Berndt, Donald

Griffiths, Jamie

English

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Association for Information SystemsAIS Electronic Library (AISeL)AMCIS 2006 Proceedings Americas Conference on Information Systems(AMCIS)December 2006Identifying Florida's 'At-Risk' Populations forDisaster Management Purposes: A Case Study andResearch FrameworkJamie GriffithsUniversity of South FloridaDonald BerndtUniversity of South FloridaFollow this and additional works at: http://aisel.aisnet.org/amcis2006This material is brought to you by the Americas Conference on Information Systems (AMCIS) at AIS Electronic Library (AISeL). It has been acceptedfor inclusion in AMCIS 2006 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contactelibrary@aisnet.org.Recommended CitationGriffiths, Jamie and Berndt, Donald, "Identifying Florida's 'At-Risk' Populations for Disaster Management Purposes: A Case Study andResearch Framework" (2006). AMCIS 2006 Proceedings. 206.http://aisel.aisnet.org/amcis2006/206Griffiths & Berndt   Identifying Florida’s ‘At-Risk’ populations for DisasterProceedings of the Twelfth Americas Conference on Information Systems, Acapulco, Mexico August 04th-06th 2006Identifying Florida’s ‘At-Risk’ Populationsfor Disaster Management Purposes:A Case Study and Research FrameworkJamie Lynn GriffithsCollege of Business AdministrationUniversity of South FloridaJgriffit@coba.usf.eduDonald J. BerndtCollege of Business AdministrationUniversity of South Floridadberndt @coba.usf.eduABSTRACTThis purpose of this research is to present a framework for the development of a spatial decisions support system (SDSS),utilizing geographic information systems (GIS), to identify Florida’s at-risk populations, those that are likely to have specialrequirements for disaster management (DM) purposes.  A coastal county in Florida is utilized as a case study in reference tothe 2004 hurricane season.  Florida provides an ideal case study as it has a large elderly population and an increasing numberof snowbirds, elderly persons residing in Florida during the winter months leaving the state during the summer months.  Inaddition to hurricane track files, spatial and non-spatial data from a variety of sources, including the U.S. Census, the FloridaGeographic Data Library (FGDL), Florida Water Management Districts, County Health Departments, and theComprehensive Assessment for Tracking Community Health (CATCH) data warehouse, will be utilized for model buildingand validation.KEYWORDSGeographic Information Systems (GIS), Spatial Decision Support Systems (DSS), Emergency Planning (EP), DisasterManagement (DM).INTRODUCTIONThis purpose of this research is to present a framework for the development of spatial decisions support system (SDSS),utilizing geographic information systems (GIS), to identify Florida’s at-risk populations (Batty & Densham, 1994).  At-riskpopulations are those that are likely to have special requirements for disaster management (DM) purposes.  Kreps (1991)defines disaster and emergency management as the “discipline and profession of applying science, technology, planning andmanagement to deal with extreme events, that can injure or kill large numbers of people, cause extensive damage to property,and widespread disruption to society.”  Geographic information systems are “computer system(s) capable of capturing,storing, analyzing, and displaying geographically referenced information; that is, data identified according to location.Practitioners  also  define  a  GIS as  including the  procedures,  operating  personnel,  and spatial  data  that  go  into  the  system."(USGS 2004)Recent events associated with the 2004 hurricane season in Florida and Hurricane Katrina in 2005 have demonstrated theneed for identifying the location of at-risk populations for disaster management purposes.  A recent study of an emergencymanagement system, currently in use by a Florida County, which allows citizens to register as a person with special needs,reported that the time required to register and be accounted for in the system took between 6.5 and 18 days.  In addition tothis delay, lack of awareness of the system by citizens results in county officials not having systematic information regardingmedical needs or locations of at-risk populations resulting in officials being unable to accommodate their needs in areasonable manner in a disaster or emergency situation. (Hikmet & Kayhan 2006) Spatial decision support systems canprovide an effective tool for improving and enhancing an organization’s level of preparation for a disaster. (Cova, 1999)The authors propose a research framework that brings together spatial and non-spatial data from a variety of sources,including the U.S. Census, the Florida Geographic Data Library (FGDL), Florida Water Management Districts, CountyHealth Departments, and the Comprehensive Assessment for Tracking Community Health (CATCH) data warehouse formodel building and validation.  Over the past few years the authors have demonstrated an application of the CATCH data 1610Griffiths & Berndt   Identifying Florida’s ‘At-Risk’ populations for DisasterProceedings of the Twelfth Americas Conference on Information Systems, Acapulco, Mexico August 04th-06th 2006warehouse in another disaster management context, this being bioterrorism surveillance (Berndt et al, 2006a; Berndt et al,2006b; Griffiths et al, 2006).  This paper provides an overview of the CATCH Data Warehouse, discusses additional datasources, the 2004 hurricane season, presents the challenge of identifying At-Risk populations in Florida, overviews theemergency management process, then presents a framework and conclusions.THE CATCH DATA WAREHOUSEThe Comprehensive Assessment for Tracking Community Health (CATCH) data warehouse, located at the University ofSouth Florida, was initially developed in 1998.  It integrates fine-grained event data such as vital statistics (birth and deathrecords), hospital discharge data, and free-standing clinical data along with other detailed disease registries.  Theinfrastructure also includes major healthcare coding systems, such as the International Classification of Disease (ICD),Common Procedure Terminology (CPT) and Diagnostic Related Group (DRG) coding systems to support analysis at varyinglevels of detail.  In addition population data from the census, economic data and even commercial marketing data are addedto the mix (Tremblay, 2006).  This data can be aggregated at several spatial levels, such as ZIP code, community, and county.Data from the CATCH data warehouse should prove valuable for model building and validation purposes.The CATCH community assessment methodology identifies both a rich collection of quantitative indicators and a frameworkfor ranking healthcare problems to support policy formulation.  The CATCH methodology has been applied to more thantwo-dozen counties throughout Florida, as well as selected applications in other states, with great success.Additional DataAdditional data sources include the U.S. Census, the Florida Geographic Data Library (FGDL), Florida Water ManagementDistricts, and county governments.2004 HURRICANE SEASONIn 2004, the estimated cost of damage to the United States from hurricanes and tropical storms exceeded $42 billion. Thus,being the most costly hurricane season ever for the United States up to that point in time.  The 1992 hurricane season, theyear  Hurricane  Andrew impacted  Florida,  was  the  second most  costly,  at  $35 billion.   The  Atlantic  Basin  had 15 tropicalstorms and 9 hurricanes, including 6 major hurricanes resulting in a more active season than average.  An estimated one infive homes in Florida was damaged by hurricanes during August and September 2004, with 117 storm-related fatalities inFlorida during that period.  (NCDC 2004)As can be seen in Figure 1, Florida was directly affected by four hurricanes ranked from two to four on the Saffir-SimpsonHurricane Scale (Charley, Francis, Ivan, and Jeanne) between August 13th and September 25th, 2004, no other state has beenaffected by four hurricanes in one season since 1886 when Texas was directly affected by four hurricanes.  Hurricane tracks,wind speeds, and geographic extent of wind speeds are displayed in Figure 1.  As can be seen, Hurricane Charley madelandfall in South West Florida as a Category 4 Hurricane (131-155 mph winds) and maintained a Category 3 (110-130 mphwinds) status midway through the state.  Hurricane Charley was the strongest and costliest storm to hit the US sinceHurricane Andrew in 1992.  Charley caused approximately $14 billion in damage.Hurricanes and tropical storms are routine occurrences in the southeast part of the US each year.  Between 1991 and 2004, atotal of 23 hurricanes struck the mainland US (NHCa 2006), eight of which were classified as “major” having a scale fromthree to five according to the Saffir-Simpson Hurricane Scale (NHCb 2006). In case of impending storms, counties that are inthe path of hurricanes are responsible for evacuating thousands – sometimes millions – of citizens living in coastal, low-lying, or flood-prone areas and relocating them to temporary housing for several days or sometimes months at a time.  In2004, when three major hurricanes struck Florida, nine million Floridians were under evacuation recommendations at somepoint in time.  Up to 350,000 of these were staying in nearly 370 inland storm shelters (Florida Cert 2004).A UNIQUE CHALLENGEThe identification of at-risk populations is a particularly interesting and challenging problem in Florida for several reasons:(1) a large and rapidly growing population; (2)  a large and rapidly growing elderly population, (3) a large and rapidlygrowing snowbird population (elderly residents that live in Florida during the winter months and return to their home state 1611Griffiths & Berndt   Identifying Florida’s ‘At-Risk’ populations for DisasterProceedings of the Twelfth Americas Conference on Information Systems, Acapulco, Mexico August 04th-06th 2006during the summer months); (4) a large and rapidly growing nursing home population; (5) a seasonal tourist population; and(6) large numbers of mobile and modular homes.Hurricane CharleyAugust 13th, 2004Hurricane FrancisSeptember 4th-5th, 2004Hurricane IvanSeptember 16th, 2004Hurricane JeanneSeptember 25th, 2004Figure 1.  Florida’s 2004 Hurricane Season’s Major Hurricanes – Charley, Francis, Ivan andJeanneFlorida’s population increased by more than 700% from the years 1950 to 2000.  Collier county Florida was the secondfastest growing Metropolitan Statistical Areas (MSA) in the United States between 1990 and 2000.  Florida is projected toovertake Texas as the third most populous state by the year 2011 and ranks above all other states in percentage of elderly.Florida is expected to remain the “oldest state” with more than 26% of it population projected to be over 65 by the year 2025.(Cambell, 1997)    Florida’s population growth isn’t evenly distributed geographically with much of the growth being incoastal areas.  Figure 2 displays the percentage of people over 50 years of age in Florida counties in the year 2000 whichranges from 21% to 55%.  This is actually an underestimate since the US Census only counts residents of Florida and notsnowbirds, those choosing to live in Florida for the winter months, while maintaining a residence in another state.An additional challenge is that the US Census population counts can be a slightly misleading measure in a state where largenumbers of tourists, sunbirds, and snowbirds temporarily reside in both the summer and winter months.  For example,Monroe  County,  the  county  that  encompasses  the  southwestern  tip  of  Florida  and the  Florida  Keys  had an  increase  of  3.5housing units for each person who became a resident of the county between 1990 and 2000.  In addition, three counties innorth Florida had housing unit to population growth rates approaching one house per person during the same time period.This reveals that population as a measure needs to be used cautiously.  Snowbirds and vacationers are building homes but notbecoming permanent residents of the State of Florida.  Seasonal migration of the elderly becomes a particularly interestingproblem when using population alone as a measure of risk.  Smith and House (2005) found that “most migration statisticsmiss temporary moves such as daily commute to work, short business trips, vacations, and seasonal migration and thesemigrations have substantial impact on the resident populations of both sending and receiving communities.”  Again, this is 1612Griffiths & Berndt   Identifying Florida’s ‘At-Risk’ populations for DisasterProceedings of the Twelfth Americas Conference on Information Systems, Acapulco, Mexico August 04th-06th 2006where utilizing a healthcare data warehouse and knowing the locations of citizens who have recently been hospitalized forcertain diagnosis codes has the potential to increase the validity of any model developed quite significantly.Figure 3:  Percentage of the Population over the Age of 50 in the Year 2000DISASTER AND EMERGENCY MANAGEMENTKreps (1991) defines disaster and emergency management as the “discipline and profession of applying science, technology,planning and management to deal with extreme events, that can injure or kill large numbers of people, cause extensivedamage to property, and widespread disruption to society.”  There are four components that comprise the disaster andemergency management process: (1) mitigation; (2) preparedness; (3) response; and (4) recovery.  As can be seen in Figure 6,geographic information systems (GIS) have the potential to play key roles in each of the four components.In case of impending storms, county governments are responsible for evacuating thousands, and potentially millions, ofcitizens living in coastal, low-lying or flood-prone areas and relocating them to temporary housing for several days or evenmonths at a time. In 2004, nearly nine million Floridians were under evacuation recommendations at some point during theyear.  Approximately 350,000 citizens were staying in nearly 370 inland storm shelters (Florida Cert 2004).  In addition theyhave a role in each of the emergency management processes displayed in Figure 6.Adapted from Cova (1999) & Christian (2004)Figure 4:  Emergency and Disaster Management Process, with examples where GIShas the potential to play a key role 1613Griffiths & Berndt   Identifying Florida’s ‘At-Risk’ populations for DisasterProceedings of the Twelfth Americas Conference on Information Systems, Acapulco, Mexico August 04th-06th 2006FRAMEWORK & MODEL DEVELOPMENTThe model developed will be a significant modification of the Florida Department of Elderly Affairs (FDOEA) “Elder NeedsIndex”.  (FDOEA, 2004) The “Elder Needs Index” ranks geographical areas according to the characteristics of the residentelder population.  The elements of the index include variables representing age, physical, mental, social and economic status.A general framework that will be utilized for model development purposes is displayed in Figure 3.Figure 5:  Framework for Identifying At-Risk PopulationsCONCLUSIONThe development of a model to identify at-risk populations in a state with an increasing percentage of elderly has thepotential to save lives, reduce costs associated with disaster management, improve emergency response and provide vitalinformation for emergency planning purposes.  The 2004 hurricane season, Hurricane Katrina in 2005 and associated eventsin New Orleans have amplified the importance for the development of a spatial decisions support system (DSS) to aid in theidentification of at-risk populations for disaster management purposes.  This research is presented as a work in progress.Further development is currently underway.REFERENCES1. Batty, M., and Densham, P (1996) Decision Support, GIS, and Urban Planning Center for Advanced SpatialAnalysis, London, U.K.2. Berndt, D., Fisher, J, Griffiths J., Hevner, Luther, S. and Studnicki (2006a) Data Warehousing for BioterrorismSurvellance, Decision Support Systems forthcomming.3. Berndt, D.J., Hevner A.R., Griffiths, J.L. (2006b) Managing Real-Time Bioterrorism Surveillance Data.  Chapter inNational Security, Edited by H. Chen, T.S. Raghu, R. Ramesh, A. Vinze and D. Zheng, Handbooks in InformationSystems Series, Elsevier, forthcoming.4. Cova, T.J. (1999) GIS in Emergency Management. In: Longley, P.A., Goodchild, M.F., Maguire, D.J.,5. Cambell, Paul (1997) Current Population Reports, Population Projections: States 1995-2025, Washington D.C.,Population Division of the United States Bureau of the Census. 1614Griffiths & Berndt   Identifying Florida’s ‘At-Risk’ populations for DisasterProceedings of the Twelfth Americas Conference on Information Systems, Acapulco, Mexico August 04th-06th 20066. Florida Cert – Makes a Differnce in the 2004 Hurricane Season, 2004<http://www.floridadisaster.org/director_office/citizen_corps/HurricaneActivations2004.pdf >7. Florida Department of Elder Affairs, FDEA (2004). Assessing the needs of elder Floridians, 2003. Tallahassee,Florida: Department of Elder Affairs.8. Griffiths, J.L., Berndt, D.J., and Hevner, A.R.  (2006) Naturally Occuring Incidents as Facsimiles for BiochemicalTerrorist Attacks.  Proceedings of the Fourth Symposium on Intellegence and Security Informatics (ISI 2006),Lecture Notes in Computer Science, LNCS, Springer-Verlag forthcoming, San Diego, CA, May 2006.9. Hikmet N. and Kayhan, O.  An Implementation of Business Process Reengineering:  Improved Healthcare PlanningDuring Emergency Relief Operations.  Northeast Decision Sciences Institute Conference, San Juan, PR, March2006.10. National Climatic Data Center, NCDC (2004) Climate of 2004 Atlantic Hurricane Season, 2004. Asheville, NorthCarolina, National Climatic Data Center11. National Hurricane Center (2006a) U.S. Mainland Hurricane Striks by State, 1851-2004 <www.nhc.noall.gov/paststate.shtml  >12. National Hurricane Center  (2006b).  Saffir-Simpson Index, 1851-2004 < www.nhc.noall.gov/paststate.shtml  >13. Smith, S.K., House, M. (2005) Snowbirds, Sunbirds, and Stayers: Seasonal Migration of Elderly in Florida, AnnualMeeting Population Association of America March 2005 Philidelphia, PA 1615

Identifying Florida\u27s \u27At-Risk\u27 Populations for Disaster Management Purposes: A Case Study and Research Framework

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Identifying Florida\u27s \u27At-Risk\u27 Populations for Disaster Management Purposes: A Case Study and Research Framework

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