nes the maps for each state are overlaid with interstate highway and federal
structure of the neighborhood LISA and weight matrices were created land areas Geo visualization of clusters with such recognizable
in GeoDa 0 9 5 I This analysis uses a rst order queen contiguity gures provides reference for illustrating the spatial location of
matrix where wij 1 if the adjacent CBG j shares a common border clusters For example in the analysis for Alabama Fig 1 red clusters
length or common vertex with the ith CBG If a common border is not or hot spots are located in the southern part of the state mostly south
shared the value is zero of Interstate 85 and US 80 Interestingly this portion of the state
contains relatively less federal land area compared to areas north of
those highways South Alabama also contains large areas of light blue
6 2 Results clusters which again indicated high social vulnerability CBGs in the
neighborhood of low wildland re risk CBGs
6 2 1 ESDA at the state level The overall pattern of high social vulnerability red and light blue
Figs 1 6 show bivariate LISA analyses for each state In each gure patches follows the spatiality of Alabama s impoverished Black Belt The
the red color indicates clusters of high wildland re risk CBGs located more socially vulnerable clusters are located almost exclusively in the
in neighborhoods or clusters with high social vulnerability High southern part of the state The present analyses demonstrate how low
High dark blue clusters denote low wildland re risk CBGs in socio economic status or socially vulnerable communities intersect with
clusters with low social vulnerability Low Low low wildland re wildland re risk In some areas of the state s Black Belt there is an inverse
risk high social vulnerability clusters are shown in light blue Low association between social well being and this type of environmental risk
High and high wildland re risk low social vulnerability clusters are light blue whereas in others the association is positive red
colored mango High Low White areas within the study area North Alabama stands out as a near antonym to the southern part
represent CBGs where the spatial association between WFSI and of the state in terms of social well being From Birmingham and
SOVUL is not statistically signi cant Tuscaloosa northward the state contains remarkably more low
Federal Lands
High Wildfire Risk High Socia l Vulnerability
Low Wildfire Risk Low Social Vulnerability
Low Wildfire Risk High Socia l Vulnerability
High Wildfire Risk Low Socia l Vulnerability
Note The clusters are based on bivariate LISA Statistic significant al p 0 05
lJI tIite areas represent census block groups where the association is insignificant
Fig 1 Bivariate LISA based spatial clusters showing the local association between wildland re risk and social vulnerability in Alabama
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C J Gaither et al Forest Policy and Economics 13 2011 24 36 29
CIfoIPP Community
Firewise Commun y
W Federal lands
High VVildfire Risk Hfgh Social Vulnerabil y
l ow Wildfire Risk l ow Social Vulnerability
l ow Wildfire Risk High Social Vulnerability
Note The clusters are based on Bivariate U SA Statistic sign ificant at p 0 05 High VVildfire Risk low Social Vulnerabilily
Vv11i1e areas represent the Census Block Groups where th e association is insignificant
Fig 2 Bivariate LISA based spatial clusters showing the local association between wild re risk and social vulnerability in Arkansas
socially vulnerable clusters The dark blue Low Low clusters predom area The only exception to this pattern is the light blue High Low
inate in the north but high re risk areas also intersect with more area of central city Birmingham The cluster here is similar to that in
well off communities in north Alabama in the Huntsville Florence the rural Black Belt south of Interstate 20 This is not surprising given
Firewise Community
CWPP Community
Federal Lands
Hig h Wildfire Risk High Social Vulnerability
Low Wildfire Risk Low Social Vulnerability
Low Wildfire Risk High Social Vulnerability
Hig h Wildfire Risk Low Social Vulnerability
Note The clusters are based on bivariate U SA Statistic significant at p 0 05
Vv11ite areas represent census block grou ps where the association is insignificant
Fig 3 Bivariate LISA based spatial clusters showing the local association between wildland re risk and social vulnerability in Florida
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30 C J Gaither et al Forest Policy and Economics 13 2011 24 36
CWPP MS CountLcentroid Merg
L CWPP Community
Firewise Community
W Federal Lands
High Wildfire Risk High Social Vulnerability
Low Wildfire Risk Low Social Vulnerability
Low Wildfire Risk High Social Vulnerability
High Wildfire Risk Low Social Vulnerability
Note The clusters are based on bivariate LISA Statistic significant at p 0 05
lMlite areas represent census block groups where the association is insignificant
The CWPP sign at the Japer City represents 7 CWPP Communities
Fig 4 Bivariate LISA based spatial clusters showing the local association between wildland re risk and social susceptibility in Georgia
that roughly 73 of Birmingham s city population is African American In Florida Fig 3 more af uent communities are located along the
U S Census Bureau 2000 A similar phenomenon occurs around coast from the Jacksonville area on the Atlantic coast down to
other major cities in the region Titusville and West Palm Beach Low socially vulnerable clusters
A moderate clustering northeast of Montgomery and in the state s extend inland to the Everglades on Florida s southern tip and up the
panhandle region is also characterized by high re risk low social Gulf coast from the Naples and Fort Myers area along the coastline of
vulnerability Near Mobile there is a small light blue cluster Sarasota up to the Tampa St Petersburg region As well higher re
approximating the location of central city Mobile 56 African risk is associated with higher income communities on both the
American that is low wildland re risk high social vulnerability Atlantic and south Gulf coasts and in the upper Everglades region Hot
Fig 2 also shows rough demarcations along socio economic lines spots are clustered in extreme north central and south central Florida
in Arkansas The eastern portion of the state south of Interstate 30 40 Similar to Alabama and Arkansas social vulnerability in Georgia also
contains more socially vulnerable CBGs however there are only two varies geographically with south Georgia containing noticeably more
distinct hot spot clusters in southeast Arkansas A light blue area is socially vulnerable clusters compared to suburban Atlanta area and
again evident near the state s capital city Little Rock but areas to the points north Fig 4 shows segments of the southern Black Belt
north and west of Little Rock are either dark blue or mango which denoted by light blue clusters and a spattering of hot spot red clusters
indicate low social vulnerability In this state too high wildland re mainly south of Atlanta running along a line from southwest Georgia
risk areas do not overlap with federal lands northeast to the South Carolina boarder In contrast dark blue clusters
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C J Gaither et al Forest Policy and Economics 13 2011 24 36 31
l CWPP Community
Firewise Community
W Federal Land s
High Wi ldfire Risk High Social Vulnerability
Low Wi ldfire Risk Low Social Vulnerability
Low Wi ldfire Risk High Social Vu lnerability
High Wi ldfire Risk Low Social Vu lnerability
Note The clusters are based on bivariate LISA Statistic significant at p 0 05
VV11ite areas represent census block groups where the association is insignificant
Fig 5 Bivariate LISA based spatial clusters showing the local association between wildland re risk and social susceptibility in Mississippi
are located mainly in the periphery of metropolitan Atlanta and in southwest Mississippi but Jackson is similar to other larger cities in
northeast Georgia around the Chattahoochee National Forest terms of low re risk and high social vulnerability With the exception
The Chattahoochee portion of the Chattahoochee Oconee National of an area to the immediate east of Interstate 55 and extreme east
Forest is located in a high re risk area along Georgia s northern border central Mississippi more areas in the western part of the state are
with North Carolina however the Oconee preserve in the Piedmont characterized by low social vulnerability In the north low social
between Interstates 20 and 16 is not The light blue coloring vulnerability intersects more with low wildland re risk whereas in
distinguishes central city Atlanta from its more af uent suburbs North the south low social vulnerability crosses with higher re risk
of Atlanta there are also mango colored areas which suggests higher re Finally Fig 6 shows a large portion of east South Carolina in hot
risk in concert with higher socio economic status As well there are spot clusters Hot spots overlap with the Francis Marion National
smaller clusters of mango in southeast Georgia near Savannah Forest along the Atlantic coast and also with the Sumpter National
Mississippi northwest of Interstate 55 contains the low lying Forest on the Georgia border There are smaller dark blue areas along
Mississippi Delta or alluvial plain which historically has been the state s east coast but these clusters are located more in the
associated with high poverty rates and is indicated in Fig 5 by light upstate region around Greenville Spartanburg and Columbia A
blue color In this region there is little overlap between social spattering of mango is also along the coast and in the extreme upstate
vulnerability and wildland re risk given the higher moisture content region near Greenville
of this terrain Wildland re risk is positively associated with social As expected our analyses identi ed socially vulnerable clusters
vulnerability in a central Mississippi cluster north of Jackson and also which coincide with the rural Black Belt across the region Again
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32 C J Gaither et al Forest Policy and Economics 13 2011 24 36
Firewise Community
1 CWPP Community
W Federal Lands
High Wi ldfire Risk High Social Vulnerability
Low Wildfire Risk Low Social Vulnerability
Low Wildfire Risk High Social Vulnerability
High Wildfire Risk Low Social Vul nerability
Note The clusters are based on bivariate LISA Statistic significant at p 0 05
VVhite areas represent census block groups where the association is insignificant
Fig 6 Bivariate LISA based spatial clusters showing the local association between wildland re risk and social vulnerability in South Carolina
however elevated wild re risk did not overlap with social vulnera theoretical size which assumes the re is spreading under steady
bility in some areas of south Alabama southwest Georgia and the conditions with no suppression activity Built infrastructure such as
Georgia Piedmont This lack of association may be explained in part roads and re ghting services contribute to re suppression ef cacy
by the three components of WFSI i e weather conditions contrib Poor road networks in some parts of west Alabama may contribute to
uting to re occurrence re behavior and suppression low re suppression scores and hence higher WFSI scores in these
Naturally occurring res are caused by lightning Peak lightning CBGs Road quality can change abruptly depending upon county
concentrations occur along the coast where sea breeze forced resources Poor roads as well as mountainous landscape are also
thunderstorms are common Higher WFSI clusters are clearly seen factors that would contribute to low re suppression effectiveness
in the Gulf areas of Alabama and Mississippi and along Florida s raising the re risk in northern Georgia Contrast the higher re risk
coastline The coastal plain is also characterized by a higher for the Chattahoochee National Forest in north Georgia with the lower
percentage of plant communities that burn with greater intensities risk for the Oconee preserve in the Georgia Piedmont southeast of
on average than upland areas In contrast to the coast and coastal Atlanta Most federal lands however have dedicated re suppression
plain south Alabama southwest Georgia and the Georgia Piedmont resources which lowers re risk in their vicinity
are not characterized by these physical conditions
Those areas of southwest Alabama and other states with adjacent 6 2 2 Spatial associations by type
Low High and High High clusters seem contradictory but may be Distribution of CBGs by cluster type was tabulated for each state
explained by the re suppression component of WFSI To recount re and is presented in Table 1 In all of the states about one quarter of
suppression effectiveness is the comparison of actual re sizes to a total CBGs were found to have negative associations between
Distribution of CBGs for Alabama Arkansas Florida Georgia Mississippi and South Carolina according to types of local spatial association between WFSI and SOVUL
Types of association Alabama Arkansas Florida Georgia Mississippi South Carolina Total
CBG N CBG CBG N CBG CBG N CBG CBG N CBG CBG N CBG CBG N CBG CBG N CBG
High wildland re risk high 85 2 55 6 0 28 405 4 46 58 1 21 44 2 05 248 8 68 846 3 48
social vulnerability
Low wildland re risk low 543 16 31 268 12 95 1425 15 68 887 18 53 301 14 02 549 19 21 3973 16 36
social vulnerability
Low wildland re risk high 589 17 69 344 16 62 1269 13 96 899 18 78 356 16 58 327 11 44 3784 15 58
social vulnerability
High wildland re risk low 142 4 27 144 6 95 890 9 79 266 5 56 144 6 71 91 3 18 1677 6 91
social vulnerability
Insigni cant 1970 59 18 1307 63 17 5099 56 11 2678 55 93 1302 60 64 1643 57 49 13 999 57 66
Total 3329 100 2069 100 9088 100 4788 100 2147 100 2858 100 24 279 100
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C J Gaither et al Forest Policy and Economics 13 2011 24 36 33
wildland re risk and social vulnerability i e either had high Firewise Community locations from a national Firewise manager for
wildland re risk and were located in higher status neighborhoods each of our states A total of 145 active Firewise Communities were
or had low wildland re risk and located in more socially vulnerable reported Alabama 1 Arkansas 91 Florida 38 Georgia 10
neighborhoods South Carolina had the highest percentage of CBGs Mississippi 1 and South Carolina 4 It was more dif cult to secure
classed as hot spots 8 68 and Arkansas had the lowest 0 28 CWPP locations The NFP website lists 730 Communities at Risk for
South Carolina also had the highest percentage of cold spots 19 21 wildland re in the South covered by a CWPP in 2008 http www
and again Arkansas had the lowest cold spot percentage 12 95 forestsandrangelands gov reports documents healthyforests 2008
Florida had the highest percentage of High Low clusters 9 79 and healthy forests report fy2008 pdf however the location of these
South Carolina the lowest 3 18 CWPPs is not mapped by NFP managers
The row totals show that roughly 3 5 of CBGs in the region were We contacted the individual state forestry agencies to obtain
hot spots About 16 of CBGs were in either cold spot areas or Low CWPP locations For some states CWPP data had not been assembled
High clusters and roughly 7 were in High Low social vulnerability at the state level In the case of Florida for instance individual re
areas About 58 of the CBGs in the region exhibited no signi cant districts forwarded latitudinal and longitudinal coordinates to us and
association between wildland re risk and social vulnerability we mapped CWPP locations at the CBG Mississippi establishes
county wide CWPPs so the CWPPs listed for that state represent a
6 2 3 Distribution of wildland re mitigation programs across the central point in the respective counties We obtained the most
Southeast complete listing of CWPP sites for each state that was available
Our primary objective is to examine the spatial relationship although these listings may not be exhaustive Alabama 1 Florida
between 1 hot spots and wildland re mitigation programs and 2 10 Georgia 10 Mississippi 34 and South Carolina 2 but we
High Low areas and wildland re mitigation programs We would did have a complete listing for Arkansas 109
assume that those areas across the region identi ed as being highly Despite their limitations these mappings represent the rst efforts
susceptible to wildland re occurrence would have a greater number of which we are aware that attempt to locate CWPP locations in the
of mitigation programs compared to low re risk communities Our South Both CWPP and Firewise programs locations are typically
aim is to determine how such programs may be distributed in areas associated with residential or a community association address rather
that are also socially vulnerable than a centralized address removed from communities thus the
There are a number of federal state and local level mitigation coordinates for mitigation programs directly re ect community
programs across the country Three key programs are Community involvement
Wild re Protection Plans CWPPs Firewise Communities and To test the hypothesis that hot spots are less likely than High Low
hazardous fuels reduction programs on federal lands The latter are areas to be engaged with wildland re mitigation programs we
funded by the USDA Forest Service and USDI Department of Interior computed the mean distance in kilometers between hot spots and
through the Healthy Forest Initiative and the National Fire Plan NFP High Low clusters respectively to the nearest CWPP location and
http www forestsandrangelands gov reports documents healthy Firewise program Distances were computed in ArcGIS using the
forests 2008 healthy forests report fy2008 pdf Fuels reduction simple distance feature to determine the straight line distance from
programs in the form of prescribed burns or mechanical thinning hot spot and High Low clusters for Firewise and CWPPs respectively
might occur on any federal lands with fuel loads suf cient to warrant CWPP and Firewise location data were also combined into a single
reductions in loadings Communities adjacent to those lands would generic layer representing the location of both types of community
accrue bene ts of such treatments mitigation programs and the distances from hot spots and High Low
We are interested in mitigation efforts involving signi cantly more CBGs to the nearest programs were estimated
community initiative and input CWPPs or Community Wild re Table 2 contains means standard deviations and t tests generated
Protection Plans are also funded by the NFP but are founded principally from the analyses Results show that the average distance from hotspots to
by communities rather than public agencies Communities at risk for CWPPs was signi cantly longer than from High Low clusters to CWPPs in
wildland re collaborate with public agencies local re departments Arkansas Georgia Mississippi and South Carolina The distance was
and municipalities to prioritize private landholdings needing hazard signi cantly shorter in Alabama but not signi cant in Florida For Firewise
ous fuel reduction and recommend appropriate treatments to reduce the mean distance between hot spots and these programs was longer for
future wildland re threats http hazardmitigation calema ca gov Florida Georgia and South Carolina but shorter for Alabama and
hazards natural re Typically state forestry agencies provide infor Mississippi and not signi cant for Arkansas For the combined programs
mation to at risk communities about CWPPs but individual commu mean hot spot distance was longer for all states except Alabama
nity groups or municipalities must take ownership of the plan by It should be noted that the mean distances between a cluster type
becoming active partners with sponsoring agencies and program locations in some cases are the same or very similar This
Similarly the national Firewise Communities program involves has to do with the way hotspots and programs are spatially arranged
signi cant community input These programs are intended to on the ground For instance if most of the hotspots in a state are
reach beyond the re service by involving homeowners community located close to a particular CWPP program their mean distance to
leaders planners developers and others in the effort to protect CWPPs and mean distance to CWPP and Firewise combined would be
people property and natural resources from the risk of wildland the same if there are no Firewise programs in the area Similar
re before a re starts http www rewise org Because of the observations were observed between distances to CWPP and
commitment and involvement required for successful implementa distances to Firewise if a state had only a few programs that are
tion and running of both CWPPs and Firewise programs we believe located close to each other
that communities with higher social and human capital assuming Of the 18 comparisons made 12 or 66 indicated a longer average
high wildland re risk would be more likely than lower capital distance between hot spot clusters and High Low clusters Because
communities or those communities rating high in social vulnerability there was only one CWPP and Firewise in Alabama one Firewise
to establish these programs location in Mississippi and two CWPPs in South Carolina these
We selected CWPPs and Firewise Communities as indicators of comparisons should be taken with some caution If these comparisons
mitigation programs on the ground We realize there are other and the combined category for Alabama are excluded from the
programs at the local and state level that could also be included but analyses eleven of the remaining thirteen means show longer
the dif culty of obtaining data on such programs across the study area distances for hot spots 84 6 Overall results support the research
prohibits their inclusion We obtained complete and current listings of hypothesis and suggest that communities with both higher re risk
Author s personal copy
34 C J Gaither et al Forest Policy and Economics 13 2011 24 36
Table 2 While we acknowledge that individual landowner preferences for
Mean distance of CWPP Firewise and combined CWPP Firewise programs to high mitigation may vary we also submit that speci c socio cultural practices
Wfsi SOVUL hotspot and high WFSI low SOVUL clusters in Alabama Arkansas
Florida Georgia Mississippi and South Carolina
regarding landownership rights inhibit more socially vulnerable groups
from engaging in mitigation Speci cally the practice or system of heir
State Types of CBG CWPP mean Firewise CWPP and property ownership among lower income southern landowners may
association N km stand mean km Firewise mean
work to constrain involvement in land improvement initiatives Building
dev stand dev km stand dev
on Collins 2008a thesis that the environmental values of distinct socio
Alabama High 85 173 30 172 40 172 40
cultural groups in uence community exposure to wildland re risk we
WFSI high 85 01 86 58 86 58
SOVUL posit that differences in hot spot and High Low community engagement
High 142 227 19 249 09 227 19 with mitigation may be explained in part by cultural norms reifying
WFSI low 15 74 168 93 157 41 communal ownership of land in the South
SOVUL t 13 50 t 16 07 t 14 35 Heir property or tenancy in common is inherited land which is
Arkansas High 6 27 42 25 10 24 75
passed on intestate without clear title typically to family members
WFSI high 13 27 13 01 12 79
SOVUL Although such owners have legal claims to land there are no
High 144 18 90 25 87 16 89 demarcations of the land specifying what amount is held by a single
WFSI low 17 22 19 87 16 53 individual Dyer et al 2009 Dyer and Bailey 2008 With each
SOVUL t 2 14 t 0 21 t 2 05
succeeding generation individual ownership interests shrink because
Florida High 405 75 45 34 28 33 56
WFSI high 44 13 23 53 23 83 of the growing number of heirs
SOVUL Mitchell 2001 estimates that 41 of African American owned land in
High 890 77 41 28 03 26 57 the southeastern U S is heir property and Craig Taylor 2000 in Dyer
WFSI low 45 06 22 77 23 00 and Bailey 2008 states that heir property represents the most wide
SOVUL t 2 87 t 15 38 t 16 92
spread form of property ownership in the African American Community
Georgia High 58 145 52 92 61 92 41
WFSI high 54 52 37 68 37 69 But Dyer et al 2009 caution against overestimates arguing that few
SOVUL systematic investigations of heir property prevalence have been con
High 266 74 65 55 49 55 37 ducted because of the meticulous methodology required to classify such
WFSI low 46 29 32 29 32 37
properties Although much of the scholarship on heir property concen
SOVUL t 16 40 t 12 51 t 12 50
Mississippi High 44 26 74 304 47 26 74
trates on southern blacks this type of ownership is also prevalent among
WFSI high 19 88 113 72 19 88 Appalachian whites Deaton et al 2009
SOVUL There are a number of problems associated with heir property and
High 144 14 68 377 01 14 68 land management Principle among these is that the lack of clear title
WFSI low 12 43 126 89 12 43
prohibits participation in any government sponsored home improvement
SOVUL t 6 15 t 11 04 t 6 15
South High 248 276 33 150 33 150 33 programs Also property owners cannot use heir property as collateral for
Carolina WFSI high 39 26 64 11 64 11 a mortgage and selling timber from such land is virtually impossible
SOVUL because a buyer would have to secure the consent of all heirs for a sale and
High 91 234 18 124 44 124 44
most buyers are unwilling to do so Besides this the lack of clear title acts
WFSI low 106 95 68 77 68 78
SOVUL t 4 83 t 8 25 t 8 25
as a disincentive to the improvement of real property attached to land If a
Total 2523 structure were remodeled the increase in value would not accrue to the
individual who paid for the upgrade but again must be disbursed among
signi cant at 0 05 or less
all heirs regardless of where they live Dyer et al 2009 Dyer and Bailey
2008 In many cases heirs may not even live in the same state as the
and higher levels of social vulnerability are less involved with these property location Drawing from economics Deaton et al 2009 argue
particular wildland re mitigation programs that such impediments result in ef ciency problems which occur when
the existing uses of the property result in lower net bene ts to the
7 Discussion and conclusion cotenants than might otherwise be achieved Viewed from the lens of
pro t maximization land use in such scenarios is underutilized
Reasons why socially vulnerable communities are less engaged We submit that heir property holders would also be less motivated
with Firewise Communities or CWPPs may have to do with a range of to participate in wildland re mitigation because of the communal
factors emanating from lack of interest to again a dearth of social and nature of their land interest Again any fees land clearing structure
human capital in these communities A state forester in Florida preparation or other time commitments to CWPP or Firewise would be
stressed that information about CWPPs Firewise and other mitiga likely be borne by the residing heir or others living closer to the
tion programs is readily available from the Florida Division of Forestry property While all heirs would not have to consent to mitigation
but individual homeowners and communities express varying levels planning the disproportionate involvement by one or a few heirs might
of interest in adopting the programs 9 Also unpublished data from our deter participation because of costs necessary to insulate structures or
recent analysis of southern landowner knowledge and understanding clear land either on or off one s property Deaton et al s 2009 case
of wildland re mitigation programs indicated that overall roughly study from Kentucky illustrates how cotenants unwillingness to cut
40 of landowners reported that they had done nothing to prevent timber from their land had the unintentional consequence of increasing
wild re on their rural land and nearly 46 of African Americans said undergrowth resulting in increased fuel loading
they had taken no action to mitigate wild re although blacks were Deaton et al 2009 describe heir property management as a
more likely than whites to say they aware of mitigation information It tragedy of the anti commons in that heirs of jointly held land can
may be that awareness or knowledge possession among African prevent any single heir from certain land uses some of which would
Americans does not translate easily into action either in the form of yield pro ts or potentially lessen hazards In contrast to the overuse
mitigation efforts on one s own land or for the formation of tragedy of the commons problem with heir property the con ict
community efforts like Firewise or CWPPs involves under or nonuse
Also a key factor in mitigation success for CWPPs is collaboration
with and federal agencies U S Forest Service U S Bureau of Land
Personal communication 2010 Gerry Lacavera Florida Division of Forestry Management Communities are expected to draw on the expertise of
Author s personal copy
C J Gaither et al Forest Policy and Economics 13 2011 24 36 35
these agencies for plan preparation and develop a trust in agency the Centers for Urban and Interface Forestry Available online at www south
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black farmers alleging systematic discrimination on the part of the downloads reports Sanborn 20 20Quantifying Wildland Fire Risk in South pdf
USDA exempli es this latter problem and also highlights the some Date accessed 11 June 2009
Buckley D Carlton D Krieter D Sabourin K 2006b Southern wild re risk assessment
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