A GIS-Based Model to Determine Site Suitability of Emergency Evacuation Shelters

: 227 248 Research Article Blackwell Oxford, TGIS Transactions 1361-1682 1467-9671 XXX Original Modeling B Kar 2008 and The UK Articles evacuation Publishing M Authors. E in Hodgson GIS Ltd Journal shelter suitability compilation 2008 Blackwell Publishing Ltd A GIS-Based Model to Determine Site Suitability of Emergency Evacuation Shelters Bandana Kar Department of Geography University of South Carolina Michael E Hodgson Department of Geography University of South Carolina Abstract In recent years, the increase in the number of hurricanes and other costal hazards in the US pose a tremendous threat to the residents of coastal states. According to the National Hurricane Center, Florida is the most vulnerable coastal state to hurricanes. Mitigation policies have been formulated to reduce mortality and provide emergency services by evacuating people from the hazard zone. Many of these evacuees, particularly the elderly or lower income populations, rely on evacuation shelters for temporary housing. Because of the cost and limited use, evacuation shelters are almost exclusively dual use shelters where the primary purpose of the facility is for some other public function (e.g. school, hospital, etc.). In 2000, the estimated shortage of public shelter spaces in Florida was about 1.5 million. The purpose of this study was to rank the existing and candidate shelters (schools, colleges, churches and community centers) available in the state based on their site suitability. The research questions examined in this study include: (1) How many candidate shelters are located in physically suitable areas (e.g. not in a flood prone area, not near hazardous facilities, etc.)?; (2) How many existing shelters are located in physically unsuitable areas, but in socially suitable areas (situated in areas with demand)?; (3) How many alternative existing and/or candidate shelters with high/very high physical suitability are located near physically unsuitable existing shelters and thus, may be better choices for a shelter?; and (4) How many existing shelters located in physically unsuitable areas are not near alternative existing and/or candidate shelters? A Geographic Information System-based suitability model integrating Weighted Linear Combination (WLC) with a Pass/Fail screening technique was implemented for the 17 counties of Southern Florida. It was found that 48% of the existing shelters are located in Address for correspondence: Bandana Kar, Department of Geography, University of South Carolina, 709 Bull Street, Columbia, SC, USA. E-mail: kar@mailbox.sc.edu

228 B Kar and M E Hodgson physically unsuitable areas. Out of all the candidate shelters, 57% are located in physically unsuitable areas. For 15 of the existing shelters in unsuitable locations, no alternative candidate or existing shelter with medium to high physical suitability exists within 10 miles (16.1 km). Keywords : accuracy standards, geocoding, Global Positioning Systems, LIDAR, NSSDA, positional error, Root Mean Square Error, TIGER 1 Introduction The east coast of the United States typically experiences a number of hurricanes and tropical storms each year. Yet, a large portion of the US population resides in coastal areas. In 2003, 53% of the US population resided in counties close to the coastline (Crosett et al. 2005). The combined exposure from hurricanes and the increase in coastal population contributes to a risk of significant damage. For example, Hurricane Andrew (1992) caused some $25 billion damage in Florida and Louisiana (Godschalk et al. 2000), and Hurricane Katrina (2005) resulted in an estimated $200 billion loss in Louisiana and Mississippi (Burby 2006). To reduce hurricane impacts and provide emergency services, a number of mitigation policies have been formulated. Mandatory evacuation is one such policy that requires people to evacuate during a governor-defined emergency declaration. Non-mandatory, but strongly encouraged evacuation is also possible in some states. However, perhaps due to advancement in forecasting techniques, and reliance of residents on personal risk perception about storm surge flooding, resident compliance with evacuation policy has diminished (Dow and Cutter 1998, 2000). Studies have shown that households with higher incomes tend to evacuate to motels/hotels and households with higher education and/or, with pets and children are more likely to stay with families/friends (Whitehead et al. 2000). Females are most likely to evacuate though they are less inclined to go to shelters (Whitehead et al. 2000). The low-income and minority families with children or elderly members are the only people likely to evacuate to shelters (Whitehead et al. 2000, Willigen et al. 2002). The limited space in shelters may cause evacuees to travel hundreds of miles from the coast to lodging or may result in them not evacuating at all. Since evacuation shelters are almost always dual-use facilities, their location in disaster situations may be less than ideal. Thus, there is a need to identify suitable shelters for evacuation. Florida is the most vulnerable coastal state to hurricanes (FDEM 2006b). Since the 1980s, the Department of Community Affairs is responsible for preparing emergency shelter plans, and assisting officials in identifying and/or constructing public shelter space (FDEM 2006a, b). As of January 2000, the state had a deficit of 1.5 million public shelter spaces (FDEM 2006b). The Department of Community Affairs has also taken a proactive approach of identifying counties with a surplus of hurricane shelter space that could be used for evacuation. Instead of constructing new shelters, the most cost effective solution is to identify existing facilities (hospitals, recreation buildings, schools, etc.) that are structurally suitable for evacuees. An analytical evaluation of the site suitability of existing shelters and available facilities, however, has not been conducted. A geographic information system (GIS) based site suitability approach is extensively used to evaluate and rank candidate facility locations, such as steam electrical generating facilities, commercial buildings, waste disposal sites, toxic release locations, and nuclear dumping sites. Ironically, the research literature is devoid of site suitability studies for emergency shelters.

Modeling evacuation shelter suitability 229 The purpose of this study was twofold. One goal was to identify physical and social suitable criteria that would enable determination of site suitability for existing and candidate shelters in hurricane prone areas. Another goal was to evaluate and rank both the existing and candidate shelter locations in Southern Florida based on their physical and social suitability. Candidate shelters include existing schools, colleges, churches, community centers, cultural centers, civic centers and social service centers that are not currently used for evacuation. A GIS-based generic site suitability model using a weighted linear combination (WLC) and pass/fail screening technique was implemented to answer the following research questions: 1. How many candidate shelters are located in physically suitable areas (e.g. not in a flood prone area, not near hazardous facilities, etc.)? 2. How many existing shelters are located in physically unsuitable areas, but in socially suitable areas (situated in areas with demand)? 3. How many alternative existing and/or candidate shelters with high/very high physical suitability are located near physically unsuitable existing shelters and thus, may be better choices for a shelter? 4. How many existing shelters located in physically unsuitable areas are not near alternative existing and/or candidate shelters? The remainder of this paper is organized in five sections. The next section discusses previous evacuation studies and identifies the relevant issues for shelter location suitability. An introduction of the study site, description of the data sets and factors used in the model, and illustration of the methodology are provided in section 3. Section 4 presents the results of the study. Finally, a summary and recommendations for future work are presented in the final section. 2 Background Florida is the most vulnerable state to hurricanes, and next to Bangladesh, has the highest predicted storm surge level in the world (FDEM 2006b). The average number of hurricanes striking Florida each year is 5.8 with an average of 2.2 of these hurricanes with sustained winds greater than 111 mph (FDEM 2006b). Since 1884, about 150 major and minor hurricanes, and approximately 260 tropical storms have affected the state (FDEM 2006b). Approximately 80% of the incoming residents to Florida (~ 693 per day) choose to live close to the coast (FDEM 2006b). In 1999, approximately 6.13 million people were living in storm surge impacted areas (Figure 1) (FDEM 2006b). Despite rapid population growth, the state has failed to provide adequate infrastructure facilities and public shelter space (FDEM 2006b). To eliminate the statewide shelter deficit, the Department of Community Affairs suggested building new school facilities using public shelter design criteria. However, due to budgetary constraints, the state decided to identify schools facilities and shelters that could be retrofitted for future evacuations (FDEM 2006b). Evacuation modeling in the United States first started in the 1970s during the Three Mile Island nuclear plant incident (Cova and Church 1997). This early work focused on identifying populations at risk and travel routes. The standard approach was to identify an emergency planning zone (EPZ) surrounding hazard sites, and estimate travel time away from EPZ based on factors that might affect network clearance time. This

230 B Kar and M E Hodgson Figure 1 Population vulnerable to future storm surge (Source: Florida Division of Emergency Management 2006b) approach is used for some hurricane events where populations are evacuated to shelters. Because time available to evacuate and accessibility to shelters determine evacuation time, models have been developed to identify suitable routes that would provide the least clearance time from the EPZ to a safe neighborhood (Cova and Church 1997, Farahmand 1997, Cova and Johnson 2002). The determination of potential hazard zones for evacuation planning is somewhat subjective. Standards for evacuation from airborne toxic releases have been defined based on the type and amount of chemical releases. However, local meteorological conditions after a release may alter the evacuation zone. Floods and forest fires are particularly sensitive to temporally varying conditions, and evacuation zones either must be very conservative or must be modified during the hazard event. Thus, modeling approaches for evacuation planning often contain a model for the hazard and a separate model for the population to be evacuated. Cova and Church (1997) researched the transportation modeling of residents, with a particular emphasis on identification of choke points in the transportation network. They developed a methodology for identifying neighborhoods that would encounter evacuation problems due to choke points. For a specific study area, the authors identified possible nodes (street junctions) that would be used for evacuation, and for each node the cluster of population to be evacuated. An Integer Programming approach was used to identify vulnerable neighborhoods facing evacuation difficulty based on the number of available lanes from each node, and the number of people evacuating at different time durations. In a similar kind of study, Chen et al. (2005) developed a methodology to estimate the evacuees and clearance times during evacuation in a hurricane. Using two different evacuation timings and Emergency Management Division specified evacuation zones, the authors estimated the total number of evacuating vehicles from the study site. Finally, an agent-based microsimulation model was implemented to estimate minimum clearance time required for complete evacuation. Using the concept of an evacuation trigger point (e.g. as in HURREVAC; FEMA 2006b), Cova et al. (2005) estimated the population at risk in a community prone to fire impacts, and the time required to evacuate. Kongsomsaksakul et al. (2005) implemented a location-allocation model to reduce travel time out of a flood-affected zone. The

Modeling evacuation shelter suitability 231 authors assumed that people will evacuate to the available shelters using passenger cars and implemented a bi-level programming approach combining location of shelters and their capacity with available traffic network to minimize evacuation time out of the hazard zone. Using psychological and social factors, and policy variables, Simonovic and Ahmad (2005) modeled human behavior and estimated the number of evacuees in flood-impacted areas. Pine et al. (2003) researched structurally suitable shelters for New Orleans, Louisiana. Based on the American Red Cross guidelines for mass care and hurricane evacuation shelters (ARC 2002), the authors determined the structural stability of existing shelters, and grouped them as acceptable, preferred or marginal. Using South Florida building code and Florida Standards for shelter construction, and the design and construction guidelines developed by FEMA and the American Society of Civil Engineers (ASCE); Coulbourne et al. (2002) evaluated the structural suitability of community shelters in three communities for tornado and hurricane winds. Other than these studies, few studies have modeled shelter location suitability. Gall (2004) implemented a suitability model for shelter location in Mozambique, where travel mode is primarily on foot. Based on a survey of locals, the suitability criteria included locations of vulnerable populations, proximity to roads, local infrastructure, and farmland. In summary, the focus of previous evacuation studies is to estimate the temporal aspect of evacuation (i.e. the estimation of travel time out of hazard zone to shelters) or determine the structural suitability of shelters. Except for the Gall study (2004) in Mozambique, no attention has been given to the site suitability of existing evacuation shelters and/or identification of potential facilities that could be used for evacuation. Although the approaches developed for fire or flood hazards could be used for hurricanes, the factors affecting evacuation out of hurricane-impacted areas are different. A generic site suitability model based on the criteria identified in this study and factor weights determined by a focus group was implemented for existing/candidate shelters to help in emergency service planning. 3 Methodology The WLC was implemented to estimate site suitability of shelters. WLC is a ranking method most commonly used in GIS-based studies such as site suitability, site selection, and resource evaluation analysis (Malczewski 2000, Basnet et al. 2001, Ayalew et al. 2004). The method can use both a quantitative and a qualitative approach. Once the factors are determined, a rule-based weight selection technique dependent on selected criteria is used to determine the ratings for values of the factors. In the qualitative phase, an opinion-based subjective technique may be used to develop weights for each variable (Malczewski 2000, Ayalew et al. 2004). Finally, the factor ratings of each variable are multiplied with their respective weights, and all the weighted layers are combined through addition to estimate suitability scores for each land unit: Score = ( FRj * wj) where Score = summary suitability score for the location, FR j = Factor Rating for factor j, n = number of factors included in the model, and w j = weight assigned to factor j such n that w j = 100. j n j (1)

232 B Kar and M E Hodgson The WLC method was combined with a Pass/Fail suitability approach to implement exclusionary criteria. The combined model is: n Score = ( FRj * wj) * ( FC1 * FC2 *... FCn) j where FC i represents a factor constraint for variable i (0 implies excluded, 1 implies acceptable for that factor). (2) 3.1 Study Site This study focused on the shelter-siting problem in Southern Florida, the region visited most frequently by hurricanes. Seventeen Florida counties were examined in the study: Broward, Charlotte, Collier, De Soto, Glades, Hardee, Hendry, Highlands, Lee, Manatee, Martin, Miami-Dade, Monroe, Okeechobee, Palm Beach, Sarasota, and St. Lucie counties (Figure 2). Five adjacent counties (Pinellas, Polk, Hillsborough, Indian River and Osceola) were included to eliminate boundary effects. 3.2 Data and Variables Evacuation shelters could be single use or multi-purpose facilities; however, shelters should be multi-purpose so that the owners would get some return from their investment (FEMA 2006a). Multi-purpose facilities like schools, colleges, universities, state- and local government-owned facilities such as community and civic centers, and other public assembly facilities (churches) are considered as potential shelters in the event of an emergency situation (FEMA 2006a). For this study, schools, colleges, community centers, civic centers, churches, social service centers, and cultural centers were considered as candidate shelters. A database of the current existing shelters was obtained from the Florida Department of Community Affairs. Both physical and social factors influence the site suitability of a shelter. However, no formal study has evaluated and specified the criteria for an emergency evacuation shelter. A list of these factors was prepared in this study using a number of shelter related studies (Table 1). Accessibility to shelters is a major concern among researchers studying evacuation modeling (Cova and Church 1997, Cova and Johnson 2002, Chen et al. 2005). The Florida Division of Emergency Management (FDEM) has also pointed out that populations tend to evacuate to shelters that have easy access to evacuation routes (FDEM 2006a). Thus, proximity to highways and evacuation routes was considered as one of the important factors to rank shelters. Shelters located in flood zones may result in additional risk to occupants. The American Red Cross (ARC), FEMA and FDEM (ARC 2002, FDEM 2006a, FEMA 2006a) indicated that shelters should not be located in a 100 or 500-year flood zone. In this study, all shelters located within a 100- or 500-year flood zone were considered as unsuitable for evacuation. FEMA Q3 flood zone data representing 100 and 500-year inundation zones was used as an exclusionary factor. The ARC (2002) standard for shelter location states that evacuation shelters should not be located within the 10-mile emergency planning zone of a nuclear power plant. The ARC guideline also states that shelters should be located at a safe distance (usually determined by the local emergency planning committee) from hazardous facilities (any facility that manufactures, stores or uses hazardous materials) (ARC 2002). Because

Modeling evacuation shelter suitability 233 Figure 2 The 17 county study area in South Florida different agencies are involved with the maintenance and cleaning of hazardous facilities, data regarding their chemical content and concentration is not always available from one database (McMaster et al. 1997). Environmental justice studies often rely on the location of hazardous sites rather than the type and toxicity of the substance to study their societal impact (Chakraborty and Armstrong 1997, Jerrett et al. 2001). Cutter et al. (2000) used the Environmental Defense Fund scorecard utilizing toxicity indices based on chemical type to determine potential impact of TRI facilities on populations. Due to lack of data for chemical concentrations at the hazardous facilities in Florida, the proximity to superfund sites, nuclear power plant locations, brownfields, toxic release inventory (TRI) sites was used as factors in siting shelters. The closer a shelter was to a hazardous facility, the less suitable the shelter was. All facilities were assumed equally hazardous for shelters. During an emergency event, evacuees might experience psychological, physical stress, or be in need of health care. It was assumed that shelters

234 B Kar and M E Hodgson Table 1 Factors included in the shelter suitability model based on other studies Factors Comment Reference Flood zone Proximity to highways and evacuation routes Proximity to hazard sites Proximity to health care facilities Total population in neighborhood Total children in neighborhood Total elders in neighborhood Total minority in neighborhood Total low-income in neighborhood Locations in flood zone are excluded Locations closer to major transportation routes is more suitable Locations closer to hazardous facility are less desirable Locations closer to health facility are more desirable Higher populations are more desirable Higher populations are more desirable Higher populations are more desirable Higher populations are more desirable Higher populations are more desirable ARC 2002, FDEM 2006a, and FEMA 2006a Cova and Church 1997, Cova and Johnson 2002, Chen et al. 2005, FEMA 2006a ARC 2002 Assumption Assumption Cutter et al. 2000 Cutter et al. 2000 Whitehead et al. 2000 Whitehead et al. 2000 should be located within close proximity of hospitals and health care facilities to provide medical aid. Cutter et al. (2000) identified the most vulnerable population groups needing assistance during a hazard event were elderly people above the age of 65 years, children under the age of 18 years, and females. According to Whitehead et al. (2000), females are less likely to evacuate as opposed to low-income and minorities. For this study, elderly, children, low-income and minority were select as the salient social factors. It was also assumed that shelters should be located at or near areas with high population concentrations. A list of the data sets used to represent the physical and social factors identified for the study is provided in Table 2. 3.3 Data Processing Only certain kinds of public facilities (i.e. civic centers, community centers, churches, schools, cultural centers and social service centers) were considered as candidate shelters (Table 3). For instance, from all categories of health care facilities ambulatory surgical center, clinic, crisis stabilization unit, family/general practice center, hospital and medical center were extracted for the model. Likewise, from cultural centers, only institutional library, library, special interest library facilities were used in the model. After removing duplicate facilities from these layers, the selected categories of each facility were merged

Modeling evacuation shelter suitability 235 Table 2 Data sources Data Source Demographic data Block group and County boundary State highways Hurricane evacuation routes Hospitals and medicare facilities Existing evacuation shelters Hazard sites (Toxic Release Inventory sites, Superfund sites, Brown fields, Nuclear sites) Schools, Colleges, Community centers, civic centers etc. Q3 Flood Zone US Census Bureau (www.census.gov) ESRI (http://www.esri.com/data/download/ census2000_tigerline/index.html) Florida Department of Transportation (http:// www.dot.state.fl.us/planning/statistics/gis/ default.htm#roads) Florida Disaster Organization (http:// www.floridadisaster.org/publicmapping/index.htm) Florida Geographic Data Library (http://www.fgdl.org/ download/download.html) Florida Disaster Organization (http:// www.floridadisaster.org/publicmapping/index.htm) Florida Geographic Data Library (http://www.fgdl.org/ download/download.html) Florida Geographic Data Library Department of Geography, University of South Carolina Table 3 Facility Facility types considered as candidate shelters Types of Facility Healthcare Community Center Social Services Center Cultural Center Civic Center Religion Center Ambulatory Surgical Center, Clinic, Crisis Stabilization Unit, Family/General Practice, Hospital, Medical Center Community Center Child Daycare Service, Government Offices, Health and Welfare Agencies Institutional Library, Library, Special Interest Library Auditorium, Conference Center, Convention Center, Civic Center Center, Church together to generate the candidate shelter database. The names and addresses of existing shelters in Florida were geocoded to produce an existing shelter geographic database. Out of the total 462 shelters existing in the study site, 440 shelters were geocoded (a 95% geocoding rate). 3.4 Shelter Suitability Model Though a vector-based model of site suitability was possible, a raster based GIS model is more efficient for this large number of facilities and the spatial resolution needed to

236 B Kar and M E Hodgson Figure 3 Flow diagram of the suitability model represent the factors. Thus, a spatial resolution of 50 m 50 m was used to adequately represent distances between features and uniquely identify each shelter facility. To generate the suitability score surface, both social and physical suitability of the entire study site were determined. Except for the flood zone, distance surfaces describing proximity to each physical factor (Table 1) were generated. Surfaces describing the distribution of social factors were also created. Each of the social and physical variable surfaces was reclassified into six intervals, and a factor rating was assigned to each interval. Each factor-rating surface was then weighted and all factors combined to create a physical and a social suitability score. These resulting score grids were multiplied with the reclassified flood grid (for the pass/fail criteria) to produce the combined suitability surface. Each of the physical and social suitability score surfaces was used in combination with the existing and candidate shelters layers to determine their physical and social suitability. Each phase of the suitability model (Figure 3) implemented in the study is described in detail in the following sections. 3.4.1 Surfaces for social variables At the spatial resolution of 50 m 50 m, population characteristics (e.g. total population, elderly, children, minority, low-income) at the block level were distributed spatially using the common dissagregation approach assuming equal densities within census units (Chakraborty and Armstrong 1997).

Modeling evacuation shelter suitability 237 Figure 4 Cumulative frequency of existing shelters surrounding evacuation routes and hospitals Previous studies argued that shelters should be located close to evacuation routes to reduce clearance time from evacuation zones to shelters (Cova and Church 1997, Cova and Johnson 2002, Chen et al. 2005). Likewise, it was assumed that proximity of shelters to health care centers is imperative to provide medical aid during evacuation. A spatial analysis of the existing shelters revealed that 75% of the existing shelters are within 2 miles of evacuation routes and health care centers (Figure 4 and Table 4). It was assumed that evacuees would move to the closest shelter. Thus, surfaces depicting the neighborhood (i.e. focal) distribution of social factors within 2 miles of each location instead of each shelter in the study site were created. Shelters should be located in areas with a high demand, such as high concentrations of total population, elderly, etc. To standardize the counts for each social factor, the neighborhood population surfaces were reclassified into six intervals using natural breaks. Each class was assigned a rating between 0 to 10. A rating of 10 and 0 was assigned to a high-valued class and low-valued class, respectively. 3.4.2 Proximity surfaces Proximity of sites to evacuation routes, hospitals and hazard sites was also derived. Each proximity surface was reclassified into six intervals, and a factor rating was assigned to each interval based on distance. Locations closer to evacuation routes and hospitals received higher ratings (Table 4). Conversely, sites located closer to hazardous sites (e.g. nuclear plants, superfund sites, brownfields, or TRI sites) received lower ratings. 3.4.3 Flood zone surface Flood zone risk was represented by the Q3-flood zone data. These data include both riverine and coastal flooding from storm surge associated with hurricanes (Table 5). The flood zone surface was reclassified into a binary flood zone/no flood zone category using a pass/fail constraint method. All areas present in either the 100 or 500-year flood zone were considered unsuitable for locating shelters. Locations in flood zones were assigned a value of 0 so that the final suitability score of locations in flood zones will be 0 (i.e. unsuitable). Locations not situated in flood zones were given a value of 1.

238 B Kar and M E Hodgson Table 4 Factor Factor ratings and frequency of existing shelters Class Interval Number of Factor Existing Shelters Rating Schedule Proximity to evacuation route < 1,609 meters 350 10 1,609 3,218 meters 75 8 3,218 4,827 meters 11 6 4,827 6,436 meters 1 4 6,436 8,045 meters 2 2 > 8,045 meters 1 0 Proximity to health care facility < 1,609 meters 271 10 1,609 3,218 meters 102 8 3,218 4,827 meters 34 6 4,827 6,436 meters 11 4 6,436 8,045 meters 7 2 > 8,045 meters 15 0 Proximity to TRI sites < 1,609 meters 76 0 1,609 3,218 meters 101 2 3,218 4,827 meters 59 4 4,827 6,436 meters 50 6 6,436 8,045 meters 33 8 > 8,045 meters 121 10 Proximity to brown fields < 1,609 meters 19 0 1,609 3,218 meters 36 2 3,218 8,045 meters 84 4 8,045 12,872 meters 65 6 12,872 16,090 meters 17 8 > 16,090 meters 219 10 Proximity to superfund sites < 1,609 meters 9 0 1,609 3,218 meters 24 2 3,218 8,045 meters 63 4 8,045 12,872 meters 44 6 12,872 16,090 meters 14 8 > 16,090 meters 286 10 Proximity to nuclear power plant < 1,609 meters 0 0 1,609 3,218 meters 0 2 3,218 8,045 meters 4 4 8,045 12,872 meters 13 6 12,872 16,090 meters 12 8 > 16,090 meters 411 10 Most of Miami Dade, Monroe, Collier, Broward, Palm Beach, Martin, and portions of Hendry, Lee, Charlotte and Glades counties are located in flood zones. Most areas in the northern counties were not situated in flood zones. As flood zone data for Okeechobee County was unavailable, and the county is inland, except for the Lake Okeechobee area, the remainder of the county was assumed to be protected from storm surges.

Modeling evacuation shelter suitability 239 Table 5 Q3 flood zone classification Zone Code Zone Definition A Area inundated by 100-year flooding, but no base flood elevation is decided AE Area inundated by 100-year flooding, and base flood elevation is determined AH Area inundated by 100 year flooding for which base elevation is determined, and flooding height is about 1 3 feet AO Alluvial fan inundated by 100-year for which average flood depths and velocities have been determined, and flood depths range from 1 to 3 feet ANI Area not included in Flood Insurance Rate Mapping D Area of undetermined but possible flood hazards X-500 Area inundated by 500-year flooding X Area not subjected to flooding VE Area inundated by 100-year flooding with velocity hazard, and base elevation is determined UNDES No flood plain information is available. Source: FEMA (http://www.yorkcounty.gov/gis/fema_flood_zone_designations.htm) 3.4.4 Modeled shelter suitability surface The selection of factor weights is one of the most controversial parts of site suitability modeling. Malczewski (2000) suggested that weights for factors should be determined based on the range in values. He suggested a swing weights technique to derive weights. In this technique, the weight for a factor is computed from the ratio of the initial weight to the total weight of all the factors used in the study. The swing weights technique ensures normalization of weights. In this study, factor ratings were assigned to each class interval defined by normal breaks. Weights were developed using expert subjects. No previous research has established factor weights for evacuation shelter siting in developed countries. In this study, a nominal group process technique was used to elicit a weight for each factor. A focus group of eight experts from the Hazards Lab and the GIS and Remote Sensing Lab (Department of Geography, University of South Carolina) were asked to weight the factors by distributing 100 points among the different factors (Table 6). An average of the assigned weights was then used as the final factor weight. Using the empirical model (equation 2), separate physical and social suitability surfaces were generated. Each suitability surface (Figures 5a, b) was categorized into five ordinal categories (using natural breaks) depicting very low through very high suitability. The summary model (equation 2) combining both the physical and social suitability was used to generate the combined site suitability surface (Figure 6). Each existing and candidate shelter, based on its physical suitability score, was classified as unsuitable due to their location in the flood zone or some level of suitability. The physically suitable existing shelters were classified into five categories. To determine a classification scheme for these shelters, a frequency graph depicting the frequency of their physical and social suitability score range was generated (Figures 7a, b). Because the distributions are non-normal, an equal interval classification instead of a standarddeviation classification scheme was used to classify these shelters.

Table 6 Weight of each factor assigned by eight human subjects Variable Subject 1 Subject 2 Subject 3 Subject 4 Subject 5 Subject 6 Subject 7 Subject 8 Average Weight Evacuation routes 30 20 20 4 10 25 5 15 16.125 Health care facilities 15 30 15 12 10 10 5 10 13.375 Hazard sites 2 0 0 10 0 20 10 0 5.25 Total Population 35 30 20 6 5 10 20 0 15.75 Children 3 10 5 15 5 5 10 10 7.875 Elders 3 10 5 25 20 5 10 25 12.875 Low income 9 0 25 16 25 25 20 25 18.125 Minority 3 0 10 12 25 0 20 15 10.625 240 B Kar and M E Hodgson

Modeling evacuation shelter suitability 241 Figure 5 Frequency graph of physical suitability (a) and social suitability (b) of existing shelters 4 Results and Findings Over 99% and 97% of the existing shelters are within 5 miles (8 km) of evacuation routes and hospitals, respectively (Table 4). This finding is consistent with recommendations that shelters be easily accessible with nearby medical care. As per the ARC guidelines for hurricane evacuation shelter location, shelters should not be located within 10 miles (16.1 km) of a nuclear power plant (ARC 2002). More than 93% of the existing shelters are situated farther than this 10 miles threshold from nuclear power plants, and hence were considered physically suitable. Of the existing shelters, 50% and 35% are located within 10 miles of a brownfield and a superfund site, respectively. The ARC guidelines do not specify a threshold distance for siting an evacuation shelter with respect to active polluting industries, such as a TRI facility. Approximately 72% of the existing shelters are located within 5 miles (8 km) of a TRI facility. Of considerable concern is that three of the 2% (9) and 17% (76) of the existing shelters are physically suitable yet are located within one mile of a superfund site and a TRI facility, respectively. The northern counties (Manatee, Hardee, DeSoto, Sarasota, Okeechobee and Highlands and St. Lucie) are more physically suitable for shelter locations than the southern counties (Figure 5a), in large part due to the absence of flood-prone areas. The social suitability surface is almost an inverse of the physical suitability surface (Figure 5b). In large part, high concentrations of the population needing shelters are present in southern, physically unsuitable coastal counties. For instance, large areas of Lee county with high social suitability are located in a flood zone. In only very small

242 B Kar and M E Hodgson Figure 6 Physical suitability (a) and social suitability (b) for evacuation shelters

Modeling evacuation shelter suitability 243 Figure 7 Combined site suitability portions of Miami-Dade, Broward, Palm Beach County, is the social need collocated with high physical suitability. In some ways, this is the paradox of risk and desirable living. Populations typically desire to live in low-lying coastal areas (i.e. near water) where the physical risk from hurricane hazards is greatest. Based on the combined suitability score (physical and social suitability combined), the northern counties (Manatee, Hardee, DeSoto, Sarasota, St. Lucie) are more suitable for shelter locations (Figure 6). Very small portions of Charlotte, Glades, Lee, Hendry, Palm Beach, Miami-Dade and Broward counties include very high suitable locations. Based on the combined suitability score, high/very high suitable existing shelters are present in Miami-Dade, Broward, Palm Beach Counties (Figure 8a). Additionally, medium suitability existing shelters are located in Manatee, Sarasota, Hendry, St, Lucie County (Figure 8b). Analogous to this pattern of the existing shelters, most of the high/very high suitable candidate shelters are also located near the coast in Miami-Dade, Broward, Palm Beach, St. Lucie, Manatee, and Sarasota County. Most of the medium suitability candidate shelters are located in Martin, Charlotte, Hendry, Collier and Monroe County. The analysis included a cross-comparison of the 440 existing shelter categorization based on physical and social suitability (Table 7). About 48% (211) of the existing shelters are physically unsuitable. Approximately 90% (191) of these physically unsuitable

244 B Kar and M E Hodgson Figure 8 Existing shelters with high/very high combined suitability rating (a) and medium combined suitability rating (b)

Modeling evacuation shelter suitability 245 Table 7 Frequency of existing shelters by physical and social suitability categories Social Suitability Physical Suitability High/Very High Medium Very Low/Low Unsuitable High/Very High 0 4 89 3 Medium 3 9 45 5 Very Low/Low 26 10 22 13 Unsuitable 31 38 122 20 Table 8 Frequency of existing shelters with poor physical suitability by proximity to alternate existing shelters with good physical suitability (a) and frequency of existing shelters with poor physical suitability by proximity to alternate candidate shelters with good physical suitability (b) (a) Distance to Alternate Existing Shelters with High/Very High/Medium (i.e. good) Physical Suitability Suitability Score of Existing Shelters 0 3,218 m 3,218 8,045 m 8,045 16,090 m > 16,090 m Total Shelters Unsuitable (poor) 26 41 55 69 191 Very Low/Low (poor) 16 23 16 3 58 (b) Distance to Alternate Candidate Shelters with High/Very High/Medium (good) Physical Suitability Suitability Score of Existing Shelters 0 3,218 m 3,218 8,045 m 8,045 16,090 m > 16,090 m Total Shelters Unsuitable (poor) 69 60 47 15 191 Very Low/Low (poor) 34 23 1 0 58 shelters are located in socially suitable locations. Thus, there is a population in need of sheltering yet the existing shelters are located in physically hazardous areas. An obvious question is Are there existing or candidate shelters with good physical suitability nearby these shelters that could fulfill the need? Existing shelters with high/very high and medium physical suitability and located within 10 miles of a physically unsuitable existing shelter were considered as desirable alternatives. Of the 191 physically unsuitable, but socially suitable existing shelters, about 64% (122) had a nearby (i.e. within 10 miles) alternative existing shelter with good physical suitability (Table 8a), However, 69 of these physically unsuitable shelters

246 B Kar and M E Hodgson Figure 9 Socially suitable yet physically unsuitable existing shelters with no access to physically suitable (high/very high/medium rating) existing or candidate shelters within 10 miles do not have an alternative existing shelter within 10 miles. Of the 58 existing shelters with very low/low physical suitability, 53 have access to alternative nearby shelters within 10 miles. An analysis of the spatial locations of physically unsuitable existing shelters with respect to the candidate shelters with good physical suitability was also conducted. Some 92% of the unsuitable existing shelters had either an alternative existing or a candidate shelter within 10 miles (Table 8b). However, it was found that 15 of the physically unsuitable existing shelters do not have any alternative shelter located within 10 miles. These physically unsuitable shelters are located in Monroe, Broward, Collier and Lee Counties (Figure 9). 5 Conclusions and Recommendations Evaluation of the site suitability of existing shelters indicated that of the total 440 existing shelters in 17 southern Florida counties 229 (52%) are physically suitable for evacuation sheltering purposes.

Modeling evacuation shelter suitability 247 Out of the total 9,048 candidate shelters, some 3,922 (43%) are in physically suitable locations for evacuation sheltering purposes. From the 3,922 suitable candidate shelters, about 50% have high/very high physical suitability scores. Due to high social suitability of the coastal areas, most of the socially suitable existing and candidate shelters are located close to the ocean. On the other hand, physically suitable locations of the northern counties, such as Manatee, Hardee, Okeechobee, Highlands, DeSoto, and St. Lucie do not have many suitable existing and candidate shelters. Again, this is the paradox of populations living in high-risk areas and their dependence on nearby infrastructures, which serve as evacuation shelters. New public facilities should be constructed in suitable locations to evacuate people during future hurricane events. Because of the high suitability score of shelters present in northern counties, those shelters should be used as potential emergency shelters. Currently, the guidelines for shelters focus mostly on structural suitability of shelters rather than their site suitability. The geographic location of shelters has not received adequate attention and may be as equally important as their structural stability. The methods developed in this study not only could be used by planners and policy makers in Florida, but also in other states to formulate guidelines, and evacuation plans that would be more effective in the event of a hurricane disaster. Location-allocation models are typically used to establish optimal location for facilities, such as hospitals, schools, and factories. Based on the number of evacuees identified in the evacuation zones, capacity of the suitable existing shelters, and possible evacuation routes connecting the evacuation zones to shelters, a location-allocation model could be used to allocate populations to nearby existing and, if required, candidate shelters. The same approach could be used to identify new shelter locations as well as optimal evacuation routes to reduce clearance times out of the evacuation zones. The limitations of the study include: (1) non-inclusion of physical characteristics of the shelters; and (2) generalization of risk posed by TRI facilities, brownfields and superfund sites without regard of their chemical content and concentration. A future screening model could be implemented to assess the impact of these two criteria on suitable shelters identified in the study. References ARC (American Red Cross) 2002 Standards for Hurricane Evacuation Shelter Selection. WWW document, http://www.floridadisaster.org/response/engineers/documents/2007srr/ 2007SRR-AppxC.pdf Ayalew L, Yamagishi H, and Ugawa N 2004 Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides 1: 73 81 Basnet B B, Apan A A, and Raine S R 2001 Selecting suitable sites for animal waste application using a raster GIS. Environmental Management 28: 519 31 Burby R J 2006 Hurricane Katrina and the paradoxes of government disaster policy: Bringing about wise governmental decisions for hazardous areas. Annals of the American Academy of Political and Social Science 60: 171 91 Chakraborty J and Armstrong M P 1997 Exploring the use of buffer analysis for the identification of impacted areas in environmental equity assessment. Cartography and Geographic Information Systems 24: 145 57 Chen X, Meaker J W, and Zhan F B 2005 Agent based modelling and analysis of hurricane evacuation procedures for the Florida Keys. Natural Hazards 38: 321 38

248 B Kar and M E Hodgson Coulbourne W L, Tezak E S, and McAllister T P 2002 Design guidelines for community shelters for extreme wind events. Journal of Architectural Engineering 8: 69 77 Cova T J and Church R L 1997 Modelling community evacuation vulnerability using GIS. International Journal of Geographical Information Science 11: 763 84 Cova T J and Johnson J P 2002 Microsimulation of neighborhood evacuations in the urbanwildland interface. Environment and Planning A 34: 2211 29 Cova T J, Dennison P E, Kim T H, and Moritz M A 2005 Setting wildfire evacuation trigger points using fire spread modeling and GIS. Transactions in GIS 9: 603 17 Crosett K M, Culliton T J, Wiley P C, and Goodspeed T R 2004 Population Trends along the Coastal United States, 1980 2008. WWW document, http://oceanservice.noaa.gov/programs/ mb/pdfs/coastal_pop_trends_complete.pdf Cutter S L, Mitchell J T, and Scott M S 2000 Revealing the vulnerability of people and places: A case study of Georgetown County, South Carolina. Annals of the Association of American Geographers 90: 713 37 Dow K and Cutter S L 1998 Crying wolf: Repeat responses to evacuation orders. Coastal Management 26: 238 52 Dow K and Cutter S L 2000 Public orders and personal opinions: Household strategies for hurricane risk assessment. Environmental Hazards 2: 143 55 FDEM (Florida Division of Emergency Management) 2006a State of Florida: 2004 Statewide Emergency Shelter Plan. WWW document, http://floridadisaster.org/response/engineers/ documents/2004sesp/2004%20sesp%20complete.pdf FDEM (Florida Division of Emergency Management) 2006b Hurricane Task Force Report. WWW document, http://www.floridadisaster.org/documents/hurricanetaskforcereport.pdf FEMA (Federal Emergency Management Agency) 2006a Design and Construction Guidance for Community Shelters. WWW document, http://www.fema.gov/plan/prevent/saferoom/ fema361.shtm FEMA (Federal Emergency Management Agency) 2006b HURREVAC Evacuation Decision Assistance Tool for Government Officials. WWW document, http://www.fema.gov/plan/ prevent/nhp/hurrevac.shtm Gall M 2004 Where to go? Strategic modelling of access to emergency shelters in Mozambique. Disasters 28: 82 97 Godschalk D R, Norton R, Richardson C, and Salvesen D 2000 Avoiding coastal hazard areas: Best state mitigation practices. Environmental Geosciences 7: 13 22 Jerrett M, Burnett R T, Kanaroglou P, Eyles J, Finkelstein N, Giovis C, and Brook J R 2001 A GIS-environmental justice analysis of particulate air pollution in Hamilton, Canada. Environment and Planning A 33: 955 73 Kongsomsaksakul S, Yang C, and Chen A 2005 Shelter location-allocation model for flood evacuation planning. Journal of the Eastern Asia Society for Transportation Studies 6: 4237 52 Malczewski J 2000 On the use of weighted linear combination method in GIS: Common and best practice approaches. Transactions in GIS 4: 5 22 McMaster R B, Leitner H, and Sheppard E 1997 GIS-based environmental equity and risk assessment: Methodological problems and prospects. Cartography and Geographic Information Systems 24: 172 89 Pine J C, Marx B D, Levitan M L, and Wilkins D 2003 Comprehensive assessment of hurricane shelters: Lessons from Hurricane Georges. Natural Hazards Review 4: 197 205 Simonovic S P and Ahmad S 2005 Computer-based model for flood evacuation emergency planning. Natural Hazards 34: 25 51 Whitehead J C, Edwards B, Willigen M V, Maiolo J R, Wilson K, and Smith K T 2000 Heading for higher ground: Factors affecting real and hypothetical hurricane evacuation behavior. Environmental Hazards 2: 133 42 Willigen M V, Edwards T, Edwards B, and Hessee S 2002 Riding out the storm: Experiences of the physically disabled during hurricanes Bonnie, Dennis, and Floyd. Natural Hazards Review 3: 98 106