Forest Policy and Economics

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1 Forest Poicy and Economics 13 (2011) Contents ists avaiabe at ScienceDirect " Forest Poicy and Economics ELSEVIER journa homepage: Widand fire risk and socia vunerabiity in the Southeastern United States: An exporatory spatia data anaysis approach Cassandra Johnson Gaither a,, Neeam C. Poudya b,1, Scott Goodrick a, J.M. Bowker a, Sparke Maone c, Jianbang Gan d a USDA Forest Service, Southern Research Station, 320 Green St., Athens, GA 30602, United States b University of Georgia, Danie B. Warne Schoo of Forestry and Natura Resources, 180 E. Green St., Bdg , Athens, GA 30602, United States c University of Forida, 136 Newins-Zieger Ha, P.O. Box , Gainesvie FL , United States d Texas A&M University, Department of Ecosystem Science and Management, 311 Horticuture/Forest Science Bdg., 2138 FAMU, Coege Station, TX , United States artice info abstract Artice history: Received 22 January 2010 Received in revised form 14 Juy 2010 Accepted 29 Juy 2010 Keywords: Socia vunerabiity Widand fire Environmenta risk Widand fire mitigation The southeastern U.S. is one of the more widand fire prone areas of the country and aso contains some of the poorest or most sociay vunerabe rura communities. Our project addresses widand fire risk in this part of the U.S and its intersection with socia vunerabiity. We examine spatia association between high widand fire prone areas which aso rank high in socia vunerabiity ( hot spots ) for Aabama, Arkansas, Forida, Georgia, Mississippi, and South Caroina. We aso ook at the proximity of hot spots to widand fire mitigation programs. We hypothesize that hot spots are ess ikey than high widand fire risk/ow socia vunerabiity communities to engage with mitigation programs (e.g., Community Widfire Protection Pans or Firewise Communities). To assess our hypothesis, we examined mean distances between: 1) hot spots and mitigation programs and 2) high widand fire risk/ow socia vunerabiity communities and mitigation programs. Overa, resuts show onger mean distances from hot spots to mitigation programs, compared to distances for high widand fire risk/ow socia vunerabiity communities. This finding provides support for our hypothesis and suggests that poorer communities in the southeast with high widand fire risk may be at a greater disadvantage than more affuent, high fire risk communities in these states. Pubished by Esevier B.V. 1. Introduction This investigation examines the association between widand fire risk and socia vunerabiity in six states in the southeastern U.S. Aabama, Arkansas, Forida, Georgia, Mississippi, and South Caroina. Recent studies conducted outside the South suggest that poorer communities such as those prevaent in the southern Back Bet 2 and esewhere across the rura South woud face greater widand fire risks than midde-cass or affuent communities (Ojerio, 2008; Ojerio et a., 2008; McCaffrey, 2004; Lynn and Geritz, 2006; Center for Watershed and Community Heath, 2001). Socia vunerabiity, in terms of ow socio-economic status of residents, has the effect of exacerbating community risk to widand fire occurrence and devastation because sociay vunerabe popuations are generay ess abe to either Corresponding author. Te.: ; fax: E-mai address: cjohnson09@fs.fed.us (C.J. Gaither). 1 Joint first author. 2 The Back Bet is comprised of 623 counties contained in eeven states of the former Confederacy Aabama, Forida, Georgia, Louisiana, Mississippi, North Caroina, South Caroina, Tennessee, Texas, and Virginia. The region hods 18% of the nation's popuation (Aen-Smith et a., 2000). These counties are mosty adjacent athough they span severa states (Wimberey and Morris, 1997). mitigate widand fire risk or recover from such events (Cutter et a., 2000; Lynn and Geritz, 2006; Evans et a., 2007; Baikie et a., 1994, p.3). For instance, Mercer and Prestemon (2005) found a positive association between poverty and area of widand burned and widand fire intensity, suggesting that once widand fires are ignited, poorer communities have fewer resources to extinguish fire. We use Exporatory Spatia Data Anayses (ESDA) to ook at possibe inks between widand fire risk and socia position. Our objective is to identify descriptive custers of widand fire risk and socia vunerabiity hot spots, defined as areas with both above average fire risk and socia vunerabiity; or cod spots, geographies with ow widand fire risk and socia vunerabiity. Further, we examine the proximity of widand fire mitigation programs to hot spots and other custers to assess whether communities facing the greatest risks, in terms of both biophysica and socio-demographic characteristics, have the requisite community-based programs to essen the effects of widand fire devastation Widand urban interface and non-widand urban interface settements in the South A study of southern poverty commissioned by former U.S. Senator Ze Mier of Georgia found that in 2000, 13.6 miion poor peope /$ see front matter. Pubished by Esevier B.V. doi: /j.forpo

2 C.J. Gaither et a. / Forest Poicy and Economics 13 (2011) ived in the South, representing 40% of tota U.S. poverty (Car Vinson Institute of Government, 2002). Aong with high poverty concentrations, however, the South aso contains areas of affuence in urban metropoises such as Atanta, Georgia and weath pockets in amenityrich widand areas. The South contained six of the fastest growing counties in the nation, in terms of percentage change in popuation from 1 Apri 2000 to 1 Juy 2009 (U.S. Census Bureau, 2009a). Popuation growth increases demand for housing and other deveopment, much of which contributes to the expanding Widand Urban Interface or the WUI the area where structures and other human deveopment meet or interminge with undeveoped widand ( WUI growth in turn, increases the ikeihood of widand fire ignition caused by humans, given the coser proximity of human dweings and activities to woodands (Macie and Hermansen, 2002). Research indicates that WUI expansion is driven argey by affuent migration to peri-urban areas (Rodrigue, 1993; Coins, 2008a,b). In many instances then, WUI settement impies higher income strata popuating woodand and widand areas. 3 Federa mandates for widand fire mitigation efforts prioritize WUI communities (Lynn and Geritz, 2006; Western Governor's Association, 2002). This is justifiabe given the combination of physica and socia factors (increasing popuation and housing density) contributing to higher widand fire risk in the WUI. However, ess densey popuated rura areas outside the WUI containing abundant vegetation may be at a comparabe risk of widand fire. Importanty, non-wui settements have been found to contain higher percentages of ower income popuations, in contrast to the WUI. In Oregon and Washington, Lynn and Geritz (2006) found a higher percentage of poor peope in a cass of widands they term Inhabited Widands, as compared with the WUI. As we, anaysis of county-eve WUI data 4 for the six states incuded in this study shows that non-wui acreage in nonmetropoitan counties 5 varies positivey with percentage of popuation beow the poverty threshod (r=0.363; pb0.0001; correation between a county's WUI acreage and percentage of popuation beow poverty is r= 0.439, pb0.0001) (Radeoff et a., 2005). Hence, those paces where deveopment is expanding into rura widands are ess ikey to be in high poverty counties in Aabama, Arkansas, Forida, Georgia, Mississippi, and South Caroina. Again, however, our interest in widand fire across these southeastern states concentrates on those sociay vunerabe popuations that ocate in nonmetropoitan areas outside the WUI. Thus, our anaysis incudes not just the WUI but aso ess densey setted, high vegetation paces outside the WUI that contain ong-estabished, sociay vunerabe groups. These popuations are prevaent in Back Bet counties such as Jefferson County, Mississippi and Perry County Aabama, where 37.5 and 31.7%, respectivey, of the popuation is cassified as impoverished (U.S. Census Bureau, 2009b). 2. Widand fire risk in the South Physiographic features contribute significanty to widand fire risk in the South (Stanturf et a., 2002; Monroe, 2002). Critica factors are ong growing seasons with frequent rainfa and wind, which contribute to abundant vegetation. This growth, aong with a high frequency of ightning strikes and ack of a persistent snow ayer, increase the ikeihood of widand fire. 3 Coins (2005) stresses that poor communities may coexist with affuent popuations in the WUI. 4 Data source: Forest and Widife Ecoogy, University of Wisconsin at Madison. Widand Interface Maps, Statistics, and GIS Downoad ( projects/wuimain.asp. 5 As measured by the USDA's Rura Urban Continuum Codes ( gov/briefing/ruraity/ruraurbcon/). The greatest number of widand fires, by region, occurs in the South (Nationa Interagency Fire Center, Widand Fire Statistics, n.d.). In 2007, one-haf of a reported widand fires in the nation occurred in the thirteen states comprising the U.S. Forest Service's Southern Region; in 2006, more than one-haf of a reported widand fires in the nation were in the South, and 42% of a arge widand fires reported were in this region (Andreu and Hermansen-Báez, 2008). In pre-industria times, Native Americans and eary European setters used fire to reduce fue oads. The advent of agricutura and industria deveopment during the nineteenth century resuted in wide-spread oss of forest cover throughout the South. To aid forest regeneration in the eary twentieth century, fire suppression programs were impemented across the region. However, decades of fire suppression have resuted in substantia fue buidup in Southern woodands, which contribute to an increased ikeihood of widand fire (Fower and Konopik, 2007; Monroe, 2002). In addition, severe drought conditions over the past severa years have made some areas in the region especiay susceptibe to widand fire. In Forida, for instance, state fire officias reported 1847 widand fires on state and private ands from January to Apri This number represents an increase of 88% over 2008 figures for the same period (Forida Division of Forestry, 2009). The Southern Group of State Foresters', 2005 report, Fire in the South, identifies a number of factors contributing to the probem of widand fire in the region. These incude the fact that there is reativey itte federay owned and in the South, which makes states responsibe for widand fire protection on greater than 94% of the region's and area. Again, the widand urban interface (WUI) exacerbates widand fire threat in many areas; and oca fire departments must contribute heaviy to fire suppression. Aso, changing demographics in heaviy forested areas makes the task of prescribed burning harder to impement, resuting in increased fue oadings in some communities. 3. Socia vunerabiity and widand fire risk Haque and Etkin (2007) write that an after-the-fact response to disaster emphasizing ceanup and recovery efforts has for the most part been repaced with a vunerabiity/resiience paradigm. This perspective paces as much emphasis on the socia dimensions of disaster, that is, on suspected societa conditions and inequities which may cause some groups to be ess prepared for and ess abe to recover from hazard events, as physica causes. In a review of the iterature on poverty and disasters in the U.S., Fothergi and Peek (2004) describe disasters as a socia phenomenon and cite a number of studies showing that poorer peope are more ikey than other income groups to perceive greater risks from natura disasters but are ess ikey to respond to disaster warnings. Poor peope aso suffer disproportionatey from the physica and psychoogica impacts of disasters, experience higher mortaity rates, and find it more difficut to recover after disasters. The authors concude that these findings iustrate a systematic pattern of stratification within the United States and that disasters often highight a priori disparities in socia we-being (Fothergi and Peek, 2004, p. 103). Cannon (in Haque and Etkin, 1994) makes expicit socia variabes that contribute to socia vunerabiity socia, economic, and poitica factors. These factors can either enhance or detract from a community's abiity to mitigate disaster. Aong simiar ines, Cutter et a. (2000) argue that sociay vunerabe groups such as the edery, ower income, racia minorities, and women are more ikey to be exposed to a arger number of hazards and or be ess abe to recover from disasters (e.g., chemica spis, hurricanes, widfire), than weathier, more abe-bodied individuas and communities. Morrow (1999) and Lynn and Geritz (2006) aso posit that poor communities are ess abe to absorb the effects of natura disasters.

3 26 C.J. Gaither et a. / Forest Poicy and Economics 13 (2011) Simiar to Cutter et a. (2000), Ojerio (2008) examined both biophysica and socia data to assess the vunerabiity of census bock groups in Arizona to widand fire risks. Resuts consistenty showed that census bock groups comprised argey of poor non-whites (Navajo and Apache) were ess ikey than majority white census bock groups to participate in either state-sponsored grants aimed at widand fire mitigation, community widfire protection programs, or the Firewise Community program. Importanty, Coins (2008a) critiques assumptions of risk exposure in the First Word which assume that higher income househods wiingy expose themseves to risk by ocating in aestheticay peasing, yet ecoogicay fragie environments. Marginaized groups, he argues, are rendered invisibe in these settings. Coins (2008a) offers instead a poitica ecoogy view of risk exposure in deveoped nations which makes marginaization reative. He stresses that sociay vunerabe popuations exist aongside the we-heeed in paces with high environmenta risk in deveoped nations. However, state and market institutions (oca fire protection and fire risk insurance) act to insuate the rich from devastating oss in the event of disaster by the provision of such services. Margina communities, conversey, absorb the risk avoided by the weathy because of their reative inabiity to access these safeguards. Coins' (2008a) focus is the contribution of institutions to the faciitation of more affuent communities. A more comprehensive ook at the advantages accruing to the rich (or disadvantages of the poor) necessitates an examination of agency; that is, not just the arger society shieding some sectors from harm, but aso the activities initiated by the we-off to insuate their properties from widfire oss. Not ony do the more affuent have better access to structura services to mitigate fire, but residents act at the individua and community eve to prevent oss by engaging with mitigation programs in the communities where they ive. Such participation distinguishes upper income areas from poor and working cass communities. 4. Research hypothesis We expect that the type of association between socia vunerabiity and widand fire risk wi vary geographicay (custer), with hot spot custers (high socia vunerabiity/high widand fire risk) prevaent in ess densey popuated, rura areas. We do not suppose that a particuar type of association, for instance, hot spots or cod spots woud characterize an entire state because, again, sociay vunerabe popuations aso ocate in urban areas with very ow widand fire risk; and more affuent popuations concentrate in or near high widand fire risk rura areas. However, we expect fewer widand fire mitigation programs to exist near hot spot custers, compared to ow socia vunerabiity/high widand fire risk custers. H1. Communities with high widand fire risk and high socia vunerabiity (hot spots) are ess ikey than communities with high widand fire risk and ow socia vunerabiity to be engaged with widand fire mitigation programs. 5. Methods To examine the association between widand fire risk and socia vunerabiity in the six-state region, we first identified indicators of widand fire risk and socia vunerabiity at the Census Bock Group (CBG) eve. We chose the CBG as the unit of anaysis because this geography approximates community groupings. The U.S. Census Bureau defines a CBG as an aggregation of bocks, with bocks being anaogous to city bocks demarcated by streets; in rura areas CBGs can contain an extensive number of square mies and do not have street boundaries. Aso, the CBG eve approximates the spatiaity at which most widfires occur; and for the variabes incuded in our anayses, the CBG provides the most detaied spatia resoution pubicy avaiabe Widand fire susceptibiity index We seected the Widand Fire Susceptibiity Index (WFSI) as our indicator of widand fire risk. The index is one of severa indices produced by the Southern Widfire Risk Assessment (SWRA). The SWRA is the first comprehensive widand fire risk assessment of its kind in the nation. It is supported by the thirteen state forestry agencies that comprise the USDA Forest Service's Southern Region, in partnership with the USDA Forest Service, USDI Fish and Widife Service, USDI Nationa Park Service, Bureau of Indian Affairs, and the Department of Defense. The WFSI measures on a scae of zero to one the probabiity 6 of an acre burning, based on surface fues and forest conditions, weather, historica fire sizes, and historica suppression effectiveness (Buckey et a., 2006a,b). The index incudes three key components: 1) probabiity of fire occurrence, 2) fire behavior, and 3) fire suppression effectiveness. The first component, probabiity of fire occurrence, is comprised principay of Fire Occurrence Areas (FOA) and Weather Infuence Zones (WIZ) (Buckey et a., 2006a, p.41 52). FOAs are determined by historica data pinpointing fire ignition. Quantitativey, FOA is the historica mean of ignitions cacuated as the number of fires per year per thousand acres. Periods of fire occurrence were not specified but rather referred to generay as fire history reports, which we assume were suppied by state and federa and management agencies. Fire ignition data were coected between 1997 and Weather aso infuences probabiity of fire occurrence. To incorporate this variabe, WIZs or weather zones were designated for the thirteen southern states, and daiy weather observations for each WIZ were recorded from 1 January 1994 to 31 December 2003 (Buckey et a., 2006a). Weather conditions were categorized into percenties that indicated conditions which were more or ess conducive to fire ignition ow, moderate, high, and extremey high percenties. Various and management agencies and the Nationa Oceanic and Atmospheric Administration suppied weather data. The second significant component of WFSI is Fire Behavior (rate of spread [ROS], crown fire potentia, and fame ength). ROS is simuated using FB3 DLL Windows software (commercia software icensed by Fire Program Soutions LLC). Fire Behavior attributes, in turn are cacuated based on surface fues, canopy cosure, canopy characteristics, 7 and topography (aspect, sope, eevation). Surface and canopy fues data were obtained from crosswaks of existing datasets. Fire behavior is estimated in m ces with specific weather conditions. ROS is cacuated for the four weather categories ow, moderate, high, and extreme. Lasty, WFSI incudes Fire Suppression Effectiveness which is a function of Fina Fire Size (FFS) and ROS. Fire suppression effectiveness is the comparison of actua fire sizes to a theoretica size which assumes fire spreads under stabe conditions with homogenous weather and fue conditions with no suppression activity. Data used for these cacuations are from states and federa agencies for the time period The fina WFSI figure for a m ce in a given WIZ is the summation of the respective WFSI cacuations for the four weather percentie areas. WFSI is avaiabe in a raster format. To faciitate anaysis at the CBG eve, basic statistics (maximum, mean, minimum, and standard deviation) were cacuated for a 30 m pixes within each CBG using the summarize zones function in the ESRI's (Environmenta Systems Research Institute) Spatia Anayst 6 Athough due to some necessary assumptions such as fue homogeneity, it is not the true probabiity. 7 Data on canopy characteristics were imited by the ack of extant data and funding to coect primary canopy fues data, canopy ceiing height, canopy base height, and canopy buk density (Buckey et a., 2006a, p. 49).

4 C.J. Gaither et a. / Forest Poicy and Economics 13 (2011) extension for ArcVIEW. Vaues ranged from 0 to 0.86, mean 0.04 (standard deviation 0.086), and median Socia vunerabiity index Concurrenty, we constructed an index to measure socia vunerabiity (). We define vunerabiity as marginaization, characterized by the ack of abiity to assertivey navigate socia systems or to move progressivey towards higher iving standards in terms of materia weath and infuence. As indicated, a number of researchers have found a range of socia indicators associated with an individua, househod, or community's abiity to mitigate and/or recover from disasters. Cutter et a. (2000) identified eeven county-eve factors that infuence socia vunerabiity. These have to do with persona weath, housing stock and tenancy (percent mobie homes in county), and race/ethnicity. Morrow (1999) incudes simiar factors physicay and mentay disabed, edery, femae-headed househods, and the homeess. Cutter et a. (2003) deveoped a Socia Vunerabiity Index (SoVi 8 )whichexamines how socio-demographic characteristics infuence cimate reated hazards drought, foods, hurricane force winds, and sea-eve rise in the southeast (Oxfam, 2009). Widand fire hazard is not incuded among the environmenta risks this group examines. Our index incudes percent of popuation beow poverty, percent of popuation 25 or oder without a high schoo dipoma, percent African American, percent of housing structures that are mobie homes, and percent of renter occupied housing units. Each of these variabes can have a direct bearing on socia vunerabiity for both individuas and communities. As discussed, persons or househods beow poverty and those with ower education eves typicay have ess efficacy in obtaining services or information about environmenta protection. Aso, race often figures into issues invoving services and information access. White, midde-cass neighborhoods and communities typicay have a greater number of faciities and services compared to poorer, minority areas (Tayor et a., 2007; Tayor, 2000; Woch et a., 2002). Racia status tends to correate positivey with other sociodemographic and economic indicators such as those incuded in our index particuary poverty and education. However, we aso beieve that the descriptor African American or Back carries an additiona weight beyond that of income or education. This reates to both overt and more subte forms of discrimination from the arger society and aso to sef-imposed racia segregation which continues de facto racia separation. Mobie homes are ess abe to withstand natura disasters such as hurricanes because the buiding materia is generay of ower quaity than constructed dweings. This may aso be the case with fire resistance, as mobie structures are ess ikey than constructed homes to be made of fire resistant, durabe materias. Finay, renters have ess contro over buiding materias, andscaping, fire insurance or other safeguards against widand fire, which coud resut in greater vunerabiity for this group. Because of overaps between race and the other variabes incuded in, we examined the degree of muticoinearity for the variabes comprising the index by examining a regression mode where WFSI was the dependent variabe and percent back, percent beow poverty, percent ow education, percent renter and mobie home were predictors. Here, we wanted to detect infated standard errors by ooking at the variance infation factor (VIF), as muticoinearity is indicated by fuctuating standard errors. Generay, VIF vaues greater than ten may indicate muticoinearity among variabes. VIF vaues for each of our predictors were beow three, which suggests ow or moderate muticoinearity. Frequencies for variabes comprising were downoaded from the 2000 U.S. 8 SoVi incudes eight variabes which expain 75% of the variance in socia vunerabiity. The variabes are weath, age, race, ethnicity, rura residence, specia needs popuations, gender, and empoyment (Oxfam, 2009). Census Bureau Summary Fie 3 sampe data tabes. Data were obtained for each CBG in the six-state region. We downoaded tota popuation; tota African American popuation; tota popuation 25 years and oder; both mae and femae popuation 25 and oder with varying degrees of educationa attainment; tota popuation for whom poverty was determined; popuation with income beow poverty; tota housing units; tota mobie home units; tota occupied housing units; and tota renter occupied housing units. From these frequencies, percent African American, percent over 25 without high schoo dipoma, percent beow poverty, percent mobie home dweer, and percent renter were cacuated. Percentages for each indicator (e.g., percent beow poverty, back, etc.) were summed to produce the vaue for a given census bock group. Vaues were not standardized, and a variabes are assumed to carry equa weight. vaues ranged from 0 to 3.64, with a mean of 1.10 (standard deviation 0.64) and median Vaues arger than the mean indicate high socia vunerabiity. Zero vaues woud be observed in the case of CBGs with no popuation. 6. Exporatory spatia data anaysis 6.1. Bivariate custers of widand fire risk and socia vunerabiity We use the LISA statistic, ocaized indicator of spatia association, to test the strength of association between WFSI and and aso to map these associations at the CBG eve (Ansein, 1995). The correation statistic indicates how observations of a variabe in a given CBG (say i) are associated with observations of a different variabe in adjacent CBGs or the neighborhood of the ith CBG. In our case, this invoves correations between WFSI in an area unit, i, and in the custer of CBGs surrounding and incuding the ith CBG. Neighboring CBGs or the neighborhood of the ith CBG was defined based on a first order contiguity weight matrix. CBGs adjacent to the ith CBG sharing a common border ength or at east a vertex were considered to be in the neighborhood. The mean neighborhood vaue for and WFSI incudes the vaue for the variabe in the ith CBG, as we as the vaues for a CBGs adjacent to it. This was achieved by manuay editing the weight matrix fies. Bivariate LISA statistics were used in GeoDa I to map four different types of spatia custers for WFSI and at the CBG eve. For WFSI, for exampe, custers incude: 1), CBGs with high widand fire risk surrounded by CBGs with high socia vunerabiity; 2) Low Low, CBGs with ow widand fire risk surrounded by CBGs with ow socia vunerabiity; 3) Low, CBGs with ow widand fire risk surrounded by CBGs with high socia vunerabiity; 4) Low, CBGs with high widand fire risk surrounded by CBGs with ow socia vunerabiity. Again, the high and ow eve of a given variabe is defined in reference to its mean vaue for the neighborhood. We defined and Low Low custers as hot spots and cod spots, respectivey, where the association between two phenomena is positive. For the other custers (Low and Low), the associations are negative and are described as spatia outiers (Ansein, 2005). LISA scores significant at p=0.05 or ess were used to map statisticay significant custers. Pseudo-p vaues were generated for LISA statistics utiizing 999 permutation criteria avaiabe in GeoDa I ( The foowing equation (Sunderin et a., 2008) provides the computation of bivariate LISA based on Ansein (1995). I = z xi N j =1;j i w ij z yj where, I is the oca Moran's I (LISA); x and y are two variabes of interest measured for CBG i, and neighborhood j, respectivey. ð1þ

5 28 C.J. Gaither et a. / Forest Poicy and Economics 13 (2011) Simiary, z x and z y represent the standardized z-scores for variabes x and y, respectivey. The term w ij is the weight matrix that defines the structure of the neighborhood. LISA and weight matrices were created in GeoDa I. This anaysis uses a first order queen contiguity matrix, where w ij =1 if the adjacent CBG j shares a common border ength or common vertex with the ith CBG. If a common border is not shared, the vaue is zero Resuts ESDA at the state eve Figs. 1 6 show bivariate LISA anayses for each state. In each figure, the red coor indicates custers of high widand fire risk CBGs ocated in neighborhoods or custers with high socia vunerabiity ( ); dark bue custers denote ow widand fire risk CBGs in custers with ow socia vunerabiity (Low Low); ow widand fire risk/high socia vunerabiity custers are shown in ight bue (Low ); and high widand fire risk/ow socia vunerabiity custers are coored mango ( Low). White areas within the study area represent CBGs where the spatia association between WFSI and is not statisticay significant. To make the interpretation easier and more meaningfu, custer maps for each state are overaid with interstate highway and federa and areas. Geo-visuaization of custers with such recognizabe figures provides reference for iustrating the spatia ocation of custers. For exampe, in the anaysis for Aabama (Fig. 1), red custers or hot spots are ocated in the southern part of the state, mosty south of Interstate-85 and US-80. Interestingy, this portion of the state contains reativey ess federa and area compared to areas north of those highways. South Aabama aso contains arge areas of ight bue custers, which again indicated high socia vunerabiity CBGs in the neighborhood of ow widand fire risk CBGs. The overa pattern of high socia vunerabiity (red and ight bue patches) foows the spatiaity of Aabama's impoverished Back Bet. The more sociay vunerabe custers are ocated amost excusivey in the southern part of the state. The present anayses demonstrate how ow socio-economic status or sociay vunerabe communities intersect with widand fire risk. In some areas of the state's Back Bet, there is an inverse association between socia we-being and this type of environmenta risk (ight bue); whereas in others the association is positive (red). North Aabama stands out as a near antonym to the southern part of the state, in terms of socia we-being. From Birmingham and Tuscaoosa northward, the state contains remarkaby more ow ~.~ uso * Firewise.r; CWpp :;:;:;:; Federa Lands Widfire Risk - Socia Vunerabiity Low Widfire Risk - Low Socia Vunerabiity Low Widfire Risk - Socia Vunerabiity Widfire Risk - Low Socia Vunerabiity Note: The custers are based on bivariate LISA Statistic significant a p = 0.05 JI..tIite areas represent census bock groups where the association is insignificant Fig. 1. Bivariate LISA based spatia custers showing the oca association between widand fire risk and socia vunerabiity in Aabama.

6 .. C.J. Gaither et a. / Forest Poicy and Economics 13 (2011) i'! L La-,/"~.. ~ -~ *.., ~,AO -4 1M 29 * * *!", Note: The custers are based on Bivariate U SA Statistic significant at p : Vv11i1e areas represent the Census Bock Groups where the association is insignificant. CIfoIPP Community * Firewise Commun~y W- Federa ands VVidfire Risk.Hfgh Socia Vunerabi~y ow Widfire Risk- ow Socia Vunerabiity ow Widfire Risk- Socia Vunerabiity VVidfire Risk ow Socia Vunerabiiy Fig. 2. Bivariate LISA based spatia custers showing the oca association between widfire risk and socia vunerabiity in Arkansas. sociay vunerabe custers. The dark bue Low Low custers predominate in the north; but high fire risk areas aso intersect with more we-off communities in north Aabama in the Huntsvie Forence area. The ony exception to this pattern is the ight bue, Low area of centra city Birmingham. The custer here is simiar to that in the rura Back Bet south of Interstate-20. This is not surprising given i i SI. P't"~'7? * Firewise Community! CWPP Community ~ Federa Lands Hig h Widfire Risk - Socia Vunerabiity Low Widfire Risk - Low Socia Vunerabiity Low Widfire Risk - Socia Vunerabiity Hig h Widfire Risk - Low Socia Vunerabiity Note: The custers are based on bivariate U SA Statistic significant at p " 0.05 Vv11ite areas represent census bock groups where the association is insignificant Fig. 3. Bivariate LISA based spatia custers showing the oca association between widand fire risk and socia vunerabiity in Forida.

7 30 C.J. Gaither et a. / Forest Poicy and Economics 13 (2011) CWPP MSCountLcentroidMerg L CWPP Community * Firewise Community W Federa Lands Widfire Risk - Socia Vunerabiity Low Widfire Risk - Low Socia Vunerabiity Low Widfire Risk - Socia Vunerabiity Widfire Risk - Low Socia Vunerabiity Note: The custers are based on bivariate LISA Statistic significant at p = 0.05 Mite areas represent census bock groups where the association is insignificant The CWPP sign at the Japer City represents 7 CWPP Communities Fig. 4. Bivariate LISA based spatia custers showing the oca association between widand fire risk and socia susceptibiity in Georgia. that roughy 73% of Birmingham's city popuation is African American (U.S. Census Bureau, 2000). A simiar phenomenon occurs around other major cities in the region. A moderate custering northeast of Montgomery and in the state's panhande region is aso characterized by high fire risk/ow socia vunerabiity. Near Mobie, there is a sma ight bue custer approximating the ocation of centra city Mobie (56% African American) that is ow widand fire risk/high socia vunerabiity. Fig. 2 aso shows rough demarcations aong socio-economic ines in Arkansas. The eastern portion of the state south of Interstate-30/40 contains more sociay vunerabe CBGs; however, there are ony two distinct hot spot custers in southeast Arkansas. A ight bue area is again evident near the state's capita city, Litte Rock; but areas to the north and west of Litte Rock are either dark bue or mango which indicate ow socia vunerabiity. In this state, too, high widand fire risk areas do not overap with federa ands. In Forida (Fig. 3), more affuent communities are ocated aong the coast from the Jacksonvie area on the Atantic coast down to Titusvie and West Pam Beach. Low sociay vunerabe custers extend inand to the Evergades on Forida's southern tip and up the Guf coast from the Napes and Fort Myers area, aong the coastine of Sarasota, up to the Tampa/St. Petersburg region. As we, higher fire risk is associated with higher income communities on both the Atantic and south Guf coasts and in the upper Evergades region. Hot spots are custered in extreme north centra and south centra Forida. Simiar to Aabama and Arkansas, socia vunerabiity in Georgia aso varies geographicay, with south Georgia containing noticeaby more sociay vunerabe custers compared to suburban Atanta area and points north. Fig. 4 shows segments of the southern Back Bet, denoted by ight bue custers and a spattering of hot spot red custers, mainy south of Atanta running aong a ine from southwest Georgia northeast to the South Caroina boarder. In contrast, dark bue custers

8 C.J. Gaither et a. / Forest Poicy and Economics 13 (2011) ~ " ' <//// ~.,.. w;;;' ,. i i f ~0; //; CWPP Community * Firewise Community W Federa Lands Widfire Risk - Socia Vunerabiity Low Widfire Risk - Low Socia Vunerabiity Low Widfire Risk - Socia Vunerabiity Widfire Risk - Low Socia Vu nerabiity Note: The custers are based on bivariate LISA Statistic significant at p = 0.05 VV11ite areas represent census bock groups where the association is insignificant Fig. 5. Bivariate LISA based spatia custers showing the oca association between widand fire risk and socia susceptibiity in Mississippi. are ocated mainy in the periphery of metropoitan Atanta and northeast Georgia around the Chattahoochee Nationa Forest. The Chattahoochee portion of the Chattahoochee Oconee Nationa Forest is ocated in a high fire risk area aong Georgia's northern border with North Caroina; however, the Oconee preserve in the Piedmont between Interstates-20 and 16 is not. The ight bue cooring distinguishes centra city Atanta from its more affuent suburbs. North of Atanta, there are aso mango coored areas which suggests higher fire risk in concert with higher socio-economic status. As we, there are smaer custers of mango in southeast Georgia near Savannah. Mississippi northwest of Interstate-55 contains the ow ying Mississippi Deta or auvia pain, which historicay has been associated with high poverty rates and is indicated in Fig. 5 by ight bue coor. In this region, there is itte overap between socia vunerabiity and widand fire risk given the higher moisture content of this terrain. Widand fire risk is positivey associated with socia vunerabiity in a centra Mississippi custer north of Jackson and aso in southwest Mississippi; but Jackson is simiar to other arger cities in terms of ow fire risk and high socia vunerabiity. With the exception of an area to the immediate east of Interstate-55 and extreme eastcentra Mississippi, more areas in the western part of the state are characterized by ow socia vunerabiity. In the north, ow socia vunerabiity intersects more with ow widand fire risk; whereas in the south, ow socia vunerabiity crosses with higher fire risk. Finay, Fig. 6 shows a arge portion of east South Caroina in hot spot custers. Hot spots overap with the Francis Marion Nationa Forest aong the Atantic coast and aso with the Sumpter Nationa Forest on the Georgia border. There are smaer dark bue areas aong the state's east coast, but these custers are ocated more in the upstate region around Greenvie, Spartanburg, and Coumbia. A spattering of mango is aso aong the coast and in the extreme upstate region near Greenvie. As expected, our anayses identified sociay vunerabe custers which coincide with the rura Back Bet across the region. Again,

9 32 C.J. Gaither et a. / Forest Poicy and Economics 13 (2011) * Firewise Community 1 CWPP Community W Federa Lands Wi dfire Risk - Socia Vunerabiity Low Widfire Risk - Low Socia Vunerabiity Low Widfire Risk - Socia Vunerabiity Widfire Risk - Low Socia Vu nerabiity '"an""n, GA Note: The custers are based on bivariate LISA Statistic significant at p = 0.05 VVhite areas represent census bock groups where the association is insignificant Fig. 6. Bivariate LISA based spatia custers showing the oca association between widand fire risk and socia vunerabiity in South Caroina. however, eevated widfire risk did not overap with socia vunerabiity in some areas of south Aabama, southwest Georgia, and the Georgia Piedmont. This ack of association may be expained, in part, by the three components of WFSI (i.e., weather conditions contributing to fire occurrence, fire behavior, and suppression). Naturay occurring fires are caused by ightning. Peak ightning concentrations occur aong the coast where sea breeze-forced thunderstorms are common. er WFSI custers are ceary seen in the Guf areas of Aabama and Mississippi and aong Forida's coastine. The coasta pain is aso characterized by a higher percentage of pant communities that burn with greater intensities on average than upand areas. In contrast to the coast and coasta pain, south Aabama, southwest Georgia, and the Georgia Piedmont are not characterized by these physica conditions. Those areas of southwest Aabama and other states with adjacent Low and custers seem contradictory but may be expained by the fire suppression component of WFSI. To recount, fire suppression effectiveness is the comparison of actua fire sizes to a theoretica size which assumes the fire is spreading under steady conditions with no suppression activity. Buit infrastructure such as roads and fire fighting services contribute to fire suppression efficacy. Poor road networks in some parts of west Aabama may contribute to ow fire suppression scores, and hence higher WFSI scores in these CBGs. Road quaity can change abrupty depending upon county resources. Poor roads, as we as mountainous andscape, are aso factors that woud contribute to ow fire suppression effectiveness, raising the fire risk in northern Georgia. Contrast the higher fire risk for the Chattahoochee Nationa Forest in north Georgia with the ower risk for the Oconee preserve in the Georgia Piedmont southeast of Atanta. Most federa ands, however, have dedicated fire suppression resources, which owers fire risk in their vicinity Spatia associations by type Distribution of CBGs by custer type was tabuated for each state and is presented in Tabe 1. In a of the states, about one-quarter of tota CBGs were found to have negative associations between Tabe 1 Distribution of CBGs for Aabama, Arkansas, Forida, Georgia, Mississippi, and South Caroina according to types of oca spatia association between WFSI and. Types of association Aabama Arkansas Forida Georgia Mississippi South Caroina Tota CBG (N) CBG (%) CBG (N) CBG (%) CBG (N) CBG (%) CBG (N) CBG (%) CBG (N) CBG (%) CBG (N) CBG (%) CBG (N) CBG (%) widand fire risk high socia vunerabiity Low widand fire risk ow socia vunerabiity Low widand fire risk high socia vunerabiity widand fire risk ow socia vunerabiity Insignificant , Tota ,

10 C.J. Gaither et a. / Forest Poicy and Economics 13 (2011) widand fire risk and socia vunerabiity (i.e., either had high widand fire risk and were ocated in higher status neighborhoods or had ow widand fire risk and ocated in more sociay vunerabe neighborhoods). South Caroina had the highest percentage of CBGs cassed as hot spots (8.68%) and Arkansas had the owest (0.28%). South Caroina aso had the highest percentage of cod spots (19.21%); and again Arkansas had the owest cod spot percentage (12.95%). Forida had the highest percentage of Low custers (9.79%) and South Caroina the owest (3.18%). The row totas show that roughy 3.5% of CBGs in the region were hot spots. About 16% of CBGs were in either cod spot areas or Low custers; and roughy 7% were in Low socia vunerabiity areas. About 58% of the CBGs in the region exhibited no significant association between widand fire risk and socia vunerabiity Distribution of widand fire mitigation programs across the Southeast Our primary objective is to examine the spatia reationship between: 1) hot spots and widand fire mitigation programs and 2) Low areas and widand fire mitigation programs. We woud assume that those areas across the region identified as being highy susceptibe to widand fire occurrence woud have a greater number of mitigation programs, compared to ow fire risk communities. Our aim is to determine how such programs may be distributed in areas that are aso sociay vunerabe. There are a number of federa, state, and oca eve mitigation programs across the country. Three key programs are Community Widfire Protection Pans (CWPPs), Firewise Communities, and hazardous fues reduction programs on federa ands. The atter are funded by the USDA Forest Service and USDI Department of Interior through the Heathy Forest Initiative and the Nationa Fire Pan (NFP) ( Fues reduction programs in the form of prescribed burns or mechanica thinning might occur on any federa ands with fue oads sufficient to warrant reductions in oadings. Communities adjacent to those ands woud accrue benefits of such treatments. We are interested in mitigation efforts invoving significanty more community initiative and input. CWPPs or Community Widfire Protection Pans are aso funded by the NFP but are founded principay by communities rather than pubic agencies. Communities at risk for widand fire coaborate with pubic agencies, oca fire departments, and municipaities to prioritize private andhodings needing hazardous fue reduction and recommend appropriate treatments to reduce future widand fire threats ( hazards/natura/fire). Typicay, state forestry agencies provide information to at risk communities about CWPPs, but individua community groups or municipaities must take ownership of the pan by becoming active partners with sponsoring agencies. Simiary, the nationa Firewise Communities program invoves significant community input. These programs are intended to reach beyond the fire service by invoving homeowners, community eaders, panners, deveopers, and others in the effort to protect peope, property, and natura resources from the risk of widand fire before a fire starts ( Because of the commitment and invovement required for successfu impementation and running of both CWPPs and Firewise programs, we beieve that communities with higher socia and human capita (assuming high widand fire risk) woud be more ikey than ower capita communities, or those communities rating high in socia vunerabiity, to estabish these programs. We seected CWPPs and Firewise Communities as indicators of mitigation programs on the ground. We reaize there are other programs at the oca and state eve that coud aso be incuded, but the difficuty of obtaining data on such programs across the study area prohibits their incusion. We obtained compete and current istings of Firewise Community ocations from a nationa Firewise manager for each of our states. A tota of 145 active Firewise Communities were reported Aabama (1), Arkansas (91), Forida (38), Georgia (10), Mississippi (1), and South Caroina (4). It was more difficut to secure CWPP ocations. The NFP website ists 730 Communities at Risk (for widand fire) in the South covered by a CWPP in 2008 ( forestsandrangeands.gov/reports/documents/heathyforests/2008/ heathyforestsreportfy2008.pdf); however, the ocation of these CWPPs is not mapped by NFP managers. We contacted the individua state forestry agencies to obtain CWPP ocations. For some states, CWPP data had not been assembed at the state eve. In the case of Forida, for instance, individua fire districts forwarded atitudina and ongitudina coordinates to us, and we mapped CWPP ocations at the CBG. Mississippi estabishes county-wide CWPPs, so the CWPPs isted for that state represent a centra point in the respective counties. We obtained the most compete isting of CWPP sites for each state that was avaiabe athough these istings may not be exhaustive: Aabama (1), Forida (10), Georgia (10), Mississippi (34), and South Caroina (2); but we did have a compete isting for Arkansas (109). Despite their imitations, these mappings represent the first efforts of which we are aware that attempt to ocate CWPP ocations in the South. Both CWPP and Firewise programs ocations are typicay associated with residentia or a community association address rather than a centraized address removed from communities; thus the coordinates for mitigation programs directy refect community invovement. To test the hypothesis that hot spots are ess ikey than Low areas to be engaged with widand fire mitigation programs, we computed the mean distance, in kiometers, between hot spots and Low custers, respectivey, to the nearest CWPP ocation and Firewise program. Distances were computed in ArcGIS using the simpe distance feature to determine the straight ine distance from hot spot and Low custers for Firewise and CWPPs, respectivey. CWPP and Firewise ocation data were aso combined into a singe generic ayer representing the ocation of both types of community mitigation programs; and the distances from hot spots and Low CBGs to the nearest programs were estimated. Tabe 2 contains means, standard deviations, and t-tests generated from the anayses. Resuts show that the average distance from hotspots to CWPPs was significanty onger than from Low custers to CWPPs in Arkansas, Georgia, Mississippi, and South Caroina. The distance was significanty shorter in Aabama but not significant in Forida. For Firewise, the mean distance between hot spots and these programs was onger for Forida, Georgia, and South Caroina but shorter for Aabama and Mississippi and not significant for Arkansas. For the combined programs, mean hot spot distance was onger for a states except Aabama. It shoud be noted that the mean distances between a custer type and program ocations in some cases are the same or very simiar. This has to do with the way hotspots and programs are spatiay arranged on the ground. For instance, if most of the hotspots in a state are ocated cose to a particuar CWPP program, their mean distance to CWPPs and mean distance to CWPP and Firewise combined woud be the same if there are no Firewise programs in the area. Simiar observations were observed between distances to CWPP and distances to Firewise if a state had ony a few programs that are ocated cose to each other. Of the 18 comparisons made, 12 or 66% indicated a onger average distance between hot spot custers and /Low custers. Because there was ony one CWPP and Firewise in Aabama, one Firewise ocation in Mississippi, and two CWPPs in South Caroina, these comparisons shoud be taken with some caution. If these comparisons and the combined category for Aabama are excuded from the anayses, eeven of the remaining thirteen means show onger distances for hot spots (84.6%). Overa, resuts support the research hypothesis and suggest that communities with both higher fire risk

11 34 C.J. Gaither et a. / Forest Poicy and Economics 13 (2011) Tabe 2 Mean distance of CWPP, Firewise, and combined CWPP/Firewise programs to high Wfsi/ (hotspot) and high WFSI/ow custers in Aabama, Arkansas, Forida, Georgia, Mississippi, and South Caroina. State Aabama Arkansas Forida Georgia Mississippi South Caroina Types of association WFSI high WFSI ow WFSI high WFSI ow WFSI high WFSI ow WFSI high WFSI ow WFSI high WFSI ow WFSI high WFSI ow CBG (N) Tota 2523 =significant at 0.05 or ess. and higher eves of socia vunerabiity are ess invoved with these particuar widand fire mitigation programs. 7. Discussion and concusion CWPP mean (km) (stand. dev.) Firewise mean (km) (stand. dev.) (85.01) (86.58) (86.58) CWPP and Firewise mean (km) (stand. dev.) (15.74) (168.93) (157.41) t= t=16.07 t= (13.27) (13.01) (12.79) (17.22) (19.87) (16.53) t= 2.14 t= 0.21 t= (44.13) (23.53) (23.83) (45.06) (22.77) (23.00) t= 2.87 t=15.38 t= (54.52) (37.68) (37.69) (46.29) (32.29) (32.37) t= t=12.51 t= (19.88) (113.72) (19.88) (12.43) (126.89) (12.43) t= 6.15 t= t= (39.26) (64.11) (64.11) (106.95) (68.77) (68.78) t= 4.83 t= 8.25 t= 8.25 Reasons why sociay vunerabe communities are ess engaged with Firewise Communities or CWPPs may have to do with a range of factors emanating from ack of interest to again, a dearth of socia and human capita in these communities. A state forester in Forida stressed that information about CWPPs, Firewise, and other mitigation programs is readiy avaiabe from the Forida Division of Forestry, but individua homeowners and communities express varying eves of interest in adopting the programs. 9 Aso, unpubished data from our recent anaysis of southern andowner knowedge and understanding of widand fire mitigation programs indicated that overa, roughy 40% of andowners reported that they had done nothing to prevent widfire on their rura and; and neary 46% of African Americans said they had taken no action to mitigate widfire athough backs were more ikey than whites to say they aware of mitigation information. It may be that awareness or knowedge possession among African Americans does not transate easiy into action, either in the form of mitigation efforts on one's own and or for the formation of community efforts ike Firewise or CWPPs. 9 Persona communication (2010). Gerry Lacavera, Forida Division of Forestry. Whie we acknowedge that individua andowner preferences for mitigation may vary, we aso submit that specific socio-cutura practices regarding andownership rights, inhibit more sociay vunerabe groups from engaging in mitigation. Specificay, the practice or system of heir property ownership among ower income southern andowners may work to constrain invovement in and improvement initiatives. Buiding on Coins' (2008a) thesis that the environmenta vaues of distinct sociocutura groups infuence community exposure to widand fire risk, we posit that differences in hot spot and Low community engagement with mitigation may be expained in part by cutura norms reifying communa ownership of and in the South. Heir property or tenancy in common is inherited and which is passed on intestate, without cear tite, typicay to famiy members. Athough such owners have ega caims to and, there are no demarcations of the and specifying what amount is hed by a singe individua (Dyer et a., 2009; Dyer and Baiey, 2008). With each succeeding generation, individua ownership interests shrink because of the growing number of heirs. Mitche (2001) estimates that 41% of African American-owned and in the southeastern U.S. is heir property, and Craig-Tayor (2000) (in Dyer and Baiey, 2008) states that heir property represents the most widespread form of property ownership in the African American Community. But Dyer et a. (2009) caution against overestimates, arguing that few systematic investigations of heir property prevaence have been conducted because of the meticuous methodoogy required to cassify such properties. Athough much of the schoarship on heir property concentrates on southern backs, this type of ownership is aso prevaent among Appaachian whites (Deaton et a., 2009). There are a number of probems associated with heir property and and management. Principe among these is that the ack of cear tite prohibits participation in any government sponsored home improvement programs. Aso, property owners cannot use heir property as coatera for a mortgage, and seing timber from such and is virtuay impossibe because a buyer woud have to secure the consent of a heirs for a sae, and most buyers are unwiing to do so. Besides this, the ack of cear tite acts as a disincentive to the improvement of rea property attached to and. If a structure were remodeed, the increase in vaue woud not accrue to the individua who paid for the upgrade, but again must be disbursed among a heirs, regardess of where they ive (Dyer et a., 2009; Dyer and Baiey, 2008). In many cases, heirs may not even ive in the same state as the property ocation. Drawing from economics, Deaton et a. (2009) argue that such impediments resut in efficiency probems, which occur when the existing uses of the property resut in ower net-benefits to the cotenants than might otherwise be achieved. Viewed from the ens of profit maximization, and use in such scenarios is underutiized. We submit that heir property hoders woud aso be ess motivated to participate in widand fire mitigation because of the communa nature of their and interest. Again, any fees, and cearing, structure preparation, or other time commitments to CWPP or Firewise woud be ikey be borne by the residing heir or others iving coser to the property. Whie a heirs woud not have to consent to mitigation panning, the disproportionate invovement by one or a few heirs might deter participation because of costs necessary to insuate structures or cear and either on or off one's property. Deaton et a.'s (2009) case study from Kentucky iustrates how cotenants' unwiingness to cut timber from their and had the unintentiona consequence of increasing undergrowth, resuting in increased fue oading. Deaton et a. (2009) describe heir property management as a tragedy of the anti commons in that heirs of jointy hed and can prevent any singe heir from certain and uses, some of which woud yied profits or potentiay essen hazards. In contrast to the overuse tragedy of the commons probem, with heir property the confict invoves under or nonuse. Aso, a key factor in mitigation success for CWPPs is coaboration with and federa agencies (U.S. Forest Service, U.S. Bureau of Land Management). Communities are expected to draw on the expertise of