USF Libraries
USF Digital Collections

The application of a modified human development index

MISSING IMAGE

Material Information

Title:
The application of a modified human development index spatial modeling of socioeconomic well-being for Florida counties
Physical Description:
Book
Language:
English
Creator:
Kelsey, Clay
Publisher:
University of South Florida
Place of Publication:
Tampa, Fla
Publication Date:

Subjects

Subjects / Keywords:
Social indicator
Territorial indicator
Composite index
HDI
Choropleth
Dissertations, Academic -- Geography -- Masters -- USF
Genre:
bibliography   ( marcgt )
theses   ( marcgt )
non-fiction   ( marcgt )

Notes

Abstract:
ABSTRACT: The Application of a Modified Human Development Index: Spatial Modeling of Socioeconomic Well-being for Florida CountiesThis thesis uses the United Nations Human Development Index as a model for comparing a selected set of socioeconomic indicators across Florida's sixty-seven counties. Whether for urban planning, hazards mitigation, transportation forecasting, or other county-level and state-level functions, information and understanding of socioeconomic conditions are keys to efficient planning and policy making, both in the early development stages as well as during implementation. A summary overview of socioeconomic well-being and its distribution across a given area offers a distinct advantage in terms of deciding where planning or policy changes are most needed and where they will prove most beneficial.This thesis takes a well-established and well documented index used for examining and comparing human development in nations across the globe, and modifies it for comparing county-level socioeconomic conditions across Florida. The results from this modified index are then displayed using choropleth maps as an aid to location interpretation of the ranked socioeconomic values, thereby providing a spatial context for the indexing.In the end, this thesis seeks to answer whether or not the modified index model is a suitable one for normalizing, aggregating, and ranking county-level socioeconomic data for Florida, and whether the use of choropleth mapping to display the rankings is a viable choice.
Thesis:
Thesis (M.A.)--University of South Florida, 2006.
Bibliography:
Includes bibliographical references.
System Details:
System requirements: World Wide Web browser and PDF reader.
System Details:
Mode of access: World Wide Web.
Statement of Responsibility:
by Clay Kelsey.
General Note:
Title from PDF of title page.
General Note:
Document formatted into pages; contains 110 pages.

Record Information

Source Institution:
University of South Florida Library
Holding Location:
University of South Florida
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
aleph - 001790595
oclc - 144807556
usfldc doi - E14-SFE0001512
usfldc handle - e14.1512
System ID:
SFS0025830:00001


This item is only available as the following downloads:


Full Text

PAGE 1

The Application of a Modified Human De velopment Index: Spatial Modeling of Socioeconomic Well-being for Florida Counties by Clay Kelsey A thesis submitted in partial fulfillment of the requirements for the degree of Master of Arts Department of Geography College of Arts and Sciences University of South Florida Major Professor: Graham A. Tobin, Ph.D. Robert Brinkmann, Ph.D. Jayajit Chakraborty, Ph.D. Date of Approval: April 06, 2006 Keywords: social indicator, territorial indicator, compos ite index, HDI, choropleth. Copyright 2006, Clay Kelsey

PAGE 2

Acknowledgments This thesis could not have been comple ted without the guidan ce and support of a number of people. I would like to acknowle dge and thank the members of my thesis committee: Dr. Graham A. Tobi n who not only served as my thesis supervisor but also provided guidance and encouragement in the de velopment of this project. I consider myself fortunate to have such a mentor. I want to also acknowledge the support, encouragement, and suggestions I received from Dr. Robert Brinkmann and Dr. Jayajit Chakraborty – your kindness and he lp was very much appreciated. I would like to thank my fellow graduate st udents, close friends all, for their help and hints. In particular, I w ould like to thank Heather for providing a shining light across the dark and turbulent waters of academia, and of course Sarah and Craig who helped keep my boat upright and my oars in the water. I am particularly thankful to my chie f editor who has read more versions and revisions of this thesis than any mortal s hould have to endure, helping me over the rough spots, and supporting me throughout: to Aydelette, from your husband.

PAGE 3

ii TABLE OF CONTENTS List of Tables................................................................................................................. ....iv List of Figures................................................................................................................ ...vi Abstract....................................................................................................................... ....viii Chapter One: Introduction...................................................................................................1 A Note on Idioms Used.............................................................................................5 A Brief Overview of Florida Counties.......................................................................7 Research Aims........................................................................................................ ...9 Chapter Two: Foundation Literature.................................................................................11 The Human Development Index.............................................................................12 Thematic Mapping..................................................................................................23 Chapter Three: Research Methods....................................................................................32 The Florida County Human Development Index Equation.....................................32 FCHDI Calculation..................................................................................................38 Mapping the FCHDI................................................................................................44 Alternative variable: the natural amenities indicator...............................................50 Chapter Four: Results........................................................................................................53 Mortality Interim Index...........................................................................................55 Mortality Rate............................................................................................. ...55

PAGE 4

iii Child Mortality Rate......................................................................................5 7 Heart Disease.............................................................................................. ...59 Malignant Neoplasm.....................................................................................61 Combined Heart Disease and Malignant Neoplasm......................................63 Mortality Interim Index.................................................................................65 Education Interim Index..........................................................................................67 Non-High School Graduate...........................................................................67 Educati on Attainment: High School Graduation and Higher........................69 Educa tion Attainment: Bachelo r’s Degree or Higher...................................71 Education Interim Index................................................................................73 Economic Interim Index..........................................................................................75 Poverty.................................................................................................... .......75 Per Capita Income.........................................................................................7 7 Price Level Index.......................................................................................... .79 Mortality Interim Index.................................................................................81 The Florida County Human Development Index (FCHDI)....................................83 The FCHDI plus Natural Amenities........................................................................90 Chapter Five: Summary and Conclusions.........................................................................99 References..................................................................................................................... ..105 Appendices Appendix A: FCHDI Data Tables........................................................................A-1 Appendix B: Florida County Locator Maps..........................................................B-1

PAGE 5

iv LIST OF TABLES Table 2-1 Maximum – minimum values used for the 2004 HDR............................16 Table 2-2 Increase of indicators relativ e to level of human development................18 Table 3-1 Dimension indicators in the UNDP HDI and proxy indicators in the modified HDI models for West Virginia, Alabama, and Florida.......33 Table 4-1 Extremes in rank chan ges after combining he art disease and cancer........63 Table 4-2 Calculating the Flor ida County Human Development Index.........83 and 84 Table 4-3 Calculating th e FCHDI and Natural Amenity Values...............................93 Table 4-4 Overall Change in Rank between the FCHDI Model and the FCHDINA Model.............................................................................96 Table A-1 Florida Resident Mortality Rate and Indicator Value............................A-2 Table A-2 Florida Child Mort ality Rate and Indicator Values...............................A-3 Table A-3 Florida Heart Disease and Indicator Values...........................................A-4 Table A-4 Florida Malignant Neoplasm and Indicator Values...............................A-5 Table A-5 Combined Florida Hear t Disease and Cancer Indicator Values.............A-6 Table A-6 Florid a Non-High School Graduate.......................................................A-7 Table A-7 Education Atta inment High School and Higher..................................A-8 Table A-8 Education Atta inment Bachelors and Higher......................................A-9 Table A-9 Florida P overty and Indicator Values..................................................A-10 Table A-10 Florida Per Capita Income and Indicator Values.................................A-11 Table A-11 Florida Price Le vel Index and Indicator Values..................................A-12

PAGE 6

v Table A-12 Mortalit y Interim Index – Alachua C ounty to Lake County................A-13 Table A-12 Mortalit y Interim Index – Lee County to Washington County............A-14 Table A-13 Education Interim Inde x – Alachua County to Lake County...............A-15 Table A-13 Education Interim Inde x – Lee County to Washington County...........A-16 Table A-14 Economic Interim Inde x – Alachua County to Lake County...............A-17 Table A-14 Economic Interim Inde x – Lee County to Washington County...........A-18 Table A-15 Florida Count y Human Development Index........................................A-19 Table A-15 Florida County Huma n Development Index (Continued)....................A-20 Table A-16 Florida Counties Ranked by FCHDI ...................................................A-21 Table A-17 Test Variable Natura l Amenities Scale and Indicator Values...........A-22 Table A-18 FCHDI + Natural Amenities Indicator...............................................A-23 Table A-19 Change in Ranking FCHDI + Natural Amenity Indicator...............A-24

PAGE 7

vi LIST OF FIGURES Figure 1-1 Locator map of Monroe County..................................................................9 Figure 2-1 The Human Devel opment Index as used by the United Nations Development Programme, 2004........................................................13 Figure 3-1 Conceptual model for calculating the FCHDI..........................................37 Figure 3-2 Example of a zscore choropleth map.......................................................43 Figure 3-3 FCHDI color code.....................................................................................47 Figure 3-4 Example of frequency hist ogram and box-and-whisker diagram.............49 Figure 4-1 Florida Resident Mort ality Rate Indicator Values....................................56 Figure 4-2 Florida Child Mortal ity Rate Indicator Values.........................................58 Figure 4-3 Florida Heart Di sease Indicator Values....................................................60 Figure 4-4 Florida Malignant Neoplasm Indicator Values.........................................62 Figure 4-5 Combined Heart Disease and Cancer Indicator Values............................64 Figure 4-6 Box-and-whisker diagrams for the Mortality Interim Index.....................65 Figure 4-7 Florida County Mortality Interim Index...................................................66 Figure 4-8 Florida Non-High School Graduate Indicator Values...............................68 Figure 4-9 Education Attainment: Hi gh School Graduate or Higher.........................70 Figure 4-10 Education Attainmen t: Bachelors Degree or Higher................................72 Figure 4-11 Box-and-whisker diagrams for the Education Interim Index....................73 Figure 4-12 Florida County Education Interim Index..................................................74

PAGE 8

vii Figure 4-13 Florida Poverty Indicator Values..............................................................76 Figure 4-14 Florida Per Capita Income Indicator Values.............................................78 Figure 4-15 Florida Price Leve l Index Indicator Values..............................................80 Figure 4-16 Box-and-whisker diagrams for the Economic Interim Index....................81 Figure 4-17 Florida County Economic Interim Index..................................................82 Figure 4-18 Florida County Human Development Index.............................................85 Figure 4-19 FCHDI by Quartile....................................................................................87 Figure 4-20 Comparing th e FCHDI to MSA counties..................................................88 Figure 4-21 Florida Natura l Amenities Indicator Values.............................................91 Figure 4-22 Comparing the FCHDI to FCHDI plus Natural Amenities.......................94 Figure 4-23 FCHDI plus Natural Amenities................................................................95 Figure 5-1 Florida County Hu man Development Index...........................................102

PAGE 9

viii The Application of a Modified Human De velopment Index: Spatial Modeling of Socioeconomic Well-being for Florida Counties Clay Kelsey Abstract This thesis uses the United Nations Hu man Development Index as a model for comparing a selected set of socioeconomi c indicators across Florida’s sixty-seven counties. Whether for urban planning, hazar ds mitigation, transportation forecasting, or other county-level and st ate-level functions, inform ation and understanding of socioeconomic conditions are keys to effi cient planning and policy making, both in the early development stages as well as dur ing implementation. A summary overview of socioeconomic well-being and its distributi on across a given area offers a distinct advantage in terms of deciding where planni ng or policy changes are most needed and where they will prove most beneficial. This thesis takes a well-establishe d and well documented index used for examining and comparing human development in nations across the globe, and modifies it for comparing county-level socioeconomic c onditions across Florida. The results from this modified index are then displayed us ing choropleth maps as an aid to location interpretation of the ranked socioeconomic values, thereby providing a spatial context for the indexing. In the end, this thesis seeks to answer wh ether or not the modified index model is a suitable one for normalizing, aggregating, a nd ranking county-level socioeconomic data for Florida, and whether the use of choropleth mapping to display the rankings is a viable choice.

PAGE 10

1 Chapter One: Introduction Using statistics to gauge so cial conditions in the United States dates to the early nineteenth century, when, for example, th e temperance movement of the 1830s used statistical data collected fr om poorhouses and jails as evid ence of the le vel of moral depravity, poverty, and economic wastefulne ss caused by consumption of alcohol (Cobb and Rixford, 1998). Through the early years of the twentieth century statistics were frequently used to investigate social issu es. Since poverty was perceived as the most prevalent social ill, and economic progress was seen as the best solution to poverty, economic indicators became the dominant measurement, until ultimately they were equated with social well-being. Then in the 1960s, indicators such as education, health, and racial inequity gained in importance in social studies, and the social indicators movement emerged with a holistic perspect ive of social well-being. This movement advocated that measurements of social well -being must include a combination of social and economic indicators rather than focusi ng solely on economics as in preceding decades. In 1973, David M. Smith developed the idea of territorial social indicators as the geographic representation of social well-being, bringing the spatial element of geography into the realm of the social indicators movement. In introducing the concept of territorial social indicators, Smith argues that it is not only impor tant to discern what the social

PAGE 11

2 conditions are, but also how these conditions are distributed across a given area, and how these conditions are spatia lly related (Smith, 1973). “Alternatively describe d as ‘social accounti ng,’ ‘social reporting,’ or ‘monitoring social change,’ the development of social indicators involves the measurement of social conditions as they vary in time and space. A basic proposition of [t he social indicator movement] is that we should be as well in formed about the nature and performance of the social system as we are about the economic system.” (Smith, 1973, p. 52) In the years since the pub lication of Smith’s work, tw o important tools closely tied to the geographic represen tation of socioeconomic condi tions have been developed. The first of these tools, spawned by modern computer technology in the 1970s and 1980s, is a set of ever evolving, ever improving ge ographic information system (GIS) programs which link the power of computerized graphi cs with massive data bases to produce a wide array of thematic maps. The second tool is the Human Development Index (HDI). In 1990 the United Nations published its fi rst annual Human Development Report, a cross-national comparative survey of soci al and economic conditions for 130 countries. In order to provide a holistic measurem ent of human development for ranking each country in the report, the United Nati ons Human Development Programme (UNDP) created the HDI, a composite index that comb ines both economic and social indicators. This model is referred to by Sharpe and Smith (2005) as the ‘gold standard’ for composite indicators: First, the HDI is by far the best -known composite indicator in the world, reflecting the fact it has been around since 1990 and that it is produced by a high-profile UN agency. Second, the HDI uses a simple framework for identifying what constitutes human development, namely income, health and education, which is intu itive and easy to understand.

PAGE 12

3 Third, despite the apparent simplicity, there is much technical sophistication be hind the HDI. Nobel Prize winning economist A.K. Sen c ontributed significantly to the conceptual development of the index. (Sharpe and Smith, 2005, p. 58) Geographer’s find the HDI model highly adaptable and therefore useful for studying comparative socioeconomic conditions at the global scale, or at the scale of smaller spatial units such as the state, county (Hanham, Burhanu, and Loveridge, 2002; Bukenya and Fraser, 2002), and city level (Agostini and Richardson, 1997). This thesis uses the basic precepts of the HDI to create a modified index for measuring a selected set of social conditions in Florida at the county level, then uses choropleth mapping to spatially s ituate the results. The model developed in this thesis is the Florida County Human Development Index (F CHDI). The construction of a modified index such as the FCHDI is s upported by existing literature. Criteria and Conceptual Boundaries Two criteria shape the FCHDI. First, the model must use secondary source data from readily accessible Federal or Florida State agencies such as the United States Census Bureau and Florida Department of H ealth, and these data need not be processed through formulae more rigorous or complex th an those used in the United Nations HDI model. Second, the model must be straightforw ard enough that it is ea sily replicated for any State, Province, or other territorial di vision where sufficient da ta as described in criterion one exist. This stipul ation represents the expressed h ope that the effectiveness of the FCHDI will encourage wider use of ge ographic socioeconomic index modeling, and the FCHDI will provide an accurate and practic al benchmark of the basic socioeconomic

PAGE 13

4 conditions of Florida’s counties, allowing th e counties to be ranke d according to their overall socioeconomic well-being. Linking back to the overall goal of geogra phic representation, this benchmark of socioeconomic well-being is plotted using choropleth mapping, bringing to light the spatial patterns and relationships of the ranke d counties. This thesis then considers how an alternative variable re presenting additional social, economic, or environmental attributes might change the FCHDI ranking of Florida counti es. The alternative attribute used in this latter section of the study is selected base d on its implicit relationship to socioeconomic conditions in Florida: natural amenities. It is vital at this point to discuss two sets of concep tual boundaries within which this thesis operates. First and foremost, this thesis attempts to synthesize socioeconomic data, statistics, index modeling, and presenta tion of results, all from a predominantly geographic viewpoint. It is th e spatial relationships rather than the cause and effect elements of socioeconomic well-being that are of primary interest here. The second conceptual boundary is the choice of scale, a topic which will be discussed in greater detail in Chapter Three Research Methods. At this point, however, it is important in discussing the goals of the study to introduce the choice of a county-level scale for the FCHDI. The aggregation of socioeconomic data, beginning at the individual level and moving progressively higher to th e neighborhood level, city level, county level, region of state and beyond tend to increasingly gene ralize socioeconomic conditions and mask extremes that influence the well-being of the individual. Therefore, it is important at the outset to clarify that the crea tion and use of the FCHDI in this thesis is to study spatial patterns and relationships of Florida’s socioeconomic conditions at the county level

PAGE 14

5 strictly from a geographer’s perspective of te rritorial units rather than the more focused scale of a sociologist, pla nner or policy-maker, although it is hoped that the index will encourage further research from va rious perspectives and scales. A Note on Idioms Used “There is an obvious need for clarifying the generic tools and terminology of the social sciences across the disciplines, as academics argue past each other, using identic al terms but attaching different meanings to them .” (Grix, 2002, p.175) In a number of scientific disciplines precise and accurat e terminology is a straightforward feature of the field, however, in an interdisciplinary social science such as human geography, great verbal battles are ofte n waged over the definition of frequentlyused yet wooly terms such as region, community rural and development The use of these widely understood yet dive rsely interpreted terms set th e tone of the research and often suggest biases not intended by the research er. Therefore, it is im portant at the onset to describe and, if not fully define, at leas t acknowledge the ambiguity of certain terms used in this thesis. Without question the most vexing problem encountered in this project is the naming of what is being measured by the index model. Essentially, the FCHDI is measuring statistical socioeconomic elements of Florida’s resident population per year 2000 data in order to estimate metaphorical socioeconomic living conditions of the populace at the county level during the 2000 census time frame. It is frustrating to use highly relative terms such as ‘social well-being’ or ‘quality of life’ to describe these

PAGE 15

6 conditions and it is therefore tempting to de lve into neologism. However, no matter how tempting it may be, creating a ‘metaphoric so cioeconomic living condition index’ is like waving a red cape before the bull of incredibility. The idiom ‘social well-being’ as used by Sm ith and others in th e social indicator movement is advocacy-oriented and closely asso ciated with social justice and the fair distribution of economic and social resources (Smith, 1973; Andrews and Withey, 1976; NRC, 2002). The essence of ‘well-being’ clearly describes a positive condition, and in application, well-being as a descriptive term is more subdued than the highly subjective ‘quality of life,’ for arguably, what passes as ‘quality’ to an i ndividual often becomes inconsequential in the larger context of society. Although th is thesis uses the term ‘socioeconomic well-being’ for the FCHDI’s m easurement, the intent is to retain the positive aspect of ‘well-being’ without the advocacy-orientation or subjectivity of ‘quality.’ In human geography, the term ‘development ’ generally refers to either social, economic, or land-use (e.g. rural to urban) transformations. Defining development is problematic on at least two fronts: cultural perspective and globaliz ing redefinition. From cultural perspective, developm ent by western value systems does not align precisely with eastern values, nor do development priorities of agrarian, industrial, or service-based social sectors match. As Straussfogel (1997) notes, development is a relative concept, and with the rapid changes and interactions brought on by globaliz ation, we periodically need to reexamine and adjust our definitions of development and progress. Development is usually positively associated with growth (with the possible exception of suburban and rural sprawl development), and is therefor e important to planning and policy-making.

PAGE 16

7 For this reason, ‘development’ is measur ed and monitored by planning and policy makers, and index modeling is commonly used as a comparative tool due to its rankingscale feature. The HDI was created for just this purpose: it m easures and compares human (socioeconomic) development at a gl obal scale as both a means to highlight socioeconomic disparities and to induce s ound development planning and policies over time, monitoring national rankings as they move up or down the HDI scale. However, in this thesis the use of ‘developm ent’ in the Florida County Human Development Index refers to the UNDP model the index was patte rned after, and, unless otherwise specified in the text, does not refer to either hu man or land-use development in Florida. There are undoubtedly additional ambiguous trigger words used in this thesis beyond socioeconomic well-being and development however, every attempt has been made to define these terms ‘in-text’ in order to make the thesis as transparent as possible. A Brief Overview of Florida Counties1 There are 67 counties in Florida, ranging in ar ea from Union County (249.71 square miles) in the north, to Monroe Count y (3,737.15 square miles) on the southern tip of the peninsula. Florida’s Monroe County t ypifies an anomaly not found in land-locked states, that is, since Monroe County incorporat es the Florida Keys, the total area includes 2,740.24 square miles of water and tidal coastline. Therefore, solely in land area, Monroe County is 996.91 square miles in size. The largest county in land area is Collier County (2,025.30 square miles) just to the north of Monroe County. 1. County locator maps are found in the appendices.

PAGE 17

8 One of the most prominent features of Florida is its long coastline, running 1,197 statute miles (Fernald and Pu rdum, 1996) from the Georgia state line on the Atlantic Coast, around peninsular Florida to the Alab ama state line on the Gu lf of Mexico. Taking the numerous islands, bays, and inlets into consideration, the Florida Coastal Management Program estimates Florida has approximately 8,400 miles of tidal coastal zone (FCMP, 2005). In their study for the Un iversity of Florida’s Electronic Data Information Source, Adams et al. (2001) repor t that, with the excep tion of a small region of Columbia County’s Pinhook Swamp, no point in Florida is more th an 60 miles from either the Atlantic Coast or the Gulf of Me xico. Of Florida’s 67 c ounties, just over onehalf (35) are situated either along the Atlantic Coast or th e Gulf of Mexico and 32 are non-coastal. In terms of total population according to Census (2003) data, Miami-Dade County ranks the highest with 2,253,362 people while Li berty County in the panhandle ranks the lowest with a population of 7,021. Population density figures for the 67 counties range from 8.4 persons per square mile, again in Liberty County, to 3,292.0 persons per square mile in Tampa Bay’s Pinellas County. The averaged population density for Florida is 296.4 persons per square mile. In broad, gene ralized terms, the Florida panhandle and northern counties tend to be more rural in ch aracter, while the southern counties of the peninsula, particularly along the coasts tend to be more urbanized. However, the distribution of population can be quite misleading in Florida. For example, according to the 2000 census, the population for Monroe C ounty at Florida’s southern tip shown in Figure 11 is 79,589. The large mainland porti on of the county has a total population of 60 persons, while the string of keys has the remainder population of 79,529.

PAGE 18

9 Figure 1-1: Monroe County: A population distribution anomaly. According to 2000 census data, the mainland has a population of 60, while the Florida Keys area of the county has a population of 79,529. Research Aims From a socioeconomic standpoint, Florida is not a homogenous State. There are heavily urbanized areas and predominantly rural sections; areas where the economy is based on agriculture, and areas where it is base d on recreation; and there are areas of the state that have a high percentage of retired and seasonal residents. To the casual observer, it is sufficient that these c onditions are spatial generaliza tions, however, for planning or policy making, a clearer delineation of socio economic well-being is needed. There are currently several useful econom ic indices available, but no t so for a composite measure

PAGE 19

10 of economic and social conditions. This thesis is, primarily, a project using descriptive statistics which attempts to answer the following: 1. Can a modified model of the HDI be effectively applied to measure socioeconomic well-being across a contiguous territorial unit such as the State of Florida at the county-level? And, 2. Is the geographic representation of th e model’s rankings advantageous in discerning territorial patterns relationships, and trends? Should this thesis satisfactorily answer th ese questions, the significance of the work then becomes useful in the realm of pla nning, mitigation, and advocacy. Having a means to model socioeconomic well-being at the coun ty level is of intere st to several groups, including planners, policy makers, public mana gers, social activists, and politicians.

PAGE 20

11 Chapter Two: Foundation Literature The literature reviewed for this thesis is sorted into two broad categories regarding first, the Human Development Inde x (the model used to normalize and rank the socioeconomic units); and second, thematic mapping (the means of presenting the results). The first section of the review details the components and formulae of the United Nations’ Human Development Index (HDI), how the model works, concerns and critiques of the model, how it has been modi fied for use in four recent socioeconomic studies similar to this thesis, and why a modified HDI model is appropriate for the FCHDI. The second section of this chap ter considers design elements of data visualization through thematic mapping. Five of the more familiar thematic maps for socioeconomic studies are discussed: dot-d istribution maps; proportional symbol maps; data maps; cartograms; and choropleth ma ps with emphasis on choropleth mapping and why it is deemed a good fit for this thesis Bearing in mind th at the index model proposed in this thesis begins as a retrofitting of the or iginal HDI, using the existing literature as a foundation and gui deline is a logical first step in the development of the FCHDI in Chapter Three. Likewise, a backgr ound for the choices of data mapping format and design elements helps clarify the choices made in chapters Three (research methods) and Four (presenting the index results).

PAGE 21

12 The Human Development Index “ As the 1990 Human Development Report argued, a basic distinction needs to be made between the means and the ends of development. Human bei ngs are the real end of all activities, and development must be centered on enhancing their achievements, freedoms, and capabilities.” (Anand and Sen, 1994, p.1) The Human Development Index (HDI) is a composite socioeconomic model used by the United Nations Development Programme (UNDP) to rank the countries listed in the annual Human Development Report. Th e HDI was originally designed as an alternative means of measuri ng a country’s development ba sed on composite social and economic conditions rather than on solely economic indicators such as the GNP (ul Haq, 2003; Estrada, 2005). In this respect, the HDI is essentially “des igned to measure the relative attainments of nations more subtly than the annual ranking by GNP per head that the World Bank provides” (People Doing Better, The Economist May 25, 1991, p. 48: In Agostini and Richardson, 1997, p. 19). The intent of the HDI is to provide a multidimensional view of development by measur ing people’s ability “to live a long and healthy life, to be educated, and to have access to the resources needed for a decent standard of living” (UNDP, 1990, p.10, Box 1.1) According to the 2004 FAQ’s page, an additional intent of the HDI is “to capture the attention of policy makers, media, and NGO’s and to draw their attention away from the more usual economic statistics to focus instead on human outcomes” (UNDP, 2004b). The HDI is a combined measurement of th ree key elements: health and longevity (mortality); knowledge (literacy); and a decent standard of living based on income and purchasing power (ul Haq, 2003).

PAGE 22

13 According to the Human Development Report 2004 – Technical Notes (UNDP, 2004a), the HDI is a straightforward m odel composed of the three dimensions mentioned above (long life, knowledge, and a decent st andard of living), four indicators (life expectancy at birth; adult li teracy; gross school enrollment ratio; and GDP per capita) and three dimension or interim indices (see Figure 2-1). DIMENSION A long and A decent standard Healthy life Knowledge of living INDICATOR Life expectancy Gross enrollment GDP per capita at birth Adult literacy ratio (GER) (PPP US$) Adult Literacy Index GER Index DIMENSION Life Expectancy Education Index GDP Index (INTERIM) Index INDEX Human Development Index (HDI) Figure 2-1: The human development index model as used by the United Nations Development Programme, 2004. : Adapted from UNDP HDR 2004 Before the HDI can be calculated, the raw i ndicator data must first be normalized to facilitate computation, a nd then converted into an interim index format. The first interim index is life expectancy, which is based on the ‘expected life-span from birth indicator.’ The second interim index of educa tion is a combined and averaged measure of the adult literacy rate indicat or and the collective primary, secondary, and tertiary gross

PAGE 23

14 enrollment ratio (GER) indicator. These two indicators are weighted with two-thirds weight given to adult literacy and one-third we ight to the GER. The third interim index is referred to as the gross domestic product or GDP index. The HDI uses a per capita GDP derived from purchasing power parity cal culations in US dollars (PPP US$). Two common methods for normalizing or st andardizing raw data are to either convert the data values into z-score values or use a linear sca ling transformation set between two bounds, generally on a scale betw een zero and positive one. The UNDP uses the latter method to normalize their data. To do this, minimum and maximum values determined by the UNDP are set for each of the four indicator data sets, and then interim index values are calculat ed using the linear scalin g transformation formula: actual value – minimum value Interim index = ma ximum value – minimum value According to Anand and Sen (1994), these minimum/maximum values need to be comparable over time in order to track a country’s human development. For this reason the minimum/maximum values for the HDI calcu lations were developed for the original 1990 HDR as follows: Life expectancy : To establish the minimum value for life expectancy at birth the UNDP used 1960 data, which is the earliest point in time when all of the count ries in the study had reliable life expectancy records. In 1960, the lowest average life expectancy for any country was 35 years, which became the minimum value for the HDI. Using projections out to the year 2050 from “Barbara Torrey and other references” (Anand and Sen, p. 10), the maximum value for life expectancy was set at 85 years.

PAGE 24

15 Knowledge : Initially, adult literacy minimum/maximum values were set using a 0 to 100 range (percent) based on whether a person is or is not literate. The UNDP defines literacy in a person 15 years or older as being able to “with understanding, both read and write a short, simple statement about their everyday lif e” (Human Development Report 1994, p. 221: In Agostini and Richardson, 1997, p. 25). Standard of living : For the minimum/maximum GDP values, the UNDP used “the logarithm of per capita GDP in 1987 Kravis dollars truncated at the average official poverty line income in nine developed countries” (Anand and Sen, p. 10), resulting in a maximum GDP value equal to the logarithm of purchasing power parity (PPP) $4,861 in 1987 prices. Since the initial 1990 report, two indicators of the HDI were modified to increase the robustness of the HDR: the GDP, and median years of schooling. The standard of living attribute was changed in 1991 by moving to a more systematic determination of income diminishing returns using the Atkins on formulation for the utility of income: {( y ) = 1/ 1 y1} in which ( y ) represents the poverty line. With this formula, any income up to the poverty line has a full weight, however any income ove r the poverty line does not, the weighting being reduced as the per capita income increases. The intent here is to measure up to an established income cut-off point that th e UNDP considers “adequate for a reasonable standard of living and for a reasonable fulfillment of human capabilities” (ul Haq, 2003,

PAGE 25

16 p. 129), and treat income above the cut-off poi nt with a diminishing return. This is perhaps one of the strongest statements against indexing methods that emphasize economic growth as a means to an end, suggest ing that well-being is not dependent solely on income. “The HDI emphasizes sufficiency rather than satiet y” (UNDP, 1994, p. 91). Using correlation and principle components an alysis, Cahill (2002) was able to support the HDI’s diminishing returns assumption. The knowledge indicator was reconfigured in 1995 to combine a dult literacy with the mean years of schooling, adult literacy be ing weighted at 2/3 and years of schooling weighted at 1/3. The literature does not clearly explain why th e indicators are weighted as they are, however, since the HDI is intended to measure basic levels of human development, it is assumable that the mere existence of literacy outweighs the level of literacy. In 1994 the minimum/maximum values were ‘set,’ and, with the exception of mean years of schooling changing to a gross enrollment ratio, and the minimum value for the GDP per capita dropping from $200 to $100, these values continue being used through 2004. The minimum/maximum values set by the UNDP and used to calculate the 2004 Human Development Report are shown in Table 2-1. Table 2-1. Maximum/minimu m values used for the 2004 HDR. Indicator Maximum value Minimum Value Life expectancy at birth (years) 85 25 Adult literacy rate (percentage) 100 0 Combined gross enrolment ratio (percentage) 100 0 GDP per capita (PPP US$) 40,000 100 Source: UNDP HDR 2004

PAGE 26

17 Once the interim index values are de termined using the minimum/maximum values against each country’s actual indicator value set, the HDI is calculated as the average of these combined interim index values: HDI = (life expectancy index + education index + GDP index) 3 Criticisms of the HDI include concerns that the minimum/maximum values are subjective and exceptional values, and that they highlight deprivation rather than development (Kelly, 1991); that there are an insufficient num ber of dimensions (addition of human rights or political freedom dimensions have been suggested); and that there is a need for improved indicators such as infant mortality rates or levels of education attainment beyond basic literacy (Agostini and Richardson, 1997; Noorbakhsh, 1998). The UNDP, however, has held steadfast to the concept that the thre e dimensions – long life, knowledge, and decent standard of living – together with the established minimum / maximum values are sufficient measurements for the Human Development Reports. In their assessment of the sufficiency of HDI’s measurements, Ivanova, Arcelus, and Srinivasan (1999) conclude d that the index held useful information about current levels of each country’s development, but o ffered little in terms of projecting future development. An early criticism by Kelly (1991) is that since countries with high development have essentially reached the ma ximum values for the three dimensions, the HDI offers little in terms of measuring progress for human development in these countries. The UNDP recognized the problem of disparity occurring when one index is applied equally to a country with a low human development level and a country with a

PAGE 27

18 high human development level. Therefore, in 1993, changes were made to the number of indicators used relative to each country’s hum an development level. For countries with a ‘low’ level, one basic indicator would be used for each dimension. For ‘medium’ level countries, two indicators would be applied, and for ‘high’ level countries, three indicators would be applied to each dimension (A nand and Sen, 1994). Table 2-2 lists these indicators: Table 2-2: The number of indicators used to calculate the HD I is relative to level of human development within each country Human Development Level Low Medium High 1.1 Life expectancy 1.1 Life expectancy 1.2 Under-5 mortality 1.1 Life expectancy 1.2 Under-5 mortality 1.3 Maternal mortality 2.1 Adult literacy 2.1 Adult literacy 2.2 Secondary school enrollment 2.1 Adult literacy 2.2 Secondary school Enrollment 2.3 Tertiary enrollment Human Development Indicators 3.1 Log per capita GDP – up to international poverty line 3.1 Log per capita GDP – up to international poverty line 3.2 Incidence of poverty 3.1 Log per capita GDP – up to international poverty line 3.2 Incidence of poverty 3.3 Gini-corrected mean national income Source: Anand and Sen, 1994, p.14 Despite the criticisms leveled agains t it, the HDI remains one of the most universally studied and accepted index mode ls available for examining and comparing socioeconomic conditions across nations (Lanteigne, 2005). By virtue of its straightforward computation method and its transparency, the HDI is also a highly adaptable model as demonstrated in severa l studies. Four studies pertinent in

PAGE 28

19 methodology and objective to this thesis have successfully used modified HDI models in their research. The first, by Agostini and Richardson (1997) uses the HDI to rank and compare twenty-five U.S. cities for the pur pose of identifying ‘benchmarks’ in the success of local government st rategic planning policies. By using a ranking system, policy makers are able to evaluate the success of implemented development policies against development in other U.S. cities, a nd prioritize or make adjustments to their strategic plans accordingly. Agostini and Richardson find that the UNDP HDI is less suited to generate subtle dis tinctions of well-being in highl y developed study areas where the indicator values do not vary widely. A dditionally, not all data required for the UNDP HDI are available at the city level. For th is reason, proxy indicator s are used and, where data are not available at the city level, data from count y or the Federal Office of Management and Budget’s Standard Metropoli tan Statistical Area are used. In the conclusion of their study, Agostini and Richar dson describe moderate success in using the modified HDI at a city-level scale to identify benchmarks among the twenty-five sample cities. This moderate success is perh aps due to the scale of study and the diversity of the sample. Even in modified form, the HDI appears to have limited capability for discerning subtle variations at the city-level scale. Th e insensitivity to subtle variation at the citylevel scale further masks dissimilarities between cities as diverse as Jacksonville Florida, San Francisco California, and Detroit Michigan. Presumably, if the sample cities were taken from the same region, for example, Jacksonville, Miami, and Tampa Florida, any variations in their similarity would be highlighted rather than masked by the HDI. In theory then, a modified HDI applied at a coun ty-level scale to counties within a similar

PAGE 29

20 region such as the State of Florida will improve the succe ss level reached by Agonstini and Richardson. The second study, by Hanham, Berhanu, a nd Loveridge (2000), remains much more closely aligned to the original intent of the HDI model, that is, measuring and ranking the levels of human development. The approach taken by Hanham et al. is focused less on policy issues than on compara tively assessing development and quality of life within the state of West Vi rginia at the county level. Th e tone of the study is set in the questions posed in the introduction: “If you had your choice of living anywhere in the state, where would you live? Where would your quality of life be the highest? How would you choose where to locate?”(Hanham et al., p. 2). As with the Agostini and Richardson study, Hanham found it necessary to modify components of the HDI indicators due to data constraints. For exam ple, the UNDP HDI uses life expectancy as a key component, however these da ta are not available at the c ounty level in West Virginia. Therefore the study uses adjusted morta lity rates per 100,000 population combined with an averaged mortality rate for children under the age of five years as a proxy for longevity. As with the Agonstini and Richar dson study, adult literacy is replaced with education attainment indicators, in this cas e: median years of schooling of persons 25 years and older, high school drop-out rate, a nd percentage of persons 25 years and older with a bachelor’s degree or higher. The resu lts at each stage of the indexing process in this study are presented in c horopleth map form using a 5 sequential color theme (low scores: dark to high scores: light). By pres enting the results in this visual manner, Hanham is able to convey effectively how th e raw data (poverty rates, high school drop-

PAGE 30

21 out rates, et cetera) are combined in the m odified HDI, and where dissimilarity patterns in the key dimensions exist, even to thos e not familiar with West Virginia. The third study, by Bukenya and Fraser (2002), is very similar to the West Virginia research, but focuses on human deve lopment at the county level in Alabama, and rather than seeking a be st location, emphasis in this study is on uncovering social inequities within the state, pa rticularly in the ‘Black belt region’ of southern Alabama. Bukenya and Fraser supplement the basic HDI with one additional environmental indicator: amenities based on the Natural Amenities Scale published by the Economic Research Service of the USDA (ERS, 1999). By running the data th rough the model both with and without the amenities indicator, Buke nya and Fraser are able to demonstrate the significance of amenities in th e overall quality of life ranking of the Alabama counties. According to McGranahan (1999), natural amen ities such as those found in Florida are a major pull factor in migration patterns, and, coupled with the results of the Bukenya and Fraser study, this suggests that an indexing of socioeconomic factors in Florida should include a natural amenities indicator. The fourth study, by Estrada (2005), us es a modified HDI to assess the effectiveness of community resources a nd economic development programs created by the Cooperative State Research, Education, a nd Extension Service (USDA). Estrada’s research focuses on evaluating the Empower ment Zone program and its impact on community well-being at the county level in the Rio Grande Valley of Texas. The intent of the study is to demonstrate the usefulness of the HDI model for ev aluating a variety of programs and policies by measuring their effec tiveness in improving qua lity of life. In order to show the adaptability of the model, Estrada has re placed the longevity dimension

PAGE 31

22 with a housing dimension, and altered th e GDP dimension to reflect economic opportunity resulting from implementation of the Federal Government’s Empowerment Zone program. The socioeconomic indicators us ed in this study in clude the total number of housing units, the number of owner-occupi ed housing units, and the median value of the owner-occupied housing units. While the in dicators have been modified or replaced, Estrada uses the same formula format used in the original UNDP HDI, replacing the UNDP minimum/maximum values with values sp ecific to Texas. For example, instead of a GDP based on a maximum value of $40,000 a nd a minimum of $100, Estrada uses the figure from the Texas county with the highest average income for the maximum, and the figure from the county with the lowest average income for the minimum value. Estrada shows through this study that the HDI can suc cessfully be used to measure at the county level, in his terms: “a holistic indica tor of community resources and economic development's goal of community well-being” (p. 2), an indicator that is equally applicable to a Florida study. From these four studies, it is evident th at the HDI is a practical and adaptable model for ranking socioeconomic well-being in Florida at the county level. It is shown that proxy indicators can be used when data fo r the original indicator s are unavailable or inadequate. Data available from secondary s ources such as the U.S. Census Bureau, the Bureau of Economic Analysis, Florida State, and others are suffi cient to produce valid results. These studies also show that altern ate indicators such as natural amenities can be used in the basic format of the HDI to check the significance of variables on socioeconomic well-being in Florida.

PAGE 32

23 Thematic Mapping “ If the map reader is to receive a pr oper understanding of the statistical intercorrelations among a set of variables then we must encode maps so that the map reader’s decoding corresponds to the intercorrelations of the variables ” (Lloyd and Steinke, 1977, p. 430). Visualizing Data For composite data such as socioeconomi c well-being measurements to be by any means useful for analysis or interpretation, th ey must be presented in an understandable format. Presentations of data are made on se veral levels, includi ng verbal description, tabular or matrix, and graphic representation. St atistical data are most often presented in either numerical tables such as frequency di stribution tables, matric es, and indices, or visual graphic representations such as scatte r plots, histograms, pie charts, bar graphs, ogives, and time series graphs. For the geographe r, spatial relationships play a key role in visualizing and comparing abst ract data, and so thematic maps are a common form of visual representation. Four of the more fam iliar thematic maps for socioeconomic studies are proportional symbol maps, data maps, car tograms and choropleth maps (Rittschof et al. 1996). To date, geographic information systems (GIS) have greatly improved our ability to quickly generate thematic maps on a wide range of topics to suit the needs of a variety of groups from sociologists to planners and policy makers. However, just prior to this technological advancement, the useful ness of maps as a tool for conveying and analyzing statistical data wa s being questioned within the geographic discipline (Board and Taylor, 1977). Smith (1975) lauds the matr ix system for displa ying numerical facts, noting that tables are more pr ecise, easier to read, and eas ier to directly manipulate in

PAGE 33

24 terms of mathematical computation. In Smith ’s view, “[a]ny geogra phical pattern, be it one of points, lines, or areas, may be depi cted as a matrix” (p. 5). In 1981, Phillip Muehrcke wrote an opinion piece in the Professional Geographer discussing what he termed the ‘demise of geographic cartography.’ Muehrcke credits this demise to the following: “Possibly the most devastating bl ow to the preeminence of maps, mapping, and map use in geogra phical methodology came with the conceptual/theoretical/quantitative revolution. Implicit in the shift to quantitative methods that took place during the 1950’s-1960‘s was the belief that maps had hurt geography. Traditional overreliance on maps was blamed in part for the lack of geographical theory. In support of this notion it was pointed out that maps are subjective and descriptive rather than explanatory; maps are weak in hypothesis testing, and maps encourage a descriptive rather than problemoriented approach to geography” (Muehrcke, 1981, p. 398). Muehrcke’s article was written, ironical ly, on the eve of the GIS revolution. GIS has done much to answer cri ticisms of maps as subjectiv e and cumbersome tools. The ability to quickly see the resu lts of data manipulation on a computer generated thematic map has greatly increased the map’s value as an analytical modeli ng tool (Carr et al., 2005). Five thematic map types : The common dot-distribution map discussed by Dent (1999) and the four thematic maps mentioned by Rittschof (proportional symbol maps, data maps, cartograms and choropleth maps) each offer particular benefits for graphically displaying data, depending on the specific goals and requirements of th e research project. Dot-distribution and

PAGE 34

25 proportional symbol mapping are effective m eans of showing spatial distribution for discrete elements (One dot or symbol representing 300 people, 100 bushels of corn harvested, et cetera). These maps generally plot the spatial units (nat ions, states, counties) at true-scale. A dot or symbol proportional to its statistical value is placed within each spatial unit. An easily imagin ed example of a proportional symbol map is a GNP map of the world where symbolic stacks of coins of varying heights are placed on each country, each coin in the stack representing a quantita tive unit of money. In this example, the symbol used is explicit (coins represent a monetary unit) and proportion is simply a matter of counting the coins in the stack. The explicit symbol and proportion are both crucial elements that allow the map reader to easily decode the map. The problem in proportional symbol mapping arises when non-exp licit symbols such as circles (or stars, blocks, cut-out human figures, et cetera) are used and the in crease or decrease in size is not easily discernable. Dot-distribution and proportional symbol maps frequently suffer the additional problem of symbol-crowding, a condition that causes more confusion than clarity for the map reader. Though effectiv e for discrete elements, these mapping techniques are less effective at showing c ontinuous phenomena such as ranking or scale. Data maps are similar to proportional sy mbol maps in that they are also geographically true-scale, however, rather than using a symbol to represent the statistical value, the actual numeric value is placed w ithin its corresponding sp atial unit. Cluttering is occasionally a problem in data maps, how ever more troubling is the shallowness of spatial analysis. Listing the data values w ithin the spatial units is only marginally different than listing the same data in a table: it gives an idea of where the values are

PAGE 35

26 located, but it can be difficult to visualize distribution patterns, rankings, or comparisons between spatial units that are not in close proximity to each other. Cartograms, in contrast to the true -scale maps, intentionally distort the spatial unit boundaries of a regional map so that the size of the distorted area is proportional to its statistical variable (Du and Liu, 1999), but in such a way that the region of the map is still recognizable (R ittschof et al. 1996; House and Kocmoud, 1998; Keim, North, and Panse, 2004). Due to the li nk between the statistic al values and the areal distortion, cartograms are also referre d to as value-by-ar ea maps. The areal distortion of a cartogram can result in conf usion for the map reader who has no prior concept of the conventional spatial boundaries, for as Ols on (1976) points out in her introduction to noncontiguous area cartograms: "Cartograms are usually visually striking and intellectually interesting, at least to those who are familiar with the ordinary map area" (p. 371). Perhaps the most daunting aspects of cartograms are the algorithms required to generate them: “The current solutions have two majo r problems: First, the high time complexity of the algorithms restricts thei r use to static applications with a small number of polygons and vertices. Second, they have very limited shape preservation” (Keim, North, and Panse, 2004, 99); “Generating a cartogram for a not-socomplex map may require hours of computation, and the resulting cartogram may not be satisfactory” (Du, Liu, 1999, 1); “Cartograms are controversial in pa rt because they are difficult to construct and the results seen to date are crude or imprecise or both” (Dougenik, Chrisman and Niemeyer, 1985, 75).

PAGE 36

27 While cartograms do provide a visual f eel for the relationship between the statistical variables and their as sociated spatial units, the effort required to generate them does not suit one of the primary objective of this thesis, that is, to develop a ‘userfriendly’ means of geographically presen ting socioeconomic information that is beneficial to a large group of users. Choropleth Maps “ Descriptive statistics and c horopleth map design go hand-in-hand .” (Kumar, 2004, p. 218) The etymology of ‘choropleth’ is Greek: choro meaning ‘area’ or ‘place’ and pleth (from plethos ) referring to ‘a crowd’ or ‘multi tude’ (Wright, 1944; Robinson et al, 1984; Dent,1999), or in the case of things rather than persons, ‘an abundance.’ Loosely interpreted then, choropleth describes ‘how many in a place.’ The International Cartographic Association defines choropleth mapping “as a geographic representation of areas, generally administrative or enumera tion units with distinct intensity of color/shading proportional to the data valu e associated with th ese units” (Kumar, 2004, p. 218). This description reflects the technological advances ma de from the time when the use of color was a rather expensive option in the map making process, a time when data values were more often distinguished one from the other on choropleth maps through cross-hatching, stippling, or gray-scaling. Although the human eye can discern and di stinguish between a large number of colors, map-makers using choropleth mapping find that too many colors cause confusion on the part of the map reader. Since it is impr actical to assign a separate color to every

PAGE 37

28 data value in cases where there are more than seven or eight values the data values are traditionally grouped into classes in order to reduce the number of colors required. There are several methods for breaking a data set into classes, each having advantages or disadvantages depending on the purpose for di splaying the data. Jenks and Caspall (1971) and Richard Smith (1986) stress the importance of selecting valid class intervals, making a convincing argument for optimization classing. This is particularly true with single variable data where the distri bution is highly skewed. Howeve r, in the case of composite indexing, where distribution tends to normalize, it appears the advantages over quantile classing may weaken. Brewer and Pickle (2002 ) compare seven classification methods to determine the most suitable for epidemio logical map-reading. These methods are: quantile; minimum boundary error; natural breaks (Jenks – optimized method); hybrid equal interval; standard deviat ion; shared area; and box plot Of these seven, Brewer and Pickle concluded that the classification methods “best suited for choropleth maps intended for a wide range of map-readi ng tasks were quantiles and minimum-boundary error” (p. 677). This research suggests that, as with epidemiological maps, quantile classification is well suited for the FCHDI. Today, color monitors, digitizing tablets, color inkjet and laser printers are ubiquitous in map development and production, common tools not readily available prior to the 1980s. This high-quality, low-cost accessi bility of color maps promotes flexibility in the design of maps, includi ng exploration into designing ma ps suitable for people with color-vision impairments (O lson and Brewer, 1997; Light and Bartleine, 2004). The color palettes available as a default feature in visualiza tion software products, including GIS, allow the map designer to choos e from a veritable rainbow of colors to

PAGE 38

29 represent data values. While such a pletho ra of choice may seem advantageous, unless the map-maker has some understanding of statistical graphic design and visual perception, there is considerab le likelihood of confusion and misinterpretation on the part of the map-reader being introduced to the map (Rogowitz and Treini sh, 1998). “Color has the potential to enhance communication, but de sign mistakes can resu lt in color figures that are less effective than gray scale displa ys of the same data” (Light and Bartlein, 2004, 385). Edward Tufte (1990), in discussi ng the complexity of coloring data concludes that when working with colors “avoiding catastrophe becomes the first principle in bringing color to information: Above all, do no harm (p. 81, emphasis in text). The psychological perception of color by th e map-reader must at least be taken into consideration when designing a chor opleth map. First of all, colors convey qualitative information more readily than qua ntitative values, that is, because a bright orange hue draws more attention than a mu ted brown, the map-reader may perceive the orange area more important, but not by how mu ch. In an experiment on assigning colors to data values, Olson finds that the subj ects often choose colors based on connotative associations: “dull colors with the dull outlook of little inco me or education, green with money, purple with academia, and so on” (Olson, 1981, p. 226). This may explain the tendency for “hot” items, or those issues th e map maker wishes to highlight as urgent being expressed in red hues. The extent of the map-reader’s prior experience with maps can also effect perception. For example, ev en brief encounters with topographical maps condition the map-reader to interpret bl ue as water and green as vegetation.

PAGE 39

30 Map coloration can inadvertently exagge rate visual weighting by drawing the map-reader’s attention to the larger geographic units (House and Kocmoud, 1998; Kumar, 2004). For example, if all the counties in Florida were the same shape and size, there would be little problem with correlati ng quantitative data with geographic area, however, when there are large counties and small counties on the same map, the eye will register the larger counties firs t. As mentioned in the first chapter, Monroe County at the southern tip of Florida is quite large, ye t over 99 percent of the population live off the county’s coast in the Florida Keys. Convers ely, the population density of Tampa Bay’s Pinellas County is the highest in the state, but the county is so small that even when the color values are clearly distinct, the map-read er will most likely ‘see’ the larger Monroe County first. This correlati on of spatial unit size to data value is a major issue with proponents of cartograms. In working on the design of a mortality atlas for the National Center for Health Statistics, Pickle (2004) notes that sequential color scales ar e well suited for determining extremes in data. Sequential color scales are a light to dark progressi on of either a single hue or color group (yellow-orange-red). The se quential scales are part icularly useful for recognizing clusters of similar data values; an important feature to the FCHDI where the spatial patterns of socioeconomic we ll-being is of special interest. In conclusion, the literature supports not only the value and usefulness of the UNDP’s human development index for gauging comparative social well-being across geographical regions, but al so the model’s adaptive characteristics, which lend themselves to modification for the purposes of the FCHDI. The lit erature supports the

PAGE 40

31 development of the FCHDI as a tool for st udying Florida’s socioec onomic well-being at the county-level. The literature also suppor ts the use of choropleth thematic mapping and quantile interval classification as an e ffective means for uniformly displaying the FCHDI’s rankings as an aid in identifying clus ters or spatial patterns of socioeconomic distribution. Using this background material as a f oundation, the methods for constructing the FCHDI and mapping the results are elucidated in Chapter Three.

PAGE 41

32 Chapter Three: Research Methods “Research…is the concentrated examination and correlation of the multitudinous phenomena co-existent in some specific field of activity.” (Theodor Seuss Geisel, 1939) The Florida County Human Development Index Equation The key components of the Florida C ounty Human Development Index (FCHDI) are based on those dimensions used by the UNDP: life expectancy, knowledge, and a decent standard of living. Due to data constr aints found at the county level but not at the national level, coupled with th e goal of using universally acc essible data sources, proxies for these components are established followi ng the works of Hanham et al (2002) and Bukenya and Frasier (2002). For example, data for life expectancy at birth are available for most nations – the aggregated life exp ectancy in the United States per the 2004 Human Development Report is 77.0 yearshowev er, these same data are not easily found disaggregated to the county level in a fo rmat useful to the FCHDI. For this reason, mortality rates are used as a proxy measurem ent for the life expectancy dimension. Table 3-1 lists the indicators used by the UNDP to measure the life expectancy, knowledge, and a decent standard of living dimensions, plus the proxy indicators used by Hanham et al, Bukenya and Frasier, and this thesis. In the FCHDI, a conscious effort is made to ensure that these proxies reflect the general socioeconomic indicators modeled in the UNDP Human Development Index.

PAGE 42

33 Table 3-1: Dimension indicators used in the UNDP HDI and proxy indicators used in the modified HDI models for West Virginia, Alabama, and Florida Dimension UNDP HDI West VA HDI Hanham, Berhanu, and Loveridge Alabama HDI Bukenya and Frasier FCHDI Kelsey A long and healthy life Life expectancy at birth Adjusted mortality rate per 100,000 Infant mortality rate Life expectancy at birth Adjusted mortality rate per 100,000 Infant mortality rate Adjusted mortality rate per 1,000 Infant mortality rate Leading cause of death Knowledge Adult Literacy Gross enrollment ratio Median years of schooling High school dropout rate Percent of population with bachelor’s degree or higher Median years of schooling High school dropout rate Percent of population with bachelor’s degree or higher Non-high school graduate Percent of population with high school degree or higher Percent of population with bachelor’s degree or higher A decent standard of living GDP per capita Poverty rate Per capita income Inequality of income distribution (Gini coefficient) Poverty rate Poverty among children Per capita income Inequality of income distribution (Gini coefficient) Poverty rate Per capita income Price level index As noted by Agostini and Richardson ( 1997), Anand and Sen (1994) and others, measuring the subtle variances in a country with a high level of human development is difficult when using only one indicator to represent a socioeconomic component. In

PAGE 43

34 order to increase the sensitivity of measurem ent across Florida’s sixty-seven counties, it is determined that each of the three key com ponents, or dimensions, should be calculated from an interim index made up of three indicators for an overa ll total of nine indicators. Proxy Socioeconomic Indicators For the FCHDI, a proxy of the life expect ancy dimension used by the UNDP is established using three indicators or measures of mortality gathered from the State of Florida’s Vital Statistics Annual Report 2000 (FDOH, 2001). The first is the resident death rate per one-thousand populat ion. This rate is taken directly from Table D-1 of the report, and reflects the death rate of Florida residents specifically as oppo sed to the larger and more general record of d eaths occurring within the stat e. The FDOH defines resident death as “events occurring to Florida resi dents regardless of the place of occurrence” (FDOH, 2001, viii), with “residen t” referring to persons whose usual place of residence is Florida. This mortality indicator raises the issue of non-resident deaths in Florida, and how a non-resident mortality variable might influence the socioeconomic well-being index. Florida is a destination state for v acationers and seasonal residents escaping the discomforts of northern winters. Therefore, to urism and the service sector play vital roles in the state’s economy. As such, socioeconomic conditions are highly sensitive to a tourist death, or the threat of tourist death as in the spate of shark attacks in 2005 or the high number of tourist muggings and murder s in the early 1990s. A non-resident mortality variable is certainly intriguing and merits further research, however, spotty data sources and inconsistent data availability run counter to the stated criteria of the FCHDI, and therefore the variable is omitted from this version of the index.

PAGE 44

35 The second mortality indicator is the death rate of child ren under the age of five years. This value requires combining the number of deaths in each county for infants (under one year in age) and the number of deat hs of children aged one to five taken from Table D-4 (Resident Deaths by Age Group) of the annual vital statis tics report. Following the rates and formulae given by the Florida De partment of Health for age-specific rates (FDOH, 2001, p. xiv ), this total is first multiplie d by 1,000 and then divided by the number of children under the age of five year s for that county as reported by the U.S. Census Bureau (CENSUS, 2003), resulting in a child mortality rate per one-thousand population. To establish a third indicato r for the mortality dimension, a process similar to the UNDP method for calculating the ed ucation interim index is used, that is, combining the rate of adult literacy with the total gross enro llment ratio. In order to reflect the health issues of the mortality dimension, values for the two leading causes of death in Florida heart disease and malignant neoplasm (cancer ) are taken from Table D-12 of the vital statistics report, normalized, combined and then averaged to produce the third indicator. Due to the assumption that the basic literacy rate as defined by the United Nations is relatively high across Florid a, a proxy education attainment dimension is developed for the FCHDI. As previously noted, the UNDP defines adult literacy as the ability by persons 15 years and older to read, comprehe nd, and write simple sentences about their everyday lives. Adult literacy is reported in the 99.0 percen t range for the United States (UNDP, 2002, Table 1), however, there are c onflicting figures in the same report indicating that in the United States, the pe rcentage of persons between 16 and 64 years who lack functional literacy ski lls is 20.7 (UNDP, 2002, Table 4) Because literacy rates

PAGE 45

36 at the county level are difficult to determ ine, the focus for the FCHDI education dimension is education attainment using th ree indicators taken di rectly from Table 4 (Education and Veteran Status) of the U.S. Census Bureau’s 2000 Census of Population and Housing: Summary Social, Economic, and Housing Characteristics (Census, 2003). The indicators are: non-high sc hool graduate (population 16 to 19 years, not enrolled in school and not high school graduate); educat ion attainment high school graduate or higher (population 25 years and over: Perc ent high school graduate or higher); and education attainment – Bachelor’s Degr ee or higher (population 25 years and over: Percent with Bachelor’s degree or higher). From a socioeconomic well-being standpoint, ‘a decent standard of living’ is a highly subjective term, for a level considered ‘decent’ by the researcher may differ greatly from various sectors of the st udy population, thereby potentially violating objectivity in analysis. Therefore, indicators fo r the standard of living dimension fall back to more traditional and well establish economic standards measurements: measures of poverty, per capita income, a nd the price level index de veloped by the Bureau of Economic and Business Research at the Univer sity of Florida (BEBR, 2003). This last indicator is the only one used to compute th e FCHDI, which is not taken directly from Federal or State data sets, however the intrinsic significance of the pecuniary consumption price level index to the FCHDI necessitates its inclusion. The data come from the 2003 Florida Price Level Index re port, Table II. The county-level poverty figures and per capita income data come from the U.S. Census Bureau’s 2000 Census of Population and Housing: Summar y Social, Economic, and Hous ing Characteristics, Table 16 (Poverty Status in 1999: 2000) and Table 10 (Work Status and Income in 1999: 2000).

PAGE 46

37 The conceptual model of the FCHDI deve loped for this thesis and shown in Figure 3-1 illustrates the flow of the ten indicators to their respective interim indices, which are then combined to produce the FCHDI. Heart Disease Rate Mortality Child Mortality Rate Rate Cancer Rate MORTALITY INTERIM INDEX High School High School Bachelors Dropout Graduate Degree Rate or Higher or Higher EDUCATION INTERIM INDEX Poverty Per Capita Price Level Rate Income Index ECONOMIC INTERIM INDEX Florida County Human Development Index Figure 3-1: Conceptual model for calculating the FCHDI

PAGE 47

38 FCHDI Calculation With the ten variables for the nine indi cators established, and the values for each collected from the data source listed above the FCHDI is calculated using Microsoft Office Excel 2003. Although there are several sp readsheet programs available, Excel is chosen with the intent of us ing one of the most common or accessible programs available to the widest number of potential users of a modified human development index. To facilitate organization, the task of calculating the FCHDI is broken into fourteen worksheets: one worksheet for each of the nine indicators where descriptive statistics and normalizing of the raw data is calculated, three worksheets for calculating the interim indices, one worksheet for calculating the FCHDI, and one worksheet for ranking the Florida counties. Breaking the calculation down in this fashion also facilitates transferring the data from table fo rmat to the choropleth maps. The values of each of the nine indi cators are normalized using the same conventional linear scali ng transformation (LST) method used for the HDI: y = (x – xmin) / (xmax – xmin) Where: y = normalized indicator value x = observed or adjusted indicator value xmin = minimum value of the indicator set xmax = maximum value of the indicator set These normalized values are th en recalculated using an arith metic average to produce an interim index value for the three dimensi ons. Following the UNDP general principle of uniform weighting for the social and economic factors, each of these indicator values carries equal weight during calculation of the interim index value. The interim indices are summed and averaged, again with unifor m weighting, to produce the FCHDI. The

PAGE 48

39 Florida counties are then ranke d by the FCHDI value as a perc entage of the sixty-seven county data set. Adjusting for net-positive results When constructing an index of social we ll-being, it is necessary to consider the issue of value directionality: whether the at tribute has a positive or negative effect on social well-being (Salzman, 2003). In a so cioeconomic index such as the FCHDI, the term for the highest positive measurement (100 percent) is unity and each gradation below it is a measure of deprivation Before normalizing the inte rim indicator values for the FCHDI, a subjective positive/negative va lue judgment is set for each of the indicators, depending on whether that indicator will have a positive or negative effect on the overall socioeconomic well-being of Florid a’s counties. The goal here is to ensure that when ranked, the higher numbers represent positive socioeconomic well-being, while progressively lower numbers represent correspondingly less positive socioeconomic wellbeing. For example, poverty is considered a deprivation or nega tive social condition. A poverty level of 6.2 percent is generally accepted as better than a poverty level of 12.4 percent, yet numerically the 6.2 percent is lower. This situation is remedied by subtracting the deprivation from unity; in this case, the povert y level in decimal form is subtracted from the number one (100 percent): 1 – 0.062 = 0.938 (or 93.8 percent non -poverty) 1 – 0.124 = 0.876 (or 87.6 percent non -poverty) By making this adjustment, a 6.2 percent poverty level becomes higher on the ranking scale. In this thesis, positive indicator values used directly from the data sets are referred

PAGE 49

40 to as “observed values” and those requiri ng modification such as the poverty level described above are termed “adjusted values.” Indicator weighting during calculation One criticism leveled against the HDI is the UNDP’s choice to use uniform weighting of the interim indices when cal culating the HDI (Kelly, 1991; Booysen, 2002; Hagerty and Land, 2004). Kelly (1991) emphasizes that, “while a priori it is difficult to justify any set of weights, testing the sensit ivity of the HDI to alternative weights would have been useful” (318). Similarly, Booysen’s concerns lay in the fact that the further apart the minimum and maximum values used in calculating the HDI, the more difficult it is to maintain relative increas es in the indicators between nations without increasing the implicit weighting (2002, p. 125). The issue of weighting indicators or interim indices in a summary index such as the FCHDI is a th orny one, since it is suggested by Cutter, Michael, and Scott (2000) that establishing non-uniform weighting schemes between social indicators (in their study case, social vulnerability) and non-social indicators (such as biophysical risk or economic indicators) tends to be subjec tive, or biased toward the agenda of the research. Of c ourse, as stated in the works of Anand and Sen (1994) and ul Haq (2003), reducing or elimin ating the economic standard bias in ranking national progress is one of the objectives of the HDI, so it is, they ar gue, logical to use uniform weighting. Bowen and Moesen (2005) counter that predetermined uniform weighting schemes applied universally to countries ha ving differing policymaki ng priorities will in fact bias the measurements.

PAGE 50

41 At a global scale, where the 2004 HDI values range from a high of 0.956 for Norway to a low of 0.273 for Sierra Leone, the wide spread of values within the closed scale of zero-to-one is great enough to validate both Booysen’s concerns that implicit weights in the HDI are being introduced during scaling, and th e potential for policy priority bias as discussed by Bowen and Mo esen. On the other hand, at a county-level scale within a comparatively homogeneous unit such as the State of Florida, calibrating the FCHDI with explicit weights would overl y complicate the index, particularly when efforts to increase the sensitivity of the s cale are made by increasing the number of relative indicators within each interim inde x. In addition, by using the linear scaling transformation to normalize the observed indicator values, the minimum/maximum spread is narrowed, and thus the need for explicit weighting is reduced (Salzman, 2003; Smith, 1975). Booysen refers to Earl Babbi e (The Practice of Social Research, 1995: Wadsworth Publishing) arguing that “equal we ighting should be the norm and the burden of proof should fall on differential weight ing” (Booysen, 2002. pp 127-128). With this in mind, it is deemed too problematic to justify, and therefore impractic al to establish a nonuniform, explicit weighting scheme for the FCHDI. Standard score as an alternative to linear scaling transformation The linear scaling transformation (LST) form ula used in the FCHDI is one of two common methods for standardizing and aggreg ating un-scaled variables. The LST works best when the value range of a data set is relatively centered about the mean and is not heavily impacted by outliers, a condition that skews the spread of indicator values in the index and diminishes the usefulness of the data set. A second common transformation

PAGE 51

42 method is the z-score, also known as Gaussian normalization, which is not as strongly influenced by extremes in the range of a data set (Smith, 1977). An example of this second method is the Natural Amenities Scal e created by the Economic Research Service of the USDA, which uses z-scores to standard ize an array of six i ndicators ranging from the mean temperature in January to land su rface typography at the county level for the 48 contiguous United States (McGranahan, 1999). As can be imagined, the range of mean January temperatures between Koochiching County, Minnesota (Inte rnational Falls) and Monroe County, Florida (Key West) is quite wide, a condition where z-score normalization provides more uniformity around the mean than using LST. Standardization of raw indicator values in to z-scores involves first finding the mean and standard deviation for the indica tor data set, then using the formula: z-score = (observed value – mean) / standard deviation A z-score can be a negative value, indicating the observed value is below the mean of the data set, a positive value indicating the valu e is above the mean, or zero indicating the value is equal to the mean. Since composite indices complicate determining the symmetry of indicator value distribution ar ound the mean, the z-score helps simplify the matter by using Chebyshev’s Inequity, which st ates that for any given distribution of variables, the probability of a z-score value being outside th e range of 2 and -2 is at most 25 percent, and the probability of being outside the range of 3 and -3 is at most 11 percent. The problem here is th at the z-score represents the value away from the mean of that particular data set, and does not standardize a ll data sets within a composite index to a common range as is the cas e using LST (Salzman, 2003).

PAGE 52

43 Ranking the z-scores is basically a matte r of sorting the values in descending order, and representing the z-scores thr ough choropleth mapping using, in the example shown in Figure 3-2, a seven-hue divergent co lor scale. In this example taken from Isserman (2005) and using the ERS/USDA Natural Amenities Scale, z-scores representing the average per centage of humidity in July for each county in the 48 contiguous United States are considered eith er a positive factor (blue), or a negative factor (red), so those humid counties in the Southeast, particularly in Florida are shown with a darker shade of red, representing a humidity z-score between -1 and -2 standard deviations, while arid Great Basin states su ch as Nevada and Utah are shown in dark blue, representing a humidity z-score betw een +2 and +3 standard deviations. Figure 3-2: Example of a z-score choropleth map using humidity data for the 48 contiguous states at the county level from the ERS/USDA Natura l Amenities Scale. (Source: Isserman, 2005)

PAGE 53

44 The FCHDI could easily be calculate d using the Gaussian normalization, however, Salzman found that between the two methods, the LST is the ‘best practice’ for standardizing variables, which assigns the lowest implicit weights and efficiently contends with the directionality issue of net-positive results for aggregated data (Salzman, 2003, p. 26). Since the FCHDI is fashioned after the UNDP model, the LST method is used on the indicator values ensuri ng that the ranges of the values are all positive and fall between the set bounds of zero to one (0.000 to 1.000) for ranking purposes. Mapping the FCHDI There are many, many mapping software pack ages on the market at the time of this writing, with new and improved releases constantly on the horizon; some are merely modified drawing programs, others are co mplex, multi-faceted, fully integrated GIS programs requiring extensive training to op timize the full scope and potential of their capabilities. High-end GIS so ftware offers fantastic possi bilities, not only for mapping data values, but also for customizing da ta compilation, analysis and comparison. Solutions to problems such as selecting an a ppropriate data value cl ass interval (natural breaks, standard deviation, equal interval, qu antile, et cetera), color scheme, font type, line quality, or even which data layers shoul d be visible and which should be suppressed, all can be explored with a click of the m ouse. Unfortunately, budget, facilities, and/or training restraints often limit justification for these types of GIS programs, particularly in the private sector when the primary functi on of the agency or department is not geographically orientated. For pragmatic purposes then, all of the FCHDI choropleth

PAGE 54

45 maps presented in this thesis were created using a simple vector graphic program (Adobe Illustrator 10), with the fu ll understanding that while this is not the optimal method for mapping, it is quite possible to create choroplet h maps suitable for this type of project using non-GIS graphic design tools. Choosing the data set class interval “In an era when maps are made from large databases with software that allows queries of individual polygons and iterative changes in classifications, it seems that facilitating map comparison is now more important than optimizing classification for a single map. Quantiles seem to be one of the best methods for facilitating comparison as well as aiding general map reading.” (Brewer and Pickle, 2002, p. 679) As discussed in the literatur e review, for purposes of di splaying data sets with a large number of data values in choropleth mapping, it is necessary to break the data sets into groups or classes. There are several me thods available for grouping data values into classes, seven of which were evaluated by Brewer and Pickle (2002). Through their testing of observed and predicted percent accuracy of ma pped epidemiologic data interpretation by classification method, the quantile method proved the most accurate at 75.6 percent overall, followed by the mini mum boundary error method at 72.6 percent overall. The Jenks, or natural breaks method ha d an overall accuracy of 69.9 percent. While it is true that cartographic resear chers find quantiles less effective for certain data displays, Brewer and Pickle de monstrate that for general comparative mapreading tasks of ranked data, the quantil e method produces an accuracy level “not significantly different from or better than two of the most optimal methods...” (p. 678).

PAGE 55

46 In both the Hanham et al. and the Bu kenya and Frasier studies, quantile classification is used to display the ranked data The data sets are brok en into quintiles, or 20 percent increments, meaning that only five co lors are required to show the results of the modified index. Quintiles were also c onsidered for the FCHDI, however, due to the use of frequency histograms and box-and-whiske r diagrams as described later in this chapter, quartiles were selected in order to provide a clear median point. Though there are several classifications to choose from, in this initial stage of research, quartiles highlight clusters and spatial patterns in th e data, which hopefully will inspire and act to focus future research, research that may require another interval cla ssification. Of utmost concern is the need to use one interval classification on al l data sets for consistency throughout this stage of research. Choosing the color schemes As discussed earlier, the color scheme used on a choropleth map is more than a function of design, it is also a critical mean s for conveying an interpretation of data, and as such, care must be taken not to introduce confusion or misinterpretation to the map and consequently the map reader through poor or unconventional color choices. In the interest of simplifying the in terpretation of the rank distribution, the index results are grouped by quartiles, meaning that only four sequential co lors are required for the maps. The web-based ColorBrewer, crea ted by Cynthia Brewer and Mark Harrower, is used to select a sequential color scheme th at does not lose defin ition or contrast across multi-functional uses such as desktop prin ting, power-point projec tion, or CRT display (http://www.ColorBrewer.org). Initially the four-class sequential yellow-orange-brown

PAGE 56

47 scheme was selected, however it was determ ined that the brown hue is not clearly distinguishable from dark orange when printe d on an inkjet printer, so the saturation value for brown is increased. For the alternative indicator map, where it is necessary to show standard deviation from the mean, a seven-class divergent colo r scheme is required: one neutral color representing the mean, three gradations of one hue representing the positive standard deviations, and three gradations of anot her hue representing the negative standard deviations. This divergen t color scheme follows the National Center for Health Statistic’s Atlas of United States Mortality which finds that the scheme brings to light extremes of data distribution and aids in cluster recognition (Pickl e, 2004). Figure 3-3 shows the colors selected for this proj ect and each color’s CMYK (cyan, magenta, yellow, black) and RGB (red, green, blue) values. Figure 3-3: FCHDI colors and CMYK/RGB values used on the choropleth maps.

PAGE 57

48 Frequency Histograms and Box-and-Whisker Graphs “One of the main objectives of a choropleth map is to provide an overall understanding of the sp atial patterns of the mapped variable. Reader’s unde rstanding of these patterns can be easily influenced by map design compone nts and the skewed distribution of the visual weight of mapping uni ts. Thus it becomes critical that the statistical information be embedded in the map to assist readers to develop (objective) statisti cal understandin g of the mapped variable” (Kumar, 2004, p. 217). Mapping the state of Florida in its entirety is a challe nge, that is to say, mapping it without lopping off the western panha ndle and placing the amputated appendage somewhere else in order to give balan ce to the map design. Confined within a rectangular neatline, the empty expanse of the Gulf of Mexico gives Florida an unstable look. This thesis takes advantage of th e vacant space with th e placement of two additional statistical graphics as legend aids to the map: frequency histograms and boxand-whisker graphs. Both Kumar (2004), proponent of the frequency histogram legend (FHL), and Kostbade (1981), proponent of the box-and-whisker legend (BWL), make compelling arguments for the use of these information enhancers on choropleth maps. Kumar recognizes the typical issue of having to compromise map space when adding components, and suggests completely replac ing the standard le gend with the FHL. However, as stated above, the layout of Florida maps is well suited for additional graphics and therefore this thes is follows the example in the Atlas of Mortality from Selected Diseases where Mason et al. (1981) suppleme nt the standard legend with the FHL. The map layout for each FCHDI indicator us ed in this thesis includes two maps of Florida counties; one showing all four ranked quartiles, and, to ai d cluster recognition,

PAGE 58

49 one showing only the upper and lower quartiles. This approach allows both the FHL and BWL to be added to the map layout. Although a frequency histogram by itself is useful for understanding the distribution of data values, the FHL is furthe r enhanced here by breaking the bars of the graph down into their respectiv e quartile and applying the colo r assigned to that quartile so that the distribution curve clearly shows the transition fr om the lower quartile to the upper quartile. The box-and-whisker diagram developed by J.W. Tukey also describes frequency distribution of variables, but give s a clearer picture of the quartiles and how they are grouped around the median of the da ta set, and identifies the position of any outliers. Figure 3-4 is an example of the FHL and BWL using the malignant neoplasm data set for Florida (FDOH, 2001). The FH L shows the lower quartile distributed between values 0.000 and 0.396, the 2nd quartile between 0.400 and 0.460, the 3rd quartile between 0.468 and 0.590, and the upper quartile between values 0.598 and 1.000. Figure 3-4: Example of Frequency Histograms and Box-and-Whisker Diagrams. The BWL in Figure 3-4 shows the distribution for the same data set, but identifies the median to be 0.468, and also three outlier data points: two at the lo wer end of the scale (0.000 and 0.089) and one at the upper end of the scale (1.000). As will be seen in

PAGE 59

50 Chapter Four, the FHL and BWL help the ma p-reader get a better sense of the county rankings by providing a visual di splay of frequency distributions to complement spatial patterns. Box-and-whiskers graphing is not included in the Office Excel 2003 chart wizard, nor is a frequency curv e plotted on Excel’s frequency histograms, so the graphs found on the maps were generated using SPSS 13.0 statistical software. Alternative variable: the natural amenities indicator In several recent studies of rural devel opment and domestic migration trends in the United States, natural amenities are cited as a major pull factor (McGranahan, 1999; Shumway and Otterstrom, 2001; Green, 2002; Kwang-Koo et al., 2005). This is particularly true in a retirement and tourist destination state such as Florida. In their study on the effects of Florida’s economi c and population growth on natural lands conservation, Kiker and Hodges (2002) find the st ate leads much of th e nation in terms of non-traditional growth, th at is, growth based on services and natural amenities (tourism) rather than on natural resour ce extraction, agricult ure, and manufacturing. If the idea that natural amenities affect in-migration and t ourism is accepted as true, and in-migration and tourism are beneficial to local economics, then it follows that natural amenities affect socioeconomic conditions. Bukenya and Fraser (2002) take an altern ative view of natural amenities in their human development index for Alabama counties; that environmental factors affect human development itself. As a measure of Alab ama’s environmental factors, Bukenya and Fraser used a proxy indicator based on the Natural Amenities Scale published by the Economic Research Service, U.S. Departme nt of Agriculture (McGranahan, 1999). This

PAGE 60

51 natural amenities scale is a county-level co mposite index of six measurements including climate, topographic variation, and surface water area, which, according to the authors, represent the natural attractiveness of an area as a place to live. Based on 1999 data from all counties in the lower 48 states, the indicators described by ERS (1999) represent measures of: Warm winter (average January temperature) Winter sun (average January days of sun) Temperate summer (low wint er-summer temperature gap) Summer humidity (low average July humidity) Water area (water area as pr oportion of total county area) Topographic variati on (topography scale) As can be imagined, Florida does no t rank high on the topographic variation indicator, which classifies counties by la nd-surface form codes ranging from 1 (flat plains) to 21 (high mountains). Eleven countie s are rated 4 (irregular plains), while the remaining fifty-six are rated 1. However, as McGranahan (2005) notes, the “six characteristics do not tend to be found toge ther; often there are tradeoffs... The natural amenities scale is designed to reflect these tradeoffs by combining these characteristics into a single scale” (p.43). After combining the indicator values, the natural amenities scale ranks each county according to its st andard deviation from the overall mean: 1 = Over -2 (Low) 2 = -1 to -2 3 = 0 to -1 4 = 0 to 1 5 = 1 to 2 6 = 2 to 3 7 = Over 3 (High) In Florida, all counties rank above a 4 on the natural amenities scale, ranging from Monroe County (overall scal e value: 6.05, rank: 6) to Li berty County (overall scale

PAGE 61

52 value: 0.36, rank: 4). In Florida there are fifteen Rank 6 counties, twenty-four Rank 5 counties, and twenty-eig ht Rank 4 counties. Although the subjectivity of what constitute s “attractiveness” may come into play here, and the fact that this is a national-level scale rather than a Florida-specific scale, as a standardized measure (z-score) the Natura l Amenities Scale values fits the format requirements of the FCHDI quite well. In summary, Chapter Three describes th e primary components and issues of the FCHDI, establishing a blueprint for the m odified index. Specifically, the nine proxy socioeconomic indicators used in buildi ng the mortality, education, and economic dimensions are discussed along with thei r secondary data sources. The method for normalizing the data (linear scaling transformation) and, where required, the method for adjusting the raw data for ne t-positive results is disc ussed. The issue of uniform weighting versus explicit weighting of the indicator values is addressed, as well as considerations for interval cl assification, color scheme choices and the inclusion of both frequency histograms and box-and-whisker graphs to supplement the map legends. The calculation and mapping software (Excel 2003; SPSS 13.0; and Adobe Illustrator 10) is also briefly discussed. In the final sec tion of Chapter Three, the natural amenities alternative variable indicator is introduced, a nd its statistical format fit with the FCHDI is discussed.

PAGE 62

53 Chapter Four: Results This chapter presents the results of th e indexing process as described in Chapter Three, which is: normalizing the raw data for each indicator, calculat ing the three interim indices, calculating the FCHDI, and ranking th e counties. In additi on, the outcome from testing the natural amenities alternative indi cator against the base FCHDI values is presented, along with the resulting cha nge in the Florid a county ranking. The processing of data through the FCHDI entails calculating, plotting, and to some degree interpreting the indicator rankings, however it is important to keep in mind the original postulates of the thesis: 1. Can the FCHDI be effectively applied to Florida at the county-level? 2. Is choropleth mapping advantageous in disc erning territorial pa tterns and trends? The results are presented in choropleth mapping format, and are represented as net-positive, that is, each indicator, interi m index, summary index, and test indicator is ranked and plotted with the index values most positive to social well-being in the upper quartile, and those least positive values in the lower quartile. To standardize the choropleth representation thr oughout the thesis, four sequential hues are used, with the darker hues representing th e upper quartiles and the lighter hues representing the lower quartiles. To spatially highlight the uppe r and lower quartile distribution, a second choropleth map is plotted showing only the respective upper and lowe r quartiles, the mid-

PAGE 63

54 quartiles are combined and converted to gray-scale. As recommended by Kostbade (1981) and Kumar (2004), box-and-whiskers diagrams and simplified frequency histograms are included on each map, furthe r highlighting data distribution around the median, and indicating whether the distribution is skewed or whether outliers are present in the data sets. Reference data tables created in Ex cel are found in Appendix A, which include the observed and adjusted data collected from the sources for each of the ten indicators, along with the calculations for the three in terim indices, the FCHDI, and the Florida county rankings per the FCHDI. The purpose of applying the alternative va riable to the FCHDI is not only to determine what rank position the counties move to, but also to analyze whether and to what degree the variable impacts the FCHDI ra nkings. Therefore, the data results for the alternative natural amenities indicator are pres ented somewhat differently than the basic FCHDI indicators. The indicator value map, show n with the standard format used for the previous indicators, is fo llowed by a figure showing the original FCHDI map and the FCHDI plus natural amenities. The final figure has the upper choropleth map showing whether each county’s ranking is raised or lowered, and the lower map showing the degree of change (if any) in plus/minus standa rd deviations. These maps visually describe whether or not the alternative indicators affect the FCHDI rankings, by how much, and whether there is a spatial component to the effect.

PAGE 64

55 Mortality Interim Index The FCHDI mortality interim index is a proxy for the ‘long and healthy life’ dimension used by the UNHDP, and is comprise d of three indicators: mortality rate; child mortality rate; and the leading cause of deat h indicator, which is a composite of the two most common causes of death in Florida: heart disease and malignant neoplasm. Mortality Rate (per 1,000 population) Death is by most accounts a negative social factor, so in the FCHDI mortality index (Figure 4-1) a high mortality rate corr esponds to a low index value and is assigned a light hue. The 2000 death rate per 1,000 popul ation for resident Floridians is 8.0, slightly less than the national death rate of 8.5, ranging from a low of 6.4 in Leon County to a high of 16.6 in Citrus County. Of th e seventeen upper-quartile counties (low mortality rate), eleven are found in the norther n tier of the state. A cluster of three low mortality rate counties (Orange, Seminole, and Osceola) is found in th e central section of peninsular Florida, with the remaining three low mortality counties (Miami-Dade, Monroe and Hendry) in the south. The majo rity of high mortality rate counties are located in the central section of the peninsul a. Interestingly, the five counties with the highest death rates (#63: Sara sota, #64: Hernando, #65: Char lotte, #66: Pasco, and #67: Citrus) are all located on Florida’s central Gulf Coast. These counties also correlate closely to the counties with the highest ratio of population ove r the age of 65: #1: Charlotte County (34.72 percent) #3: Citrus County (32.19 percent) #4: Sarasota County (31.47 percent) #5: Hernando County (30.85 percent) #10: Pasco County (26.80 percent)

PAGE 65

56 Figure 4-1: Florida Resident Mortality Rate Indicator Values. A choropleth map of the Florida resident mortality rate indicator values in spatial context, showing the highest ranked counties (lowest resident mortality rates) in dark brown and the lowest ranked counties (highest mortality rates) in pale yellow.

PAGE 66

57 Child Mortality Rate As with resident mortality, child mortality is also a negative social indicator and therefore a high child mortality rate is show n in the lower quartile with a light yellow hue. The child mortality rate is calculated by dividing the number of child deaths (under the age of five years) per county from th e Florida Department of Health 2000 vital statistics report by the numb er of under five year-old children by county per the 2000 U.S. Census. The child mortality rate in Figure 4-2 ranged from 0.00 percent to 0.65 percent, a relatively narrow range. Three of the northern counties (Hamilton, Jefferson, and Lafayette) reported no child deaths in 2000 for a 100 percent survival rate. Glades County in the south-central se ction of the peninsula had a 0.65 percent child mortality rate (or a 99.35 percent survival rate), the lowest in the state. Judging by the choropleth maps alone, it would appear that the upper and lo wer quartiles are quite mixed in the northern tier of the state, w ith an odd cluster of low quart ile counties in close proximity to Lake Okeechobee in the south-central pe ninsula. The frequency histogram for this indicator shows a negative or left-skew in the distribution, an d the box-and-whisker diagram shows a narrow interquartile range centered closely on the median. The disturbing results of the box-and-whisker di agram are the three outliers (Okeechobee, Gadsden, and Suwannee counties) and two extreme outliers (Gulf and Glades counties) in the data set. This raises questions on th e soundness of using child mortality rate as an indicator in the FCHDI, suggesti ng that in future research ei ther more analysis be done, or another proxy indicator should be sought. See Table A-2 in Appendix A for values and conversion of the child mortality indicator.

PAGE 67

58 Figure 4-2: Florida Child Mortality Rate Indicator Values. A choropleth map of the Florida child mortality rate indicator values in spatial context, showing the highest ranked counties (lowest child mortality rates) in dark brown and the lowest ranked counties (highest child mortality rates) in pale yellow.

PAGE 68

59 Heart Disease As shown in Figure 4-3, with the excepti on of Monroe County at the southern tip of Florida, all of the counties with the lowe st incidence of death attributed to heart disease are found in the northern ti er of the state. The rate of deaths attributed to heart disease range from a low of 16.4 percent in Union County to a high of 34 percent in Charlotte County. Charlotte County has Florid a’s highest percentage of its population over the age of 65, however, the ag e factor does not appear as closely correlated to heart disease across the rest of the state as it does with the resident mortality rate. The largest cluster of counties with low incidence of h eart disease is in the north-central region, centered roughly on Union County. The major cl uster of counties with a high number of deaths due to heart disease is in south Florida, quite notic eably along the Atlantic coast (See St. Lucie, Martin, Palm Beach, Brow ard, and Miami-Dade counties). Both the frequency histogram and the box-and-whisker diagram indicate that the majority of indicator values are in the lo wer end of the scale, the dist ribution being skewed to the right (positive), with the lowe st heart disease rate county (Union County) actually falling as an outlier. See Table A-3 in appendix A for values a nd conversion of the heart disease indicator.

PAGE 69

60 Figure 4-3: Florida Heart Disease Indicator Values. A choropleth map of the Florida heart disease death rate indicator values in spatial context, showing the highest ranked counties (lowest heart disease rates) in dark brown and the lowest ranked counties (highest heart disease rates) in pale yellow.

PAGE 70

61 Malignant Neoplasm (Cancer) The death rate due to malignant neoplasm, or cancer, in Florida ranges from a low of 16.9 percent in Hardee County to a high of 30.8 percent in Glades County. It is interesting that the countie s representing the tw o opposite extremes on this scale are situated in close proximity to each other in south-central peninsular Florida (Figure 4-4), suggesting that the prevalence or absence of cancer may not be st rongly correlated to location. However, thirteen of the seventeen c ounties with the highest cancer levels are situated along the coast, while only five of the seventeen counties with the lowest cancer levels are coastal counties. There are too many unknown vari ables from the source data table such as type of cancer, race factors, or the accessibility to cancer treatment facilities to place more significance to the distribution pattern other than to note that clusters of counties with high cancer levels are mo re prevalent along the coast. It is interesting to note that comparing the distribution between heart disease and cancer, seven counties completely swap quart iles. Most notable is Union County, which is in the number one position on the heart diseas e scale (low rate), but drops to sixty-sixth position on the cancer scale (high rate). The re maining six counties th at reverse quartiles are Miami-Dade and Monroe counties at the southern tip of the peninsula, Holmes, Jackson, and Madison counties along the State’ s northern border, and St. Johns County on the northern Atlantic Coast. The box-and-whisker diagram shows one out lier (Hardee County) in the upper range of the indicator scale and two outlier s (Union County and Glades County) in the lower range. See Table A-4 in appendix A for values and conversion of the malignant neoplasm indicator.

PAGE 71

62 Figure 4-4: Florida Malignant Neoplasm Indicator Values. A choropleth map of the Florida cancer rate indicator values in sp atial context, showing the highest ranked counties (lowest cancer rates) in dark brown and the lowest ranked counties (highest cancer rates) in pale yellow.

PAGE 72

63 Combined Heart Disease and Malignant Neoplasm Values After the heart disease data and cancer data are normalized, combined, and averaged, the resulting indicator values show a distinct dist ribution pattern in Figure 4-5: all upper quartile coun ties with the exception of Ha rdee County are located in the northern tier of the state; and all lower quartile counties with the exception of Walton County are spread throughout the peninsula. Although no county maintained its exact rank position through all three permutations of the heart disease, mali gnant neoplasm, and composite indices, six counties (Baker, Bradford, Escambia, Jefferson, Lafayette and Suwannee) remain in the upper quartile throughout, and thr ee counties ( Martin, St. Luci e, and Sumter) remain in the lower quartile. When the county ranking fo r each index is compared against the other two and the cumulative position change up or down the scale for each county is summed, (a possible 201 rank changes) the range between the greatest and least overall number of rank changes in Table 4-1 is from 130 ch anges (Union County) to 6 changes (Lake County): Table 4-1: Extremes in rank changes a fter combining heart disease and cancer Counties with greater rank changes Counties with fewer rank changes Union.................................................130 Holmes..............................................118 Miami-Dade......................................110 Monroe..............................................108 Madison.............................................106 St. Johns............................................100 Lake......................................................6 Duval....................................................8 Escambia..............................................8 Orange..................................................8 Baker..................................................10 Gadsden..............................................10 Hendry................................................10 Jefferson.............................................10 Volusia...............................................10 See Table A-5 in appendix A for values a nd conversion of the combined heart disease and malignant neoplasm indicators.

PAGE 73

64 Figure 4-5: Combined Heart Di sease and Cancer Indicator Values. A choropleth map of the leading causes of death values in spatial context, showing the highest ranked counties (low death rate from heart disease or cancer) in dark brown and the lowest ranked counties (high death rate from h eart disease or cancer) in pale yellow.

PAGE 74

65 Mortality Interim Index The mortality interim index is calculated by averaging the three mortality indicator values using the formula: Resident Mortality Child Mortality Combined Heart Indicator + Indica tor + Disease/Cancer Indicator 3 Table A-12 in appendix A shows the calcula tion of the mortality interim index for all of the Florida counties. The box-and-whis ker diagram in Figure 46 clearly illustrates how using this method modifies the indicato r values by reducing skew of distribution around the mean and reducing the effect of ou tliers. In the mortality interim index, the values range from a high of 0.834 (Lafayette County) to a low of 0.247 (Glades County). Figure 4-6: Box-and-whisker diagrams for the Mortality Interim Index The map layout for the interim indices is changed in Figure 4-7 to show the quartile spatial patterns, and also id entify the counties by rank a nd include their corresponding index value.

PAGE 75

66 Figure 4-7: Florida County Mortality Interim Index. A choropleth map of the mortality interim index values in spatial context, showing the highest ranked counties for low mortality rates (per the FCHDI indicators) in dark brown and the lowest ranked counties in pale yellow.

PAGE 76

67 Education Interim Index The FCHDI education interim index is a proxy measurement for the ‘knowledge’ dimension used by the UNDP. As previously noted, the focus of this index is education attainment, and is comprised of three indi cators: non-high school graduates, high school graduation or higher, and b achelor’s degree or higher. Non-High School Graduate Preferring not to use the subjective term “dropout,” th is indicator measures only the percentage of what the U.S. Census Bu reau defines as each county’s population of 16 to 19 year olds who are not enrolled in school and are not high school graduates. Since lacking a high school diploma can have a ne gative social and economic impact on the individual, the percentage of this age-sp ecific population of nongraduates affects the overall socioeconomic well-being of the count y. As with the other negative socioeconomic indicators, a high indicator valu e correlates to a lo w quartile ranking. The state average of non-h igh school graduates in 2000 is 11.9 percent, somewhat higher than the national average of 10.9 percen t. At the county level, as shown in Figure 4-8, the non-high school graduate percentages range from a low of 3.6 in Leon County to a high of 46.3 in DeSoto County. Loose clusters of upper quartile count ies are located in the northwestern panhandle, the northeastern counties, and the southern peninsula. There is a cluster of low quartile counties in the north central region of the state, but by far, the largest cluster is in the central region of the peninsula. The box-and-whisker diagram shows a high median value and a narrow range of interquartile values with DeSoto and Lafayette as outliers.

PAGE 77

68 Figure 4-8: Florida Non-High School Graduate Indicator Values. A choropleth map of the Florida high school non-graduate indicator values in spatial context, showing the highest ranked counties (lowest non-graduate rates) in dark brown and the lowest ranked counties (highest non-graduate rates) in pale yellow.

PAGE 78

69 Education Attainment : High School Graduation and Higher The ‘high school graduation and higher’ indicator measures a positive socioeconomic attribute and requires no net-pos itive conversion. The indicator uses the percentage of each county’s population 25 years or older who have either graduated from high school or graduated and continued their education. The percen tages for this group range from 89.1 in Leon County to 47.8 in Po lk County. The distribut ion in Figure 4-9 of upper and lower quartiles is in teresting in that the upper quartile counties tend to be coastal counties while the lowe r quartile counties tend to be interior counties in the central region of the peninsula and in th e north-central panhandle. The three counties with the highest indicator values are Leon County (1.000), Seminole County (0.990), and Alachua County (0.976), all interi or counties. It is tempting to conclude that Leon and Alachua counties have such high values because Florida State University and the University of Florida are located in these count ies respectively, particul arly in the case of Leon County, which is in close proximity to predominately lower quartile counties. However, other factors must be taken into consideration such as high-skill employment and, in the case of Leon County, the polit ical establishment of the capitol city: Tallahassee. The frequency histogram and the box-andwhisker diagram also show an unusual negative or left-skewed distri bution pattern coinciding with the interquartile located above the mid-scale point, with Polk County being the only negative outlier. See Table A-7 in appendix A for values and conversion of the edu cation attainment: high school graduate or higher indicator.

PAGE 79

70 Figure 4-9: Education Attainment: High School Graduate or Higher. A choropleth map of the education attainment (high school graduate or higher) indicator values in spatial context, showing the highest ranked counties in dark brown and the lowest ranked counties in pale yellow.

PAGE 80

71 Education Attainment : Bachelor’s Degree and Higher The ‘bachelor’s degree and higher’ indicat or is a refinement of the previous education attainment indicator in that thes e levels of education are not state mandated like compulsory elementary education. Access to these levels of education can be restricted for economically marginalized popul ations, and therefore, measures of this level of attainment indicate a county’s general socioeconomic health that either promotes, supports, or hinders hi gher education. As shown in Figure 4-10, the percentage of Florida’s 2000 population that is 25 years of age or older who have received a b achelor’s degree or higher ranges from a high of 41.7 in Leon County to a low of 6.8 in Di xie County. As might be expected, the distribution pattern of the upper 50 percent of the indica tor values show a strong correlation to the proximity of institutes of higher educati on, while those in the lower 50 percent tend to be more removed. The two count ies that appear to hi ghlight this trend are Leon County (ranked number one with an indi cator value of 1.000) and Alachua County (ranked number two with an indicator value of 0.914). Both the frequency histogram and the box-and-whisker diagrams show a strong positive or right-skewed distribution. The distri bution curve in the histogram is relatively flat compared to other histograms in th e FCHDI model (kurtosis = 0.468), and the frequency distribution does not fit the curve well. The boxand-whisker diagram shows a relatively wide interquartile with a median that is located quite low on the indicator scale, a long upper whisker indicating a right-sk ew, and one outlier (Leon County). See Table A-8 in appendix A for values and conversion of the education attainment: Bachelor’s De gree or higher indicator.

PAGE 81

72 Figure 4-10: Education Attainment: Bachelors Degree or Higher. A choropleth map of the education attainment (bachelor’s degree or higher) indicator values in spatial context, showing the highest ranked counties in dark brown and the lowest ranked counties in pale yellow.

PAGE 82

73 Education Interim Index The education interim index is calcul ated by averaging the three education indicator values using the formula: Non-High School High School Plus Bachelor’s Degree Graduate Indicator + Indicato r + Plus Indicator 3 Table A-13 in appendix A shows the calculation of the education interim index for all of the Florida counties. The box-and-whisker di agram in Figure 4-11 shows the median for the interim index to be slightly above mid-scale (0.573), and a wider range between maximum and minimum value than in the mo rtality interim index. In the education interim index (Figure 4-12), the values ra nge from a high of 1.000 (Leon County) to a low of 0.142 (DeSoto County). Figure 4-11: Box-and-whisker diagrams for the Education Interim Index

PAGE 83

74 Figure 4-12: Florida County Education Interim Index. A choropleth map of the interim education index values in spatial context, showing the highest ranked counties for education attainment (per the FCHDI indicators) in dark brown and the lowest ranked counties in pale yellow.

PAGE 84

75 Economic Interim Index The FCHDI economic interim index is a proxy measurement for the ‘decent standard of living’ dimension used by th e UNDP. The focus of this index is the economic well-being not directly addressed in the previous indices, and is comprised of three indicators: county poverty level, per ca pita income, and a pric e level index value. Poverty Once again, because poverty has a nega tive socioeconomic impact, the source data values are adjusted for net-positive resu lts. In Figure 4-13 the percent of poverty at the county level ranges from a low of 6.8 in Clay County (upper quartile) to a high of 26.0 in Hamilton County (lower quartile). Th ere is a major cluster of low quartile counties in the central region of the northern tier and panhandle, with a scattering of four counties (Hardee, DeSoto, Hendry, and Miami-Da de) in the central and southern area of the peninsula. The majority of the central peninsula is in the tw o upper quartiles (low poverty), with a cluster in the north-east section of the state and a cluster in the western area of the panhandle. In this indicator, the clustering is more prominent when comparing the upper two quartiles against the lower two rather than only the fourth and first quartiles. The box-and-whisker diagram indicates a negative or left-skew with a wide quartile range of indicator values coveri ng the scale from 1.000 to 0.000 (no outliers), and a high median point of 0.641. See Table A-9 in appendix A for values and conversion of the poverty indicator.

PAGE 85

76 Figure 4-13: Florida Poverty Indicator Values. A choropleth map of the Florida poverty rate indicator values in spatial context, showing the highest ranked counties (lowest poverty rates) in dark brown and the lowest ranked counties (highest poverty rates) in pale yellow.

PAGE 86

77 Per Capita Income Since in most of the world, and certainly in Florida, a higher per capita income is associated with a higher level of socioec onomic well-being, this indicator uses the observed data values with no adjustment for net-positive results. Issue may be taken with this indicator in that it uses aggregated data and therefore is not sens itive to disparities of income distribution within the counties. Prev ious studies compensate for this by using a Gini coefficient to emphasize inequities (Bukenya and Fraser, 2002, Hanham et al., 2002), however, in that this thesis is concer ns itself with the ove rall ranking of each county and not inequities, th e aggregated per capita income values were deemed sufficient. In Figure 4-14 there is a substantial range of county-level per capita income, from a low of $10,562 in Hamilton County to a high of $31,195 in Collier County. With the exception of Seminole County in the east-centr al peninsula, all upper quartile counties are located along the coast. The majority of lower quartile counties are in the central section of the panhandle with a smaller, loos er cluster in the s outh-central peninsula (Hardee, DeSoto, Hendry, and Okeechobee). Both the box-and-whisker and frequency histogram show a distinctly positive or right-skewed distribution with a low median. Collier County is the only outlier, with the quartiles ranging from 0.000 (Hamilt on) to 0.922 ($29,584 – Martin County). See Table A-10 in appendix A for values and conversion of the per capita income indicator.

PAGE 87

78 Figure 4-14: Florida Per Capita Income Indicator Values. A choropleth map of the Florida per capita income indicator values in spatial context, showing the highest ranked counties in dark brown and the lowest ranked counties in pale yellow

PAGE 88

79 Price Level Index The Price Level Index developed by th e Bureau of Economic and Business Research at the University of Florida measures the dissimilarity in the cost of living (purchasing a specific set of goods and services) across the state at the county level. Because a high cost of living negatively a ffects social well-being, adjusted indicator values are used. These adjusted values ra nged from a low of 90.68 percent of the state averaged cost of living in Suwannee County to 108.53 percent in Palm Beach County. In Figure 4-15 the cluster patterns of the price level index show distinct delineation between the western and central panhandl e to the southern peninsula. The more populous southern coastal counties have a highe r average cost of living than the le ss populous interior counties of the central peninsula, and much higher than the predominantly rural northern counties. Assuming the median in the box-and-whisker diagram is a fair measure of the state averaged cost of living, there is a nega tive or left-skew dist ribution supported by the histogram, and three outliers representing counties with a high cost of living (Palm Beach, Monroe, and Broward counties). See Table A-11 in appendix A for the data of the Price Level Index.

PAGE 89

80 Figure 4-15: Florida Price Level Index Indicator Values. A choropleth map of the Florida Price Level Index values in spatial context, showing the highest ranked counties (lowest cost of living) in dark brown and the lowest ranked counties (highest cost of living) in pale yellow.

PAGE 90

81 Economic Interim Index The economic interim index is calcul ated by averaging the three economic indicator values using the formula: Poverty Per Capita Income Price Level Index Indicator + Indicator + Indicator 3 Table A-14 in appendix A shows the calculation of the economic interim index for all of the Florida counties. The box-and-whisker di agram in Figure 4-16 shows the median for the interim index to be slightly above mi d-scale (0.574), but with a narrower range between maximum and minimum value than in the mortality interim (0.834 to 0.247) and education interim (1.000 to 0.142) indices. In the economic interim index shown in Figure 4-17, the education interim index valu es range from a high of 0.818 (Saint Johns County) to a low of 0.302 (Hendry County). Figure 4-16: Box-and-whis ker diagrams for the Economic Interim Index

PAGE 91

82 Figure 4-17: Florida County Economic Interim Index. A choropleth map of the interim economic index values in spatial context, showing the economically highest ranked counties (per the FCHDI indicators)in dark brown and the lowest ranked counties in pale yellow.

PAGE 92

83 The Florida County Human Development Index (FCHDI) The final FCHDI is calculated by averaging the three interim indices using the formula: Mortality Education Economic Interim Index + Interim Index + Interim Index 3 For the FCHDI, the data tables are show n in context with the choropleth map. Table 4-2 shows the calculation and ranking of counties for the FCHDI (see also Table A-15 in appendix A). On a 0.000 to 1.000 scal e, the index values in Table 4-2 and Figure 4-18 range from a high of 0.793 (St. Johns County) to a low of 0.352 (DeSoto County) with a median value of 0.554 (Calhoun County). Table 5-2: Calculating the Florida County Human Development Index. Saint Johns County to Bay County Rank County Mortality Interim Index Education Interim Index Economic Interim Index Sum FCHDI (sum / 3) 1 Saint Johns 0.6770.8840.8182.380 0.793 2 Seminole 0.6980.8580.7582.314 0.771 3 Leon 0.7461.0000.5292.276 0.759 4 Santa Rosa 0.7200.7610.7292.210 0.737 5 Okaloosa 0.6370.7970.7332.168 0.723 6 Clay 0.6770.7280.7602.165 0.722 7 Alachua 0.7330.9610.4542.148 0.716 8 Nassau 0.6150.6750.7822.072 0.691 9 Orange 0.7000.7200.5922.012 0.671 10 Collier 0.5910.6710.7321.995 0.665 11 Monroe 0.6850.7570.5431.984 0.661 12 Brevard 0.5540.7470.6801.980 0.660 13 Wakulla 0.7510.5810.6321.963 0.654 14 Duval 0.6470.6900.6241.961 0.654 15 Sarasota 0.4060.7820.7591.947 0.649 16 Martin 0.4160.7180.8021.936 0.645 17 Osceola 0.7250.6010.5941.919 0.640 18 Escambia 0.6100.7050.6001.916 0.639 19 Flagler 0.4690.7330.7101.912 0.637 20 Bay 0.6140.6520.6371.904 0.635

PAGE 93

84Table 5-2: Calculatin g the Florida Count y Human Development Index. Indian River County to DeSoto County RankCounty Mortality Interim Index Education Interim Index Economic Interim IndexSum FCHDI (sum / 3) 21Indian River0.4320.6920.7711.894 0.631 22Hillsbor ough0.6190.6980.5691.887 0.629 23Pinellas0.5070.7080.6201.835 0.612 24Lee0.4780.6560.6991.833 0.611 25Walton0.6130.5970.6181.828 0.609 26Jefferson0.7230.5610.5081.792 0.597 27Palm Beach0.4760.7420.5741.792 0.597 28Lake0.4990.6020.6911.791 0.597 29Broward0.5630.7320.4941.789 0.596 30Volusia0.4690.6510.6591.778 0.593 31Manatee0.4510.6340.6841.769 0.590 32Charlotte0.3550.6660.7261.747 0.582 33Lafayette0.8340.3240.5121.670 0.557 34Calhoun0.7110.4980.4511.661 0.554 35Baker0.7020.3820.5691.653 0.551 36Marion0.4670.5560.6271.651 0.550 37Jackson0.5910.5220.5351.648 0.549 38Hernando0.3830.5730.6891.646 0.549 39Franklin0.6580.4900.4861.634 0.545 40Union0.6410.4110.5681.621 0.540 41Bradford0.6220.4630.5341.620 0.540 42Pasco0.4240.5650.6201.609 0.536 43Columbia0.5330.4990.5731.604 0.535 44Saint Lucie0.4570.5520.5801.589 0.530 45Gilchrist0.5180.4770.5851.580 0.527 46Citrus0.3330.5730.6731.578 0.526 47Sumter0.4280.5150.6131.556 0.519 48Miami-Dade0.6310.5860.3061.523 0.508 49Levy0.5320.4800.5041.516 0.505 50Taylor0.5810.4350.4951.511 0.504 51Washington0.4960.4880.5091.492 0.497 52Liberty0.6070.3740.5001.481 0.494 53Holmes0.5520.4510.4631.466 0.489 54Suwannee0.4630.4720.5301.465 0.488 55Gadsden0.5080.5030.4491.460 0.487 56Gulf0.3740.5170.5301.421 0.474 57Polk0.5150.3010.6011.417 0.472 58Highlands0.3440.5040.5651.413 0.471 59Hamilton0.7390.3370.3181.394 0.465 60Putnam0.4650.4460.4591.370 0.457 61Madison0.5390.4410.3861.366 0.455 62Dixie0.4830.3920.4641.339 0.446 63Hardee0.7500.2580.3301.338 0.446 64Okeechobee0.4030.3350.5031.241 0.414 65Glades0.2470.4690.4981.215 0.405 66Hendry0.6530.2270.3021.1820.394 67Desoto0.5470.1420.3681.057 0.352

PAGE 94

85 Figure 4-18: Florida County Human Development Index. A choropleth map of the FCHDI values in spatial context, showing the highest ranked socioeconomic well-being counties in dark brown and the lowest ranked socioeconomic well-being counties in pale yellow.

PAGE 95

86 The choropleth map of the FCHDI in Fi gure 4-18 shows several interesting patterns of socioeconomic conditions. The cluster-patterns for the upper and lower quartiles appear quite different between the panhandle/northe rn-tier region of the state where clustering is smaller a nd generally looser, and the pe ninsular/Atlantic Coast region where the clusters are larger and more defi ned. Three of the more prominent of these distinguishable groups are the upper quartile clusters in the north-east corner around Jacksonville and central-east around Orlando, and the large lowe r quartile cluster to the west of Lake Okeechobee in the central re gion of the peninsula. A prominent midquartile clustering is the group of second quart ile (lower-mid) countie s that run north to south from the Georgia border we st of Jacksonville to the no rth of the Tampa Bay area on the Gulf Coast. Two aspects of these cluster patterns ar e that the lower and lower-mid quartile clusters tend to be larger than the upper quartile clusters, and they are more homogenous in terms of rank. For example, the large upper quartile cluster near Jacksonville has counties ranked #1, #6, #8 and #14, a spread of fourteen rank positions across four counties, and the Orlando cluster has coun ties ranked #2, #9, #12, and #17, a spread of sixteen rank positions, again, across four countie s. In comparison, the Georgia to Gulf Coast cluster has eleven of the quartile’ s sixteen counties, ranked #3536, #38, #40-43, #45-47, and #49, for a narrower (more homogeno us) ranking spread, while the Lake Okeechobee cluster is made up of counties ra nked #57 and #58 along with the five lowest ranked counties: #63 #67. The quartile clus tering is more recognizable in Figure 4-19 where each FCHDI quartile is broken out and plotted separately, allowing for comparisons that are visually less cluttered.

PAGE 96

87 Figure 4-19: FCHDI results broken out by quartile. By breaking the quartiles out, one quartile per map, the clustering patterns become more evident It is in this grouping that the visual relationship be tween the FCHDI and urban areas begins to emerge. To highlight this pattern, Florida’s urban areas are mapped in

PAGE 97

88 Figure 4-20 according to metropolitan statisti cal area (MSA) countie s as defined by the Federal Office of Management and Budget. Figure 4-20: Comparing the upper quartiles of the FCHDI to MSA counties The Federal Office of Management a nd Budget (OMB) define these large urbanized areas as “an area containing a recognized population nuc leus and adjacent communities that have a high degree of in tegration with that nucleus” (OMB, 2000,

PAGE 98

89 p.82228), specifically a high degree of social and economic in tegration. These areas are not constrained by county boundaries, and often consist of more than one county. For example, the Orlando MSA incorporates Osceo la, Orange, Seminole, and Lake County. According to the OMB’s 1999 standards, there are 20 MSAs in Florida, spreading across 34 of the state’s 67 counties as shown in Fi gure 4-20 above. When the pattern of MSA counties is compared to the pattern of combined third and fourth quartile FCHDI counties, definite similaritie s are seen, suggesting a patt ern linking urban areas and socioeconomic well-being. As noted in the text, there are data ta bles in Appendix A that correspond to the FCHDI indicators and indices. The tables for each of the nine indicators (plus the two sub-indicators: heart disease and cancer) co ntain the raw data, adjusted data where required for net-positive, and th e calculated FCHDI values. At the bottom of these tables are descriptive statistics information generate d in Excel for the raw data (observed or adjusted) and the calculated FCHDI values, and the upper quartile delineation values. The indicator tables are followed by interim index and FCHDI calculation tables, which in the case of the interim indices are the converted indicator values su mmed and averaged, and in the case of the FCHDI, are the three interim indices summed and averaged. Table A-16 shows the county rankings according to each co unty’s FCHDI value, the quartile, and the rank value as a percentage of the FCHDI data set.

PAGE 99

90 FCHDI plus Natural Amenities Using the FCHDI data set as a base sta ndard for the dimensions of mortality, education, and economics, the addition of an environmental dimension to the model can now be analyzed. As described earlier, the Natural Amenities Scale (NAS) developed by the USDA Economic Research Service is used for the environment indicator. It is important to reiterate that this is a nationa l county-level scale a nd does not indicate the subjective quality of a Florida county’s environm ent. Rather, the scale measures to what degree a combined set of climate and topologi c factors exist within the counties of the lower 48 United States. As with the previously selected indicators, the data values for Florida counties taken directly from the NAS are normalized to the FCHDI model using the linear scaling transformati on formula. This not only formats the data to the FCHDI model, but also confines the data set to Florida minimum/maximum values rather than national values. The normalized values for the Florida c ounties are plotted onto the choropleth map in Figure 4-21 using the same layout as the previous FCHDI indicators. The Florida natural amenities values from the NAS range from a high of 6.05 in Monroe County to a low of 0.36 in Liberty County (see Table A-17 in appendix A). As can be seen in Figure 4-21, the clustering of the quartiles is not only quite strong, but also highly regional, with the upper quartiles grouped predominantly in s outhern peninsular Florida, and the lower quartiles in the northern tier of the state. Only two counties break from this general pattern: Wakulla County in the north panhandl e and Hardee County in the south-central peninsula.

PAGE 100

91 Figure 4-21: Florida Natural Amenities Indicator Values. A choropleth map of the Florida Natural Amenities Index values in spatial context, showing the highest ranked counties in dark brown and the lowest ranked counties in pale yellow

PAGE 101

92 Now that the Florida natural amenities indicator values are established, they can be applied to the FCHDI model. Because the FCHDI is calculated using averaged interim index values, which in turn are calculated using averaged indicator values, the natural amenity interi m values can not simply be added to the FCHDI. First the FCHDI must be disaggregat ed to the level of its nine indicators, and then these nine values for each county ar e summed. It is at this point that the Florida natural amenities indicator values are added as shown in Table 4-3. This combined value is then averaged to provide the FCHDI plus Natural Amenity (FCHDINA) values. It is interesting to note that when th e Florida natural amenities values are added to the FCHDI, the highest ranked county (Saint Johns) and the lowest ranked county (DeSoto) maintain their rank positions. When the values for the FCHDI and the FCHDINA models are plo tted by quartiles onto comparative choropleth maps as shown in Figure 4-22, the natural amenities values do not appear to have quit the impact on the co mbined values that their strong northsouth regional pattern suggest ed when taken alone. This is not to conclude that natural amenities do not significantly influence the FCHDI ranking, but to spatially analyze the influence, a met hod other than quartile choropleth mapping is required

PAGE 102

93 Table 4-3: Calculation of FCHDI plus Florida Natural Amenities Values. The FCHDI is disaggregated to the level of its nine indicators, and the natural amenities values are added. County 9 FCHDI Indicators (summed) Natural Amenity Indicator Sum / 10 County 9 FCHDI Indicators (summed) Natural Amenity Indicator Sum / 10 Alachua 6.444 0.366 0.681 Lee 5.499 0.856 0.636 Baker 4.959 0.051 0.501 Leon 6.827 0.244 0.707 Bay 5.711 0.315 0.603 Levy 4.549 0.371 0.492 Bradford 4.859 0.172 0.503 Liberty 4.443 0.000 0.444 Brevard 5.940 0.627 0.657 Madison 4.098 0.165 0.426 Broward 5.366 0.812 0.618 Manatee 5.306 0.756 0.606 Calhoun 4.982 0.134 0.512 Marion 4.952 0.392 0.534 Charlotte 5.241 0.833 0.607 Martin 5.809 0.875 0.668 Citrus 4.735 0.540 0.527 Miami-Dade 4.570 0.900 0.547 Clay 6.494 0.290 0.678 Monroe 5.952 1.000 0.695 Collier 5. 984 0.815 0.680 Nassau 6.217 0.295 0.651 Columbia 4.813 0.040 0.485 Okaloosa 6.503 0.290 0.679 Desoto 3.171 0.418 0.359 Okeechobee 3. 722 0.763 0.448 Dixie 4.016 0.362 0.438 Orange 6.037 0.457 0.649 Duval 5.883 0.343 0.623 Osceola 5.757 0.728 0.648 Escambia 5.747 0.348 0.609 Palm Beach 5.376 0.840 0.622 Flagler 5.736 0.411 0.615 Pasco 4.826 0.529 0.535 Franklin 4.903 0.404 0.531 Pinellas 5.506 0.824 0.633 Gadsden 4.381 0.227 0.461 Polk 4.251 0.636 0.489 Gilchrist 4.741 0.149 0.489 Putnam 4.109 0.350 0.446 Glades 3.645 0.842 0.449 Saint Johns 7.139 0.460 0.760 Gulf 4.264 0.332 0.460 Saint Lucie 4.767 0.821 0.559 Hamilton 4.182 0.039 0.422 Santa Rosa 6.630 0.278 0.691 Hardee 4.014 0.332 0.435 Sarasota 5.841 0.777 0.662 Hendry 3.547 0.678 0.423 Seminole 6.941 0.489 0.743 Hernando 4.938 0.589 0.553 Sumter 4.668 0.436 0.510 Highlands 4.239 0.664 0.490 Suwannee 4.395 0.060 0.445 Hillsborough 5.660 0.696 0.636 Taylor 4.533 0.344 0.488 Holmes 4.399 0.093 0.449 Union 4.862 0.218 0.508 Indian River 5.683 0.766 0.645 Volusia 5.335 0.543 0.588 Jackson 4.944 0.246 0.519 Wakulla 5.889 0.279 0.617 Jefferson 5.376 0.288 0.566 Walton 5.484 0.320 0.580 Lafayette 5.009 0.084 0.509 Washington 4.477 0.279 0.476 Lake 5.374 0.534 0.591

PAGE 103

94 Figure 4-22: Comparing the FCHDI to the FCHDI plus Natural Amenities. This set of choropleth maps compares the or iginal FCHDI rankings to the FCDHI rankings where natural amenities indicator values are added.

PAGE 104

95 In Table 4-4, Florida’s sixty-seven countie s are first listed with their ranking by the original FCHDI, and second, with their ranking by the FCHDINA model. When the FCHDINA ranks are subtracted from the FCHDI ranks, the number of any rank position changes is found, and the changes are measured as either increasing (positive number) or decreasing (negative number). Fr om Table 4-4 it is clear that with the addition of natural amenities, 29 counties decrease in rank, 30 counties increase, and in 8 counties, no change in rank takes place. These changes ra nge from a drop of 11 (Baker County) to a gain of 13 rank positions (Miami-Dade County). In Figure 4-23, the counties where rank position increased are plotted in dark brow n, where rank position decreased in pale yellow, and neutral gray where no change takes place. Using the Descriptive Statistics function in Excel, the standard deviation (STDV) for this data set of rank changes is calculat ed at 5.562, providing a method to measure the changes in rank relative to the data set. Ther efore, a change between zero and 6 equals one STDV, between 7 and 11 equals two STDV, and between 12 and 17 equals three STDV. All together there is 1 increasing county at 3 STDV (MiamiDade), 16 counties at 2 STDV (7 increasing an d 9 increasing counties) at 2 STDV, 42 counties at 1 STDV, and 8 unchanged. Usi ng the seven-class divergent color scheme discussed earlier in Chapter Three, these th ree standard deviations of rank change are plotted in Figure 4-23 with neutral gray representing no change, three progressively darker shades of brown representing positive STDV, and three progressively darker shades of green representing negative STDV.

PAGE 105

96 Table 4-4: Overall Change in Rank between the FCHDI Model and the FCHDINA Model. Each county in this table is listed by its FCHDI rank followed by its FCHDINA rank. Subtracting the FCHDINA from the FCHDI gives the number of position changes in rank. FCHDI FCHDI + Natural Amenity Change in Rank FCHDI FCHDI + Natural Amenity Change in Rank 7 Alachua 6 Alachua 1 24 Lee 18 Lee 6 35 Baker 46 Baker -11 3 Leon 3 Leon 0 20 Bay 28 Bay -8 49 Levy 47 Levy 2 41 Bradford 45 Bradford -4 52 Liberty 61 Liberty -9 12 Brevard 12 Brevard 0 61 Madison 64 Madison -3 29 Broward 22 Broward 7 31 Manatee 27 Manatee 4 34 Calhoun 41 Calhoun -7 36 Marion 37 Marion -1 32 Charlotte 26 Charlotte 6 16 Martin 10 Martin 6 46 Citrus 39 Citrus 7 48 Miami-Dade 35 Miami-Dade 13 6 Clay 9 Clay -3 11 Monroe 4 Monroe 7 10 Collier 7 Collier 3 8 Nassau 13 Nassau -5 43 Columbia 52 Columbia -9 5 Okaloosa 8 Okaloosa -3 67 Desoto 67 Desoto 0 64 Okeechobee 58 Okeechobee 6 62 Dixie 62 Dixie 0 9 Orange 14 Orange -5 14 Duval 20 Duval -6 17 Osceola 15 Osceola 2 18 Escambia 25 Escambia -7 27 Palm Beach 21 Palm Beach 6 19 Flagler 24 Flagler -5 42 Pasco 36 Pasco 6 39 Franklin 38 Franklin 1 23 Pinellas 19 Pinellas 4 55 Gadsden 54 Gadsden 1 57 Polk 50 Polk 7 45 Gilchrist 49 Gilchrist -4 60 Putnam 59 Putnam 1 65 Glades 57 Glades 8 1 Saint Johns 1 Saint Johns 0 56 Gulf 55 Gulf 1 44 Saint Lucie 33 Saint Lucie 11 59 Hamilton 66 Hamilton -7 4 Santa Rosa 5 Santa Rosa -1 63 Hardee 63 Hardee 0 15 Sarasota 11 Sarasota 4 66 Hendry 65 Hendry 1 2 Seminole 2 Seminole 0 38 Hernando 34 Hernando 4 47 Sumter 42 Sumter 5 58 Highlands 48 Highlands 10 54 Suwannee 60 Suwannee -6 22 Hillsborough 17 Hillsborough 5 50 Taylor 51 Taylor -1 53 Holmes 56 Holmes -3 40 Union 44 Union -4 21 Indian River 16 Indian River 5 30 Volusia 30 Volusia 0 37 Jackson 40 Jackson -3 13 Wakulla 23 Wakulla -10 26 Jefferson 32 Jefferson -6 25 Walton 31 Walton -6 33 Lafayette 43 Lafayette -10 51 Washington 53 Washington -2 28 Lake 29 Lake -1

PAGE 106

97 Figure 4-23: FCHDI plus Natural Amenities. The upper map describes whether the addition of a natural amenities indicator to the FCHDI has a positive (increase), negative (decrease), or neutral effect to the FCHDI rankings. The lower map uses increments of standard deviation to measure the changes in rank.

PAGE 107

98 The upper choropleth map in Figure 4-23 si mply indicates whether there is an increase, a decrease, or no change in rank position when natural amenities values are added to the FCHDI. In this map, the str ong regional influence noted in Figure 4-21 (Florida Natural Amenities Indicator Values) is more apparent than in the quartile map in Figure 4-22. The lower choropleth map in Figure 4-23 indicates the amount of increase or decrease in rank position change, effectively illustrating the degree of influence natural amenities has on the FCHDI, or in the case of eight counties, the lack of influence. In summary, Chapter Four covers the piece-by-piece construction of the FCHDI; from calculating the nine indica tor values, three interim inde x values, and the cumulative ranking of the FCHDI itself, to a method of displaying the calculated results of the FCHDI so that spatial relations hips and patterns can be discer ned. The adaptability of the FCHDI model to incorporate additional socio economic dimensions is demonstrated with the inclusion of an environmental indicator, natural amenities.

PAGE 108

99 Chapter Five: Summary and Conclusions This chapter concludes the current effort with a summary of goals, methods, and results. This thesis began as a search for a composite measure of socioeconomic conditions for comparing Florida regions. In the socioeconomic indices literature, perhaps the most studied and best documented model is the Human Development Index (HDI) created by the United Nations De velopment Programme in 1989. Lanteigne (2005) states that the HDI is one of the most universally accepted index models available for examining and comparing socioeconomic conditions across nations, and Sharpe and Smith (2005) go so far as to refer to the HDI as the ‘gold standard’ for composite indicators. The HDI is a combined measurement of three key dimensions: health and longevity (mortality); knowledge (literacy) ; and a decent standard of living based on income and purchasing power (ul Haq, 2003). Fo r reasons stated in the literature, it seemed logical to use these same three dime nsions to compare socioeconomic conditions across Florida using the sixty-se ven counties as territorial unit s. A problem arose in data acquisition due to data constr aints at the county-level: data readily available at the national level such as life expectancy at birth were either difficult to find or non-existent at the county level. For this reason proxy indica tors using county-level data were selected for the three dimensions to create a modified version of the HDI. Precedent for this was found in two previous studies, one for a West Virginia HDI (Hanham, Berhanu, and

PAGE 109

100 Loveridge, 2000) and one for an Alabama HDI (Bukenya and Frasier, 2002), both using county-level data. Establishing a socioeconomic index only pa rtially addressed territorial indicators since spatial patterns and relationships were difficult to discern from a numeric scale alone. Therefore choropleth thematic mapping was considered for spatial context. The research aims for this thesis were to answer the following questions: 1. Can a modified model of the HDI be effectively applied to measure socioeconomic well-being across a conti guous territorial unit such as the State of Florida at the county-leve l using readily available data? And, 2. Is the geographic representation of the model’s rankings via choropleth mapping advantageous in di scerning territorial patte rns, relationships, and trends? For the Florida Counties Human Developm ent Index (FCHDI), nine indicators were used to calculate the mortality, education, and economic dimension indices, which in turn were used to compute the final FCHDI. The proxy indicators used here were similar to those used in the West Virginia and Alabama indices, and were intended to reflect those used in the HDI. The Florida county i ndicators selected were: Mortality Resident mortality rate Child mortality rate Leading causes of death Education Percent of high school non-graduates Percent of high school graduates or higher Percent of bachelo r’s degree or higher Economics Poverty rate Per capita income Price level Index (cost of living)

PAGE 110

101 Several of these indicators have a negative effect on socioeconomics. For example, the higher the poverty rate, the mo re negative the socioeconomic effect. For this reason, those indicators having an intr insic negative effect were adjusted by subtracting the observed value from unit y. The issue of implicit weighting (equal weighting) versus applying explicit weights on the indicators was looked into, however, it was impractical to establish and then justif y a non-uniform, explicit weighting scheme for the FCHDI at this stage of research. The data sets were all normalized using the linear scaling transformation (LST) method rather th an Gaussian normalization (z-score) for two reasons. First, the LST is the preferred method used in the HDI, and second, Salzman (2003) found that between the two me thods, the LST is the ‘best practice’ for standardizing variables because it assigns th e lowest implicit weights and efficiently contends with the directionality issue of ne t-positive results for aggregated data. To geographically display the FCHDI data five thematic mapping methods were considered (dot-distributi on, proportional symbol maps, data maps, cartograms and choropleth maps); however, for the goals a nd requirements of this thesis, choropleth mapping was deemed the most suitable. As noted by Kumar, “Descriptive statistics and choropleth map design go hand-in-hand” (Kum ar, 2004, p. 218). In order to further aid the spatial analysis of the FCHDI, two statis tical graphs were incorporated into the choropleth maps to clarify data distributio n: frequency histograms (Kumar, 2004) and box-and-whisker diagrams (Kostbade, 1981). Qu antile intervals were selected for the choropleth maps based on resear ch by Brewer and Pickle (2002) indicating that of seven classification methods tested, quantile intervals ar e easier to interpret by the general mapreader, and best suited for comparative study.

PAGE 111

102 The results of the FCHDI calculations were presented in choropleth mapping format, and were represented as net-positiv e, that is, each indicator, interim index, summary index, and test indicator was ranked and plotted with the index values most positive to social well-being in the upper quart ile, and those least positive values in the lower quartile. The results s how interesting county -level spatial patterns of FCHDI data distribution (See Figure 5-1). Figure 5-1: Florida County Human Development Index. The highest ranked socioeconomic well-being counties are shown in dark brown and the lowest ranked socioeconomic well-being counties in pale yellow

PAGE 112

103 The cluster size and distribution patterns of the quartiles differ between smaller, looser patterns in Florida’s northern tier and larger, more defined patterns in the peninsular region. This suggested a weak er socioeconomic influence between the counties in the northern tier, as in the cas es of upper quartile Sa int Johns County (#1), Clay County (#6) and Alachua County (#7) abutting low quartile Putnam County (#60); or upper quartile Leon County (#3) and Wa kulla County (#13) abutting lower quartile Gadsden County (#55) and Liberty County (#52). With the notable exception of the low-qua rtile cluster to the west and north-west of Lake Okeechobee, the distribution pattern in the peninsula tends to show more incremental diffusion from upper quartile cluste rs to third and second quartile clusters. The quartile clusters also tend to contain a greater number of c ounties in peninsular Florida, suggesting a stronger inter-socio economic influence between these counties, with less polarization between the upper and lower quartiles. The exception to this trend is the inland low-quartile cluster of Polk, Highlands, Hardee, Okeechobee, Glades, Hendry, and DeSoto counties. This is a clea r illustration that the coastal counties of peninsular Florida fare better on the FCHDI s cale than interior counties, however further research would be useful to validate this result. There is evidence in Figure 5-1 that metropolitan areas tend to rank higher on the FCHDI, examples being the Jacksonville, Or lando, Naples, Tallahassee, and PensacolaFort Walton clusters. This suggests a link be tween urban areas and greater socioeconomic well-being. The most notable exception to this trend is Miami-Dade, which, although certainly a metropolitan area, ranks #48, low in the second quartile.

PAGE 113

104 A degree of caution must be taken when interpreting Figure 5-1, since the actual distribution of socioeconomic well-bei ng is not bound by c ounty lines, nor is socioeconomic well-being homogeneously distri buted across an enti re county. The use of quartiles can also be called into question, however, the FCHDI does adequately suggest broad socioeconomic trends, and does draw atte ntion to areas that might warrant further and more detailed research by several gr oups, including planners, policy makers, public managers, social activists, and politicians. After compiling and processing selected socioeconomic data through the FCHDI, plotting the results on choropleth maps, and then referring to the ini tial postulations of this thesis, two conclusions can be drawn: 1. The FCHDI is a useful model for normaliz ing, aggregating, and ranking social and economic data at the county level, and these rankings are apposite for choropleth mapping. 2. When the FCHDI rankings are plot ted on choropleth maps, clusters and location patterns (e.g. coastal versus inland counties) are easily recognizable and potential socioeconomi c relationships between counties and within regions emerge. The aim of this thesis was to detail th e development of an index based on an existing model, and to geographically plot th e index rankings. The resulting visualization of socioeconomic patterns and spatial rela tionships offer a provocative conclusion and suggest the value of further study. To this effect, there is a positive conclusion to the project.

PAGE 114

105 LIST OF REFERENCES Adams, C., Philippakos, E., Hodges, A., a nd Mulkey, D. (2001). An overview of the relative economic importance of Florida’s coastal counties. EDIS Document FE 306 Institute of Food and Agricultural Sciences : University of Florida, Gainesville. Agostini, S.J. and Richardson, S.J. (1997). A human development index for U.S. cities: Methodological issues a nd preliminary findings. Real Estate Economics, 25 (1), 13-41. Alexander, C., Ishikawa, S. and Silverstein, M. (1977). A pattern language: Towns, buildings, construction New York: Oxford University Press Anand, S. and Sen, A.K. (1994) Human Development Index: Methodology and measurement. Occasional Papers, #12 New York: United Nations Development Programme. [Internet site: http:// hdr.undp.org/publications/occ asional_papers/oc12.pdf] Andrews, F.M. and Whithey, S.B. (1976). Soci al indicators of we ll-being: America’s perceptions of life quality. New York: Plenum Press. BEBR (Bureau of Economic and Business Research). (2003). The 2003 Florida price level index Gainesville: University of Florida. [Internet site: http://bebr.ufl.edu/ Publications/FPLI2003.pdf ] Board, C. and Taylor, R.M., (1977). Perception and maps: Hu man factors in map design and interpretation. Transactions of the Institute of British Geographers New Series, 2(1), Contemporary Cartography, 19-36. Booysen, F. (2002). An overview and evaluation of composite indices of development. Social Indicators Research, 59 115-151. Bowen, H.P. and Moesen, W. (2005). Benchm arking the competitiveness of nations: Non-uniform weighting a nd non-economic dimensions. Vlerick Leuven Gent Working Paper Series 2005/2 K.U. Leuven: Belgium Brewer, C.A. and Pickle, L. (2002). Ev aluation of methods for classifying epidemiological data on choropleth maps in series. Annals of the Association of American Geographers, 92 (4), 662-681.

PAGE 115

106 Bukenya, J.O. and Fraser, R. (2002) Estimation of the human development index for Alabama counties [Research Paper # AGB200202: Draft] Alabama A&M University, AL. [Internet site: http://saes.aamu.edu/agb/AGB200202.pdf ] Cahill, M.B. (2002). Diminishing returns to the GDP and the Human Development Index. Applied Economic Letters, 9 885-887. Carr, D.B., White, D., MacEachren, and A.M., (2005). Conditioned choropleth maps and hypothesis generation. Annals of the Association of American Geographers, 95 (1), 32-53. Census (U.S. Census Bureau) (2003). Florida: 2000. Summary social, economic, and housing characteristics (PHC-2-11). Washington, DC: U.S. Census Bureau. Cutter, S.L., Mitchell, J.T., and Scott, M.S. (2000). Revealing the vulnerability of people and places: A case study of Geor getown County, South Carolina. Annals of the Association of American Geographers, 90 (4), 713-737. Dent, B.D. (1999). Cartography thematic map design (Fifth Edition). Boston: McGraw-Hill Dougenik, J.A., Chrisman, N.R. and Niemeyer, D.R. (1985). An algorithm to construct continuous area cartograms. Professional Geographer, 37 (1), 75-81. Du, C. and Liu, L. (1999). Constructing co ntiguous area cartogr am using ArcView Avenue. In Li, B., et al., (eds.) Geoinformatics and Socioinformatics : The Proceedings of Geoinformatics ’99 C onference. Ann Arbor: Michigan, pp. 1-7. ERS (Economic Research Service), (1997). Natural Amenities Scale. Washington, D.C.: United Stated Department of Agriculture. [Internet site: http://www.ers.usda.gov/Data/Natural/Amenities/] Estrada, J.K. (2005). Assessing community resources and economic development programming efforts using a modified human development index. Journal of Extension, 43 (2) 2IAW1. [Internet site: http://www.joe.org/joe/2005april/iw1.shtml .] Fernald, E.A. and Purdum, E.D. (Eds.) (1996). Atlas of Florida (revised edition). Gainesville: University Press of Florida. FCMP (Florida Coastal Management Program). (2005) [Internet site: http://www.orcm.nos.noaa.gov/czm/czmflorida.html .] [Internet site: http://www.dep.state.fl.us/cmp/.]

PAGE 116

107 FDOH (Florida Department of Health). (2001). Florida vital statis tics annual report 2000 Tallahassee: Office of Vital Statistics. FDOH (Florida Department of Health). (2004). Florida vital statis tics annual report 2003 Tallahassee: Office of Vital Statistics. FNAI (Florida Natural Areas Inventory) (2005). [Internet site: http://www.fnai.org]. Geisel, T.S. (1939). The Seven Lady Godivas New York: Random House Green, G. P. (2001). Amenities and community economic development: Strategies for sustainability. The Journal of Regional Analysis and Policy, 32 (2), 61-75. Grix, J. (2002). Introducing students to the generic terminology of social research. Politics: 2002 22 (3), 175-186. Hagerty, M.R. and Land, K.C. (2004). Constr ucting summary indices of social wellbeing: A model for the effect for heteroge neous importance weight s. Revision of a paper presented at the annual meeting of the American Soci ological Association, Chicago, IL, 2002. Hanham, A.C., Berhanu, S. and Loveridge, S. (2002). A human development index for West Virginia counties Research paper 2005. Center for Community, Economic, and Workforce Development. West Virginia University Extension Service. ul Haq, M. (2003). The birth of the human development index. In Readings in Human Development eds. S. Fukuda-Parr and A.K. Siva Kuma, Pp. 127-137. Oxford, UK: Oxford University Press. Holcombe, R.G. (1995). Florida’s growth management experiment: An analysis Florida State University, Tallahassee, FL. House, D.H. and Kocmoud, C.J. (1998) Continuous cartogram construction. Proceedings IEEE Visualization 197-204. Isserman, A. (2005). Rating places: Why and how. Department of Agricultural and Consumer Economics: University of Illinois at Urbana-Champaign [Internet site: http:/ /www.ace.uiuc.edu/cour ses/up506/Feb_14_slides.pdf] Ivanova, I., Arcelus, F.J. and Srinivasan, (1999). An assessment of the measurement properties of the Human Development Index. Social Indicators Research, 46 157179 Jenks, G.F. and Caspall, F.C. (1971). Error on choroplethic maps: Definition, measurement, reduction. Annals of the Association of American Geographers, 61 (2), 217-244.

PAGE 117

108 Keim, D.A., North, S.C and Panse, C. (2004). CartoDraw: A fast algorithm for generating contiguous cartograms. IEEE Transactions on Visualization and Computer Graphics, 10 (1), 95-110. Kelly, A.C. (1991). The human devel opment index: “Handle with care.” Population and Development Review, 17 (2), 315-324. Kiker, C. F., & Hodges, A. W., (2002) Economic benefits of natural land conservation: Case study of Northeast Florida. Final Report Submitted to Defenders of Wildlife Kostbade, J.T. (1981) Mapping frequency distributions by the box-and-whisker. Professional Geographer, 33 (4), 413-418. Kumar, N. (2004). Frequency histogram legend in the choropleth map: A substitute for traditional legends. Cartography and Geography Information Science, 31 (4), 217-236. Kwang-Koo, K., Marcouiller, D., and Deller, S.C. (2005). Natural amenities and rural development: Understanding spatia l and distributional attributes. Growth and Change, 36 (2), 273-297. Lanteigne, C.A. (2005). Quality of life in cities Thesis: University of New Brunswick. Lloyd, R. and Steinke, T. (1977). Visual and st atistical comparison of choropleth maps. Annals of the Association of American Geographers, 67 (3), 429-436. McGranahan, D.A. (1999). Natural amenities drive rural population change AER-781; USDA/ERS: Washington. [Internet site: http://www.ers .usda.gov/publicat ions/aer781/] McGranahan, D.A. (2005). Natural Amenities Scale. Amber Waves, April 2005 ; USDA/ERS: Washington. [Internet site: http://www.ers .usda.gov/AmberWaves/April05/] Mason, T.J., Fraumenie, J.F., Hoover, R., and Blot, W.J. (1981). An atlas of mortality from selected diseases Washington: USGPO (NIH Publication No. 81-2397). May, J. W. (1998). 1990 coastal population in Fl orida: A report to Florida’s coastal managers.

PAGE 118

109 Muehrcke, P.C., (1981). Whatever ha ppened to geographic cartography? Professional Geographer, 33 (4), 397-405. NASS (National Agricultural Statistics Se rvice) 2002 Census of Agriculture (FL). Washington: USDA. [Internet site: http://nass.usda.gov/]. Noorbakhsh, F. (1998). The human developmen t index: Some technical issues and alternative indices. Journal of International Development, 10, 589 – 605. NRC (National Research Council) (2002). Community and quality of life: Data needs for informed decision making Washington: National Academy Press. OEDR (Office of Economic and Demographic Research). (2005). Florida Demographic Summary. Tallahassee: Florida Legislature.[Internet site: http://www.state.fl.us/edr ] Olson, J.M. (1981). Spectrally encoded two-variable maps. Annals of the Association of American Geographers, 71 (2), 259-276. Olson, J.M. and Brewer, C.A. (1997). An eval uation of color selections to accommodate map users with color-vision impairments. Annals of the Association of American Geographers, 87 (1), 103-134. Pacione, M. (2003). Urban environmental qu ality and human wellbeing: A social geographical perspective. Landscape and Urban Planning,65 19-30. Pickle, L.W., (2004). Usabil ity testing of map designs. Proceedings of the symposium on the Interface of Computi ng Science and Statistics, 35 :42-56 Reynolds, J. E. (2001). Urbanization and land use change in Florida and the South. Proceedings of a Regional Workshop SER-IEG-30 Southern Rural Development Center and Farm Foundation, # 220 (pp. 28-49). Mississippi State, MS Rittschof, K.A., Stock, W.A., Kulhavy, R.W ., Verdi, M.P. and Johnson, J.T. (1996). Learning from cartograms: The effects of regional familiarity. Journal of Geography, 95 (2), 50-58. Robinson, A.H., Sale, R.D., Morrison, J.L. and Muehrcke, P.C. (1984). Elements of Cartography (Fifth Edition). New York: John Wiley and Sons Salzman, J. (2003). Methodological choices encountered in the construction of composite indices of economic and social well-being Center for the Study of Living Standards: Ottawa, Canada

PAGE 119

110 Shumway, J.M. and Otterstrom, S.M. (2001). Sp atial patterns of migration and income change in the mountain West: The domi nance of service-based, amenity-rich counties. Professional Geographer, 53 (4), 492-502. Slottje, D.J. (1991). Measuring the quality of life across countries. The Review of Economics and Statistics, 73 (4), 684-693. Smith, D.M. (1973). The Geography of Social Well-being in the United States: An Introduction to Terr itorial Social Indicators New York: McGraw-Hill Book Company. Smith, D.M. (1975). Patterns in Human Geography: An Introduction to Numerical Methods New York: Crane, Russak & Company, Inc. Smith, R.M. (1986). Comparing traditional me thods for selecting class intervals on choropleth maps. Professional Geographer, 38 (1), 62-67. Solecki, W.D. (2001). South Florida: The re ality of change and the prospects for sustainability: The role of global-to-local linkages in la nd use/land cover change in South Florida. Ecological Economics 37 (3), 339-356. Straussfogel, D. (1997). Redefining deve lopment as humane and sustainable. Annals of the Association of American Geographers, 87 (2), 280-305. Tufte, E.R. (1990). Envisioning Information Cheshire, CT: Graphics Press. UNDP (United Nations Deve lopment Programme) (1990). Human Development Report 1990 Online. [Internet Site: http://hdr.undp.org/repor ts/global/1990/en/ ] UNDP (United Nations Deve lopment Programme) (1994). Human Development Report 1994 Online. [Internet Site: http://hdr.undp.org/repor ts/global/1994/en/ ] UNDP (United Nations Deve lopment Programme) (2002). Human Development Report 2002 Online. [Internet Site: http ://hdr.undp.org/reports/global/2002] UNDP (United Nations Devel opment Programme) (2004a). Human Development Report 2004 Online. [Internet Site: http ://hdr.undp.org/reports/global/2004] UNDP (United Nations Deve lopment Programme) (2004b). FAQ on HDR statistics: Examples from HDR 2004 Online. [Internet Site: http://hdr.undp.org/statistics/faq/ ] Wright, J.K., (1944). The terminol ogy of certain map symbols. Geographical Review, 34 (4), 653-654.

PAGE 120

APPENDICES

PAGE 121

A-1 APPENDIX A: FCHDI Data Tables The following tables, built from Excel worksheet calculations, include the observed and adjusted data collected from the sources listed in Chapter Three for each of the nine indicators, the three interi m indices, and the FCHDI calculated in Chapter Four. Table A-1: Florida Resi dent Mortality Rate a nd Indicator Value..................................A-2 Table A-2: Florida Child Mortality Rate and In dicator Values......................................A-3 Table A-3: Florida Heart Diseas e and Indicator Values.................................................A-4 Table A-4: Florida Ma lignant Neoplasm and Indicator Values......................................A-5 Table A-5: Combined Flor ida Heart Disease and Can cer Indicator Values...................A-6 Table A-6: Florida Non-Hi gh School Graduate .............................................................A-7 Table A-7: Education Attainment High School and Higher.........................................A-8 Table A-8: Education Attainment Bachelors and Higher.............................................A-9 Table A-9: Florida Poverty and Indicator Values.........................................................A-10 Table A-10: Florida Per Capita In come and Indicator Values.....................................A-11 Table A-11: Florida Price Level Index and Indicator Values.......................................A-12 Table A-12: Mortality Inte rim Index..............................................................A-13 to A-14 Table A-13: Education Inte rim Index.............................................................A-15 to A-16 Table A-14: Economic Inte rim Index.............................................................A-17 to A-18 Table A-15: Florida County Human Development Index..............................A-19 to A-20 Table A-16: Florida Coun ties Ranked by FCHDI .......................................................A-21 Table A-17: Test Variable Natural Am enities Scale and Indicator Values................A-22 Table A-18: FCHDI + Natural Amenities Indicator ....................................................A-23 Table A-19: Change in Ranking FCHDI + Natural Amenity Indicator.....................A-24

PAGE 122

Appendix A (Continued) A-2 Table A-1: Florida Resident Mortality Rate and Indicator Value (2000) This data table includes the observed and adjusted mortality rates for each county and the calculated indicator value used in building the Mortality Interim Index. Also included are the descriptive statistics and the quartile break-down for the data set. County Mortality Rate Adjusted Mortality Rate Indicator Value County Mortality Rate Adjusted Mortality Rate Indicator Value Alachua 7.0 93.0 0.941 Lee 11.7 88.3 0.480 Baker 8.2 91.8 0.824 Leon 6.4 93.6 1.000 Bay 9.3 90.7 0.716 Levy 12.6 87.4 0.392 Bradford 10.1 89.9 0.637 Liberty 7.8 92.2 0.863 Brevard 10.8 89.2 0.569 Madison 10.1 89.9 0.637 Broward 10.4 89.6 0.608 Manatee 13.0 87.0 0.353 Calhoun 8.7 91.3 0.775 Marion 13.1 86.9 0.343 Charlotte 15.4 84.6 0.118 Martin 12.5 87.5 0.402 Citrus 16.6 83.4 0.000 Miami-Dade 8.6 91.4 0.784 Clay 7.5 92.5 0.892 Monroe 7.5 92.5 0.892 Collier 10.2 89.8 0.627 Nassau 8.9 91.1 0.755 Columbia 10.9 89.1 0.559 Okaloosa 7.2 92.8 0.922 Desoto 10.9 89.1 0.559 Okeechobee 10.4 89.6 0.608 Dixie 11.2 88.8 0.529 Orange 7.2 92.8 0.922 Duval 8.6 91.4 0.784 Osceola 7.6 92.4 0.882 Escambia 9.1 90.9 0.735 Palm Beach 12.0 88.0 0.451 Flagler 12.2 87.8 0.431 Pasco 15.6 84.4 0.098 Franklin 10.5 89.5 0.598 Pinellas 13.8 86.2 0.275 Gadsden 8.8 91.2 0.765 Polk 10.9 89.1 0.559 Gilchrist 11.5 88.5 0.500 Putnam 12.3 87.7 0.422 Glades 10.4 89.6 0.608 Saint Johns 9.5 90.5 0.696 Gulf 10.0 90.0 0.647 Saint Lucie 12.1 87.9 0.441 Hamilton 8.4 91.6 0.804 Santa Rosa 7.7 92.3 0.873 Hardee 9.4 90.6 0.706 Sarasota 14.7 85.3 0.186 Hendry 8.5 91.5 0.794 Seminole 7.2 92.8 0.922 Hernando 14.8 85.2 0.176 Sumter 11.9 88.1 0.461 Highlands 14.6 85.4 0.196 Suwannee 12.1 87.9 0.441 Hillsborough 8.8 91.2 0.765 Taylor 11.9 88.1 0.461 Holmes 11.4 88.6 0.510 Union 10.6 89.4 0.588 Indian River 13.0 87.0 0.353 Volusia 13.0 87.0 0.353 Jackson 10.4 89.6 0.608 Wakulla 8.7 91.3 0.775 Jefferson 11.2 88.8 0.529 Walton 10.4 89.6 0.608 Lafayette 9.0 91.0 0.745 Washington 11.3 88.7 0.520 Lake 13.4 86.6 0.314 Raw Data FCHDI Mean 89.381 0.586 Quartile 1 0.441 Standard Deviation 2.345 0.230 Quartile 2 0.608 Minimum 83.4 0.000 Quartile 3 0.770 Maximum 93.6 1.000 Quartile 4 1.000

PAGE 123

Appendix A (Continued) A-3 Table A-2: Florida Child Mortality Rate and Indicator Values (2000) This data table includes the observed and adjusted ch ild mortality rates for each county and the calculated indicator value used in building the Mortality Interim Index. Also included are the descriptive statistics and the quartile break-down for the data set. County Child Mortality Adjusted Mortality Rate Indicator Values County Child Mortality Adjusted Mortality Rate Indicator Values Alachua 0.29 99.71 0.557 Lee 0.20 99.80 0.691 Baker 0.19 99.81 0.703 Leon 0.24 99.76 0.629 Bay 0.14 99.86 0.776 Levy 0.20 99.80 0.687 Bradford 0.28 99.72 0.572 Liberty 0.26 99.74 0.598 Brevard 0.13 99.87 0.794 Madison 0.28 99.72 0.568 Broward 0.15 99.85 0.775 Manatee 0.19 99.81 0.710 Calhoun 0.13 99.87 0.799 Marion 0.16 99.84 0.748 Charlotte 0.17 99.83 0.735 Martin 0.25 99.75 0.614 Citrus 0.20 99.80 0.689 Miami-Dade 0.16 99.84 0.757 Clay 0.21 99.79 0.682 Monroe 0.20 99.80 0.688 Collier 0.13 99.87 0.793 Nassau 0.22 99.78 0.653 Columbia 0.30 99.70 0.532 Okaloosa 0.18 99.82 0.715 Desoto 0.16 99.84 0.753 Okeechobee 0.40 99.60 0.383 Dixie 0.39 99.61 0.398 Orange 0.18 99.82 0.723 Duval 0.23 99.77 0.637 Osceola 0.12 99.88 0.815 Escambia 0.30 99.70 0.534 Palm Beach 0.18 99.82 0.715 Flagler 0.15 99.85 0.775 Pasco 0.14 99.86 0.778 Franklin 0.20 99.80 0.696 Pinellas 0.16 99.84 0.758 Gadsden 0.46 99.54 0.283 Polk 0.20 99.80 0.698 Gilchrist 0.36 99.64 0.440 Putnam 0.28 99.72 0.571 Glades 0.65 99.35 0.000 Saint Johns 0.11 99.89 0.836 Gulf 0.59 99.41 0.083 Saint Lucie 0.20 99.80 0.684 Hamilton 0.00 100.00 1.000 Santa Rosa 0.12 99.88 0.819 Hardee 0.10 99.90 0.850 Sarasota 0.21 99.79 0.674 Hendry 0.18 99.82 0.727 Seminole 0.10 99.90 0.839 Hernando 0.17 99.83 0.738 Sumter 0.24 99.76 0.634 Highlands 0.31 99.69 0.523 Suwannee 0.48 99.52 0.264 Hillsborough 0.21 99.79 0.673 Taylor 0.09 99.91 0.864 Holmes 0.19 99.81 0.699 Union 0.14 99.86 0.790 Indian River 0.21 99.79 0.677 Volusia 0.21 99.79 0.679 Jackson 0.16 99.84 0.758 Wakulla 0.07 99.93 0.886 Jefferson 0.00 100.00 1.000 Walton 0.05 99.95 0.928 Lafayette 0.00 100.00 1.000 Washington 0.32 99.68 0.512 Lake 0.09 99.91 0.859 Raw Data FCHDI Mean 99.792 0.678 Quartile 1 0.621 Standard Deviation 0.120 0.185 Quartile 2 0.699 Minimum 99.353 0.000 Quartile 3 0.777 Maximum 100.000 1.000 Quartile 4 1.000

PAGE 124

Appendix A (Continued) A-4 Table A-3: Florida Heart Disease and Indicator Values (2000) This data table includes the observed and adjusted he art disease rates for each county and the calculated indicator value used in building the leading cause of death indicator. Also included are the descriptive statistics and the quartile break-down for the data set. County Heart Disease Rate Adjusted Rate Indicator Values County Heart Disease Rate Adjusted Rate Indicator Values Alachua 19.5 80.5 0.835 Lee 32.6 67.4 0.124 Baker 24.6 75.4 0.557 Leon 21.2 78.8 0.744 Bay 29.7 70.3 0.278 Levy 25.4 74.6 0.514 Bradford 21.9 78.1 0.703 Liberty 30.3 69.7 0.248 Brevard 29.8 70.2 0.273 Madison 33.7 66.3 0.065 Broward 34.0 66.0 0.048 Manatee 32.5 67.5 0.126 Calhoun 29.6 70.4 0.286 Marion 31.3 68.7 0.194 Charlotte 34.9 65.1 0.000 Martin 33.5 66.5 0.076 Citrus 30.3 69.7 0.247 Miami-Dade 34.8 65.2 0.005 Clay 26.0 74.0 0.482 Monroe 22.7 77.3 0.659 Collier 29.2 70.8 0.308 Nassau 30.1 69.9 0.257 Columbia 23.6 76.4 0.610 Okaloosa 28.8 71.2 0.330 Desoto 31.4 68.6 0.186 Okeechobee 34.8 65.2 0.002 Dixie 23.6 76.4 0.613 Orange 28.4 71.6 0.351 Duval 26.2 73.8 0.472 Osceola 28.5 71.5 0.347 Escambia 25.3 74.7 0.522 Palm Beach 33.9 66.1 0.053 Flagler 29.7 70.3 0.281 Pasco 30.0 70.0 0.266 Franklin 19.8 80.2 0.816 Pinellas 28.4 71.6 0.352 Gadsden 27.9 72.1 0.380 Polk 32.7 67.3 0.115 Gilchrist 26.7 73.3 0.443 Putnam 26.8 73.2 0.436 Glades 29.9 70.1 0.269 Saint Johns 22.8 77.2 0.654 Gulf 27.5 72.5 0.399 Saint Lucie 32.6 67.4 0.124 Hamilton 27.3 72.7 0.408 Santa Rosa 28.3 71.7 0.355 Hardee 27.7 72.3 0.389 Sarasota 29.0 71.0 0.319 Hendry 28.8 71.2 0.327 Seminole 30.9 69.1 0.213 Hernando 29.3 70.7 0.303 Sumter 34.0 66.0 0.048 Highlands 32.8 67.2 0.114 Suwannee 24.7 75.3 0.555 Hillsborough 29.5 70.5 0.289 Taylor 26.4 73.6 0.458 Holmes 33.8 66.2 0.058 Union 16.4 83.6 1.000 Indian River 30.3 69.7 0.250 Volusia 29.1 70.9 0.311 Jackson 33.4 66.6 0.080 Wakulla 27.1 72.9 0.420 Jefferson 24.8 75.2 0.544 Walton 32.1 67.9 0.150 Lafayette 19.7 80.3 0.824 Washington 29.0 71.0 0.320 Lake 30.8 69.2 0.223 Raw Data FCHDI Mean 71.460 0.343 Quartile 1 0.190 Standard Deviation 4.165 0.226 Quartile 2 0.311 Minimum 65.136 0.000 Quartile 3 0.465 Maximum 83.553 1.000 Quartile 4 1.000

PAGE 125

Appendix A (Continued) A-5 Table A-4: Florida Malignant Neoplasm and Indicator Values (2000) This data table includes the observed and adjusted can cer rates for each county and the calculated indicator value used in building the leading cause of death indicator. Also included are the descriptive statistics and the quartile break-down for the data set. County Malignant Neoplasm Rate Adjusted Rate Indicator Values County Malignant Neoplasm Rate Adjusted Rate Indicator Values Alachua 22.9 77.1 0.567 Lee 25.3 74.7 0.400 Baker 22.5 77.5 0.601 Leon 24.2 75.8 0.477 Bay 25.0 75.0 0.420 Levy 23.6 76.4 0.523 Bradford 22.3 77.7 0.612 Liberty 24.2 75.8 0.473 Brevard 26.3 73.7 0.324 Madison 20.3 79.7 0.756 Broward 23.0 77.0 0.563 Manatee 24.5 75.5 0.453 Calhoun 19.2 80.8 0.835 Marion 24.9 75.1 0.428 Charlotte 24.9 75.1 0.426 Martin 25.4 74.6 0.387 Citrus 25.6 74.4 0.374 Miami-Dade 21.1 78.9 0.700 Clay 24.9 75.1 0.429 Monroe 26.8 73.2 0.289 Collier 25.3 74.7 0.400 Nassau 22.3 77.7 0.615 Columbia 25.2 74.8 0.405 Okaloosa 27.8 72.2 0.218 Desoto 24.2 75.8 0.475 Okeechobee 24.8 75.2 0.433 Dixie 24.8 75.2 0.430 Orange 23.1 76.9 0.557 Duval 22.9 77.1 0.569 Osceola 22.4 77.6 0.606 Escambia 22.5 77.5 0.599 Palm Beach 24.3 75.7 0.468 Flagler 29.2 70.8 0.119 Pasco 23.5 76.5 0.527 Franklin 23.3 76.7 0.543 Pinellas 22.1 77.9 0.628 Gadsden 22.9 77.1 0.570 Polk 24.4 75.6 0.460 Gilchrist 19.9 80.1 0.787 Putnam 25.7 74.3 0.366 Glades 30.8 69.2 0.000 Saint Johns 26.0 74.0 0.345 Gulf 25.5 74.5 0.383 Saint Lucie 25.8 74.2 0.365 Hamilton 25.0 75.0 0.419 Santa Rosa 22.7 77.3 0.582 Hardee 16.9 83.1 1.000 Sarasota 25.3 74.7 0.396 Hendry 23.2 76.8 0.547 Seminole 24.5 75.5 0.454 Hernando 28.5 71.5 0.168 Sumter 26.2 73.8 0.332 Highlands 23.7 76.3 0.515 Suwannee 19.5 80.5 0.811 Hillsborough 23.1 76.9 0.552 Taylor 25.6 74.4 0.375 Holmes 19.2 80.8 0.837 Union 29.6 70.4 0.089 Indian River 26.9 73.1 0.281 Volusia 24.8 75.2 0.436 Jackson 20.6 79.4 0.734 Wakulla 20.2 79.8 0.762 Jefferson 20.6 79.4 0.734 Walton 24.5 75.5 0.456 Lafayette 21.2 78.8 0.691 Washington 22.6 77.4 0.590 Lake 24.9 75.1 0.426 Raw Data FCHDI Mean 76.044 0.494 Quartile 1 0.398 Standard Deviation 2.552 0.183 Quartile 2 0.460 Minimum 69.159 0.000 Quartile 3 0.594 Maximum 83.099 1.000 Quartile 4 1.000

PAGE 126

Appendix A (Continued) A-6 Table A-5: Combined Florida Hear t Disease and Cancer Indicator Values This data table is the combined h eart disease / cancer rates for each coun ty used in building the Mortality Interim Index. Also included are the descriptive st atistics and the quartile break -down for the data set. County Sum of Heart Disease / Malignant Neoplasm Combined Indicator Value (Sum / 2) County Sum of Heart Disease / Malignant Neoplasm Combined Indicator Value (Sum / 2) Alachua 1.403 0.701 Lee 0.524 0.262 Baker 1.159 0.579 Leon 1.220 0.610 Bay 0.698 0.349 Levy 1.036 0.518 Bradford 1.315 0.657 Liberty 0.721 0.361 Brevard 0.597 0.299 Madison 0.822 0.411 Broward 0.611 0.306 Manatee 0.578 0.289 Calhoun 1.121 0.560 Marion 0.622 0.311 Charlotte 0.426 0.213 Martin 0.463 0.232 Citrus 0.622 0.311 Miami-Dade 0.705 0.353 Clay 0.911 0.456 Monroe 0.948 0.474 Collier 0.708 0.354 Nassau 0.872 0.436 Columbia 1.015 0.507 Okaloosa 0.548 0.274 Desoto 0.661 0.331 Okeechobee 0.435 0.218 Dixie 1.044 0.522 Orange 0.908 0.454 Duval 1.041 0.520 Osceola 0.952 0.476 Escambia 1.120 0.560 Palm Beach 0.521 0.260 Flagler 0.400 0.200 Pasco 0.793 0.396 Franklin 1.359 0.680 Pinellas 0.979 0.490 Gadsden 0.950 0.475 Polk 0.576 0.288 Gilchrist 1.229 0.615 Putnam 0.801 0.401 Glades 0.269 0.135 Saint Johns 1.000 0.500 Gulf 0.782 0.391 Saint Lucie 0.489 0.245 Hamilton 0.827 0.414 Santa Rosa 0.936 0.468 Hardee 1.389 0.695 Sarasota 0.715 0.358 Hendry 0.874 0.437 Seminole 0.667 0.333 Hernando 0.471 0.236 Sumter 0.380 0.190 Highlands 0.629 0.314 Suwannee 1.366 0.683 Hillsborough 0.842 0.421 Taylor 0.834 0.417 Holmes 0.895 0.448 Union 1.089 0.544 Indian River 0.531 0.265 Volusia 0.747 0.374 Jackson 0.814 0.407 Wakulla 1.183 0.591 Jefferson 1.278 0.639 Walton 0.606 0.303 Lafayette 1.514 0.757 Washington 0.910 0.455 Lake 0.649 0.324 FCHDI Mean 0.411 Quartile 1 0.308 STDV 0.145 Quartile 2 0.411 Minimum 0.135 Quartile 3 0.513 Maximum 0.757 Quartile 4 0.757

PAGE 127

Appendix A (Continued) A-7 Table A-6: Florida Non-High School Graduate (2000) This data table includes the observed and adjusted non-high school graduate rates for each county and the calculated indicator value used in building the Education Interim Index. Also included are the descriptive statistics and the quartile break-down for the data set. County NonH.S. Graduate Adjusted Non-H.S. Graduate Indicator Values County NonH.S. Graduate Adjusted Non-H.S. Graduate Indicator Values Alachua 3.9 96.1 0.993 Lee 15.4 84.6 0.724 Baker 24.0 76.0 0.522 Leon 3.6 96.4 1.000 Bay 10.4 89.6 0.841 Levy 16.5 83.5 0.698 Bradford 16.2 83.8 0.705 Liberty 17.5 82.5 0.674 Brevard 11.0 89.0 0.827 Madison 14.3 85.7 0.749 Broward 9.6 90.4 0.859 Manatee 16.9 83.1 0.689 Calhoun 5.6 94.4 0.953 Marion 14.9 85.1 0.735 Charlotte 9.7 90.3 0.857 Martin 16.9 83.1 0.689 Citrus 12.3 87.7 0.796 Miami-Dade 10.3 89.7 0.843 Clay 9.2 90.8 0.869 Monroe 10.6 89.4 0.836 Collier 21.3 78.7 0.585 Nassau 8.9 91.1 0.876 Columbia 15.2 84.8 0.728 Okaloosa 7.0 93.0 0.920 Desoto 46.3 53.7 0.000 Okeechobee 23.9 76.1 0.525 Dixie 14.8 85.2 0.738 Orange 12.8 87.2 0.785 Duval 12.5 87.5 0.792 Osceola 12.6 87.4 0.789 Escambia 8.8 91.2 0.878 Palm Beach 13.8 86.2 0.761 Flagler 9.4 90.6 0.864 Pasco 12.5 87.5 0.792 Franklin 11.6 88.4 0.813 Pinellas 12.7 87.3 0.787 Gadsden 13.0 87.0 0.780 Polk 17.6 82.4 0.672 Gilchrist 13.8 86.2 0.761 Putnam 15.7 84.3 0.717 Glades 12.6 87.4 0.789 Saint Johns 6.0 94.0 0.944 Gulf 9.7 90.3 0.857 Saint Lucie 16.6 83.4 0.696 Hamilton 19.4 80.6 0.630 Santa Rosa 7.4 92.6 0.911 Hardee 25.8 74.2 0.480 Sarasota 11.9 88.1 0.806 Hendry 25.5 74.5 0.487 Seminole 8.3 91.7 0.890 Hernando 11.8 88.2 0.808 Sumter 17.4 82.6 0.677 Highlands 17.7 82.3 0.670 Suwannee 16.6 83.4 0.696 Hillsborough 13.4 86.6 0.770 Taylor 16.1 83.9 0.707 Holmes 9.0 91.0 0.874 Union 20.0 80.0 0.616 Indian River 12.6 87.4 0.789 Volusia 11.5 88.5 0.815 Jackson 8.8 91.2 0.878 Wakulla 14.4 85.6 0.747 Jefferson 13.0 87.0 0.780 Walton 10.5 89.5 0.838 Lafayette 26.4 73.6 0.466 Washington 10.9 89.1 0.829 Lake 14.3 85.7 0.749 Raw Data FCHDI Mean 85.961 0.756 Quartile 1 0.697 Standard Deviation 6.365 0.149 Quartile 2 0.785 Minimum 53.700 0.000 Quartile 3 0.842 Maximum 96.400 1.000 Quartile 4 1.000

PAGE 128

Appendix A (Continued) A-8 Table A-7: Education Attainment High School and Higher (2000) This data table includes the obser ved high school grad uate and higher rates for each county and the calculated indicator value used in building the Education Interim Index. Also included are the descriptive statistics and the quartile break-down for the data set. County Education Attainment HS + Indicator Values County Education Attainment HS + Indicator Values Alachua 88.1 0.976 Lee 82.3 0.835 Baker 71.9 0.584 Leon 89.1 1.000 Bay 81.0 0.804 Levy 73.9 0.632 Bradford 74.2 0.639 Liberty 65.6 0.431 Brevard 86.3 0.932 Madison 67.5 0.477 Broward 82.0 0.828 Manatee 81.4 0.814 Calhoun 69.1 0.516 Marion 78.2 0.736 Charlotte 82.1 0.831 Martin 85.3 0.908 Citrus 78.3 0.738 Miami-Dade 67.9 0.487 Clay 86.4 0.935 Monroe 84.9 0.898 Collier 81.8 0.823 Nassau 81.0 0.804 Columbia 74.7 0.651 Okaloosa 88.0 0.973 Desoto 63.5 0.380 Okeechobee 65.1 0.419 Dixie 65.9 0.438 Orange 81.8 0.823 Duval 82.7 0.845 Osceola 79.1 0.758 Escambia 82.1 0.831 Palm Beach 83.6 0.867 Flagler 85.9 0.923 Pasco 77.6 0.722 Franklin 68.3 0.496 Pinellas 84.0 0.877 Gadsden 70.7 0.554 Polk 47.8 0.000 Gilchrist 72.4 0.596 Putnam 70.4 0.547 Glades 69.8 0.533 Saint Johns 87.2 0.954 Gulf 72.6 0.600 Saint Lucie 77.7 0.724 Hamilton 62.9 0.366 Santa Rosa 85.4 0.910 Hardee 58.0 0.247 Sarasota 87.1 0.952 Hendry 54.2 0.155 Seminole 88.7 0.990 Hernando 78.5 0.743 Sumter 77.3 0.714 Highlands 74.5 0.646 Suwannee 73.2 0.615 Hillsborough 80.8 0.799 Taylor 70.0 0.538 Holmes 65.2 0.421 Union 72.5 0.598 Indian River 81.6 0.818 Volusia 82.0 0.828 Jackson 69.1 0.516 Wakulla 78.4 0.741 Jefferson 73.2 0.615 Walton 76.0 0.683 Lafayette 68.2 0.494 Washington 71.2 0.567 Lake 79.8 0.775 Raw Data FCHDI Mean 76.075 0.685 Quartile 1 0.542 Standard Deviation 8.707 0.211 Quartile 2 0.724 Minimum 47.800 0.000 Quartile 3 0.831 Maximum 89.100 1.000 Quartile 4 1.000

PAGE 129

Appendix A (Continued) A-9 Table A-8: Education Attainment Bachelors and Higher (2000) This data table includes the observed bachelor’s degr ee and higher rates for each county and the calculated indicator value used in building the Education Interim Index. Also included are the descriptive statistics and the quartile break-down for the data set. County Education Attainment BS + Indicator Values County Education Attainment BS + Indicator Values Alachua 38.7 0.914 Lee 21.1 0.410 Baker 8.2 0.040 Leon 41.7 1.000 Bay 17.7 0.312 Levy 10.6 0.109 Bradford 8.4 0.046 Liberty 7.4 0.017 Brevard 23.6 0.481 Madison 10.2 0.097 Broward 24.5 0.507 Manatee 20.8 0.401 Calhoun 7.7 0.026 Marion 13.7 0.198 Charlotte 17.6 0.309 Martin 26.3 0.559 Citrus 13.2 0.183 Miami-Dade 21.7 0.427 Clay 20.1 0.381 Monroe 25.5 0.536 Collier 27.9 0.605 Nassau 18.9 0.347 Columbia 10.9 0.117 Okaloosa 24.2 0.499 Desoto 8.4 0.046 Okeechobee 8.9 0.060 Dixie 6.8 0.000 Orange 26.1 0.553 Duval 21.9 0.433 Osceola 15.7 0.255 Escambia 21.0 0.407 Palm Beach 27.7 0.599 Flagler 21.2 0.413 Pasco 13.1 0.181 Franklin 12.4 0.160 Pinellas 22.9 0.461 Gadsden 12.9 0.175 Polk 14.9 0.232 Gilchrist 9.4 0.074 Putnam 9.4 0.074 Glades 9.8 0.086 Saint Johns 33.1 0.754 Gulf 10.1 0.095 Saint Lucie 15.1 0.238 Hamilton 7.3 0.014 Santa Rosa 22.9 0.461 Hardee 8.4 0.046 Sarasota 27.4 0.590 Hendry 8.2 0.040 Seminole 31.0 0.693 Hernando 12.7 0.169 Sumter 12.2 0.155 Highlands 13.6 0.195 Suwannee 10.5 0.106 Hillsborough 25.1 0.524 Taylor 8.9 0.060 Holmes 8.8 0.057 Union 7.5 0.020 Indian River 23.1 0.467 Volusia 17.6 0.309 Jackson 12.8 0.172 Wakulla 15.7 0.255 Jefferson 16.9 0.289 Walton 16.2 0.269 Lafayette 7.2 0.011 Washington 9.2 0.069 Lake 16.6 0.281 Raw Data FCHDI Mean 16.734 0.285 Quartile 1 0.080 Standard Deviation 8.077 0.231 Quartile 2 0.238 Minimum 6.800 0.000 Quartile 3 0.447 Maximum 41.700 1.000 Quartile 4 1.000

PAGE 130

Appendix A (Continued) A-10 Table A-9: Florida Poverty and Indicator Values (2000) This data table includes the observed and adjusted poverty rates for each county and the calculated indicator value used in building the Economic Interim Index. Also included are the descriptive statistics and the quartile break-down for the data set. County Poverty Level Adjusted Level Indicator Values County Poverty Level Adjusted Level Indicator Values Alachua 22.8 77.2 0.167 Lee 9.7 90.3 0.849 Baker 14.7 85.3 0.589 Leon 18.2 81.8 0.406 Bay 13.0 87.0 0.677 Levy 18.6 81.4 0.385 Bradford 14.6 85.4 0.594 Liberty 19.9 80.1 0.318 Brevard 9.5 90.5 0.859 Madison 23.1 76.9 0.151 Broward 11.5 88.5 0.755 Manatee 10.1 89.9 0.828 Calhoun 20.0 80.0 0.313 Marion 13.1 86.9 0.672 Charlotte 8.2 91.8 0.927 Martin 8.8 91.2 0.896 Citrus 11.7 88.3 0.745 Miami-Dade 18.0 82.0 0.417 Clay 6.8 93.2 1.000 Monroe 10.2 89.8 0.823 Collier 10.3 89.7 0.818 Nassau 9.1 90.9 0.880 Columbia 15.0 85.0 0.573 Okaloosa 8.8 91.2 0.896 Desoto 23.6 76.4 0.125 Okeechobee 16.0 84.0 0.521 Dixie 19.1 80.9 0.359 Orange 12.1 87.9 0.724 Duval 11.9 88.1 0.734 Osceola 11.5 88.5 0.755 Escambia 15.4 84.6 0.552 Palm B each 9.9 90.1 0.839 Flagler 8.7 91.3 0.901 Pasco 10.7 89.3 0.797 Franklin 17.7 82.3 0.432 Pinellas 10.0 90.0 0.833 Gadsden 19.9 80.1 0.318 Polk 12.9 87.1 0.682 Gilchrist 14.1 85.9 0.620 Putnam 20.9 79.1 0.266 Glades 15.2 84.8 0.563 Saint Johns 8.0 92.0 0.938 Gulf 16.7 83.3 0.484 Saint Lucie 13.4 86.6 0.656 Hamilton 26.0 74.0 0.000 Santa Rosa 9.8 90.2 0.844 Hardee 24.6 75.4 0.073 Sarasota 7.8 92.2 0.948 Hendry 24.1 75.9 0.099 Seminole 7.4 92.6 0.969 Hernando 10.3 89.7 0.818 Sumter 13.7 86.3 0.641 Highlands 15.2 84.8 0.563 Suwannee 18.5 81.5 0.391 Hillsborough 12.5 87.5 0.703 Taylor 18.0 82.0 0.417 Holmes 19.1 80.9 0.359 Union 14.0 86.0 0.625 Indian River 9.3 90.7 0.870 Volusia 11.6 88.4 0.750 Jackson 17.2 82.8 0.458 Wakulla 11.3 88.7 0.766 Jefferson 17.1 82.9 0.464 Walton 14.4 85.6 0.604 Lafayette 17.5 82.5 0.443 Washington 19.2 80.8 0.354 Lake 9.6 90.4 0.854 Raw FCHDI Mean 85.648 0.607 Quartile 1 0.417 Standard Deviation 4.834 0.252 Quartile 2 0.641 Minimum 74.000 0.000 Quartile 3 0.826 Maximum 93.200 1.000 Quartile 4 1.000

PAGE 131

Appendix A (Continued) A-11 Table A-10: Florida Per Capita In come and Indicator Values (2000) This data table includes the observed per capita income rates for each county and the calculated indicator value used in building the Economic Interim Index. Also included are the descriptive statistics and the quartile break-down for the data set. County Census PCI Indicator Values County Census PCI Indicator Values Alachua $18,465 0.383 Lee $24,542 0.678 Baker $15,164 0.223 Leon $21,024 0.507 Bay $18,700 0.394 Levy $14,746 0.203 Bradford $14,226 0.178 Liberty $17,225 0.323 Brevard $21,484 0.529 Madison $12,511 0.094 Broward $23,170 0.611 Manatee $22,388 0.573 Calhoun $12,379 0.088 Marion $17,848 0.353 Charlotte $21,806 0.545 Martin $29,584 0.922 Citrus $18,585 0.389 Miami-Dade $18,497 0.385 Clay $20,868 0.499 Monroe $26,102 0.753 Collier $31,195 1.000 Nassau $22,836 0.595 Columbia $14,598 0.196 Okaloosa $20,918 0.502 Desoto $14,000 0.167 Okeechobee $14,553 0.193 Dixie $13,559 0.145 Orange $20,916 0.502 Duval $20,753 0.494 Osceola $17,022 0.313 Escambia $18,641 0.392 Palm B each $28,801 0.884 Flagler $21,879 0.548 Pasco $18,439 0.382 Franklin $16,140 0.270 Pinellas $23,497 0.627 Gadsden $14,499 0.191 Polk $18,302 0.375 Gilchrist $13,985 0.166 Putnam $15,603 0.244 Glades $15,338 0.231 Saint Johns $28,674 0.878 Gulf $14,449 0.188 Saint Lucie $18,790 0.399 Hamilton $10,562 0.000 Santa Rosa $20,089 0.462 Hardee $12,445 0.091 Sarasota $28,326 0.861 Hendry $13,663 0.150 Seminole $24,591 0.680 Hernando $18,321 0.376 Sumter $16,830 0.304 Highlands $17,222 0.323 Suwannee $14,678 0.199 Hillsborough $21,812 0.545 Taylor $15,281 0.229 Holmes $14,135 0.173 Union $12,333 0.086 Indian River $27,227 0.808 Volusia $19,664 0.441 Jackson $13,905 0.162 Wakulla $17,678 0.345 Jefferson $17,006 0.312 Walton $18,198 0.370 Lafayette $13,087 0.122 Washington $14,980 0.214 Lake $20,199 0.467 Raw Data FCHDI Mean $ 18,641 0.392 Quartile 1 0.198 Standard Deviation $ 4,773 0.392 Quartile 2 0.375 Minimum $ 10,562 0.000 Quartile 3 0.518 Maximum $ 31,195 1.000 Quartile 4 1.000

PAGE 132

Appendix A (Continued) A-12 Table A-11: Florida Price Level Index and Indicator Values (2000) This data table includes the observed and adjusted price level index values for each county and the calculated indicator value used in building the Economic Interim Index. Also included are the descriptive statistics and the quartile break-down for the data set. County FPLI 2000 Adjusted FPLI Indicator Values County FPLI 2000 Adjusted FPLI Indicator Values Alachua 94.04 5.96 0.812 Lee 98.34 1.66 0.571 Baker 92.54 7.46 0.896 Leon 96.49 3.51 0.675 Bay 93.52 6.48 0.841 Levy 92.03 7.97 0.924 Bradford 93.70 6.30 0.831 Liberty 93.20 6.80 0.859 Brevard 96.92 3.08 0.650 Madison 92.25 7.75 0.912 Broward 106.45 -6.45 0.117 Manatee 96.93 3.07 0.650 Calhoun 91.52 8.48 0.953 Marion 93.25 6.75 0.856 Charlotte 95.94 4.06 0.705 Martin 98.02 1.98 0.589 Citrus 92.75 7.25 0.884 Miami-Dade 106.42 -6.42 0.118 Clay 94.61 5.39 0.780 Monroe 107.60 -7.60 0.052 Collier 101.77 -1.77 0.379 Nassau 92.97 7.03 0.872 Columbia 91.58 8.42 0.950 Okaloosa 94.21 5.79 0.802 Desoto 94.04 5.96 0.812 Okeechobee 94.33 5.67 0.796 Dixie 92.71 7.29 0.886 Orange 98.69 1.31 0.551 Duval 97.04 2.96 0.644 Osceola 95.81 4.19 0.713 Escambia 93.22 6.78 0.858 Palm Beach 108.53 -8.53 0.000 Flagler 96.38 3.62 0.681 Pasco 96.38 3.62 0.681 Franklin 95.02 4.98 0.757 Pinellas 101.41 -1.41 0.399 Gadsden 93.54 6.46 0.840 Polk 95.24 4.76 0.745 Gilchrist 91.22 8.78 0.970 Putnam 93.05 6.95 0.867 Glades 96.03 3.97 0.700 Saint Johns 97.11 2.89 0.640 Gulf 92.15 7.85 0.918 Saint Lucie 96.30 3.70 0.685 Hamilton 91.50 8.50 0.954 Santa Rosa 92.79 7.21 0.882 Hardee 93.78 6.22 0.826 Sarasota 100.20 -0.20 0.467 Hendry 96.79 3.21 0.658 Seminole 97.39 2.61 0.624 Hernando 92.93 7.07 0.874 Sumter 92.58 7.42 0.894 Highlands 94.08 5.92 0.810 Suwannee 90.68 9.32 1.000 Hillsborough 100.32 -0.32 0.460 Taylor 93.52 6.48 0.841 Holmes 93.23 6.77 0.857 Union 90.78 9.22 0.994 Indian River 97.18 2.82 0.636 Volusia 94.50 5.50 0.786 Jackson 90.95 9.05 0.985 Wakulla 94.53 5.47 0.784 Jefferson 95.19 4.81 0.747 Walton 92.82 7.18 0.880 Lafayette 91.22 8.78 0.970 Washington 91.44 8.56 0.957 Lake 95.13 4.87 0.751 Raw FCHDI Mean 4.645 0.738 Quartile 1 0.654 Standard Deviation 3.963 0.222 Quartile 2 0.802 Minimum -8.530 0.000 Quartile 3 0.881 Maximum 9.320 1.000 Quartile 4 1.000

PAGE 133

Appendix A (Continued) A-13 Table A-12: Mortality Interim Index Alachua County to Lake County This data table sums and then averages the calculated mortality rate, child mortality rate, and the combined heart disease and cancer rate to create the Mortality Interim Index value for each county. These interim index values will be used to create the FCHDI. County Mortality Rate Child Mortality Heart Disease / Cancer Sum Mortality Interim Index (Sum / 3) Alachua 0.941 0.557 0.701 2.200 0.733 Baker 0.824 0.703 0.579 2.106 0.702 Bay 0.716 0.776 0.349 1.841 0.614 Bradford 0.637 0.572 0.657 1.867 0.622 Brevard 0.569 0.794 0.299 1.661 0.554 Broward 0.608 0.775 0.306 1.688 0.563 Calhoun 0.775 0.799 0.560 2.134 0.711 Charlotte 0.118 0.735 0.213 1.066 0.355 Citrus 0.000 0.689 0.311 1.000 0.333 Clay 0.892 0.682 0.456 2.030 0.677 Collier 0.627 0.793 0.354 1.774 0.591 Columbia 0.559 0.532 0.507 1.598 0.533 Desoto 0.559 0.753 0.331 1.642 0.547 Dixie 0.529 0.398 0.522 1.449 0.483 Duval 0.784 0.637 0.520 1.942 0.647 Escambia 0.735 0.534 0.560 1.830 0.610 Flagler 0.431 0.775 0.200 1.406 0.469 Franklin 0.598 0.696 0.680 1.974 0.658 Gadsden 0.765 0.283 0.475 1.523 0.508 Gilchrist 0.500 0.440 0.615 1.554 0.518 Glades 0.608 0.000 0.135 0.742 0.247 Gulf 0.647 0.083 0.391 1.121 0.374 Hamilton 0.804 1.000 0.414 2.218 0.739 Hardee 0.706 0.850 0.695 2.251 0.750 Hendry 0.794 0.727 0.437 1.958 0.653 Hernando 0.176 0.738 0.236 1.150 0.383 Highlands 0.196 0.523 0.314 1.033 0.344 Hillsborough 0.765 0.673 0.421 1.858 0.619 Holmes 0.510 0.699 0.448 1.657 0.552 Indian River 0.353 0.677 0.265 1.295 0.432 Jackson 0.608 0.758 0.407 1.773 0.591 Jefferson 0.529 1.000 0.639 2.168 0.723 Lafayette 0.745 1.000 0.757 2.502 0.834 Lake 0.314 0.859 0.324 1.498 0.499 (Continued)

PAGE 134

Appendix A (Continued) A-14 Table A-12: Mortality Interi m Index (Continued) Lee County to Washington County County Mortality Rate Child Mortality Heart Disease / Cancer Sum Mortality Interim Index (Sum / 3) Lee 0.480 0.691 0.262 1.433 0.478 Leon 1.000 0.629 0.610 2.239 0.746 Levy 0.392 0.687 0.518 1.597 0.532 Liberty 0.863 0.598 0.361 1.821 0.607 Madison 0.637 0.568 0.411 1.617 0.539 Manatee 0.353 0.710 0.289 1.352 0.451 Marion 0.343 0.748 0.311 1.402 0.467 Martin 0.402 0.614 0.232 1.247 0.416 Miami-Dade 0.784 0.757 0.353 1.894 0.631 Monroe 0.892 0.688 0.474 2.054 0.685 Nassau 0.755 0.653 0.436 1.844 0.615 Okaloosa 0.922 0.715 0.274 1.910 0.637 Okeechobee 0.608 0.383 0.218 1.209 0.403 Orange 0.922 0.723 0.454 2.099 0.700 Osceola 0.882 0.815 0.476 2.174 0.725 Palm Beach 0.451 0. 715 0.260 1.427 0.476 Pasco 0.098 0.778 0.396 1.273 0.424 Pinellas 0.275 0.758 0.490 1.522 0.507 Polk 0.559 0.698 0.288 1.545 0.515 Putnam 0.422 0.571 0.401 1.394 0.465 Saint Johns 0.696 0.836 0.500 2.032 0.677 Saint Lucie 0.441 0.684 0.245 1.370 0.457 Santa Rosa 0.873 0.819 0.468 2.160 0.720 Sarasota 0.186 0.674 0.358 1.218 0.406 Seminole 0.922 0.839 0.333 2.094 0.698 Sumter 0.461 0.634 0.190 1.284 0.428 Suwannee 0.441 0.264 0.683 1.388 0.463 Taylor 0.461 0.864 0.417 1.742 0.581 Union 0.588 0.790 0.544 1.923 0.641 Volusia 0.353 0.679 0.374 1.406 0.469 Wakulla 0.775 0.886 0.591 2.252 0.751 Walton 0.608 0.928 0.303 1.839 0.613 Washington 0.520 0.512 0.455 1.487 0.496

PAGE 135

Appendix A (Continued) A-15 Table A-13: Education Interim Index Alachua County to Lake County This data table sums and then averages the calculated Non-high school graduate rate, high school graduate and higher rate, and the bachelor’s degree and higher rate to create the Education Interim Index value for each county. These interim index valu es will be used to create the FCHDI. County Non-HS Graduate Education Attainment HS + Education Attainment BS + Sum Education Interim Index (Sum / 3) Alachua 0.993 0.976 0.914 2.883 0.961 Baker 0.522 0.584 0.040 1.146 0.382 Bay 0.841 0.804 0.312 1.957 0.652 Bradford 0.705 0.639 0.046 1.390 0.463 Brevard 0.827 0.932 0.481 2.240 0.747 Broward 0.859 0.828 0.507 2.195 0.732 Calhoun 0.953 0.516 0.026 1.495 0.498 Charlotte 0.857 0.831 0.309 1.997 0.666 Citrus 0.796 0.738 0.183 1.718 0.573 Clay 0.869 0.935 0.381 2.185 0.728 Collier 0.585 0.823 0.605 2.013 0.671 Columbia 0.728 0.651 0.117 1.497 0.499 Desoto 0.000 0.380 0.046 0.426 0.142 Dixie 0.738 0.438 0.000 1.176 0.392 Duval 0.792 0.845 0.433 2.069 0.690 Escambia 0.878 0.831 0.407 2.116 0.705 Flagler 0.864 0.923 0.413 2.199 0.733 Franklin 0.813 0.496 0.160 1.469 0.490 Gadsden 0.780 0.554 0.175 1.509 0.503 Gilchrist 0.761 0.596 0.074 1.431 0.477 Glades 0.789 0.533 0.086 1.408 0.469 Gulf 0.857 0.600 0.095 1.552 0.517 Hamilton 0.630 0.366 0.014 1.010 0.337 Hardee 0.480 0.247 0.046 0.773 0.258 Hendry 0.487 0.155 0.040 0.682 0.227 Hernando 0.808 0.743 0.169 1.720 0.573 Highlands 0.670 0.646 0.195 1.511 0.504 Hillsborough 0.770 0.799 0.524 2.094 0.698 Holmes 0.874 0.421 0.057 1.352 0.451 Indian River 0.789 0.818 0.467 2.075 0.692 Jackson 0.878 0.516 0.172 1.566 0.522 Jefferson 0.780 0.615 0.289 1.684 0.561 Lafayette 0.466 0.494 0.011 0.971 0.324 Lake 0.749 0.775 0.281 1.805 0.602 (Continued)

PAGE 136

Appendix A (Continued) A-16 Table A-13: Education Interim Index (Continued) Lee County to Washington County County Non-HS Graduate Education Attainment HS + Education Attainment BS + Sum Education Interim Index (Sum / 3) Lee 0.724 0.835 0.410 1.969 0.656 Leon 1.000 1.000 1.000 3.000 1.000 Levy 0.698 0.632 0.109 1.439 0.480 Liberty 0.674 0.431 0.017 1.123 0.374 Madison 0.749 0.477 0.097 1.324 0.441 Manatee 0.689 0.814 0.401 1.903 0.634 Marion 0.735 0.736 0.198 1.669 0.556 Martin 0.689 0.908 0.559 2.155 0.718 Miami-Dade 0.843 0.487 0.427 1.757 0.586 Monroe 0.836 0.898 0.536 2.270 0.757 Nassau 0.876 0.804 0.347 2.026 0.675 Okaloosa 0.920 0.973 0.499 2.392 0.797 Okeechobee 0.525 0.419 0.060 1.004 0.335 Orange 0.785 0.823 0.553 2.161 0.720 Osceola 0.789 0.758 0.255 1.802 0.601 Palm Beach 0.761 0. 867 0.599 2.227 0.742 Pasco 0.792 0.722 0.181 1.694 0.565 Pinellas 0.787 0.877 0.461 2.125 0.708 Polk 0.672 0.000 0.232 0.904 0.301 Putnam 0.717 0.547 0.074 1.338 0.446 Saint Johns 0.944 0.954 0.754 2.651 0.884 Saint Lucie 0.696 0.724 0.238 1.657 0.552 Santa Rosa 0.911 0.910 0.461 2.283 0.761 Sarasota 0.806 0.952 0.590 2.347 0.782 Seminole 0.890 0.990 0.693 2.574 0.858 Sumter 0.677 0.714 0.155 1.546 0.515 Suwannee 0.696 0.615 0.106 1.417 0.472 Taylor 0.707 0.538 0.060 1.305 0.435 Union 0.616 0.598 0.020 1.234 0.411 Volusia 0.815 0.828 0.309 1.953 0.651 Wakulla 0.747 0.741 0.255 1.743 0.581 Walton 0.838 0.683 0.269 1.791 0.597 Washington 0.829 0.567 0.069 1.464 0.488

PAGE 137

Appendix A (Continued) A-17 Table A-14: Economic Interim Index – Alachua County to Lake County This data table sums and then averages the calculated poverty rate, per capita income rate, and the Florida Price Level Index values to create the Economic Interi m Index value for each county. These interim index values will be used to create the FCHDI. County Poverty Level Census PCI FPLI 2000 Sum Economic Interim Index (Sum / 3) Alachua 0.167 0.383 0.812 1.361 0.454 Baker 0.589 0.223 0.896 1.707 0.569 Bay 0.677 0.394 0.841 1.912 0.637 Bradford 0.594 0.178 0.831 1.602 0.534 Brevard 0.859 0.529 0.650 2.039 0.680 Broward 0.755 0.611 0.117 1.483 0.494 Calhoun 0.313 0.088 0.953 1.354 0.451 Charlotte 0.927 0.545 0.705 2.177 0.726 Citrus 0.745 0.389 0.884 2.018 0.673 Clay 1.000 0.499 0.780 2.279 0.760 Collier 0.818 1.000 0.379 2.196 0.732 Columbia 0.573 0.196 0.950 1.718 0.573 Desoto 0.125 0.167 0.812 1.103 0.368 Dixie 0.359 0.145 0.886 1.391 0.464 Duval 0.734 0.494 0.644 1.872 0.624 Escambia 0.552 0.392 0.858 1.801 0.600 Flagler 0.901 0.548 0.681 2.130 0.710 Franklin 0.432 0.270 0.757 1.459 0.486 Gadsden 0.318 0.191 0.840 1.348 0.449 Gilchrist 0.620 0.166 0.970 1.755 0.585 Glades 0.563 0.231 0.700 1.494 0.498 Gulf 0.484 0.188 0.918 1.590 0.530 Hamilton 0.000 0.000 0.954 0.954 0.318 Hardee 0.073 0.091 0.826 0.991 0.330 Hendry 0.099 0.150 0.658 0.907 0.302 Hernando 0.818 0.376 0.874 2.068 0.689 Highlands 0.563 0.323 0.810 1.695 0.565 Hillsborough 0.703 0.545 0.460 1.708 0.569 Holmes 0.359 0.173 0.857 1.390 0.463 Indian River 0.870 0.808 0.636 2.313 0.771 Jackson 0.458 0.162 0.985 1.605 0.535 Jefferson 0.464 0.312 0.747 1.523 0.508 Lafayette 0.443 0.122 0.970 1.535 0.512 Lake 0.854 0.467 0.751 2.072 0.691 (Continued)

PAGE 138

Appendix A (Continued) A-18 Table A-14: Economic Interim Index (Continued) – Lee County to Washington County County Poverty Level Census PCI FPLI 2000 Sum Economic Interim Index (Sum / 3) Lee 0.849 0.678 0.571 2.097 0.699 Leon 0.406 0.507 0.675 1.588 0.529 Levy 0.385 0.203 0.924 1.513 0.504 Liberty 0.318 0.323 0.859 1.499 0.500 Madison 0.151 0.094 0.912 1.158 0.386 Manatee 0.828 0.573 0.650 2.051 0.684 Marion 0.672 0.353 0.856 1.881 0.627 Martin 0.896 0.922 0.589 2.407 0.802 Miami-Dade 0.417 0.385 0.118 0.919 0.306 Monroe 0.823 0.753 0.052 1.628 0.543 Nassau 0.880 0.595 0.872 2.347 0.782 Okaloosa 0.896 0.502 0.802 2.200 0.733 Okeechobee 0.521 0.193 0.796 1.510 0.503 Orange 0.724 0.502 0.551 1.777 0.592 Osceola 0.755 0.313 0.713 1.781 0.594 Palm Beach 0.839 0. 884 0.000 1.723 0.574 Pasco 0.797 0.382 0.681 1.859 0.620 Pinellas 0.833 0.627 0.399 1.859 0.620 Polk 0.682 0.375 0.745 1.802 0.601 Putnam 0.266 0.244 0.867 1.377 0.459 Saint Johns 0.938 0.878 0.640 2.455 0.818 Saint Lucie 0.656 0.399 0.685 1.740 0.580 Santa Rosa 0.844 0.462 0.882 2.187 0.729 Sarasota 0.948 0.861 0.467 2.276 0.759 Seminole 0.969 0.680 0.624 2.273 0.758 Sumter 0.641 0.304 0.894 1.838 0.613 Suwannee 0.391 0.199 1.000 1.590 0.530 Taylor 0.417 0.229 0.841 1.486 0.495 Union 0.625 0.086 0.994 1.705 0.568 Volusia 0.750 0.441 0.786 1.977 0.659 Wakulla 0.766 0.345 0.784 1.895 0.632 Walton 0.604 0.370 0.880 1.854 0.618 Washington 0.354 0.214 0.957 1.526 0.509

PAGE 139

Appendix A (Continued) A-19 Table A-15: Florida County Human Developmen t Index Alachua County to Lake County This data table sums and then averages the calculated Mortality Interim Index, Education Interim Index, and Economic Interim Index values to create the final FCHDI. County Mortality Interim Index Education Interim Index Economic Interim Index Sum Florida County Human Development Index (Sum / 3) Alachua 0.733 0.961 0.454 2.148 0.716 Baker 0.702 0.382 0.569 1.653 0.551 Bay 0.614 0.652 0.637 1.904 0.635 Bradford 0.622 0.463 0.534 1.620 0.540 Brevard 0.554 0.747 0.680 1.980 0.660 Broward 0.563 0.732 0.494 1.789 0.596 Calhoun 0.711 0.498 0.451 1.661 0.554 Charlotte 0.355 0.666 0.726 1.747 0.582 Citrus 0.333 0.573 0.673 1.578 0.526 Clay 0.677 0.728 0.760 2.165 0.722 Collier 0.591 0.671 0.732 1.995 0.665 Columbia 0.533 0.499 0.573 1.604 0.535 Desoto 0.547 0.142 0.368 1.057 0.352 Dixie 0.483 0.392 0.464 1.339 0.446 Duval 0.647 0.690 0.624 1.961 0.654 Escambia 0.610 0.705 0.600 1.916 0.639 Flagler 0.469 0.733 0.710 1.912 0.637 Franklin 0.658 0.490 0.486 1.634 0.545 Gadsden 0.508 0.503 0.449 1.460 0.487 Gilchrist 0.518 0.477 0.585 1.580 0.527 Glades 0.247 0.469 0.498 1.215 0.405 Gulf 0.374 0.517 0.530 1.421 0.474 Hamilton 0.739 0.337 0.318 1.394 0.465 Hardee 0.750 0.258 0.330 1.338 0.446 Hendry 0.653 0.227 0.302 1.182 0.394 Hernando 0.383 0.573 0.689 1.646 0.549 Highlands 0.344 0.504 0.565 1.413 0.471 Hillsborough 0.619 0.698 0.569 1.887 0.629 Holmes 0.552 0.451 0.463 1.466 0.489 Indian River 0.432 0.692 0.771 1.894 0.631 Jackson 0.591 0.522 0.535 1.648 0.549 Jefferson 0.723 0.561 0.508 1.792 0.597 Lafayette 0.834 0.324 0.512 1.670 0.557 Lake 0.499 0.602 0.691 1.791 0.597 (Continued)

PAGE 140

Appendix A (Continued) A-20 Table A-15: Florida County Huma n Development Index (Continued) Lee County to Washington County County Mortality Interim Index Education Interim Index Economic Interim Index Sum Florida County Human Development Index (Sum / 3) Lee 0.478 0.656 0.699 1.833 0.611 Leon 0.746 1.000 0.529 2.276 0.759 Levy 0.532 0.480 0.504 1.516 0.505 Liberty 0.607 0.374 0.500 1.481 0.494 Madison 0.539 0.441 0.386 1.366 0.455 Manatee 0.451 0.634 0.684 1.769 0.590 Marion 0.467 0.556 0.627 1.651 0.550 Martin 0.416 0.718 0.802 1.936 0.645 Miami-Dade 0.631 0.586 0.306 1.523 0.508 Monroe 0.685 0.757 0.543 1.984 0.661 Nassau 0.615 0.675 0.782 2.072 0.691 Okaloosa 0.637 0.797 0.733 2.168 0.723 Okeechobee 0.403 0.335 0.503 1.241 0.414 Orange 0.700 0.720 0.592 2.012 0.671 Osceola 0.725 0.601 0.594 1.919 0.640 Palm Beach 0.476 0. 742 0.574 1.792 0.597 Pasco 0.424 0.565 0.620 1.609 0.536 Pinellas 0.507 0.708 0.620 1.835 0.612 Polk 0.515 0.301 0.601 1.417 0.472 Putnam 0.465 0.446 0.459 1.370 0.457 Saint Johns 0.677 0.884 0.818 2.380 0.793 Saint Lucie 0.457 0.552 0.580 1.589 0.530 Santa Rosa 0.720 0.761 0.729 2.210 0.737 Sarasota 0.406 0.782 0.759 1.947 0.649 Seminole 0.698 0.858 0.758 2.314 0.771 Sumter 0.428 0.515 0.613 1.556 0.519 Suwannee 0.463 0.472 0.530 1.465 0.488 Taylor 0.581 0.435 0.495 1.511 0.504 Union 0.641 0.411 0.568 1.621 0.540 Volusia 0.469 0.651 0.659 1.778 0.593 Wakulla 0.751 0.581 0.632 1.963 0.654 Walton 0.613 0.597 0.618 1.828 0.609 Washington 0.496 0.488 0.509 1.492 0.497

PAGE 141

Appendix A (Continued) A-21 Table A-16: Florida Counties Ranked by FCHDI This data table lists the Florida counties by rank according to their FCHDI values, the FCHDI values, and the rank value as a percentage of the data set. County FCHDI Value Percent County FCHDI Value Percent 1 Saint Johns 0.793 100.00% 41 Bradford 0.540 39.30% 2 Seminole 0.771 98.40% 42 Pasco 0.536 37.80% 3 Leon 0.759 96.90% 43 Columbia 0.535 36.30% 4 Santa Rosa 0.737 95.40% 44 Saint Lucie 0.530 34.80% 5 Okaloosa 0.723 93.90% 45 Gilchrist 0.527 33.30% 6 Clay 0.722 92.40% 46 Citrus 0.526 31.80% 7 Alachua 0.716 90.90% 47 Sumter 0.519 30.30% 8 Nassau 0.691 89.30% 48 Miami-Dade 0.508 28.70% 9 Orange 0.671 87.80% 49 Levy 0.505 27.20% 10 Collier 0.665 86.30% 50 Taylor 0.504 25.70% 11 Monroe 0.661 84.80% 51 Washington 0.497 24.20% 12 Brevard 0.660 83.30% 52 Liberty 0.494 22.70% 13 Wakulla 0.654 81.80% 53 Holmes 0.489 21.20% 14 Duval 0.654 80.30% 54 Suwannee 0.488 19.60% 15 Sarasota 0.649 78.70% 55 Gadsden 0.487 18.10% 16 Martin 0.645 77.20% 56 Gulf 0.474 16.60% 17 Osceola 0.640 75.70% 57 Polk 0.472 15.10% 18 Escambia 0.639 74.20% 58 Highlands 0.471 13.60% 19 Flagler 0.637 72.70% 59 Hamilton 0.465 12.10% 20 Bay 0.635 71.20% 60 Putnam 0.457 10.60% 21 Indian River 0.631 69.60% 61 Madison 0.455 9.00% 22 Hillsborough 0.629 68.10% 62 Dixie 0.446 7.50% 23 Pinellas 0.612 66.60% 63 Hardee 0.446 6.00% 24 Lee 0.611 65.10% 64 Okeechobee 0.414 4.50% 25 Walton 0.609 63.60% 65 Glades 0.405 3.00% 26 Jefferson 0.597 62.10% 66 Hendry 0.394 1.50% 27 Palm Beach 0.597 60.60% 67 Desoto 0.352 .00% 28 Lake 0.597 59.00% 29 Broward 0.596 57.50% 30 Volusia 0.593 56.00% 31 Manatee 0.590 54.50% 32 Charlotte 0.582 53.00% 33 Lafayette 0.557 51.50% 34 Calhoun 0.554 50.00% 35 Baker 0.551 48.40% 36 Marion 0.550 46.90% 37 Jackson 0.549 45.40% 38 Hernando 0.549 43.90% 39 Franklin 0.545 42.40% 40 Union 0.540 40.90%

PAGE 142

Appendix A (Continued) A-22 Table A-17: Test Variable Natural Amenities Scale and Indicator Values County Natural Amenity Scale Indicator Values County Natural Amenity Scale Indicator Values Alachua 2.44 0.366 Lee 5.23 0.856 Baker 0.65 0.051 Leon 1.75 0.244 Bay 2.15 0.315 Levy 2.47 0.371 Bradford 1.34 0.172 Liberty 0.36 0.000 Brevard 3.93 0.627 Madison 1.30 0.165 Broward 4.98 0.812 Manatee 4.66 0.756 Calhoun 1.12 0.134 Marion 2.59 0.392 Charlotte 5.10 0.833 Martin 5.34 0.875 Citrus 3.43 0.540 Miami-Dade 5.48 0.900 Clay 2.01 0.290 Monroe 6.05 1.000 Collier 5.00 0.815 Nassau 2.04 0.295 Columbia 0.59 0.040 Okaloosa 2.01 0.290 Desoto 2.74 0.418 Okeechobee 4.70 0.763 Dixie 2.42 0.362 Orange 2.96 0.457 Duval 2.31 0.343 Osceola 4.50 0.728 Escambia 2.34 0.348 Palm Beach 5.14 0.840 Flagler 2.70 0.411 Pasco 3.37 0.529 Franklin 2.66 0.404 Pinellas 5.05 0.824 Gadsden 1.65 0.227 Polk 3.98 0.636 Gilchrist 1.21 0.149 Putnam 2.35 0.350 Glades 5.15 0.842 Saint Johns 2.98 0.460 Gulf 2.25 0.332 Saint Lucie 5.03 0.821 Hamilton 0.58 0.039 Santa Rosa 1.94 0.278 Hardee 2.25 0.332 Sarasota 4.78 0.777 Hendry 4.22 0.678 Seminole 3.14 0.489 Hernando 3.71 0.589 Sumter 2.84 0.436 Highlands 4.14 0.664 Suwannee 0.70 0.060 Hillsborough 4.32 0.696 Taylor 2.32 0.344 Holmes 0.89 0.093 Union 1.60 0.218 Indian River 4.72 0.766 Volusia 3.45 0.543 Jackson 1.76 0.246 Wakulla 1.95 0.279 Jefferson 2.00 0.288 Walton 2.18 0.320 Lafayette 0.84 0.084 Washington 1.95 0.279 Lake 3.40 0.534 Raw Alt. Indicator Mean 2.943 0.454 Quartile 1 0.279 Standard Deviation 1.497 0.263 Quartile 2 0.392 Minimum 0.360 0.000 Quartile 3 0.687 Maximum 6.050 1.000 Quartile 4 1.000

PAGE 143

Appendix A (Continued) A-23 Table A-18: FCHDI + Natural Amenities Indicator County 9 FCHDI Indicators (summed) Natural Amenity Indicator Sum / 10 County 9 FCHDI Indicators (summed) Natural Amenity Indicator Sum / 10 Alachua 6.444 0.366 0.681 Lee 5.499 0.856 0.636 Baker 4.959 0.051 0.501 Leon 6.827 0.244 0.707 Bay 5.711 0.315 0.603 Levy 4.549 0.371 0.492 Bradford 4.859 0.172 0.503 Liberty 4.443 0.000 0.444 Brevard 5.940 0.627 0.657 Madison 4.098 0.165 0.426 Broward 5.366 0.812 0.618 Manatee 5.306 0.756 0.606 Calhoun 4.982 0.134 0.512 Marion 4.952 0.392 0.534 Charlotte 5.241 0.833 0.607 Martin 5.809 0.875 0.668 Citrus 4.735 0.540 0.527 Miami-Dade 4.570 0.900 0.547 Clay 6.494 0.290 0.678 Monroe 5.952 1.000 0.695 Collier 5.984 0.815 0.680 Nassau 6.217 0.295 0.651 Columbia 4.813 0.040 0.485 Okaloosa 6.503 0.290 0.679 Desoto 3.171 0.418 0.359 Okeechobee 3.722 0.763 0.448 Dixie 4.016 0.362 0.438 Orange 6.037 0.457 0.649 Duval 5.883 0.343 0.623 Osceola 5.757 0.728 0.648 Escambia 5.747 0.348 0.609 Palm Beach 5.376 0.840 0.622 Flagler 5.736 0.411 0.615 Pasco 4.826 0.529 0.535 Franklin 4.903 0.404 0.531 Pinellas 5.506 0.824 0.633 Gadsden 4.381 0.227 0.461 Polk 4.251 0.636 0.489 Gilchrist 4.741 0.149 0.489 Putnam 4.109 0.350 0.446 Glades 3.645 0.842 0.449 Saint Johns 7.139 0.460 0.760 Gulf 4.264 0.332 0.460 Saint Lucie 4.767 0.821 0.559 Hamilton 4.182 0.039 0.422 Santa Rosa 6.630 0.278 0.691 Hardee 4.014 0.332 0.435 Sarasota 5.841 0.777 0.662 Hendry 3.547 0.678 0.423 Seminole 6.941 0.489 0.743 Hernando 4.938 0.589 0.553 Sumter 4.668 0.436 0.510 Highlands 4.239 0.664 0.490 Suwannee 4.395 0.060 0.445 Hillsborough 5.660 0.696 0.636 Taylor 4.533 0.344 0.488 Holmes 4.399 0.093 0.449 Union 4.862 0.218 0.508 Indian River 5.683 0.766 0.645 Volusia 5.335 0.543 0.588 Jackson 4.944 0.246 0.519 Wakulla 5.889 0.279 0.617 Jefferson 5.376 0.288 0.566 Walton 5.484 0.320 0.580 Lafayette 5.009 0.084 0.509 Washington 4.477 0.279 0.476 Lake 5.374 0.534 0.591

PAGE 144

Appendix A (Continued) A-24 Table A-19: Change in Ranking FCHDI + Natural Amenity Indicator FCHDI FCHDI + Natural Amenity Change in Rank FCHDI FCHDI + Natural Amenity Change in Rank 7 Alachua 6 Alachua 1 24 Lee 18 Lee 6 35 Baker 46 Baker -11 3 Leon 3 Leon 0 20 Bay 28 Bay -8 49 Levy 47 Levy 2 41 Bradford 45 Bradford -4 52 Liberty 61 Liberty -9 12 Brevard 12 Brevard 0 61 Madison 64 Madison -3 29 Broward 22 Broward 7 31 Manatee 27 Manatee 4 34 Calhoun 41 Calhoun -7 36 Marion 37 Marion -1 32 Charlotte 26 Charlotte 6 16 Martin 10 Martin 6 46 Citrus 39 Citrus 7 48 Miami-Dade 35 Miami-Dade 13 6 Clay 9 Clay -3 11 Monroe 4 Monroe 7 10 Collier 7 Collier 3 8 Nassau 13 Nassau -5 43 Columbia 52 Columbia -9 5 Okaloosa 8 Okaloosa -3 67 Desoto 67 Desoto 0 64 Okeechobee 58 Okeechobee 6 62 Dixie 62 Dixie 0 9 Orange 14 Orange -5 14 Duval 20 Duval -6 17 Osceola 15 Osceola 2 18 Escambia 25 Escambia -7 27 Palm Beach 21 Palm Beach 6 19 Flagler 24 Flagler -5 42 Pasco 36 Pasco 6 39 Franklin 38 Franklin 1 23 Pinellas 19 Pinellas 4 55 Gadsden 54 Gadsden 1 57 Polk 50 Polk 7 45 Gilchrist 49 Gilchrist -4 60 Putnam 59 Putnam 1 65 Glades 57 Glades 8 1 Saint Johns 1 Saint Johns 0 56 Gulf 55 Gulf 1 44 Saint Lucie 33 Saint Lucie 11 59 Hamilton 66 Hamilton -7 4 Santa Rosa 5 Santa Rosa -1 63 Hardee 63 Hardee 0 15 Sarasota 11 Sarasota 4 66 Hendry 65 Hendry 1 2 Seminole 2 Seminole 0 38 Hernando 34 Hernando 4 47 Sumter 42 Sumter 5 58 Highlands 48 Highlands 10 54 Suwannee 60 Suwannee -6 22 Hillsborough 17 Hillsborough 5 50 Taylor 51 Taylor -1 53 Holmes 56 Holmes -3 40 Union 44 Union -4 21 Indian River 16 Indian River 5 30 Volusia 30 Volusia 0 37 Jackson 40 Jackson -3 13 Wakulla 23 Wakulla -10 26 Jefferson 32 Jefferson -6 25 Walton 31 Walton -6 33 Lafayette 43 Lafayette -10 51 Washington 53 Washington -2 28 Lake 29 Lake -1 Standard Deviation 5.562319115 1 STDV (+/-) 5.562 Range 24 2 STDV (+/-) 11.125 Minimum -11 3 STDV (+/-) 16.687 Maximum 13

PAGE 145

B-1 APPENDIX B: Locator Ma ps of Florida Counties The following locator maps are for those readers who are unfamiliar with Florida’s sixty-seven Counties. Alachua..............Map B-3 Baker..................Map B-3 Bay.....................Map B-1 Bradford.............Map B-3 Brevard...............Map B-4 Broward..............Map B-5 Calhoun..............Map B-1 Charlotte.............Map B-5 Citrus..................Map B-4 Clay....................Map B-3 Collier................Map B-5 Columbia............Map B-3 Desoto................Map B-4 Dixie...................Map B-2 Duval..................Map B-3 Escambia............Map B-1 Flagler................Map B-3 Franklin..............Map B-2 Gadsden..............Map B-2 Gilchrist..............Map B-3 Glades................Map B-5 Gulf....................Map B-1 Hamilton............Map B-2 Hardee................Map B-4 Hendry................Map B-5 Hernando............Map B-4 Highlands...........Map B-4 Hillsborough......Map B-4 Holmes...............Map B-1 Indian River.......Map B-4 Jackson...............Map B-1 Jefferson.............Map B-2 Lafayette............Map B-2 Lake....................Map B-4 Lee......................Map B-5 Leon...................Map B-2 Levy...................Map B-3 Liberty................Map B-2 Madison..............Map B-2 Manatee..............Map B-4 Marion................Map B-3 Martin.................Map B-5 Miami-Dade.......Map B-5 Monroe...............Map B-5 Nassau................Map B-3 Okaloosa............Map B-1 Okeechobee........Map B-4 Orange................Map B-4 Osceola...............Map B-4 Palm Beach........Map B-5 Pasco..................Map B-4 Pinellas...............Map B-4 Polk....................Map B-4 Putnam...............Map B-3 Saint Johns.........Map B-3 Saint Lucie.........Map B-4 Santa Rosa..........Map B-1 Sarasota..............Map B-4 Seminole............Map B-4 Sumter................Map B-4 Suwannee...........Map B-2 Taylor.................Map B-2 Union..................Map B-3 Volusia...............Map B-3 Wakulla..............Map B-2 Walton................Map B-1 Washington........Map B-1

PAGE 146

Appendix B (Continued) B-2 Map B-1: Northwest Florida...........................................................................................B-3 Bay County Calhoun County Escambia County Gulf County Holmes County Jackson County Okaloosa County Santa Rosa County Walton County Washington County Map B-2: North Central Florida.....................................................................................B-4 Dixie County Franklin County Gadsden County Hamilton County Jefferson County Lafayette County Leon County Liberty County Madison County Suwannee County Taylor County Wakulla County Map B-3: Northeast Florida............................................................................................B-5 Alachua County Baker County Bradford County Clay County Columbia County Duval County Flagler County Gilchrist County Levy County Marion County Nassau County Putnam County St. Johns County Union County Volusia County Map B-4: Central Florida................................................................................................B-6 Brevard County Citrus County Desoto County Hardee County Hernando County Highlands County Hillsborough County Indian River County Lake County Manatee County Okeechobee County Orange County Osceola County Pasco County Pinellas County Polk County Sarasota County Seminole County St. Lucie County Sumter County Map B-5: South Florida..................................................................................................B-7 Broward County Charlotte County Collier County Glades County Hendry County Lee County Martin County Miami-Dade County Monroe County Palm Beach County


xml version 1.0 encoding UTF-8 standalone no
record xmlns http:www.loc.govMARC21slim xmlns:xsi http:www.w3.org2001XMLSchema-instance xsi:schemaLocation http:www.loc.govstandardsmarcxmlschemaMARC21slim.xsd
leader nam Ka
controlfield tag 001 001790595
003 fts
005 20070621133728.0
006 m||||e|||d||||||||
007 cr mnu|||uuuuu
008 070621s2006 flu sbm 000 0 eng d
datafield ind1 8 ind2 024
subfield code a E14-SFE0001512
040
FHM
c FHM
035
(OCoLC)144807556
049
FHMM
090
G116 (ONLINE)
1 100
Kelsey, Clay.
4 245
The application of a modified human development index :
b spatial modeling of socioeconomic well-being for Florida counties
h [electronic resource] /
by Clay Kelsey.
260
[Tampa, Fla] :
University of South Florida,
2006.
3 520
ABSTRACT: The Application of a Modified Human Development Index: Spatial Modeling of Socioeconomic Well-being for Florida CountiesThis thesis uses the United Nations Human Development Index as a model for comparing a selected set of socioeconomic indicators across Florida's sixty-seven counties. Whether for urban planning, hazards mitigation, transportation forecasting, or other county-level and state-level functions, information and understanding of socioeconomic conditions are keys to efficient planning and policy making, both in the early development stages as well as during implementation. A summary overview of socioeconomic well-being and its distribution across a given area offers a distinct advantage in terms of deciding where planning or policy changes are most needed and where they will prove most beneficial.This thesis takes a well-established and well documented index used for examining and comparing human development in nations across the globe, and modifies it for comparing county-level socioeconomic conditions across Florida. The results from this modified index are then displayed using choropleth maps as an aid to location interpretation of the ranked socioeconomic values, thereby providing a spatial context for the indexing.In the end, this thesis seeks to answer whether or not the modified index model is a suitable one for normalizing, aggregating, and ranking county-level socioeconomic data for Florida, and whether the use of choropleth mapping to display the rankings is a viable choice.
502
Thesis (M.A.)--University of South Florida, 2006.
504
Includes bibliographical references.
516
Text (Electronic thesis) in PDF format.
538
System requirements: World Wide Web browser and PDF reader.
Mode of access: World Wide Web.
500
Title from PDF of title page.
Document formatted into pages; contains 110 pages.
590
Adviser: Graham A. Tobin, Ph.D.
653
Social indicator.
Territorial indicator.
Composite index.
HDI.
Choropleth.
690
Dissertations, Academic
z USF
x Geography
Masters.
773
t USF Electronic Theses and Dissertations.
0 856
u http://digital.lib.usf.edu/?e14.1512