Wildland fire risk and social vulnerability in the Southeastern United States

13 Pages·2010·1.89 MB·English

by  Cassandra Johnson Gaither

ForestPolicyandEconomics13(2011)24–36
ContentslistsavailableatScienceDirect
Forest Policy and Economics
journal homepage: www.elsevier.com/locate/forpol
Wildland fire risk and social vulnerability in the Southeastern United States: An
exploratory spatial data analysis approach
Cassandra Johnson Gaithera,⁎, Neelam C. Poudyalb,1, Scott Goodricka, J.M. Bowkera,
Sparkle Malonec, Jianbang Gand
aUSDAForestService,SouthernResearchStation,320GreenSt.,Athens,GA30602,UnitedStates
bUniversityofGeorgia,DanielB.WarnellSchoolofForestryandNaturalResources,180E.GreenSt.,Bldg.4-126,Athens,GA30602,UnitedStates
cUniversityofFlorida,136Newins-ZieglerHall,P.O.Box110410,GainesvilleFL32611-0410,UnitedStates
dTexasA&MUniversity,DepartmentofEcosystemScienceandManagement,311Horticulture/ForestScienceBldg.,2138FAMU,CollegeStation,TX77843-2138,UnitedStates
a r t i c l e i n f o a b s t r a c t
Articlehistory: ThesoutheasternU.S.isoneofthemorewildlandfireproneareasofthecountryandalsocontainssomeof
Received22January2010 thepoorestormostsociallyvulnerableruralcommunities.Ourprojectaddresseswildlandfireriskinthis
Receivedinrevisedform14July2010 partoftheU.Sanditsintersectionwithsocialvulnerability.Weexaminespatialassociationbetweenhigh
Accepted29July2010 wildlandfireproneareaswhichalsorankhighinsocialvulnerability(“hotspots”)forAlabama,Arkansas,
Florida,Georgia,Mississippi,andSouthCarolina.Wealsolookattheproximityofhotspotstowildlandfire
Keywords:
mitigationprograms.Wehypothesizethathotspotsarelesslikelythanhighwildlandfirerisk/lowsocial
Socialvulnerability
Wildlandfire vulnerabilitycommunitiestoengagewithmitigationprograms(e.g.,CommunityWildfireProtectionPlansor
Environmentalrisk FirewiseCommunities).Toassessourhypothesis,weexaminedmeandistancesbetween:1)hotspotsand
Wildlandfiremitigation mitigation programs and 2) high wildland fire risk/low social vulnerability communities and mitigation
programs.Overall,resultsshowlongermeandistancesfromhotspotstomitigationprograms,comparedto
distancesforhighwildlandfirerisk/lowsocialvulnerabilitycommunities.Thisfindingprovidessupportfor
ourhypothesisandsuggeststhatpoorercommunitiesinthesoutheastwithhighwildlandfireriskmaybeat
agreaterdisadvantagethanmoreaffluent,highfireriskcommunitiesinthesestates.
PublishedbyElsevierB.V.
1.Introduction mitigatewildlandfireriskorrecoverfromsuchevents(Cutteretal.,
2000;LynnandGerlitz,2006;Evansetal.,2007;Blaikieetal.,1994,
Thisinvestigationexaminestheassociationbetweenwildlandfire p.3). For instance, Mercer and Prestemon (2005) found a positive
risk and social vulnerability in six states in the southeastern U.S.— association between poverty and area of wildland burned and
Alabama,Arkansas,Florida,Georgia,Mississippi,andSouthCarolina. wildlandfireintensity,suggestingthatoncewildlandfiresareignited,
Recent studies conducted outside the South suggest that poorer poorercommunitieshavefewerresourcestoextinguishfire.
communitiessuchasthoseprevalentinthesouthernBlackBelt2and We use Exploratory Spatial Data Analyses (ESDA) to look at
elsewhereacrosstheruralSouthwouldfacegreaterwildlandfirerisks possible links between wildland fire risk and social position. Our
thanmiddle-classoraffluentcommunities(Ojerio,2008;Ojerioetal., objective is to identify descriptive clusters of wildland fire risk and
2008;McCaffrey,2004;LynnandGerlitz,2006;CenterforWatershed social vulnerability—“hot spots,” defined as areas with both above
and CommunityHealth,2001). Socialvulnerability, in terms of low averagefireriskandsocialvulnerability;or“coldspots,”geographies
socio-economic status of residents, has the effect of exacerbating with low wildland fire risk and social vulnerability. Further, we
communityrisktowildlandfireoccurrenceanddevastationbecause examine the proximity of wildland fire mitigation programs to hot
socially vulnerable populations are generally less able to either spots and other clusters to assess whether communities facing the
greatest risks, in terms of both biophysical and socio-demographic
characteristics, have the requisite community-based programs to
⁎ Correspondingauthor.Tel.:[phone];fax:[phone]. lessentheeffectsofwildlandfiredevastation.
E-mailaddress:[email protected](C.J.Gaither).
1 Jointfirstauthor. 1.1. Wildland urban interface and non-wildland urban interface
2 The Black Belt is comprised of 623 counties contained in eleven states of the settlementsintheSouth
formerConfederacy—Alabama,Florida,Georgia,Louisiana,Mississippi,NorthCarolina,
SouthCarolina,Tennessee,Texas,andVirginia.Theregionholds18%ofthenation's
AstudyofsouthernpovertycommissionedbyformerU.S.Senator
population(Allen-Smithetal.,2000).Thesecountiesaremostlyadjacentalthough
theyspanseveralstates(WimberleyandMorris,1997). Zell Miller of Georgia found that in 2000, 13.6million poor people
1389-9341/$–seefrontmatter.PublishedbyElsevierB.V.
doi:10.1016/j.forpol.2010.07.009
C.J.Gaitheretal./ForestPolicyandEconomics13(2011)24–36 25
livedintheSouth,representing40%oftotalU.S.poverty(CarlVinson The greatest number of wildland fires, by region, occurs in the
InstituteofGovernment,2002).Alongwithhighpovertyconcentra- South(NationalInteragencyFireCenter,WildlandFireStatistics,n.d.).
tions, however, the South also contains areas of affluence in urban In2007,one-halfofallreportedwildlandfiresinthenationoccurred
metropolisessuchasAtlanta,Georgiaandwealthpocketsinamenity- in the thirteen states comprising the U.S. Forest Service's Southern
rich wildland areas. The South contained six of the fastest growing Region;in2006,morethanone-halfofallreportedwildlandfiresin
countiesinthenation,intermsofpercentagechangeinpopulation the nation were in the South, and 42% of all large wildland fires
from1April2000to1July2009(U.S.CensusBureau,2009a). reportedwereinthisregion(AndreuandHermansen-Báez,2008).
Population growth increases demand for housing and other In pre-industrial times, Native Americans and early European
development,muchofwhichcontributestotheexpandingWildland settlersusedfiretoreducefuelloads.Theadventofagriculturaland
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-spreadlossofforestcoverthroughouttheSouth.Toaidforest
land” (http://silvis.forest.wisc.edu/projects/WUI_Main.asp). WUI regeneration in the early twentieth century, fire suppression
growth in turn, increases the likelihood of wildland fire ignition programs were implemented across the region. However, decades
caused by humans, given the closer proximity of human dwellings of fire suppression have resulted in substantial fuel buildup in
andactivitiestowoodlands(MacieandHermansen,2002).Research Southernwoodlands,whichcontributetoanincreasedlikelihoodof
indicatesthatWUIexpansionisdrivenlargelybyaffluentmigrationto wildlandfire(FowlerandKonopik,2007;Monroe,2002).
peri-urban areas (Rodrigue, 1993; Collins, 2008a,b). In many Inaddition,severedroughtconditionsoverthepastseveralyears
instances then, WUI settlement implies higher income strata havemadesomeareasintheregionespeciallysusceptibletowildland
populatingwoodlandandwildlandareas.3 fire.InFlorida,forinstance,statefireofficialsreported1847wildland
Federal mandates for wildland fire mitigation efforts prioritize fires on state and private lands from January to April 2009. This
WUI communities (Lynn and Gerlitz, 2006; Western Governor's numberrepresentsanincreaseof88%over2008figuresforthesame
Association, 2002). This is justifiable given the combination of period(FloridaDivisionofForestry,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 fire risk in the WUI. South,identifiesanumberoffactorscontributingtotheproblemof
However, less densely populated rural areas outside the WUI wildland fire in the region. These include the fact that there is
containing abundant vegetation may be at a comparable risk of relativelylittlefederallyownedlandintheSouth,whichmakesstates
wildlandfire. responsible for wildland fire 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 fire threat in many areas; and local fire
WUI. In Oregon and Washington, Lynn and Gerlitz (2006) found a departments must contribute heavily to fire suppression. Also,
higher percentage of poor people in a class of wildlands they term changingdemographics in heavily forested areas makes the task of
InhabitedWildlands,ascomparedwiththeWUI.Aswell,analysisof prescribedburninghardertoimplement,resultinginincreasedfuel
county-levelWUIdata4forthesixstatesincludedinthisstudyshows loadingsinsomecommunities.
thatnon-WUIacreageinnonmetropolitancounties5variespositively
with percentage of population below the poverty threshold 3.Socialvulnerabilityandwildlandfirerisk
(r=0.363; pb0.0001; correlation between a county's WUI acreage
andpercentageofpopulationbelowpovertyisr=−0.439,pb0.0001) HaqueandEtkin(2007)writethatanafter-the-factresponseto
(Radeloff et al., 2005). Hence, those places where development is disasteremphasizingcleanupand recoveryeffortshas forthemost
expandingintoruralwildlandsareless likelytobe in highpoverty 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
SouthCarolina. disaster,thatis,onsuspectedsocietalconditionsandinequitieswhich
Again, however, our interest in wildland fire across these maycausesomegroupstobelesspreparedforandlessabletorecover
southeasternstatesconcentratesonthosesociallyvulnerablepopula- fromhazardevents,asphysicalcauses.
tionsthatlocateinnonmetropolitanareasoutsidetheWUI.Thus,our InareviewoftheliteratureonpovertyanddisastersintheU.S.,
analysisincludesnotjusttheWUIbutalsolessdenselysettled,high FothergillandPeek(2004)describedisastersasa“socialphenome-
vegetation places outside the WUI that contain long-established, non” and cite a number of studies showing that poorer people are
sociallyvulnerablegroups.ThesepopulationsareprevalentinBlack morelikelythanotherincomegroupstoperceivegreaterrisksfrom
BeltcountiessuchasJeffersonCounty,MississippiandPerryCounty naturaldisastersbutarelesslikelytorespondtodisasterwarnings.
Alabama, where 37.5 and 31.7%, respectively, of the population is Poor people also suffer disproportionately from the physical and
classifiedasimpoverished(U.S.CensusBureau,2009b). psychologicalimpactsofdisasters,experiencehighermortalityrates,
and find it more difficult to recover after disasters. The authors
2.WildlandfireriskintheSouth conclude that these findings “…illustrate a systematic pattern of
stratification within the United States” and that disasters often
Physiographicfeaturescontributesignificantlytowildlandfirerisk highlight a priori disparities in social well-being (Fothergill and
intheSouth(Stanturfetal.,2002;Monroe,2002).Criticalfactorsare Peek,2004,p.103).
long growing seasons with frequent rainfall and wind, which Cannon(inHaqueandEtkin,1994)makesexplicitsocialvariables
contribute to abundant vegetation. This growth, along with a high thatcontributetosocialvulnerability—social,economic,andpolitical
frequency of lightning strikes and lack of a persistent snow layer, factors.Thesefactorscaneitherenhanceordetractfromacommu-
increasethelikelihoodofwildlandfire. nity's ability to mitigate disaster. Along similar lines, Cutter et al.
(2000)arguethatsociallyvulnerablegroupssuchastheelderly,lower
3 Collins (2005) stresses that poor communities may coexist with affluent income,racialminorities,andwomenaremorelikelytobeexposedto
populationsintheWUI. a larger number of hazards and or be less able to recover from
4 Data source: Forest and Wildlife Ecology, University of Wisconsin at Madison. disasters (e.g., chemical spills, hurricanes, wildfire), than wealthier,
WildlandInterfaceMaps,Statistics,andGISDownload(http://silvis.forest.wisc.edu/
moreable-bodiedindividualsandcommunities.Morrow(1999)and
projects/WUI_Main.asp.
5 AsmeasuredbytheUSDA'sRural–UrbanContinuumCodes(http://www.ers.usda. LynnandGerlitz(2006)alsopositthatpoorcommunitiesarelessable
gov/briefing/Rurality/RuralUrbCon/). toabsorbtheeffectsofnaturaldisasters.
26 C.J.Gaitheretal./ForestPolicyandEconomics13(2011)24–36
Similar to Cutter et al. (2000), Ojerio (2008) examined both analyses, the CBG provides the most detailed spatial resolution
biophysicalandsocialdatatoassessthevulnerabilityofcensusblock publiclyavailable.
groupsinArizonatowildlandfirerisks.Resultsconsistentlyshowed
that census block groups comprised largely of poor non-Whites
5.1.Wildlandfiresusceptibilityindex
(Navajo and Apache) were less likely than majority white census
blockgroupstoparticipateineitherstate-sponsoredgrantsaimedat
wildlandfiremitigation,communitywildfireprotectionprograms,or WeselectedtheWildlandFireSusceptibilityIndex(WFSI)asour
indicator of wildland fire risk. The index is one of several indices
theFirewiseCommunityprogram.
produced by the Southern Wildfire Risk Assessment (SWRA). The
Importantly, Collins (2008a) critiques assumptions of risk expo-
SWRAisthefirstcomprehensivewildlandfireriskassessmentofits
sureintheFirstWorldwhichassumethathigherincomehouseholds
kind in the nation. It is supported by the thirteen state forestry
willingly expose themselves to risk by locating in aesthetically
agenciesthatcomprisetheUSDAForestService'sSouthernRegion,in
pleasing,yetecologicallyfragileenvironments.Marginalizedgroups,
partnership with the USDA Forest Service, USDI Fish and Wildlife
he argues, are rendered invisible in these settings. Collins (2008a)
Service,USDINationalParkService,BureauofIndianAffairs,andthe
offersinsteadapoliticalecologyviewofriskexposureindeveloped
DepartmentofDefense.TheWFSImeasuresonascaleofzerotoone
nations which makes marginalization relative. He stresses that
theprobability6ofanacreburning,basedonsurfacefuelsandforest
socially vulnerable populations exist alongside the well-heeled in
conditions, weather, historical fire sizes, and historical suppression
placeswithhighenvironmentalriskindevelopednations.However,
state and market institutions (local fire protection and fire risk effectiveness(Buckleyetal.,2006a,b).
The index includes three key components: 1) probability of fire
insurance)acttoinsulatetherichfromdevastatinglossintheeventof
occurrence,2)firebehavior,and3)firesuppressioneffectiveness.The
disaster by the provision of such services. Marginal communities,
first component,probability of fire occurrence,is comprised princi-
conversely,absorbtheriskavoidedbythewealthybecauseoftheir
pally of Fire Occurrence Areas (FOA) and Weather Influence Zones
relativeinabilitytoaccessthesesafeguards.
(WIZ) (Buckley et al., 2006a, p.41–52). FOAs are determined by
Collins' (2008a) focus is the contribution of institutions to the
facilitationofmoreaffluentcommunities.Amorecomprehensivelook historical data pinpointing fire ignition. Quantitatively, FOA is the
historicalmeanofignitionscalculatedasthenumberoffiresperyear
attheadvantagesaccruingtotherich(ordisadvantagesofthepoor)
perthousandacres.Periodsoffireoccurrencewerenotspecifiedbut
necessitates an examination of agency; that is, not just the larger
ratherreferredtogenerallyas“firehistoryreports,”whichweassume
society shielding some sectors from harm, but also the activities
initiatedbythewell-offtoinsulatetheirpropertiesfromwildfireloss. weresuppliedbystateandfederallandmanagementagencies.Fire
Notonlydothemoreaffluenthavebetteraccesstostructuralservices ignitiondatawerecollectedbetween1997and2002.
to mitigate fire, but residents act at the individual and community Weather also influences probability of fire occurrence. To
incorporate this variable, WIZs or weather zones were designated
level to prevent loss by engaging with mitigation programs in the
forthethirteensouthernstates,anddailyweatherobservationsfor
communitieswheretheylive.Suchparticipationdistinguishesupper
eachWIZwererecordedfrom1January1994to31December2003
incomeareasfrompoorandworkingclasscommunities.
(Buckley et al., 2006a). Weather conditions were categorized into
percentiles that indicated conditions which were more or less
4.Researchhypothesis conducivetofireignition—low,moderate,high,andextremelyhigh
percentiles. Various land management agencies and the National
Weexpectthatthetypeofassociationbetweensocialvulnerability OceanicandAtmosphericAdministrationsuppliedweatherdata.
andwildlandfireriskwillvarygeographically(cluster),withhotspot ThesecondsignificantcomponentofWFSIisFireBehavior(rateof
clusters(highsocialvulnerability/highwildlandfirerisk)prevalentin spread[ROS],crownfirepotential,andflamelength).ROSissimulated
less densely populated, rural areas. We do not suppose that a usingFB3DLLWindowssoftware(commercialsoftwarelicensedby
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 calculatedbasedonsurfacefuels,canopyclosure,canopycharacter-
vulnerable populations also locate in urban areas with very low istics,7andtopography(aspect,slope,elevation).Surfaceandcanopy
wildland fire risk; and more affluent populations concentrate in or fuels data were obtained from crosswalks of existing datasets. Fire
near high wildland fire risk rural areas. However, we expect fewer behavior is estimated in 30×30m cells with specific weather
wildland fire mitigation programs to exist near hot spot clusters, conditions. ROS is calculated for the four weather categories—low,
comparedtolowsocialvulnerability/highwildlandfireriskclusters. moderate,high,andextreme.
Lastly, WFSI includes Fire Suppression Effectiveness which is a
H1. Communities with high wildland fire risk and high social
functionofFinalFireSize(FFS)andROS.Firesuppressioneffective-
vulnerability(hotspots)arelesslikelythancommunitieswithhigh nessisthecomparisonofactualfiresizestoatheoreticalsizewhich
wildland fire risk and low social vulnerability to be engaged with assumes fire spreads under stable conditions with homogenous
wildlandfiremitigationprograms.
weatherandfuelconditionswithnosuppressionactivity.Dataused
forthesecalculationsarefromstatesandfederalagenciesforthetime
period1997–2002.ThefinalWFSIfigurefora30×30mcellinagiven
5.Methods
WIZisthesummationoftherespectiveWFSIcalculationsforthefour
Toexaminetheassociationbetweenwildlandfireriskandsocial weatherpercentileareas.WFSIisavailableinarasterformat.
vulnerabilityinthesix-stateregion,wefirstidentifiedindicatorsof TofacilitateanalysisattheCBGlevel,basicstatistics(maximum,
wildlandfireriskandsocialvulnerabilityattheCensusBlockGroup mean,minimum,andstandarddeviation)werecalculatedforall30m
pixelswithineachCBGusingthe“summarizezones”functioninthe
(CBG) level. We chose the CBG as the unit of analysis because this
ESRI's (Environmental Systems Research Institute) Spatial Analyst
geography approximates community groupings. The U.S. Census
BureaudefinesaCBGasanaggregationofblocks,withblocksbeing
analogoustocity blocksdemarcatedbystreets;inruralareasCBGs 6 Althoughduetosomenecessaryassumptionssuchasfuelhomogeneity,itisnot
thetrueprobability.
can contain an extensive number of square miles and do not have
7 Dataoncanopycharacteristicswerelimitedbythelackofextantdataandfunding
streetboundaries.Also,theCBGlevelapproximatesthespatialityat
tocollectprimarycanopyfuelsdata,canopyceilingheight,canopybaseheight,and
which most wildfires occur; and for the variables included in our canopybulkdensity(Buckleyetal.,2006a,p.49).
C.J.Gaitheretal./ForestPolicyandEconomics13(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
(standarddeviation0.086),andmedian0.005. obtainedforeachCBGinthesix-stateregion.Wedownloadedtotal
population; total African American population; total population
25years and older; both male and female population 25 and older
5.2.Socialvulnerabilityindex
withvaryingdegreesofeducationalattainment;totalpopulationfor
whom poverty was determined; population with income below
Concurrently,weconstructedanindextomeasuresocialvulnera-
bility(SOVUL).Wedefinevulnerabilityasmarginalization,character- poverty;totalhousingunits;totalmobilehomeunits;totaloccupied
housingunits;andtotalrenteroccupiedhousingunits.
izedbythelackofabilitytoassertivelynavigatesocialsystemsorto
Fromthesefrequencies,percentAfricanAmerican,percentover25
moveprogressivelytowardshigherlivingstandardsintermsofmaterial
wealthandinfluence.Asindicated,anumberofresearchershavefound withouthighschooldiploma,percentbelowpoverty,percentmobile
home dweller, and percent renter were calculated. Percentages for
arangeofsocialindicatorsassociatedwithanindividual,household,or
eachindicator(e.g.,percentbelowpoverty,black,etc.)weresummed
community'sabilitytomitigateand/orrecoverfromdisasters.Cutteret
al. (2000) identified eleven county-level factors that influence social toproducetheSOVULvaluefora givencensusblockgroup.Values
werenotstandardized,andallvariablesareassumedtocarryequal
vulnerability.Thesehavetodowithpersonalwealth,housingstockand
weight.
tenancy(percentmobilehomesincounty),andrace/ethnicity.Morrow
(1999) includes similar factors—physically and mentally disabled, SOVULvaluesrangedfrom0to3.64,withameanof1.10(standard
deviation0.64)andmedian1.03.Valueslargerthanthemeanindicate
elderly, female-headed households, and the homeless. Cutter et al.
(2003)developedaSocialVulnerabilityIndex(SoVi8)whichexamines highsocialvulnerability.Zerovalueswouldbeobservedinthecaseof
how socio-demographic characteristics influence climate related CBGswithnopopulation.
hazards—drought,floods,hurricaneforcewinds,andsea-levelrise—in
the southeast (Oxfam, 2009). Wildland fire hazard is not included 6.Exploratoryspatialdataanalysis
amongtheenvironmentalrisksthisgroupexamines.
6.1.Bivariateclustersofwildlandfireriskandsocialvulnerability
OurSOVULindexincludespercentofpopulationbelowpoverty,
percent of population 25 or older without a high school diploma,
WeusetheLISAstatistic,localizedindicatorofspatialassociation,
percent African American, percent of housing structures that are
totestthestrengthofassociationbetweenWFSIandSOVULandalso
mobilehomes,andpercentofrenteroccupiedhousingunits.Eachof
to map these associations at the CBG level (Anselin, 1995). The
these variablescan have a direct bearingon social vulnerability for
correlationstatisticindicateshowobservationsofavariableinagiven
both individuals and communities. As discussed, persons or house-
CBG(sayi)areassociatedwithobservationsofadifferentvariablein
holdsbelowpovertyandthosewithlowereducationlevelstypically
have less efficacy in obtaining services or information about adjacentCBGsorthe“neighborhood”oftheithCBG.Inourcase,this
environmentalprotection.Also,raceoftenfiguresintoissuesinvolving involvescorrelationsbetweenWFSIinanarealunit,i,andSOVULin
the cluster of CBGs surrounding and including the ith CBG.
servicesandinformationaccess.White,middle-classneighborhoods
NeighboringCBGsorthe“neighborhood”oftheithCBGwasdefined
and communities typically have a greater number of facilities and
basedonafirstordercontiguityweightmatrix.CBGsadjacenttothe
services compared to poorer, minority areas (Taylor et al., 2007;
ith CBG sharing a common border length or at least a vertex were
Taylor,2000;Wolchetal.,2002).
consideredtobeintheneighborhood.Themeanneighborhoodvalue
Racial status tends to correlate positively with other socio-
forSOVULandWFSIincludesthevalueforthevariableintheithCBG,
demographicandeconomicindicatorssuchasthoseincludedinour
index—particularlypovertyandeducation.However,wealsobelieve aswellasthevaluesforallCBGsadjacenttoit.Thiswasachievedby
thatthedescriptor“AfricanAmerican”or“Black”carriesanadditional manuallyeditingtheweightmatrixfiles.
BivariateLISAstatisticswereusedinGeoDa™0.9.5-Itomapfour
weightbeyondthatofincomeoreducation.Thisrelatestobothovert
differenttypesofspatialclustersforWFSIandSOVULattheCBGlevel.
andmoresubtleformsofdiscriminationfromthelargersocietyand
ForWFSI,forexample,clustersinclude:1)High–High,CBGswithhigh
alsotoself-imposedracialsegregationwhichcontinuesdefactoracial
wildlandfirerisksurroundedbyCBGswithhighsocialvulnerability;
separation.Mobilehomesarelessabletowithstandnaturaldisasters
2) Low–Low, CBGswith low wildlandfire risk surrounded byCBGs
suchashurricanesbecausethebuildingmaterialisgenerallyoflower
qualitythanconstructeddwellings.Thismayalsobethecasewithfire withlowsocialvulnerability;3)Low–High,CBGswithlowwildland
firerisksurroundedbyCBGswithhighsocialvulnerability;4)High–
resistance,asmobilestructuresarelesslikelythanconstructedhomes
tobemadeoffireresistant,durablematerials.Finally,rentershave Low,CBGswithhighwildlandfirerisksurroundedbyCBGswithlow
less control over building materials, landscaping, fire insurance or socialvulnerability.
othersafeguardsagainstwildlandfire,whichcouldresultingreater Again, the high and low level of a given variable is defined in
referencetoitsmeanvaluefortheneighborhood.WedefinedHigh–
vulnerabilityforthisgroup.
High and Low–Low clusters as “hot spots” and “cold spots,”
Becauseofoverlapsbetweenraceandtheothervariablesincluded
respectively, where the association between two phenomena is
in SOVUL, we examined the degree of multicollinearity for the
positive. For the other clusters (Low–High and High–Low), the
variables comprising the index by examining a regression model
associations are negative and are described as spatial outliers
whereWFSIwasthedependentvariableandpercentblack,percent
(Anselin,2005).LISAscoressignificantatp=0.05orlesswereused
below poverty, percent low education, percent renter and mobile
homewerepredictors.Here,wewantedtodetectinflatedstandard to map statistically significant clusters. Pseudo-p values were
errors by looking at the variance inflation factor (VIF), as multi- generated for LISA statistics utilizing 999 permutation criteria
collinearityisindicatedbyfluctuatingstandarderrors.Generally,VIF availableinGeoDa™0.9.5-I(www.geodacenter.asu.edu).
The following equation (Sunderlin et al., 2008) provides the
values greater than ten may indicate multicollinearity among
computationofbivariateLISAbasedonAnselin(1995).
variables. VIF values for each of our predictors were below three,
which suggests low or moderate multicollinearity. Frequencies for
N
variables comprising SOVUL were downloaded from the 2000 U.S. I =z ∑ w z ð1Þ
l xi ij yj
j=1;j≠i
8 SoVi includes eight variables which explain 75% of the variance in social
where, I is the local Moran's I (LISA); x and y are two variables of
vulnerability. Thevariablesarewealth,age,race,ethnicity,ruralresidence, special l
needspopulations,gender,andemployment(Oxfam,2009). interest measured for CBG i, and neighborhood j, respectively.
28 C.J.Gaitheretal./ForestPolicyandEconomics13(2011)24–36
Similarly,z andz representthestandardizedz-scoresforvariablesx To make the interpretation easier and more meaningful, cluster
x y
andy,respectively.Thetermw istheweightmatrixthatdefinesthe mapsforeachstateareoverlaidwithinterstatehighwayandfederal
ij
structureoftheneighborhood.LISAandweightmatriceswerecreated land areas. Geo-visualization of clusters with such recognizable
in GeoDa™ 0.9.5-I. This analysis uses a first order queen contiguity figures provides reference for illustrating the spatial location of
matrix,wherew =1iftheadjacentCBGjsharesacommonborder clusters.Forexample,intheanalysisforAlabama(Fig.1),redclusters
ij
lengthorcommonvertexwiththeithCBG.Ifacommonborderisnot orhotspotsarelocatedinthesouthernpartofthestate,mostlysouth
shared,thevalueiszero. of Interstate-85 and US-80. Interestingly, this portion of the state
containsrelativelylessfederallandareacomparedtoareasnorthof
thosehighways.SouthAlabamaalsocontainslargeareasoflightblue
6.2.Results clusters,whichagainindicatedhighsocialvulnerabilityCBGsinthe
neighborhoodoflowwildlandfireriskCBGs.
6.2.1.ESDAatthestatelevel The overall pattern of high social vulnerability (red and light blue
Figs.1–6showbivariateLISAanalysesforeachstate.Ineachfigure, patches)followsthespatialityofAlabama'simpoverishedBlackBelt.The
theredcolorindicatesclustersofhighwildlandfireriskCBGslocated moresocially vulnerable clusters are locatedalmost exclusively in the
in neighborhoods or clusters with high social vulnerability (High– southernpartofthestate.Thepresentanalysesdemonstratehowlow
High); dark blue clusters denote low wildland fire risk CBGs in socio-economicstatusorsociallyvulnerablecommunitiesintersectwith
clusterswithlowsocialvulnerability(Low–Low);lowwildlandfire wildlandfirerisk.Insomeareasofthestate'sBlackBelt,thereisaninverse
risk/highsocialvulnerabilityclustersareshowninlightblue(Low– associationbetweensocialwell-beingandthistypeofenvironmentalrisk
High);andhighwildlandfirerisk/lowsocialvulnerabilityclustersare (lightblue);whereasinotherstheassociationispositive(red).
colored mango (High–Low). White areas within the study area NorthAlabamastandsoutasanearantonymtothesouthernpart
represent CBGs where the spatial association between WFSI and of the state, in terms of social well-being. From Birmingham and
SOVULisnotstatisticallysignificant. Tuscaloosa northward, the state contains remarkably more low
Fig.1.BivariateLISAbasedspatialclustersshowingthelocalassociationbetweenwildlandfireriskandsocialvulnerabilityinAlabama.
C.J.Gaitheretal./ForestPolicyandEconomics13(2011)24–36 29
Fig.2.BivariateLISAbasedspatialclustersshowingthelocalassociationbetweenwildfireriskandsocialvulnerabilityinArkansas.
sociallyvulnerableclusters.ThedarkblueLow–Lowclusterspredom- area.Theonlyexceptiontothispatternisthelightblue,High–Low
inatein thenorth;but highfirerisk areasalso intersectwith more areaofcentralcityBirmingham.Theclusterhereissimilartothatin
well-off communities in north Alabama in the Huntsville–Florence theruralBlackBeltsouthofInterstate-20.Thisisnotsurprisinggiven
Fig.3.BivariateLISAbasedspatialclustersshowingthelocalassociationbetweenwildlandfireriskandsocialvulnerabilityinFlorida.
30 C.J.Gaitheretal./ForestPolicyandEconomics13(2011)24–36
Fig.4.BivariateLISAbasedspatialclustersshowingthelocalassociationbetweenwildlandfireriskandsocialsusceptibilityinGeorgia.
thatroughly73%ofBirmingham'scitypopulationisAfricanAmerican InFlorida(Fig.3),moreaffluentcommunitiesarelocatedalongthe
(U.S. Census Bureau, 2000). A similar phenomenon occurs around coast from the Jacksonville area on the Atlantic coast down to
othermajorcitiesintheregion. Titusville and West Palm Beach. Low socially vulnerable clusters
AmoderateclusteringnortheastofMontgomeryandinthestate's extendinlandtotheEvergladesonFlorida'ssoutherntipandupthe
panhandle region is also characterized by high fire risk/low social GulfcoastfromtheNaplesandFortMyersarea,alongthecoastlineof
vulnerability. Near Mobile, there is a small light blue cluster Sarasota,uptotheTampa/St.Petersburgregion.Aswell,higherfire
approximating the location of central city Mobile (56% African risk is associated with higher income communities on both the
American)thatislowwildlandfirerisk/highsocialvulnerability. AtlanticandsouthGulfcoastsandintheupperEvergladesregion.Hot
Fig.2alsoshowsroughdemarcationsalongsocio-economiclines spotsareclusteredinextremenorthcentralandsouthcentralFlorida.
inArkansas.TheeasternportionofthestatesouthofInterstate-30/40 SimilartoAlabamaandArkansas,socialvulnerabilityinGeorgiaalso
containsmoresociallyvulnerableCBGs;however,thereareonlytwo variesgeographically,withsouthGeorgiacontainingnoticeablymore
distincthotspotclustersinsoutheastArkansas.Alightblueareais socially vulnerable clusterscomparedto suburbanAtlanta area and
againevidentnearthestate'scapitalcity,LittleRock;butareastothe points north. Fig. 4 shows segments of the southern Black Belt,
north and west of Little Rock are either dark blue or mangowhich denotedbylightblueclustersandaspatteringofhotspotredclusters,
indicatelowsocialvulnerability.Inthisstate,too,highwildlandfire mainlysouthofAtlantarunningalongalinefromsouthwestGeorgia
riskareasdonotoverlapwithfederallands. northeasttotheSouthCarolinaboarder.Incontrast,darkblueclusters
C.J.Gaitheretal./ForestPolicyandEconomics13(2011)24–36 31
Fig.5.BivariateLISAbasedspatialclustersshowingthelocalassociationbetweenwildlandfireriskandsocialsusceptibilityinMississippi.
are located mainly in the periphery of metropolitan Atlanta and insouthwestMississippi;butJacksonissimilartootherlargercitiesin
northeastGeorgiaaroundtheChattahoocheeNationalForest. termsoflowfireriskandhighsocialvulnerability.Withtheexception
TheChattahoocheeportionoftheChattahoochee–OconeeNational ofanareatotheimmediateeastofInterstate-55andextremeeast-
ForestislocatedinahighfireriskareaalongGeorgia'snorthernborder central Mississippi, more areas in the western part of the state are
with North Carolina;however,theOconeepreservein thePiedmont characterized by low social vulnerability. In the north, low social
between Interstates-20 and 16 is not. The light blue coloring vulnerabilityintersectsmorewithlowwildlandfirerisk;whereasin
distinguishescentralcityAtlantafromitsmoreaffluentsuburbs.North thesouth,lowsocialvulnerabilitycrosseswithhigherfirerisk.
ofAtlanta,therearealsomangocoloredareaswhichsuggestshigherfire Finally,Fig.6showsalargeportionofeastSouthCarolinainhot
risk in concert with higher socio-economic status. As well, there are spot clusters. Hot spots overlap with the Francis Marion National
smallerclustersofmangoinsoutheastGeorgianearSavannah. Forest along the Atlantic coast and also with the Sumpter National
Mississippi northwest of Interstate-55 contains the low lying ForestontheGeorgiaborder.Therearesmallerdarkblueareasalong
Mississippi “Delta” or alluvial plain, which historically has been the state's east coast, but these clusters are located more in the
associatedwithhighpovertyratesandisindicatedinFig.5bylight upstate region around Greenville, Spartanburg, and Columbia. A
blue color. In this region, there is little overlap between social spatteringofmangoisalsoalongthecoastandintheextremeupstate
vulnerabilityandwildlandfireriskgiventhehighermoisturecontent regionnearGreenville.
ofthisterrain.Wildlandfireriskispositivelyassociatedwithsocial As expected, our analyses identified socially vulnerable clusters
vulnerabilityinacentralMississippiclusternorthofJacksonandalso which coincide with the rural Black Belt across the region. Again,
32 C.J.Gaitheretal./ForestPolicyandEconomics13(2011)24–36
Fig.6.BivariateLISAbasedspatialclustersshowingthelocalassociationbetweenwildlandfireriskandsocialvulnerabilityinSouthCarolina.
however,elevatedwildfireriskdidnotoverlapwithsocialvulnera- theoretical size which assumes the fire is spreading under steady
bility in some areas of south Alabama, southwest Georgia, and the conditionswithnosuppressionactivity.Builtinfrastructuresuchas
GeorgiaPiedmont.Thislackofassociationmaybeexplained,inpart, roadsandfirefightingservicescontributetofiresuppressionefficacy.
by the three components of WFSI (i.e., weatherconditions contrib- PoorroadnetworksinsomepartsofwestAlabamamaycontributeto
utingtofireoccurrence,firebehavior,andsuppression). low fire suppression scores, and hence higher WFSI scores in these
Naturally occurring fires 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 fire suppression effectiveness,
in the Gulf areas of Alabama and Mississippi and along Florida's raisingthefireriskinnorthernGeorgia.Contrastthehigherfirerisk
coastline. The coastal plain is also characterized by a higher fortheChattahoocheeNationalForestinnorthGeorgiawiththelower
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.Mostfederallands,however,havededicatedfiresuppression
plain,southAlabama,southwestGeorgia,andtheGeorgiaPiedmont resources,whichlowersfireriskintheirvicinity.
arenotcharacterizedbythesephysicalconditions.
ThoseareasofsouthwestAlabamaandotherstateswithadjacent 6.2.2.Spatialassociationsbytype
Low–High and High–High clusters seem contradictory but may be DistributionofCBGsbyclustertypewastabulatedforeachstate
explainedbythefiresuppressioncomponentofWFSI.Torecount,fire andispresentedinTable1.Inallofthestates,aboutone-quarterof
suppression effectiveness is the comparison of actual fire sizes to a total CBGs were found to have negative associations between
Table1
DistributionofCBGsforAlabama,Arkansas,Florida,Georgia,Mississippi,andSouthCarolinaaccordingtotypesoflocalspatialassociationbetweenWFSIandSOVUL.
Typesofassociation Alabama Arkansas Florida Georgia Mississippi SouthCarolina Total
CBG(N) CBG(%) CBG(N) CBG(%) CBG(N) CBG(%) CBG(N) CBG(%) CBG(N) CBG(%) CBG(N) CBG(%) CBG(N) CBG(%)
Highwildlandfirerisk–high 85 2.55 6 0.28 405 4.46 58 1.21 44 2.05 248 8.68 846 3.48
socialvulnerability
Lowwildlandfirerisk–low 543 16.31 268 12.95 1425 15.68 887 18.53 301 14.02 549 19.21 3973 16.36
socialvulnerability
Lowwildlandfirerisk–high 589 17.69 344 16.62 1269 13.96 899 18.78 356 16.58 327 11.44 3784 15.58
socialvulnerability
Highwildlandfirerisk–low 142 4.27 144 6.95 890 9.79 266 5.56 144 6.71 91 3.18 1677 6.91
socialvulnerability
Insignificant 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
C.J.Gaitheretal./ForestPolicyandEconomics13(2011)24–36 33
wildland fire risk and social vulnerability (i.e., either had high FirewiseCommunitylocationsfromanationalFirewisemanagerfor
wildland fire risk and were located in higher status neighborhoods eachofourstates.Atotalof145activeFirewiseCommunitieswere
orhadlowwildlandfireriskandlocatedinmoresociallyvulnerable reported—Alabama (1), Arkansas (91), Florida (38), Georgia (10),
neighborhoods).SouthCarolinahadthehighestpercentageofCBGs Mississippi(1),andSouthCarolina(4).Itwasmoredifficulttosecure
classed as hot spots (8.68%) and Arkansas had the lowest (0.28%). CWPPlocations.TheNFPwebsitelists730CommunitiesatRisk(for
SouthCarolinaalsohadthehighestpercentageofcoldspots(19.21%); wildlandfire)intheSouthcoveredbyaCWPPin2008(http://www.
and again Arkansas had the lowest cold spot percentage (12.95%). forestsandrangelands.gov/reports/documents/healthyforests/2008/
FloridahadthehighestpercentageofHigh–Lowclusters(9.79%)and healthy_forests_report_fy2008.pdf); however, the location of these
SouthCarolinathelowest(3.18%). CWPPsisnotmappedbyNFPmanagers.
Therowtotalsshowthatroughly3.5%ofCBGsintheregionwere We contacted the individual state forestry agencies to obtain
hotspots.About16%ofCBGswereineithercoldspotareasorLow– CWPPlocations.Forsomestates,CWPPdatahadnotbeenassembled
Highclusters;androughly7%wereinHigh–Lowsocialvulnerability at thestatelevel.In thecaseof Florida,forinstance,individualfire
areas. About58% of the CBGs in the regionexhibited no significant districtsforwardedlatitudinalandlongitudinalcoordinatestous,and
associationbetweenwildlandfireriskandsocialvulnerability. 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 fire 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 althoughtheselistingsmaynotbeexhaustive:Alabama(1),Florida
between:1)hotspotsandwildlandfiremitigationprogramsand2) (10),Georgia(10),Mississippi(34),andSouthCarolina(2);butwe
High–Low areas and wildland fire mitigation programs. We would didhaveacompletelistingforArkansas(109).
assumethatthoseareasacrosstheregionidentifiedasbeinghighly Despitetheirlimitations,thesemappingsrepresentthefirstefforts
susceptibletowildlandfireoccurrencewouldhaveagreaternumber ofwhichweareawarethatattempttolocateCWPPlocationsinthe
ofmitigationprograms,comparedtolowfireriskcommunities.Our South. Both CWPP and Firewise programs locations are typically
aimistodeterminehowsuchprogramsmaybedistributedinareas associatedwithresidentialoracommunityassociationaddressrather
thatarealsosociallyvulnerable. 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 reflect community
programs across the country. Three key programs are Community involvement.
Wildfire Protection Plans (CWPPs), Firewise Communities, and TotestthehypothesisthathotspotsarelesslikelythanHigh–Low
hazardousfuelsreductionprogramsonfederallands.Thelatterare areas to be engaged with wildland fire mitigation programs, we
fundedbytheUSDAForestServiceandUSDIDepartmentofInterior computedthemeandistance,inkilometers,betweenhotspotsand
throughtheHealthyForestInitiativeandtheNationalFirePlan(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 “simpledistance”featuretodeterminethestraightlinedistancefrom
programs in the form of prescribed burns or mechanical thinning hotspotandHigh–LowclustersforFirewiseandCWPPs,respectively.
mightoccuronanyfederallandswithfuelloadssufficienttowarrant CWPP and Firewise location data were also combined into a single
reductionsin loadings.Communities adjacentto those landswould genericlayer representing thelocation ofboth typesof community
accruebenefitsofsuchtreatments. mitigationprograms;andthedistancesfromhotspotsandHigh–Low
Weareinterestedinmitigationeffortsinvolvingsignificantlymore CBGstothenearestprogramswereestimated.
community initiative and input. CWPPs or Community Wildfire Table 2 contains means, standard deviations, and t-tests generated
ProtectionPlansarealsofundedbytheNFPbutarefoundedprincipally fromtheanalyses.Resultsshowthattheaveragedistancefromhotspotsto
bycommunitiesratherthanpublicagencies.Communitiesatriskfor CWPPswassignificantlylongerthanfromHigh–LowclusterstoCWPPsin
wildlandfirecollaboratewithpublicagencies,localfiredepartments, Arkansas, Georgia, Mississippi, and South Carolina. The distance was
andmunicipalitiestoprioritizeprivatelandholdingsneedinghazard- significantlyshorterinAlabamabutnotsignificantinFlorida.ForFirewise,
ousfuelreductionandrecommendappropriatetreatmentstoreduce themeandistancebetweenhotspotsandtheseprogramswaslongerfor
future wildland fire threats (http://hazardmitigation.calema.ca.gov/ Florida, Georgia, and South Carolina but shorter for Alabama and
hazards/natural/fire).Typically,stateforestryagenciesprovideinfor- MississippiandnotsignificantforArkansas.Forthecombinedprograms,
mationtoatriskcommunitiesaboutCWPPs,butindividualcommu- meanhotspotdistancewaslongerforallstatesexceptAlabama.
nity groups or municipalities must take ownership of the plan by Itshouldbenotedthatthemeandistancesbetweenaclustertype
becomingactivepartnerswithsponsoringagencies. andprogramlocationsinsomecasesarethesameorverysimilar.This
Similarly, the national Firewise Communities program involves hastodowiththewayhotspotsandprogramsarespatiallyarranged
significant community input. These programs are intended to “… on the ground. For instance, if most of the hotspots in a state are
reachbeyondthefireservicebyinvolvinghomeowners,community locatedclosetoaparticularCWPPprogram,theirmeandistanceto
leaders, planners, developers, and others in the effort to protect CWPPsandmeandistancetoCWPPandFirewisecombinedwouldbe
people, property, and natural resources from the risk of wildland the same if there are no Firewise programs in the area. Similar
fire—beforeafirestarts”(http://www.firewise.org/).Becauseofthe 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
tionandrunningofbothCWPPsandFirewiseprograms,webelieve locatedclosetoeachother.
that communities with higher social and human capital (assuming Ofthe18comparisonsmade,12or66%indicatedalongeraverage
high wildland fire risk) would be more likely than lower capital distance between hot spot clusters and High/Low clusters. Because
communities,orthosecommunitiesratinghighinsocialvulnerability, there was only one CWPP and Firewise in Alabama, one Firewise
toestablishtheseprograms. location in Mississippi, and two CWPPs in South Carolina, these
We selected CWPPs and Firewise Communities as indicators of comparisonsshouldbetakenwithsomecaution.Ifthesecomparisons
mitigation programs on the ground. We realize there are other and the combined category for Alabama are excluded from the
programsatthelocalandstatelevelthatcouldalsobeincluded,but analyses, eleven of the remaining thirteen means show longer
thedifficultyofobtainingdataonsuchprogramsacrossthestudyarea distancesforhotspots(84.6%).Overall,resultssupporttheresearch
prohibitstheirinclusion.Weobtainedcompleteandcurrentlistingsof hypothesisandsuggestthatcommunitieswithbothhigherfirerisk