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- Taylor & Francis
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- © 2013 Taylor & Francis
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- 1947-5713
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- 1947-5705
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- 10.1080/19475705.2013.832406
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Abstract
Geomatics, Natural Hazards and Risk, 2015 Vol. 6, No. 3, 195–211, http://dx.doi.org/10.1080/19475705.2013.832406 CHARLES YORKE*y, F.B. ZHANz, YONGMEI LUz and RON HAGELMANz yMurray State University, Geoscience, 334 Blackburn Hall, Murray, KY 42071, USA zTexas State University-San Marcos, Geography, 601 University Drive, San Marcos, TX 78666, USA (Received 15 February 2013; accepted 1 August 2013) The purpose of this research is to illustrate a three-component operationalization of the Hazards-of-Place Model (HPM) by integrating urban infrastructure (using the capacity of road networks to facilitate evacuation as an example) to describe place vulnerability. This approach is informed by the HPM first articulated by Cutter (Vulnerability to environmental hazards. Prog Hum Geog. 20:529–39, 1996). The HPM is a conceptual framework through which place vulnerability is defined as a combination of social characteristics (expressed by selected socioeco- nomic demographics) and geophysical risk (expressed by probabilities of occur- rence). Using a geographic information system (GIS), the study models the capacity of road networks to facilitate evacuation and used it as an example of urban infrastructure within which place vulnerability occurs. The output of the model was integrated with a geophysical risk layer and social vulnerability index layer as components for assessing the overall place vulnerability. The three- component approach to operationalizing the HPM provides a detailed and nuanced illustration of place-based vulnerability. As an applied tool, the three- component approach presents emergency planners with a new method of integrat- ing diverse geographic data when illustrating spatial patterns of vulnerability to environmental hazards. Introduction The analysis of vulnerability to environmental hazards provides the means or justifi- cation for hazard mitigation agencies to channel their efforts and resources to meet the needs of the communities they serve. Over the last three decades, most hazard researchers that focused on vulnerability analyses have paid particular attention to the intersection of geophysical risk and social factors in generating place vulnerabil- ity (Clark et al. 1998; Montz and Tobin 1998; Mileti 1999; Cutter et al. 2000;Wu et al. 2002; Brooks et al. 2005; Chakraborty et al. 2005; Cutter & Finch 2008; Wood et al. 2010; Schmidtlein et al. 2011; Kar & Hodgson 2012). This approach is deriva- tive of the Hazards-of-Place Model (HPM) developed by Cutter (1996) which offers a conceptual framework through which place vulnerability is defined as a combina- tion of social characteristics (expressed by selected socioeconomic demographics) and geophysical risk (expressed by probabilities of occurrence). *Corresponding author. Email: cyorke@murraystate.edu 2013 Taylor & Francis 196 C. Yorke et al. However, as indicated by Cutter et al. (2000), the simple overlap of social demo- graphics and geophysical risk does not give a complete picture of a community’s overall vulnerability. Cutter et al. (2000) argued that the infrastructure context of vulnerability must also be established. The focus of this paper is to show how the infrastructure context of vulnerability can be established. The focus of the paper is not on how one can obtain a complete picture of a community’s overall vulnerability. There are certain key infrastructure features (e.g. transportation networks, levees, evacuation shelters), the characteristics of which can influence a community’s place vulnerability (Piegorsch et al. 2007). One such infrastructure feature is a road net- work (Dow & Cutter 2002), particularly in instances where evacuation and emer- gency response are integral to the mitigation of impacts. During certain hazardous events, the affected communities have the option to evacuate (Cova & Johnson 2002, 2003). The inability to gain effective egress can therefore be conceptualized as part of a community’s overall place vulnerability. In general, neighbourhoods that have higher evacuation difficulties are more vulnerable to environmental hazards (Cova & Church 1997; Chakraborty et al. 2005). A high evacuation difficulty simply means that the number of people needed to be evacuated at a given time interval exceeds the capacity of the road network. The capacity of a road network is defined by the number of lanes of the road, the speed limit, as well as the travel direction of the road. From the above description, it can be said that an interstate highway, with two lanes, with a speed limit of 70 miles per hour and a one-way travel direction, has a higher capacity to facilitate evacuation than a county road with one lane, with a speed limit of 45 miles per hour and a two-way travel direction. Several factors determine whether an evacuation can be carried out with ease. These factors include weather conditions during the time of the evacuation, the size of the vulnerable population and the capacity of road networks (Southworth 1991; Dow & Cutter 2002; Sorensen et al. 2002). Of particular importance to this research is the capacity of road networks to facilitate evacuation. Though the capacity of road networks to facilitate evacuation has been identified as a major infrastructure context that contributes to community’s overall vulnerability, the role of a road network in generating a community’s place vulnerability is not well articulated in previous place vulnerability modelling. The objective of this study is to illustrate the utility of the HPM by integrating the capacity of road networks to facilitate evacuation in describing place vulnerability. The data integration is accomplished in a geographic information system (GIS). The illustration of three- component approach to operationalizing the HPM is conducted using geospatial data for Hillsborough County, Florida and is intended to answer the following research questions: (1) Using the current HPM approach, what are the spatial distributions of geo- physical risk and social vulnerability with the study area? (2) Which areas within the county are likely to experience difficulty during an evacuation due to the capacity of the road network? (3) What are the differences between the spatial overall vulnerability based on the two-component HPM and the overall vulnerability based on our three-compo- nent approach? Using a GIS as a tool, various information layers representing geophysical risk and social vulnerability indicators are combined with the capacity of a road Incorporating evacuation potential into place vulnerability analysis 197 network to facilitate evacuation in order to determine the overall vulnerability of Hillsborough County. Related work In the simplest terms, vulnerability is defined as the potential for loss (Cutter et al. 2003). However, the definition of vulnerability varies with disciplines and topics (Wu et al. 2002). According to Weichselgartner (2001), despite the diversity among scholars, three models are dominant in vulnerability studies. These include (1) the Risk-Hazard or the Exposure model; (2) the Pressure and Release model; and (3) the HPM. The first of the three models to emerge is the Risk-Hazard or Exposure model which perceives vulnerability as a situation that existed before human occupation (Burton et al. 1993; Blaikie et al. 1994; Anderson 2000). The Risk-Hazard model pays attention to the origins of hazardous conditions. This vulnerability model centres on the distribution of hazards and their potential impact on people and infra- structure (Wu et al. 2002). The second model to emerge is the Pressure and Release model which focuses on adaptive responses (including community resistance) to environmental hazards (Hewitt 1997). The Pressure and Release Model emphasizes circumstances that make geophysical conditions perilous and the causes of these conditions, leading to vulner- ability (Turner II et al. 2003). The nature of hazard is viewed as a social condition (Weichselgartner 2001). The third model (i. e. the HPM) integrates components of the Risk-Hazard and the Pressure and Release models, but it is essentially more place based (Cutter 1996). According to the HPM, vulnerability is derived from the interaction of both a geophysical risk and a social response at a specific geographic location. To date, many vulnerability studies that implement the HPM have focused on the combination of geophysical risk and social factors in creating a composite place vul- nerability index or map (Clark et al. 1998; Cutter et al. 2000; Chakraborty et al. 2005; Wood et al. 2010; Schmidtlein et al. 2011; Kar & Hodgson 2012). For example, Cutter et al. (2000) combined several geophysical variables and socioeconomic data of Georgetown County, South Carolina to develop geophysical and social vulnera- bility maps, respectively. The combination of the two maps was called a placed vul- nerability map. Similarly, Wu et al. (2002) created geophysical and social vulnerability maps for Cape May County, New Jersey. They argued that the compos- ite of the two maps reveals the vulnerability of May County to sea level rise. In addition to geophysical risk and social vulnerability, Cutter et al. (2000) argued that vulnerability analysis should include the infrastructure context within which geo- physical risk and social vulnerability occur. They indicated that overlaying the infra- structure over the place vulnerability may yield valuable information for mitigation planning. However, a simple overlay of critical infrastructure such as schools, hospi- tals and police stations on geophysical and social vulnerability maps does not clearly articulate how infrastructural context can work together with geophysical and social factors in generating overall place vulnerability. A better approach is to model the infrastructural context using similar techniques used to model the geophysical and the social vulnerability indices or maps. This study models the capacity of road networks to facilitate evacuation and used it as an example of urban infrastructure to illustrate a three-component approach to operationalization of the HPM. 198 C. Yorke et al. Advancing the utility of the HPM The HPM argues that risks from hazardous conditions can be mitigated by commu- nity responses and reduce the overall impacts of hazards (Cutter 1996). Therefore, hazard potential is influenced by place conditions and social vulnerability is influ- enced by socio-economic characteristics of the population at-risk. The social compo- nent and the geophysical risk levels interact to generate place vulnerability. Previous research also indicates that evacuation reduces vulnerability (Cova & Johnson 2003). Communities or neighbourhoods that can evacuate, or are able to do so more effi- ciently, are generally less vulnerable to hazards. This perspective stresses the role of infrastructure in mitigation. This research augments the operationalization of the HPM by incorporating geospatial representations of egress potential in an effort to advance the models’ utility in community planning. Hence, the HPM can be modified to incorporate infrastructural context as illustrated in figure 1. The study area Hillsborough County, Florida (figure 2) was selected for this study because the county experiences a range of environmental hazards in addition to problems associ- ated with high population density. According to Chakraborty et al. (2005), since the beginning of the twentieth century, more than 60 tropical storms and hurricanes have threatened the county. Also, the county is frequently flooded. Moreover, the county is one of the most polluted counties in the U.S. (Chakraborty & Armstrong 2001). Furthermore, the Port of Tampa handles about one-half of all seaborne imports and exports that pass through the state. The Port relies on bulk cargo Figure 1. A three-component modelling of HPM. Adapted from Cutter (1996). Incorporating evacuation potential into place vulnerability analysis 199 Figure 2. The study area – Hillsborough County, Florida. including phosphate, liquid sulphur and petroleum. Transportation and storage of these materials represent other potential hazards that threaten the local population (Albury 2004). Unfortunately, the hazards in the county have been exacerbated by high popula- tion growth, and more specifically by rapid urban growth that has placed people in an extremely risky environment. The 2010 U.S. population census shows that the population of the county was 1229,226. Hillsborough County Emergency Planning Operations (2006) indicated that 20000 additional people are added to the popula- tion during the winter months and 11,000 migrant workers come to the County during harvesting period. Methods Our integration of road-network-capacity-to-facilitate-evacuation data layer into the HPM was divided into four phases. These four phases include: (1) geophysical vul- nerability phase; (2) social vulnerability phase; (3) infrastructure context phase; and (4) overall vulnerability phase. figure 3 shows these four phases. The geophysical vul- nerability phase was implemented using the procedure for determining geophysical risk outlined by Cutter et al. (2000). The first step was to identify the types of hazard events that occur in the study area and estimate the rate of occurrence. The hazard history of the county was obtained by examining previous studies (Montz 2000; Montz & Evans 2001; Montz & Tobin 2003; Chakraborty et al. 2005). Based on pre- vious studies, the following hazards were identified: flooding, hurricanes and toxic release. The data sources for this research are presented in table 1. 200 C. Yorke et al. Figure 3. Implementation flow chart of the three component modelling of HPM. A flood hazard map for the county was created in ArcGIS 9.2 using the 100-year and 500-year flood zone data acquired from the Federal Emergency Management Agency (FEMA). The rates of occurrence (1 per cent likelihood of occurrence per year in the 100-year flood zone; 0.20 per cent likelihood of occurrence per year in the 500-year flood zone) were calculated from the flood data. Two forms of hurricane hazards were considered: storm surge and wind hazard. A hazard map for each com- ponent was created and the combination of the individual components resulted in a composite hurricane hazard map. The data for wind hazards were obtained from Mapping for Emergency Management, Parallel Hazard Information System (MEM- PHIS) website. The storm surge data were obtained from Tampa Bay Regional Plan- ning Council. The occurrence rates for each hurricane category as presented in table 2 were associated with the data mapped in ArcGIS 9.2. Two forms of toxic release were identified: (1) transportation spill and (2) toxic releases from industries. The transportation spill hazard was assumed to occur on highway roads and railways. The roads and railways datasets were obtained from Hillsborough County. A one-half mile buffer was created around each railroad and highway roads. The one-half mile distance buffer is the default distance recommended by the U.S. Department of Transportation (U.S.DOT) for a fire involving hazardous chemicals (U.S. DOT 1993).Tomeasure thevul- nerability for toxic release from industrial locations, the study used those Toxic Table 1. Data sources. Data Source Flood layers FEMA Hurricane wind layer MEMPHIS website Storm surge layer Tampa Bay Regional Planning Council Transportation layers Hillsborough County, Florida Toxic Release Inventories EPA Socio-economic layers U.S. Census Bureau Incorporating evacuation potential into place vulnerability analysis 201 Table 2. Hurricane probabilities adapted from Chakraborty et al. (2005). Category Wind speed (knots) Return period (years) Probability of occurrence 1 64–83 19 0.050 2 84–96 65 0.020 3 97–113 160 0.006 4 114–135 400 0.003 5 > 135 500 0.002 Release Inventory (TRI) sites reported for the year 2009 by the Environmental Protection Agency (EPA) together with climatic data from National Oceanic and Atmospheric Administration (NOAA). The TRI data contain attributes such as the type and volume of chemical released. The climatic data contain daily wind speed, wind direction, temperature and other variables. First, ALOHA (an atmospheric dispersion model) software was used to calculate a threat zone for each TRI site using the climatic data together with the TRI data. The threat zone distance was used to create an equidistant buffer around each TRI facility to account for changes in weather conditions. Fourteen years (from 1996 to 2009) of TRI data was acquired to determine the rate of occurrence for each buffer. After creating all the hazard maps, each hazard was normalized to have a value ranging from 0 to 1 to make each hazard equally important. The normalized layers were combined to form a composite geophysical vulnerability map. The social vulnerability phase was implemented using the social vulnerability indicator computation outlined by Cutter et al. (2000). Eight variables were considered: total housing units, number of females, number of non-white population, individuals over age 65-year old, children under age 18, number of mobile homes, number of households below poverty line and occupied housing units with no vehicles. These variables were chosen because they have been identified as the fundamental causes of social vulnerability (Mileti 1999;Morrow 1999; Cutter et al. 2003). The eight variables were obtained from the U.S. Census Bureau website. Following Cutter et al.’s (2000) procedure, each of the socio-economic variables was standardized and aggregated to an index that ranges from 0 to 1.00. In ArcGIS, the standardized social index was joined to a spatial-referenced block group layer to create a social vulnerability map. The capacity of a road network was used as an example of the infrastructure con- text within which vulnerability occurs. The capacity of a road network was chosen as an example due to its role in working with social and geophysical factors in creating overall vulnerability of a place. The capacity of a road network was modelled into a vulnerability map using a spatial evacuation analysis model developed by Cova and Church (1997). A spatial evacuation analysis model uses road network characteris- tics combined with population distribution to compute evacuation difficulty for dif- ferent neighbourhoods within a community. As indicated earlier, a high evacuation difficulty simply means that the number of people needed to be evacuated at a given time interval exceeds the capacity of the road network. Two datasets that were required for the model to run are street network and population data for the individual block groups within the county. The population data were obtained from the U.S. Census Bureau website. The street networks data was obtained from Hillsborough County office. 202 C. Yorke et al. The spatial evacuation analysis model can be run under different assumptions or conditions. First, an analyst can run the model under a condition that the road networks travel in one-way or two-way (or normal-way) directions. The one-way road direction is common during most evacuation events. Thus, the model was run under the one-way conditions. An analyst can also run the model using daytime or night-time population distribution data. For this study, census population data that represent night-time population distribution were used. The night-time population distribution was chosen because most people start their evacuation from their residential locations. The night-time population distribu- tion was also chosen because emergency workers based their evacuation estimate on a night-time population distribution. After the computation of the maximum evacuation difficulty for all the block groups within the county, the results were exported to ArcGIS 9.2. Using the maximum evacuation difficulty values for the individual block groups as an input, the ordinary kriging interpolation function with spherical semivariogram model was then used to create a continuous spatial evacuation difficulty map that represents the capacity of road networks to facili- tate evacuation for the County. The ordinary kriging interpolation was used because it compensates for the effects of data clustering and in the end gives a better prediction. During the overall vulnerability phase, the geophysical risk and social vulnerability layers were combined with the infrastructure context layer (represented as capacity of road networks to facilitate evacuation data layer) in ArcGIS 9.2 to create overall community vulnerability index map. Before the com- bination, each indicator (i.e. geophysical, social and infrastructure context) was standardized to have a value ranging from 0 to 1. This standardization was neces- sary to make each component equally important. The choice of making each vul- nerability component equally important was made for the sake of simplicity and also to following previous hazard researchers. This paper focuses on examining the impact of urban infrastructure in defining place vulnerability and therefore investigating comparative importance of vulnerability components is beyond the scope of the research. To examine significant differences between overall vulnerability based on the two-component approach to operationalizing the HPM and the overall vulnera- bility based on our (three-component) approach, the following steps were taken. First, the geophysical risk index and the social vulnerability index layers were combined to generate a composite vulnerability based on the two-component approach to operationalizing the HPM. Afterwards, the capacity of road net- works to facilitate evacuation data layer was combined with the social and the geophysical vulnerability layers to represent output from the three-component approach. The two composite overall vulnerability layers were further standard- ized to be on the same scale. Second, the vulnerability values based on the two approaches were extracted for each block group using the extraction function in ArcGIS 9.2. The top 5 per cent most vulnerable block groups based on the two approaches were then selected in ArcGIS 9.2. The two selection sets of block groups were combined to create a single map. The map shows differences between the outputs of two approaches in terms of identifying top 5 per cent most vulner- able block groups within Hillsborough County. The top 5 per cent most vulnera- ble block groups were chosen because emergency workers usually focus their attention on the most vulnerable areas within their community to make maximum use of their limited resources. Incorporating evacuation potential into place vulnerability analysis 203 Results Spatial distribution of geophysical vulnerability The map in figure 4 represents the spatial distribution of the overall geophysical vulnera- bility within the county and it is based on combination of hazards from hurricanes (storm surge and winds), floods from precipitation, transportation spill and toxic releases from industries. The areas with scores close to 4 experience the greatest vulnerability, while areas with scores close to 0 have the lowest geophysical vulnerability. From the map, it can be observed that geophysical risk level varies within the county. High geo- physical vulnerability can be found along the coast, the floodplains, the roads and rail- ways networks, and near the industrial facility locations. It can also be observed that the most vulnerable cities within the county include the City of Tampa, Apollo Beach, Gibsonton, Sun City, Palm River and the City of Ruskin. Less vulnerable areas can be found in the southeast part of the county with exception of the areas within floodplains and locations along the major roads. Figure 4. Geophysical vulnerability in Hillsborough County, Florida. 204 C. Yorke et al. Spatial distribution of social vulnerability Figure 5 shows the spatial distribution of the social vulnerability within the county. The map shows uneven distribution of social vulnerability within the county. For instance, the blockgroups around Wimuama and Sun City experience relatively high levels of social vulnerability. Similarly, block groups in the northern and the north- west parts of the county experience high levels of social vulnerability. The combina- tion of high social vulnerability and high geophysical risk implies high overall vulnerability. Infrastructure context (road network capacity to facilitate evacuation) Figure 6 shows evacuation difficulty distribution within the county. The thematic map units can be read as the worst-case number of people per intersection of a road network during an evacuation within Hillsborough County. From the map, the most Figure 5. Composite social vulnerability in Hillsborough County, Florida. Incorporating evacuation potential into place vulnerability analysis 205 Figure 6. Evacuation difficulty distribution in Hillsborough County, Florida. vulnerable cities include Ruskin, Gibsonton, Apollo Beach, Valrico, Sun City, Bloomingdale, Riverview, Town ‘n’ Country, Mango, Dover, Brandon, Carrollwood Village, Egypt Lake, West Park, Temple Terrace and Lutz. Patterns of overall vulnerability Figure 7 shows the spatial distribution of the overall vulnerability in the county based on the geophysical risk, social vulnerability and the infrastructure context. From the map, areas with moderate to high vulnerability levels can be found in the coastal zones, the northwestern, southwestern, the eastern and the central parts of the county. Also, the impacts of each of the three vulnerability compo- nents can be seen on the map. The footprints of infrastructure context are visi- ble at the northwestern area, the central and the eastern part of the county. The footprints of geophysical risk are visible along the transportation lines, coastal 206 C. Yorke et al. Figure 7. Distribution of overall vulnerability in Hillsborough County, Florida. areas and along the floodplain areas. Social vulnerability footprints are visible in large block groups. Spatial differences between the outputs of the two approaches Figure 8 shows both the output of the two-component (figure 8A) and the out- put of the three-component (figure 8B) operationalization of the HPM. It can be observed that a location’s vulnerability based on the two approaches can vary. For example, the central to eastern as well as the northwest parts of the two maps show significant differences. The vulnerabilities of those areas were underestimated based on the two-component approach. The higher vulnerability in the central to eastern as well as the northwest parts of the county based on the three-component approach is due to the fact that those locations experienced high vulnerability based on the infrastructure context. This observation is critical Incorporating evacuation potential into place vulnerability analysis 207 Figure 8. Comparsion of the two- and three-component modelling of HPM. for emergency planners because it highlights the importance of the infrastructure context in generating place vulnerability. Figure 9 shows the differences between the two approaches in identifying the top 5 per cent most vulnerable block groups within Hillsborough County. The grey areas are blockgroups that were identified as most vulnerable by both approaches. The areas that were identified as most vulnerable by the two-component approach but experienced low vulnerability based on the three-component approach are shaded yellow. In the yellow shaded area, the better transportation network and low evacua- tion difficulty reduced the overall vulnerability under the three-component approach. The red shaded areas experienced high vulnerability under the three-component approach but received low vulnerability values based on the two-component approach. In the red shaded area, high evacuation difficulty amplified the overall vul- nerability under the three-component approach. Discussions This study shows that vulnerability levels within Hillsborough County vary in terms of space. Assessment of the distribution of geophysical risk and social vulnerability within the county revealed that the distributions of vulnerability are not uniform. The uneven distribution of geophysical risk and social vulnerability observed within the county is consistent with results obtained by previous studies (Montz & Tobin 2003; Albury 2004; Chakraborty et al. 2005). It was observed that the infrastructure context (specifically the road network capacity to enable evacuation) together with 208 C. Yorke et al. Figure 9. Spatial differences between the two- and three-component modelling of HPM. the social vulnerability and geophysical risk can increase or decrease the overall vul- nerability of a place. For example, in the yellow shaded area of figure 9, a better transportation network and a low evacuation difficulty reduced the overall vulnera- bility of the place. On the other hand, in the red shaded area, high evacuation diffi- culty amplified the overall vulnerability of the place. These observations implied that place vulnerability can be reduced by building better urban infrastructures. Furthermore, evacuation difficulty based on the population distribution and the capacity of the road network was found to be unevenly distributed within the county. Some locations and cities experienced high evacuation difficulty which simply means the number of people needed to be evacuated at a given time interval exceeds the Incorporating evacuation potential into place vulnerability analysis 209 capacity of the road network. Church and Cova (2000) stated that problems exist whenever the number of people needed to be evacuated at a given time interval exceeds the capacity of the road network. Evacuation of large numbers of people safely during an environmental hazard when the capacities of road network is exceeded leads to problems of choke points in road networks and longer clearance times which can further increase people’s vulnerability (Church & Sexton 2002). This observation is consistent with Chakraborty et al. (2005) assertion that different groups of people can be more or less vulnerable depending on their accessibility to transportation routes or facilities during evacuation. The study also observed significant differences between the outputs of the three- component and the two-component approaches to the operationalization of the HPM. This observation has important implications. First, the results show how dif- ferent approaches to vulnerability analysis can lead to differences in conclusion. Dif- ferences in conclusion of the most vulnerable areas within a given community complicate mitigation measures. Second, differences in the results mean one of the models underestimated the vulnerability of some places. Underestimations of vulner- ability can lead to decisions that can have unintended consequences including hazard induced fatalities. As indicated earlier, the concept of vulnerability is multifaceted and requires consideration of all of its components. However, the focus of this paper is to show how the infrastructure context of vulnerability can be established. The focus of the paper is not on how one can obtain a complete picture of a community’s overall vulnerability. Therefore, determining the best model among the two models is beyond the scope of this paper. There are three issues that need to be addressed. First, population attributes are captured in the phase II (social vulnerability) and phase III (infrastructural vulnera- bility). There is a chance that these demographic attributes are duplicated in the final index of vulnerability. However, there is no way of verify this concern. Second, the capacity of road networks to facilitate evacuation is just one example of urban infra- structure that can affect place vulnerability. More urban infrastructure variables are needed to improve the accuracy of the three-component approach to the operational- ization of the HPM. The third and most important issue is how the two models can be compared. Unfortunately, there is no standard reference in the hazards and vul- nerability literature for assessing accuracy and differences between two hazard or vulnerability models. The lack of standard reference for model comparisons makes it difficult to quantitatively assess the theoretical contributions of new models. More research is needed in this area. As indicated earlier, the purpose of this research is to illustrate a three-component operationalization of the HPM by integrating urban infrastructure in describing place vulnerability. The focus is not on model compari- sons. Further research will focus on this area. Conclusion Vulnerability analysis is a critical requirement for hazard mitigations. The lack of integration of infrastructure context with the geophysical risk and social factors in community vulnerability analysis presented a gap in the vulnerability research. The three-component approach to the operationalization of the HPM in this paper contributes both theoretically and practically to the hazard research community. Theoretically, the approach advances our knowledge on how to model urban 210 C. Yorke et al. infrastructure to estimate the overall vulnerability of a place. The integration of the infrastructure context with geophysical and the social components improves our understanding of the concept of vulnerability. In a practical sense, the approach presents emergency planners with effective method of identifying vulnerable areas for disaster mitigation measures. References Albury KA. 2004. 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Journal
"Geomatics, Natural Hazards and Risk" – Taylor & Francis
Published: Apr 3, 2015