Vulnerability to Tropical Cyclone Related Mortalities Temporal Analysis 88 6 Applications of Findings

VULNERABILITY TO TROPICAL CYCLONE

RELATED MORTALITIES ON HISPANIOLA

A thesis submitted

To Kent State University in partial

Fulfillment of the requirements for the

Degree of Masters of Arts

Bryce Kastelein

December 2014

Except for previously published materials

Thesis written by

Bryce Kastelein

B.S., Southern Illinois University Edwardsville, 2011

M.A., Kent State University, 2014

Approved by

Thomas Schmidlin, Professor, Ph.D., Geography, Masters Advisor

Mandy Munro-Stasiuk, Professor, Ph.D., Chair, Department of Geography

James L. Blank, Ph.D. Dean, College of Arts and Science

Table of Contents

TABLE OF CONTENTS...... III LIST OF FIGURES ...... V LIST OF TABLES ...... VI PREFACE ...... VII 1 INTRODUCTION ...... 1 2 LITERATURE REVIEW ...... 3 2.1 GEOGRAPHY OF THE HISPANIOLA ...... 3 2.2 TROPICAL CYCLONES ...... 6 2.3 VULNERABILITY...... 10 2.3.1 SOCIAL VULNERABILITY ...... 11 2.3.2 PHYSICAL VULNERABILITY ...... 13 3 RESEARCH DESIGN ...... 15 3.1 DATA COLLECTION ...... 15 3.1.1 VULNERABILITY DATA ...... 18 3.1.1.1 SELECTION OF MEASURES OF SOCIAL AND PHYSICAL VULNERABILITY .. 18 3.1.1.2 DESCRIPTION OF VARIABLES ...... 21 3.1.1.3 REDUCTION OF CORRELATED OR COLINEAR VARIABLES ...... 30 3.1.2 TROPICAL CYCLONE EVENT DATA ...... 31 3.1.3 TROPICAL CYCLONE HUMAN FATALITY DATA ...... 31 3.2 METHODS ...... 36 3.2.1 STORM EVENT SEVERITY SCORING ...... 36 3.2.2 INCLUSION CRITERIA ...... 39 3.2.3 CORRELATING VULNERABILITY FACTORS AND STORM CLUSTER GROUPS TO HUMAN FATALITY IMPACT ...... 41 3.2.4 RESULTING CORRELATIONS TO HUMAN FATALITY DATA ...... 41 3.2.5 ANALYZE CHANGES IN VULNERABILITY OVER TIME ...... 42 4 RESULTS...... 43 4.1 TROPICAL CYCLONE RESULTS ...... 43 4.2 VULNERABILITY RESULTS ...... 47 4.3 STATISTICAL RESULTS ...... 52 5 DISCUSSION ...... 60 5.1 TROPICAL CYCLONE FINDINGS ...... 60 5.2 VULNERABILITY VARIABLES ...... 64 5.2.1 PHYSICAL VARIABLES ...... 64 5.2.1.1 SLOPE ...... 64 5.2.2 SOCIAL VARIABLES ...... 66 5.2.2.1 POPULATION ...... 66 5.2.2.2 POVERTY ...... 68 5.2.2.3 PERCENT OF URBAN POPULATION ...... 71 5.2.2.4 UNEMPLOYMENT RATE ...... 73 5.2.2.5 PERCENT OF WOMEN IN THE WORKFORCE ...... 75 5.2.2.6 HOUSING MATERIALS: EARTHEN OR SCRAP MATERIALS...... 77

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5.2.2.7 HOUSING MATERIALS: UPPER SCALE ...... 79 5.2.2.8 HEALTH CENTERS PER 10,000 ...... 81 5.3 WITH HURRICANE JEANNE ...... 82 5.3.1 PERCENT OF FOREST COVERAGE ...... 83 5.3.2 DENSITY ...... 85 5.3.3 PERCENT OF POPULATION WITH VULNERABLE AGE ...... 87 5.4 VULNERABILITY TO TROPICAL CYCLONE RELATED MORTALITIES TEMPORAL ANALYSIS 88 6 APPLICATIONS OF FINDINGS ...... 91 6.1 DANGEROUS TROPICAL CYCLONES ...... 91 6.2 VULNERABILITY VARIABLE FINDINGS ...... 92 6.2.1 PHYSICAL VARIABLE FINDINGS...... 93 6.2.2 SOCIAL VARIABLE FINDINGS ...... 93 7 CONCLUSION ...... 97 7.1 SUMMARY ...... 97 7.2 RESEARCH QUESTIONS ...... 98 7.3 RELATIONSHIP TO LITERATURE ...... 100 7.4 ACTIONS FOR OFFICIALS ...... 101 7.5 LIMITATIONS OF THE STUDY ...... 103 7.6 FUTURE CONSIDERATIONS ...... 104 LITERATURE CITED ...... 105 APPENDIX A- HURRICANE MORTALITY SOURCES CITED ...... 112 SOCIAL AND PHYSICAL SOURCES CITED ...... 123 HURRICANE DATA ...... 124

List of Figures

Figure 1 - Caribbean Basin ...... 3 Figure 2 - Population Distribution on Hispaniola ...... 4 Figure 3 - Topography of Hispaniola ...... 5 Figure 4 - Saffir Simpson Scale of Hurricane Intensity ...... 6 Figure 5 - Caribbean Island Grouping ...... 7 Figure 6 - Region of Cyclogenesis ...... 8 Figure 7 - Research Design ...... 15 Figure 8 - Map of Hispaniola ...... 17 Figure 9 - Statistical Analysis of Factors Found in Hazard Literature ...... 20 Figure 10 - Hispaniola Land Use ...... 22 Figure 11 - Hispaniola Slope ...... 24 Figure 12 - Tropical Cyclone Mortality Map ...... 34 Figure 13 - Tropical Cyclone Mortality Rate Map ...... 35 Figure 14 - Tropical Cyclones Impacting Hispaniola (1994-2012)...... 38 Figure 15 - Inclusion Criteria ...... 40 Figure 16 - Storm Cluster Analysis ...... 44 Figure 17 - Frequency of Impact and Cluster Type ...... 45 Figure 18 – Determination of Twelve Factors for Detailed Analysis ...... 49 Figure 19 - Tropical Cyclone Related Mortalities ...... 53 Figure 20 - Tropical Cyclone Related Mortality Rate ...... 54 Figure 21 – Contribution to model prediction from average slope ...... 65 Figure 22 – Contribution to model prediction from Population ...... 67 Figure 23 – Contribution to model prediction from poverty level ...... 69 Figure 24 – Contribution to model prediction from Percent Urban ...... 71 Figure 25 - Contribution to model prediction from Unemployment Rate ...... 73 Figure 26 – Contribution to model prediction for Percent Women in the Workforce .... 75 Figure 27 – Contribution to model prediction from Housing: Earthen/Scrap Materials . 77 Figure 28 – Contribution to model prediction from Housing: Upper Scale...... 79 Figure 29 – Contribution to model prediction from Health Centers Per 10,000 ...... 81 Figure 30 – Contribution to model prediction from Percent of Forestation ...... 83 Figure 31 – Contribution to model prediction from Density ...... 85 Figure 32 – Contribution to model prediction from Population with Vulnerable Age ..... 87 Figure 33 - Actual fatalities in Haiti since 1994 ...... 88 Figure 34 - Actual Fatalities in Dominican Republic since 1994 ...... 89

List of Tables

Table 1 - Fatality data by province and department per stormError! Bookmark not defined. Table 2 - Cluster Means and Standard Deviations ...... 44 Table 3 - VIF Without Jeanne ...... 50 Table 4 - VIF With Jeanne ...... 50 Table 5 - Simple Correlation Matrix of Vulnerability Factors ...... 51 Table 6 - Without Jeanne Poisson Regression Results ...... 57 Table 7 - With Jeanne Poisson Regression Results ...... 57 Table 8 - Model Predicted and Actual Fatalities by District With and Without Jeanne ... 58 Table 9 - - Phantom Storm Fatalities by District ...... 62

Preface

VULNERABILITY TO TROPICAL CYLONE RELATED MORTALITIES ON HISPANIOLA (104 pp.)

The destructive powers of tropical cyclones impact the Caribbean almost every hurricane season. In contrast to many other regions, the Caribbean’s vulnerability to potential tropical cyclone related mortalities remains high, particularly on the Island of Hispaniola, in spite of an increase in forecasting proficiency and technology. The level of social and physical vulnerability within Haiti and the Dominican Republic increases the potential impact on people from tropical cyclones. This study aims to identify social and physical vulnerabilities to tropical cyclone induced fatalities in Haiti and the Dominican Republic. Mortality data was collected at Haiti’s department scale and the Dominican Republic’s province scale and was then compared to both nations’ social and physical vulnerabilities to illustrate any relationship between vulnerability and mortality. Us- ing an inclusion criterion of selecting departments or provinces that are impacted by a given storm through Geographic Information Systems (GIS), allowed for storm event scoring to be applied equally across these departments or provinces. Social and physical vulnerability indicators for both nations were statistically analyzed through a simple correlation matrix and variance of inflation factors in order to eliminate any collinear variables. A Poisson regression was used to analyze storm related mortality, and social and physical variables. The result was used in a predictive model to gain an understanding of a district’s potential vulnerability to tropical cyclone related mortality. An overall examination of the potential vulnerability of the population of His- paniola can allow for a better understanding into why the nations still experience such vulnerability to tropical cyclone related mortalities and lead to possible mitigation efforts to reduce storm related mortality.

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1 INTRODUCTION

There is an average of eighty to ninety tropical cyclones annually around the world. These storms cause an estimated $13 billion in global damages and kill an average of 10,000 people annually (Mendelsohn et al. 2012, Adler 2005). Storm related mortality can be influenced by the characteristics of a tropical cyclone as well as the levels of physical and social vulnerability of the population in the region impacted. Tropical cyclones have killed approximately 17,000 – 20,000 people in the Caribbean region since 1945 (Organization of American States 1990, National Hur- ricane Center) and caused over 5,500 deaths on the Island of Hispaniola since 1994. The goal of this thesis is to examine the relationship between vulnerability factors and cyclone-related mortality on Hispaniola.

This research seeks to answer several questions about the relationships between tropical cyclones and cyclone-related mortality in the nations of Haiti and the Dominican Republic over the past 19 years (1994-2012). What makes the island so vulnerable to tropical cyclone-related mortality? Does this vulnerability differ between the two nations? Does this vulnerability vary within the nations? Are fatality rates associated with tropical cyclones related to the level of vulnerability of each nation? Answers to these questions may help mitigate against future fatalities on the Island of Hispaniola.

This study will build upon the conclusion of Pielke Jr. et al. (2003) that the Caribbean region is just as vulnerable to tropical cyclone mortality as it was many decades ago, in spite of an

increase in forecasting skills and technology. Studies by Cutter (Cutter 1996, Cutter et al. 2003,

Cutter and Emerich 2006, Cutter and Finch 2008) have illustrated how social vulnerabilities have a large influence on the people most affected by a natural hazard. While many places, such as the U.S., have seen a dramatic drop in the mortality associated with tropical cyclones, the Carib- bean and particularly Hispaniola continues to have high fatality rates (Pielke Jr. et al. 2003). The answers to the research questions may explain why Haiti and the Dominican Republic have such a high level of vulnerability and identify the most significant factors related to the storm related fatalities.

2 LITERATURE REVIEW

To understand the societal impacts of meteorological events, it is important to understand the climate and the society (Pielke Jr. et al. 2003). In this study, the climate aspect includes tropical cyclone frequency, location, and intensity. The societal aspects include: social vulnerability, the probability of being impacted, level of preparedness, and response to tropical cyclone impacts (Cutter and Finch 2008). The physical aspects include: land use, island size, topography, flooding, and landslides. The geography of Hispaniola, climatology of tropical cyclones, and the social and physical vulnerabilities among the islands will be studied to understand their potential roles with the fatalities that occur within these two nations.

2.1 Geography of the Hispaniola

The Caribbean region (Figure 1) is made up of twenty-two island nations. The total population for the region is 39,169,962 (CIA

World Factbook). This region contains around

7,000 islands. Hispaniola is the second largest island and has a population of almost 19 mil-

(geology.com/world/caribbean-satellite-image.shtml) lion (CIA World Factbook). The population of the Dominican Republic is located primarily in Figure 1 - Caribbean Basin the south near its capital Santo Domingo as well as throughout the middle of the nation as seen in Figure 2. Haiti’s population is dispersed slightly more evenly throughout the whole nation but has large concentrations around its capital

Port Au Prince and several cities north of the capital as seen in Figure 2. The one island is shared by the two nations of Haiti and the Dominican Republic. The Dominican Republic accounts for more than half of the island with the remaining western section belonging to Haiti. Countries of the Caribbean are relatively small so most parts of the country can be simultaneously impacted by a single storm event (Crowards 2000, Pelling and Uitto 2001). This occurred in 1998 when

Hurricane Georges traversed Hispaniola causing considerable damage and over 500 fatalities.

(http://earthobservatory.nasa.gov/Features/Haiti2004/)

Figure 2 - Population Distribution on Hispaniola

(http://earthobservatory.nasa.gov/Features/Haiti2004/)

Figure 3 - Topography of Hispaniola

While small, the climate of the island does vary. The northeast of the island is Tropical

Monsoon (Am) while the rest is Tropical Savanna (Aw) (Peterson et al. 2011). A large portion of the island is mountainous as seen in Figure 3 and thus some regions can have aspects of a High- land climate.

The Caribbean Islands are considered Small Island Developing States (SIDS). This means they are inhibited in the world market due to one or more of the following characteristics: land area, population, economic, or environmental degradation (Pelling and Uitto 2001). As such, they are more vulnerable to natural disasters such as tropical cyclones (Pelling and Uitto 2001). Pelling and Uitto (2001) showed that the Greater Antilles (Cuba, Jamaica, Haiti, The Dominican Republic, and Puerto Rico) are the most vulnerable island group based upon data collected on natural disaster impacts and losses.

2.2 Tropical Cyclones

Tropical Cyclones are areas of low pressure with a strong cyclonic movement of air and heavy rainfall. Typically, a cyclone in the Atlantic begins as a low pressure zone called an easterly wave (Petersen et al. 2011). Significant storm development can occur once the depression is over a warm ocean surface of at least 27 degrees Celsius (Petersen et al. 2011). The need for warm water is the reason why cyclones form in the tropics. The area of low pressure causes warm, moist air to quickly rise and cool releasing its energy as latent heat, increasing the strength of the tropical cyclone (Petersen et al. 2011). The temperature of the water, mid-tropospheric winds, and other atmospheric and oceanic processes determine the size and strength of a tropical cyclone (Webster et al. 2005). A tropical cyclone develops into a tropical storm when its maximum sustained wind speed reaches 39mph up to 74mph. Once it reaches 75mph, it becomes a hurricane. There are five different categories of hurricanes based on wind speed as seen in Figure 4.

The trade winds carry a majority of the Atlantic tropical cyclones west towards the Caribbean

(Petersen et al. 2011). The U.S. National Weather Service issues hurricane watches and warnings for the whole basin. The governments on Hispaniola rely on these warnings and some of their own weather technologies to alert their people to the potential for storm impact.

However, the warning system often has difficulty in reaching rural populations.

Figure 4 - Saffir Simpson Scale of Hurricane Intensity

The risk of experienc- ing a tropical cyclone varies within the region. The South- ern Caribbean Islands, as seen in Figure 5 (Antigua and

Barbuda, Barbados, Domi-

nica, Grenada, Guadeloupe, (http://www.caribbean-on-line.com/maps/#.U8Xqn_ldWLw)

Martinique, Montserrat, St. Figure 5 - Caribbean Island Grouping Kitts, Nevis, St. Lucia, St. Vin- cent, the Grenadines, and Trinidad and Tobago) have a lower frequency, 0.4 hurricane strikes per year, than the Northern Caribbean Islands (British Virgin Islands, Cayman Islands, Cuba, Domini- can Republic, Haiti, Jamaica, Puerto Rico, Turks and Caicos, and the United States Virgin Islands),

1.0 hurricane strike per year; with the long term average accounting for variations in ENSO, ver- tical windshear, sea surface temperatures, sea-level pressure, and several oceanic and atmospheric oscillations (Pielke Jr. et al. 2003). Since Hispaniola is located in the Northern Caribbean it is more vulnerable to a tropical cyclone strike in a given year.

The Island of Hispaniola is located

in the North Atlantic Ocean’s region of cy-

clogenesis as seen in Figure 6 (Webster et

al. 2005). Sea surface temperatures (SST) in

this region play a major role in determining

http://www.srh.noaa.gov/crp/?n=education-hurricanes the frequency and intensity of tropical cy- Figure 6 - Region of Cyclogenesis clones (tropical storms to hurricanes)

(Webster et al. 2005, Emanuel 2005, Vecchi and Soden 2007, Zhang and Delworth 2006). An increase in SST in the North Atlantic Ocean has a strong statistical relationship with an increase in frequency of severe hurricanes (category 3-5) (Webster et al. 2005). High SST alone do not always lead to powerful hurricanes but lead to an increase in the potential for the most severe hurricanes as they only develop in regions of high SST (Emanuel 2005).

Other atmospheric phenomena are important in understanding the potential direct impact for the islands from a tropical cyclone. The influence of SST plays the main role but evidence has shown that El Niño-Southern Oscillation (ENSO) has an influence on the location and frequency of landfall tropical cyclones (Tartaglione 2003, Jury and Enfield 2010, Pielke Jr. and

Landsea 1999).

Tropical cyclone numbers in the North Atlantic have increased since 1995, with a positive phase in the Atlantic Multidecadal Oscillation (AMO) and an increase in the SST of the basin of cyclogenesis (Goldenberg et al. 2001, Jury and Enfield 2010). The AMO oscillates between nega-

tive (cool) phases and positive (warm) phases. An increase in the number of hurricane days experienced in the Caribbean is highly associated to the AMO being in a positive phase (Jury and

Enfield 2010). While the total damage, based on assessments from NOAA, of storms has been increasing, the main societal result has been economic (Pielke Jr. and Landsea 1998). Fatalities have generally decreased over time in the Atlantic basins despite an increase of frequency and intensity of hurricanes and number of hurricanes days experienced in the North Atlantic (Rap- paport 2000, Webster et al. 2005, Emanuel 2005). This has occurred in spite of dramatic increases in coastal population and construction of valuable assets such as houses, cars, and busi- nesses. The presence of these assets has increased the damage totals but fatalities have trended downward almost everywhere in the North Atlantic, apart from Latin America and the Caribbean

Islands (Pielke Jr. et al. 2003).

The current period of increased storm activity in the Caribbean (beginning in 1995) has been just as active if not more thus far than the previous active period (1940’s to 1960’s) and could also potentially end with an increased number of fatalities as well (Pielke Jr. et al. 2003).

This is significant because the vulnerability of the people of Hispaniola could potentially be increasing due to rising population, and socio-economic conditions that can cause people to live in hazard prone areas which are subject to flooding, landslides, and storm surge (Degg 1992). These dangers are cause for some of the largest death tolls that occur on Hispaniola (Rappaport and

Fernadez-Partagas 1997). In 1994, Hurricane Gordon caused numerous floods, landslides, and mudslides which killed around 1,000 people while Hurricane Jeanne in 2004 impacted an area prone to flooding causing about 3,000 fatalities in Haiti (See Appendix A). The amount of rainfall that occurs with some tropical cyclones can cause large sections of hilly terrain to turn into deadly

landslides and mudlsides as well as causing rivers and streams to suddenly turn into deadly flash floods.

2.3 Vulnerability

Just as the frequency and potential intensity of tropical cyclones vary, so does the social vulnerability of the two nations’ people (Pielke Jr. et al. 2003). Vulnerability is often defined as the susceptibility of a place or people to a natural hazard and their potential losses (Cutter 1996

& Schmidtlein et al. 2008). The vulnerability of a place may not be exposed until after a natural disaster has caused massive devastation (Cutter and Emrich 2006). To understand the vulnerability of a place, many indicators can be used (Pelling and Uitto 2001, Brooks et al. 2005, Rygel et al. 2006, Cutter and Finch 2008). These indicators can change depending on social factors and regions under study, and they may not be appropriate for every study region (Rygel et al. 2006,

Cutter and Finch 2008). Pielke Jr. et al. 2003, Cutter et al. 2003 and other hazard literature describe many variables used in vulnerability studies. These are used as an initial investigation into the main themes of potential variables that could be used for further statistical analysis. For the

Island of Hispaniola, the socioeconomic status, topography of the island, population increase, deforestation, education, urbanization, and inequality in farmland are the focal themes of vulnerability to tropical cyclones (Pielke Jr. et al. 2003). Investigating these themes can help determine what individual factors may be notable and those related to be collected to illustrate which region on the island has the most risk of tropical cyclone related mortalities.

2.3.1 Social Vulnerability

While the primary way of observing social vulnerability is to examine social, political, and geographic indicators; natural disaster vulnerability is also examined by incorporating the number of fatalities that occur (Pelling and Uitto 2001). While these social factors are statistics at the province and department level, and may be very beneficial in determining a national socioeconomic vulnerability, they do not fully represent the vulnerability of individuals. With natural hazards, it is the individuals themselves that are at risk (Brooks et al. 2005). This is an important distinction in relating the social vulnerability of the Dominican Republic and Haiti to tropical cyclone related mortality and in assessing why these nations are unusually vulnerable to fatalities caused by tropical cyclones. It important to recognize that “mortality” is associated with individuals and not with the entire nation (Brooks et al. 2005). Therefore social vulnerability indicators need to be accompanied by observations that account for the actual individual’s mortality risk related to tropical cyclones.

Socioeconomic status plays a major role in the social vulnerability of a population (Donner and Rodriguez 2008, Cutter and Emrich 2006). People are less able to properly protect themselves if they are living in poverty. Without sufficient resources, many people either live in dwellings that are not safe during a tropical cyclone or are unable to properly repair any damage from previous storms. Socioeconomic status has been the leading variable in many of Cutter’s analyses on social vulnerabilities (Cutter 1996, Cutter and Emerich 2006, Cutter and Finch 2008).

Many people living in poverty move to an urban setting in an attempt to seek out an improved lifestyle (DESA 2011). This is the case in the Caribbean where over sixty-four percent

of the population is living in urban areas (Pielke Jr. et al. 2003). This presents unique challenges as urban areas, particularly the core areas, experience many different risks that rural populations do not experience such as segregation, neighborhood decline, socioeconomic deprivation, inequalities in health, well-being, and health care accessibility (Cutter and Emrich 2006). The impact felt by rural regions increases with the remoteness of their location from services, while the urban communities suffer from endemic inequalities in wealth, health care and housing (Cutter and

Emrich 2006, Donner and Rodriguez 2008). This can cause poor to seek housing in areas that are not suitable for proper building, such as slopes prone to landslides (Rodriguez 1997).

This problem of social inequality is exacerbated when coupled with population growth.

Hispaniola has had increased fertility rates, especially during the period of low tropical cyclone activity (Pielke Jr. et al. 2003). This can lead to larger numbers of people living in vulnerable locations, increasing the overall vulnerability when coupled with a higher frequency of storms.

Social vulnerability varies within Hispaniola and may not be fully exposed until a disaster occurs. Various indicators can be used to illuminate the vulnerability of the population of Hispan- iola: a poor socioeconomic status, urbanization, lack of education and population growth can cause people to have insufficient resource for protection, inequalities in living place and condi- tion. This can increase the overall number of individuals vulnerable to tropical cyclone impacts.

While many indicators are important in determining vulnerability, observing the mortality associated to tropical cyclones represent the actual individuals at risk during an impact.

2.3.2 Physical Vulnerability

The topography of the island and the land use changes that have occurred in the two nations can have detrimental consequences to their people through a potential increase in the vulnerability to natural hazards. The largest numbers of fatalities that occur from tropical storms on these islands are from fresh water flooding and landslides (Rappaport and Fernandez-Partagas

1997, Negri et al. 2005). The topography of Hispaniola includes hilly or mountainous terrain.

When tropical cyclones bring heavy rains, it can trigger landslides.

Changes in land use can influence the potential for the occurrence of landslides. Haiti has seen deforestation of a large portion of the land and this can lead to landslides on hilly terrain as vegetation is no longer there to hold the soil (Pielke Jr. et al. 2003). The Dominican Republic has

12% of the farmland distributed to 82% of the farmers; causing most farmers to work farmland that is on hilly terrain (Pielke Jr. et al. 2003). This is a pattern seen in Haiti also. This land was deforested to make room for crops, increasing the potential for a landslide. Deforestation can also concentrate the flow of rain water downhill helping to produce an increase in the amount of flooding that can occur (Arenas 1983).

Floods are the largest cause of fatalities associated with tropical cyclones in Caribbean

Islands (Rappaport 2000, Rappaport and Fernandez-Partagas 1997). The Island of Hispaniola suffers significantly from tropical cyclone induced flooding. The large quantity of rainfall from a single tropical cyclone coupled with the topography can cause flash floods (Arenas 1983). Poor substandard housing located in flood prone areas means that large numbers of low income people

can be affected adversely by flooding. This is illustrated by the population statistics from the Do- minican Republic which had an overall increase in the population from 7.8 million to 10.1 million from 1994-2011 (The World Bank 2013). Increasing population density can result in people building housing where ever they can, including flood prone areas (Rodriguez 1997).

3 RESEARCH DESIGN

Figure 7 demonstrates the flow of how the research was conducted. The study area is the

Island of Hispaniola in the Caribbean as seen in Figure 8. The study was conducted by recording the mortality data that occurred during a tropical cyclone impact and comparing this to measures of physical and social vulnerability acquired for both Haiti and the Dominican Republic. These indicators of vulnerability along with tropical cyclone impact data were analyzed statistically for both nations on the Island of Hispaniola to understand their relationship. This research looked at the data collected in a time series to analyze any change in the level of vulnerability Haiti or the

Dominican Republic have had over time.

Figure 7 - Research Design

3.1 Data Collection

To illustrate the relationship between vulnerability and tropical cyclone impacts on His- paniola, data were collected covering three main areas of focus: population vulnerability including social and physical risk factors, tropical cyclones characteristics relating to severity of storm events, and the human impact of specific events. All of this data came in the form of secondary data, collected by other agencies, governments, media sources, and publications. The World

Bank, United Nations World Income Inequality Database, and C.I.A. World Factbook, ReliefWeb, and government census websites offer many statistics on social and physical vulnerabilities ex- isting on the island. Population vulnerability was gathered primarily from each nation’s census data with a few variables from other government sources. NOAA’s Hurricane Best Track data contained information needed for gathering statistics on tropical cyclones. The cyclone data collected focused on the frequency, intensity, storm speed, and barometric pressure during tropical cyclone events. The impact data included storm related fatalities at the province and department spatial scale.

Map of Hispaniola Mapof

Figure Figure

3.1.1 Vulnerability Data

3.1.1.1 Selection of Measures of Social and Physical Vulnerability

The social vulnerability variables specific to Hispaniola, as discussed by Pielke Jr. et al.

(2003), and those commonly used in natural hazard research such as Cutter et al. (2003), were used as a basis for the initial collection of possible variables to study. The initial data that were able to be sourced from each country’s censuses and surveys included around 50 variables

(Figure 9). Some of these variables included: percent of males, percent of females, percent of poverty, level of education, percentage of people obtaining their drinking water from a stream or river, number of dwellings, 3 types of structures, total population, percent of urban population, percent of rural population, density, average size of household, total fertility rate, percent of female headed households, percent of people with a vulnerable age, average age, percent with a disability, birth rate, rate of urbanization, percent renters, participation in the labor force, the female participation in the labor force, infant mortality, the number of health care centers, the percentage of unemployment, four different levels of education obtained, and sixteen types of occupation (Brooks et al. 2005, Rygel et al. 2006, Pielke Jr. et al. 2003, Cutter et al. 2003, Heinz

Center for Science, Economics, and the Environment 2000, Rappaport and Fernandez-Partagas

1997, Donner and Rodriguez 2008).

The physical geography of the island also plays a role in affecting the level of vulnerability.

The impact from natural hazards is exacerbated by this in various ways including the level of deforestation, agriculture, average elevation, and the slope of the island (Pielke Jr. et al. 2003).

These data were collected from the International Food Policy Research Institute and NASA. These data were presented at the district scale using ESRI’s ArcGIS.

Data for Haiti and the Dominican Republic, at the department and province spatial scale, are not always readily available. As poor nations, the amount and availability of data is not as accessible as with the United States. This is highlighted by the fact that Haiti conducted its census in 2003 for the first time since 1982. Even so, not all of the data has been released to the public.

The Dominican Republic has been more capable in taking consistent censuses and releasing the data. Despite challenges, both governments have obtained important data needed and used in the vulnerability analysis. The variables collected for this analysis were obtained through both

Haiti’s and the Dominican Republic’s government survey and census data as seen in Appendix A.

Through further research within the literature on vulnerabilities on Hispaniola and align- ing the data gathered from the two nation’s censuses, a reduced number of variables were chosen with seven being specific to Hispaniola and twenty-six variables being prominent in Cutter et al. 2003 and other hazard literature (Chapter 3.1.1.2) that increase vulnerability. These factors are listed in Figure 9. The twenty-six variables derived from Cutter et al. 2003 were: the total population, the percentage of urban population, the percentage of rural population, the density of provinces and departments, the percentage of people who are at a vulnerable age, the percentage of renters, the percentage of female participation in the labor force, infant mortality, the number of health care centers per 10,000, total unemployment, and sixteen types of occupation.

The seven Hispanola specific variables derived from Pielke Jr. et al. 2003 and other hazard litera-

ture (Chapter 3.1.1.2) were: the percentage of forest coverage, the average slope, and percentage of poverty, obtaining drinking water from a stream or river, and three types of dwelling structures.

Between both the island specific vulnerabilities, identified by Pielke Jr. et al. 2003, and those prominent in Cutter et al. 2003, a total of thirty-three variables were selected for further statistical analysis.

Figure 9 - Statistical Analysis of Factors Found in Hazard Literature

3.1.1.2 Description of Variables

Deforestation – Deforestation increases runoff which can lead to potential escalation in flooding.

It can destabilize the soil on hillsides which can increase the potential for landslides and mudslides. Deforestation on Hispaniola has been widespread. While both Haiti and the Dominican

Republic have experienced deforestation, Haiti’s deforestation has been much worse, as seen in

Figure 10. This vast difference may have a role in increasing Haiti’s vulnerability. Flooding and landslides are the most common cause of tropical cyclone related mortalities. (Pielke Jr. et al.

(2003), Peduzzi (2005), Smith and Hersey (2008))

Hispaniola Land Use Hispaniola

10 Figure Figure

Slope – The greater the slope of the terrain the greater the potential for landslides. With the amount of rain tropical cyclones can produce, the soil on the slopes can become saturated with water and slide down the hill or mountain. Hispaniola is a very mountainous island (Figure 11) and has experienced landslides due to the slope of these mountains. Figure 11 slope angles are manually classified based on studies by Chalkias, C. et al. 2014 and GSI 2012, to represent slope angles on Hispaniola that have low to high relationships to increasing potential for landslides.

Landslides are one of the main causes of tropical cyclone related mortalities. (Pielke Jr. et al.

(2003), Rappaport & Fernandez-Partagas (1997), Mora (1995))

Hispaniola Slope Hispaniola

11 Figure Figure

Water Source – Hispaniola suffers from deficient infrastructure. This can be illustrated in how people obtain their drinking water. In some places within Hispaniola, there is not the proper infrastructure capable of bringing drinking water to homes. The number of people who have to obtain their drinking water from a river, stream, or spring can illustrate the level of this built environment. It also may indicate potential populations whose proximity is close or in potential flood prone rivers. (Cutter et al. (2003), Brooks et al. (2005), Tran, P. et al. (2009))

Poverty – The amount of people living in poverty in Haiti is much higher than in the Dominican

Republic. People living in poverty do not have the necessary means to protect themselves from tropical cyclones (proper housing structures, living in vulnerable areas, proper resources) and recover after an impact. Having a high level of poverty can cause Haiti and the Dominican Repub- lic to have increased vulnerability to tropical cyclone related mortalities. (Cutter et al. (2003),

Pielke Jr. et al. (2003), Donner and Rodriguez (2008))

Population – Haiti and the Dominican Republic have similar total populations of around ten million each. They are a part of a trend in the Caribbean of rapid population growth. This can lead to an increased number of people in hazard prone regions or with a potentially elevated vulnerability. Unlike the United States where it is mostly coastal population growth which increases vulnerability, Hispaniola is a smaller island and thus its entire population is vulnerable and any population growth adds to this vulnerability. An increased population can have an elevated risk from improper housing, social services, improper settling areas, and a higher number of people exposed to a tropical cyclone impact. These can all lead to an increase in tropical cyclone induced

mortalities. (Cutter et al. (2003), Pielke Jr. et al. (2003), Heinz Center for Science, Economics, and the Environment (2000))

Type of Dwelling – Three general categories of structures on Hispaniola vary from houses built from local earthen materials, to more architecturally developed buildings. These structures can offer a wide range of protection. Populations living in inadequate housing may be vulnerable to potential mortalities from wind related impacts from tropical cyclones, less resilient to floods and landslides, as well as not being as capable to recover after an impact due to the economically challenged populations that generally live in these inadequate structures. Many poor also tend to build housing on unsuitable land, particularly in urban areas, such as hillsides, increasing their vulnerability. Infrastructure that is not built to withstand the elements presented by tropical cyclones can cause an increase in vulnerability. (Cutter et al. (2003), Shultz et al. (2005), Cutter

(2005))

Vulnerable Age – Hispaniola’s growing population has resulted in a large portion of the population being very young and dependent. Care for the elderly is usually a responsibility of the family as they can become dependent on their families for support. The combination of these two age groups, commonly accepted as 0-5 and 65 and greater, can increase the vulnerability to tropical cyclone related mortalities due to lack of mobility, resources, and less of an ability to recover after an impact. (Cutter et al. (2003), Donner and Rodriguez (2008), Cutter and Finch (2008))

Percent Renter – The affordability of home ownership is out of reach for some. Renting is an alternative for many people. The percent of those who rent can vary dramatically between de-

partments (Haiti) and Provinces (Dominican Republic). Renters can have an increased vulnerability to tropical cyclone mortalities because they tend to lack the financial capabilities to afford a home or the ability to obtain housing again if their current dwelling becomes uninhabitable due to an impact. Information on assistance during recovery periods tends to be weaker towards renters than those who own their homes. (Cutter et al. (2003), Cutter and Finch (2008), Heinz

Center for Science, Economics, and the Environment (2000))

Occupation Type – The type of occupation one has can influence whether there is potential for increased vulnerability. There were sixteen different occupation types that were collected for

Hispaniola to illustrate a range of potential increases in vulnerability. Haiti and the Dominican

Republic are no different than any other nation in the fact that many in certain occupations may suffer from a tropical cyclone impacts or lack the ability to recover quickly after. Some occupations do not create financial ability to access needed resources to properly endure an impact.

Some can be impacted during the event itself such as resource extraction, farming, and fishing.

Others can suffer after the event during recovery such as the tourism industry. The departments and provinces with higher percentages in such occupations may increase their potential vulnerability to tropical cyclone mortalities. (Cutter et al. (2003), Heinz Center for Science, Economics, and the Environment (2000))

Percent of Unemployment – Both nations on Hispaniola have fairly high unemployment rates.

The people unemployed are not accruing any financial ability to afford basic needs, increasing their potential vulnerability to tropical cyclone impacts. If unemployment increases post tropical

cyclone impact, it can result in a decreased ability to recover quickly. The higher the unemployment is for a given province or department the higher the potential vulnerability is. (Cutter et al.

(2003))

Percent of Female Participation in Labor Force – The percentage of females participating in the labor force can increase the financial well-being of a household or of a single female headed household. Women tend to have more difficulty during the recovery period than men. This means the greater participation of females in the labor force the less vulnerable a department or province may be. It can also indicate a growing and healthier economy. (Cutter et al. (2003),

Donner and Rodriguez (2008), Cutter (1996))

Density – Hispaniola has experienced a large increase in population which has led to an increase in its density. Though the second largest island in the Caribbean, Hispaniola is still small and there is less space for a growing population than many other nations. The island is becoming more urbanized too. The few main cities are being inundated with large numbers of people. This can cause those provinces or departments which contain those cities to have an increase in density.

A high density can lead to an increase in vulnerability as there are more people in a smaller area that can suffer from an impact and require assistance. Denser locations cause many poor to choose to have a dwelling in an unsuitable location such as flood or landslide prone zones. (Cutter et al. (2003), Brooks et al. (2005))

Percent Urban – Hispaniola has seen a large amount of urbanization. There still is however, some difference between Haiti and the Dominican Republic as well as between the different provinces and departments. Urbanization has the potential to present unique challenges particularly during

an evacuation. High urbanization can cause poor people to create unsuitable settlements in flood or landslide prone zones. Many urban centers could have the capability to provide government assistance and health care to the urban population. The needs of highly urbanized areas can be dynamic and potentially difficult for governments to properly assist before or after a tropical cyclone impacts. This makes the percent of urban population an interesting variable to consider on Hispaniola in how it may potentially impact tropical cyclone mortalities (Cutter et al. (2003),

Pelling and Uitto (2001), Pielke Jr. et al. (2003))

Percent Rural – While Hispaniola has seen a large increase in urbanization there is large percentage of the population that still lives in rural areas. There still is however, some difference between

Haiti and the Dominican Republic as well as between the different provinces and departments.

Rural populations tend to have smaller incomes, be potentially further away from health centers, generally more reliant on economic activities such as farming that can be vulnerable to tropical cyclones, and further away from government assistance post tropical cyclone impact. These potential factors make it an intriguing variable for further investigation in how it may affect potential vulnerability to tropical cyclone related mortalities on Hispaniola. (Cutter et al. (2003), Brooks et al. (2005))

Number of Health Centers – As a very poor island, Hispaniola suffers from the lack of medical infrastructure. The number of health centers is very important after tropical cyclones have impacted an area as the health centers provide health care to a province or department population.

It is vital to have the necessary health care or vulnerability to tropical cyclone mortalities may

increase. (Cutter et al. (2003), Brooks et al. (2005), Heinz Center for Science, Economics, and the

Environment (2000))

Infant Mortality – Many on Hispaniola lack proper access to proper medical care. This is evident in the number of infant mortalities. The higher the number of infants dying during or shortly after birth, the worse the access to proper medical care there is. It is vital to have proper health care or potential vulnerability to tropical cyclone mortalities can increase. (Cutter et al. (2003), Brooks et al. (2005), Heinz Center for Science, Economics, and the Environment (2000))

3.1.1.3 Reduction of Correlated or Colinear Variables

Due to the large number of variables and the potential multi-collinearity of several similar variables, it was decided that a further reduction in the overall number of variables was needed.

This initial group variables were then analyzed for any multi-collinearity. This was conducted by an initial review with a simple correlation matrix. A correlation matrix is a bivariate analysis of the relationship one variable may have with another variable. It has a scale of negative one to positive one, with negative one being a perfectly inverse relationship and positive one being a perfect direct relationship. A correlation coefficient threshold of -0.75 and below and 0.75 and above were used to initially identify correlated relationships as these coefficients are typically used to identify a high degree of correlation (Jain and Aggarwal 2009). Variables that had an obvious strong relationship with other variables in the matrix could be considered for removal as will be seen in Chapter 4.2. A multivariate analysis will also be used to evaluate the variables for more complex interrelationships. The Variance Inflation Factor (VIF) will be used as a final analysis of any multi-collinear relationships that could exist among the variables. Variables with a VIF

score at or above five are generally considered to have a high multi-collinear relationship (Rog- erson, P.A. 2001). Using these techniques variables with interrelationships with other variables can be removed until only variables that have a low VIF score remain for further statistical analyses as will be seen in Chapter 4.2.

3.1.2 Tropical Cyclone Event Data

Data on intensity, coordinates of track, storm speed, and the barometric pressure of individual tropical cyclone events were obtained from the National Hurricane Center’s Best Track

Data (See Appendix A). This database contains information along the entire path of tropical cyclones in the Caribbean. The meteorological data associated with a tropical cyclone will be sam- pled from the best track database for each tropical cyclone that impacted or recorded mortalities on Haiti or the Dominican Republic. These data were used for comparison with the mortality database, where the track information is used to assess the level of impact to the location where fatalities have occurred. The coordinates of the storm’s track are analyzed along its path near

Hispaniola as several tropical cyclones have caused fatalities in both the Dominican Republic and

Haiti. The storm frequency for the island will be calculated based on the total number of storm events experienced by an island divided by the observation period of 19 years (1994-2012).

3.1.3 Tropical Cyclone Human Fatality Data

To examine the human impact associated with tropical cyclones, a database was set up containing the number of fatalities per district by storm as seen in Error! Reference source not found.. This data was assembled through the collection of data from the Monthly Weather Re- view, ReliefWeb, AlterPresse, United Nations Office for the Coordination of Humanitarian Affairs,

and many others as seen in Appendix A. These sources allowed for the spatial resolution to be increased by providing the fatality data at the Departmental level for Haiti and the Provincial level in the Dominican Republic as seen in Figure 12 and Figure 13. Figure 12 illustrates the locations of the aggregated mortality data. This figure is for illustration purposes to simply show geograph- ically, the numeric values for mortality. This map can be misleading as population may very high in some districts as opposed to others thus making them appear to have had a greater impact.

For this reason Figure 13 is also used to illustrate the mortality rate. This illustrates the amount of mortalities per district’s population to gain a more clear understanding of which districts had greater impacts based on their aggregated mortalities and population impacted. The data in the maps were classified using the Quantile method in ESRI’s ArcGIS. This places the same number of districts in each of the five classification ranges (0 mortalities were excluded so they could be distinguishable from all other districts). As the mortality data for Hispaniola is highly skewed, with a few districts having much higher mortality counts, it was difficult to classify the districts in a defined interval to illustrate anything meaningful. Quantile classification allowed for there to be greater clarity as to the different mortality groups. The groups help illustrate those districts that sustained high, medium, and low mortalities. This process was also done in the same way with

Figure 19 and Figure 20 as will be seen in Chapter 4.3. This database was then utilized to compare tropical cyclone impact and vulnerability data.

Error! Reference source not found.

Tropical Cyclone Mortality Map Mortality Cyclone Tropical

Figure Figure

Mortality Rate Map Rate Mortality

Cyclone

Tropical Tropical

13 Figure Figure

3.2 METHODS

Vulnerability to storm related loss at the national level will be assessed through the following method:

Score storm events  Apply Inclusion criteria  Correlate vulnerability factors and cluster groups to fatality impact (Poisson Regression)  Analyze vulnerability factors Ex- amine mortality rate changes over time

3.2.1 Storm Event Severity Scoring

Because tropical cyclones vary significantly in size, strength, and proximity to an island; the impact of an individual storm is widely variable. In order to compare different storm events between different departments and provinces over time on an equal basis, a severity measure was created for each storm event through a principal components analysis of storm characteristics. Nixon and Qui (2004) describe a method for developing a storm severity index using physical measurements of storm characteristics. A principal component analysis was used to reduce any multicollinearity within the observed tropical cyclone data. The data for each tropical cyclone was gathered at the data point from NOAA Best Track database when it impacted Hispaniola. In a manner similar to Nixon and Qui (2004) and Senkbeil and Sheridan (2006), the barometric pressure, maximum wind speed, and speed of forward progress were used in the principal components analysis. The resulting variables from the analysis were then placed in a Cluster analysis in order to group storms based on similarity in characteristics until each group was statistically different from each other. This is coupled with a province’s or department’s distance from the tropical cyclone which allowed for an even comparison to be made between physical and social vulnerabilities and mortalities to each storm group while maintaining the impact that strong versus weak tropical cyclones have on mortalities. This allowed impact of a given storm event for each

department or province to be more fairly measured to determine the nations vulnerability across multiple storms where the variation due to storm severity is factored out.

Tropical cyclones that impacted either nation during 1994-2012 were used in obtaining the storm’s historical record from the Best Track Hurricane Database as illustrated in Figure 14.

Being collected with coordinate records, the storms data points and paths can be easily plotted within ArcGIS. This allows for a visual representation of the data collected in its spatial boundary.

This data record provided the wind speed, barometric pressure and Saffir-Simpson score during impact. The data record time stamps along with the latitude and longitude of the neighboring two data records from the Best Track Database were used to calculate the forward speed of the storm’s progress past the island.

2012) 2012)

(1994

Tropical Cyclones Impacting Hispaniola Impacting Cyclones Tropical

igure igure F

3.2.2 Inclusion Criteria

To determine the location of impact in relation to the spatial scale at which mortalities were collected, inclusion criteria was set up. This was conducted through the use of GIS. Using the path of the storm, a buffer zone was applied to each storm path. Using satellite data and four different algorithms to determine rainfall from tropical cyclones, Cecil and Wingo (2009) illustrate that the most significant amount of rainfall recorded in tropical cyclone events fall within 500

Kilometers of the center of a given storm. Using this extent as the distance at which a potential tropical cyclone can impact, only those data points within 500 kilometers of Hispaniola were kept in the study as seen in Figure 14. Each tropical cyclone path was given a buffer that extended 500 kilometers as a zone where potential impacts could take place. This buffer was used to select any of the departments and/or provinces for Haiti and the Dominican Republic as being impacted by a tropical cyclone for each nation as illustrated in Figure 15. Any departments or provinces that had their centroid within the 500 kilometer buffer or sustained fatalities were included for analysis in the impact of that given tropical cyclone. All of the provinces or departments that did not have their centroid within the buffer were excluded from any impact analysis for that given storm. This was conducted for each tropical cyclone that impacted Haiti and the Dominican Re- public within the 500 kilometer boundary. The selected departments or provinces were used to compare any fatalities with storm and vulnerability factors at the respective spatial scale. This technique accounted for 99.7% of known deaths with Hurricane Jeanne and 99.4% of known deaths without Jeanne caused by all tropical cyclones in the collected database (Error! Reference source not found.).

Inclusion Criteria Inclusion

15 Figure Figure

3.2.3 Correlating Vulnerability Factors and Storm Cluster Groups to Human Fatality Impact

The potential indicators of social and physical vulnerability at the department and province spatial scale, as seen in Chapter 3.1.1.2, and storm cluster classifications were analyzed statistically to illustrate their relationships to the human fatality data by using a Poisson regression.

A Poisson regression was used because its distribution is positive, as a negative fatality is not possible. Its distribution is also preferred for statistical analysis in count data such as deaths because deaths as a variable have a low probability to occur (Samir 2013, Donner 2007). The Poisson regression has been used by researchers in observing the relationship between vulnerability and hazard related injuries or deaths (Samir 2013, Donner 2007, Gouveia and Fletcher 2000). This allowed for several independent variables to be correlated to the dependent variable of human fatality data.

3.2.4 Resulting Correlations to Human Fatality Data

Based on the significance of the vulnerability factors revealed in the Poisson regression, the identification of the factors most strongly related to each department’s or province’s vulnerability to tropical cyclone related mortality became apparent. An understanding of the relationship between storm clusters and fatalities also became apparent. These were utilized to gain an observation of those districts with the greatest risk. Some discussion of political and social dynamics that lead to these conditions was possible. Potential mitigation measures are also discussed on how to reduce a population’s risk to tropical cyclone related morality. The individual variables from the results of the model for each district were used to create maps in ArcGIS. This provided a good way to visually present this data. The differences within and between the two

nations were more easily identified with the help of these visual representations of the model results. This helped aid in the analysis of each variable from the model

3.2.5 Analyze Changes in Vulnerability over Time

Reliable tropical cyclone data collection is recognized beginning in 1944 and since that time there has been a period of high activity lasting from about 1944-1970, a period of low activity from about 1970-1995, and the current period of high activity 1995-present. Pielke Jr. et al.

(2003) found that the vulnerability, both economically and through fatalities, of the Caribbean has stayed the same from this initial period of high activity to the present period of high activity despite technological increases over this time period. To illustrate this, the vulnerability levels for both Haiti and the Dominican Republic during this latest period of high activity were compared to the previous analysis done by Pielke Jr. et al. (2003). Changes that have occurred over the last decade since this analysis will demonstrate whether the island remains as vulnerable currently.

4 RESULTS

4.1 Tropical Cyclone Results

The Best Track Data was used to create a database of the storm data for storms that fell within the impact buffer zone around the island. This data contained the maximum sustained wind speed, lowest barometric pressure reading, and the forward progression of the tropical cyclone. The data were first analyzed using factor analysis to remove any multicollinearity. This analysis produced three variables with eigenvalues of 1.919, 0.991, and 0.090. Eigenvalues are used to determine how much of the total variation in the database is explained by each individual factor. Within factor analysis eigenvalues of 1 and greater are considered to have significant explanation of the variance. Since SPSS automatically only retains components with a significant eigenvalue of 1 or higher, SPSS was manually made to extract two components as the component at 0.991 would have significant explanation of the variance. The two components have a cumu- lative percentage of explanation of variance of 97%. The two components retained were a pressure/wind speed component and a forward progression component. These two components were then used to define the characteristics of each tropical cyclone data point.

Once the factor analysis was completed each storm was given a category. Cluster analysis was used to complete this step. The clustering placed each data point into one of four clusters as seen in Figure 17, Figure 16 and Table 1.

The four clusters represented:

Cluster 1 - Medium to low wind speed/pressure and quicker forward progression component. Cluster 2 - High wind speed/pressure and quicker forward progression. Cluster 3 - Low wind speed/pressure and slower forward progression. Cluster 4 - Medium wind speed/pressure and slower forward progression component.

An ANOVA one-way statistic test was performed to make sure the clusters were statistically different from one another.

Storm Cluster Analysis 160 140 120 100 Cluster 1 80 Cluster 2 60 Cluster 3 40

Forward Progress (KPH) ForwardProgress(KPH) Cluster 4 20 0 0 50 100 150 200 Windspeed (MPH)

Figure 16 - Storm Cluster Analysis

Table 1 - Cluster Means and Standard Deviations Windspeed Forward Progress

Cluster Average StDev Average StDev 1 45.8 15.5 41.2 14.3 2 105.6 15.5 21.9 7.9 3 35.0 9.3 15.0 6.2 4 66.7 11.2 16.0 6.8

Type

Frequency of Impact Cluster Impact of and Frequency

17 Figure Figure

Using the storm track data, each tropical cyclone data point was given its corresponding cluster group classification. With each storm data point being assigned cluster number, a database was created to give each impacted province and department a storm category number and distance for each impacting tropical cyclone. This was completed through the use of a lookup function in Microsoft Excel. In order to best reflect the category of storm for a specific district, the statistical mode was chosen as the district’s score for the event, rather than mean or median, to give weight to the most common cluster type that a given district was subject to during the

‘exposure period’ of the storm event. The sample point at the closest distance and from the twenty-four hours prior to the time of the closest impacting point were included in the sample set from which the mode was selected. A few tropical cyclones had two modes associated with an event for some of the districts. It was determined that the mode which was registered closer in proximity to a given province or department would be selected in these cases. The districts that were not in the 500km impact zone did not receive a cluster category unless they recorded a fatality.

There were three tropical cyclones, Wilma, Paloma, and Kyle whose impacting data points did not have any data points for the twenty-four hours prior. This resulted in the impacting data points not receiving a cluster category from the Excel macro. This was resolved through an examination of the data points and use of the cluster for the storm at its closest proximity. Wilma’s impacting data point was the first data point recorded when Wilma formed. This point was similar in maximum sustained wind speed, barometric pressure, and its forward progression to the second point as the initial set of data points that received a cluster category of three. This was then applied to the individual department impacted by Wilma. The few data points within the five

hundred kilometer buffer for Kyle did not combine for a twenty-four hour period. Those that were recorded within the impact zone were all recorded at the cluster category of three. Due to the consistency in characteristics and the cluster classification, this same category was applied to the provinces and departments impacted. Similarly to Wilma, Paloma’s data point was the first recorded point in the storm’s formation. The maximum sustained wind speed, barometric pressure, and forward progression to the second data point was consistent with the second data point being recorded at a cluster category of one. This was then applied to the individual department impacted within Haiti.

The distance from the center of a tropical cyclone to a given impacted province or department was determined again using a macro within Excel through the utilization of the Best Track

Data and through obtaining the centroids of each department and province within ESRI’s ArcGIS.

The Best Track Data contains the latitude and longitude of every tropical cyclone data point. This was used to determine a tropical cyclones closest point to the centroid for each province and department. The closest distance was recorded for each tropical cyclone that impacted any province or department. Those that had a distance of greater than five hundred kilometers were not recorded unless a fatality resulted from a tropical cyclone impact.

4.2 Vulnerability Results

The initial collection of fifty social and physical variables found in the hazard literature was reduced to thirty-three variables (Figure 9). This was done by selecting the factors that were significant from respected hazard literature as well as by examining literature that focused on

the vulnerabilities within the Caribbean and Hispaniola and selecting factors that were specifi- cally applicable to Hispaniola (Pielke Jr. et al. 2003, Cutter et al. 2003).

A simple correlation matrix was created containing these thirty-three variables ((Red

Indicates Strong Correlation)

Table 4). This bivariate analysis allowed for the determination that some of the variables were highly correlated as noted by the red highlighting in (Red Indicates Strong Correlation)

Table 4. To aid the statistical analysis, some of these “redundant” factors were removed from supplementary analysis. Further examination led to the conclusion that the sixteen variables defining various types of occupations contained a large amount of multi-collinearity among themselves and other variables and thus should be considered for removal from supplementary analysis as well. Additionally, Haiti and the Dominican Republic used somewhat different occupation classifications thus making a straightforward use of occupation as a risk factor more difficult. The poverty level correlated well with many of the occupation categories and is a respected measure of social vulnerability; so it was felt that the sixteen occupation categories could safely be left out of further consideration. After removal of the occupation variables, the correlation matrix had a reduction in variables with strong correlations. As a simple correlation matrix is a bivariate analysis, the Variance Inflation Factor (VIF) was also utilized within SPSS to conduct an analysis for any remaining variables with multi-collinearity as seen in Table 2 and Table 3. The VIF allowed for the reduction in a few more variables to the final twelve: Total Population, Average

Slope, Percent Urban, Earthen/Scrap Housing, Upper Scale Housing, Number of Health Centers

Per 10,000, Percent of Women in the Workforce, Poverty Level, and Unemployment Rate, Den- sity, Percent of Vulnerable Age, and Percent of Forestation as seen in Figure 18.

Figure 18 – Determination of Twelve Factors for Detailed Analysis

At this point, the correlation matrix showed that none of the remaining variables had a significant direct or inverse relationship. So it is these twelve variables that were evaluated more closely to determine if they predicted a person’s vulnerability to being a victim of a severe storm.

Coefficientsa

Model Unstandardized Coefficients Standardized t Sig. Collinearity Statistics Coefficients B Std. Error Beta Tolerance VIF (Constant) -16.852 9.218 -1.828 .068 Population 3.919E-006 .000 .097 2.457 .014 .376 2.662

AvgSlope .422 .326 .037 1.291 .197 .714 1.401 PovertyLevel .058 .036 .077 1.614 .107 .256 3.905 HouseEarthScrap -.044 .082 -.019 -.541 .588 .450 2.222 1 HouseUpperScale -.200 .243 -.028 -.825 .410 .521 1.920 PctUrban -.041 .051 -.035 -.817 .414 .315 3.176

PctWomenWorkforce .273 .162 .062 1.690 .091 .431 2.319 Health Centers per 10000 .507 .754 .023 .672 .502 .497 2.012 UnemploymentRate .174 .168 .044 1.036 .300 .322 3.106 a. Dependent Variable: Fatalities Table 2 - VIF Without Jeanne

Coefficientsa

Model Unstandardized Coefficients Standardized t Sig. Collinearity Statistics Coefficients

B Std. Error Beta Tolerance VIF

(Constant) 25.730 40.023 .643 .520

Population 7.029E-006 .000 .056 1.296 .195 .306 3.270

AvgSlope .386 1.044 .011 .370 .711 .657 1.523

PctForest -.077 .075 -.033 -1.029 .303 .562 1.780

PovertyLevel .107 .114 .046 .943 .346 .240 4.167

HouseEarthScrap .322 .258 .046 1.250 .212 .426 2.350

1 HouseUpperScale -.473 .805 -.021 -.587 .557 .446 2.244 PctUrban .099 .176 .027 .566 .572 .246 4.060

Density -.001 .001 -.025 -.825 .409 .630 1.587 PctVulnerableAge -1.743 1.468 -.040 -1.188 .235 .509 1.966 PctWomenWorkforce -.033 .497 -.002 -.067 .946 .430 2.327

Health Centers per 10000 .580 2.330 .009 .249 .804 .491 2.039 UnemploymentRate -.173 .540 -.014 -.320 .749 .293 3.411 a. Dependent Variable: Fatalities Table 3 - VIF With Jeanne

(RedIndicates Strong Correlation)

of Vulnerability Factors Vulnerability of

Simple Correlation Matrix Correlation Simple

4 Table Table

4.3 Statistical Results

It was observed within the fatality database that Hurricane Jeanne in 2003 caused a total of 2,967 deaths across Hispaniola, with 2,826 in the Haitian Department of L’Artibonite alone.

This comprised over 51% of the total fatalities experienced on Hispaniola and over 84% of L’Arti- bonite’s total fatalities. Such a massive amount of deaths recorded in only one storm could overly affect the results and thus the analysis. Hurricane Jeanne was therefore considered a potential outlier. This altered the spatiality of the fatalities some as seen in Figure 19 and Figure 20. Alt- hough Hurricane Jeanne is considered a potential outlier it is an actual significant event that occurred and caused many deaths and thus should not be fully ignored. For this reason two central databases were constructed. The two databases were exact in every way except for one contained Jeanne and one did not contain Jeanne.

Tropical Cyclone Related Mortalities Cyclone Tropical

19 Figure Figure

Tropical Cyclone Related Rate Mortality Cyclone Tropical

Figure Figure

The central databases were created containing the storm characteristics and physical and social variables for each province or department impacted by every storm. These databases were statistically analyzed using a Poisson Regression in SPSS. The Poisson Regression was completed using fatalities as the dependent variable and storm clusters multiplied by distance, physical, and social variables as the independent variables. The Poisson Regression was used for both databases: one containing Jeanne, and one without. The Poisson Regression was run multiple times on the twelve remaining variables (Total Population, Average Slope, Percent of Forest Coverage,

Poverty Level, Health Centers per 10,000, Earthen/Scrap Housing, Upper Scale Housing, Percent

Urban, Percent of Women in the Workforce, Unemployment Rate, Density, Percent of Population with Vulnerable Age) eliminating any variables that were insignificant until all remaining variables were statistically significant. For the model with Jeanne, none of the twelve factors were elimi- nated, with all twelve being significant. For the model without Jeanne, nine of the twelve re- mained significant with Percent of Forestation, Percent of Population with Vulnerable Age, and

Density being removed.

This resulted in the two predictive models containing mostly similar significant variables but with some differences in the total number of variables, a few different variables, and their differences in their Beta values as seen in Table 5 and Table 6. The Poisson Regression allowed for the Beta values for each factor to be calculated and used with a generalized linear model:

푝 = 푒훽0+∑ 훽푖푋푖

The Beta values, as seen in column B in Table 5 and Table 6, represent the positive or negative contribution towards the predicted number of fatalities p. These Beta values are multiplied by the numeric values for the data collected for each district. When all of beta values are

used with the observed data in the generalized linear model, the result is the predicted number of mortalities for each district. Because the beta values are scaled for the data values they are associated with, the magnitude of the beta value is generally not comparable between different factors. However, whether the beta value is positive or negative is associated with increasing or decreasing the model’s prediction and is thus worth examining. A negative Beta value means that the given variable is associated with a reduction in the predicted number of mortalities represented. A positive Beta value means that the given variable increases the predicted number of mortalities represented. The Wald Chi-Square is also provided by SPSS and is a way to assess how significant the variable is to the model’s predictive capability.

Since the value p represents the predicted number of fatalities based on the factors present in a given province during a given storm, the predictive capabilities of the model can be checked by comparing the total number of predicted fatalities against the actual number that occurred. These totals were rolled up for every province and department and compared to the actual number of fatalities as seen in Table 7.

95% Wald Hypothesis Test Parameter B Std. Error Confidence Interval Lower Upper Wald Chi-Square df Sig. (Intercept) -0.4905 0.4822 -1.436 0.455 1.035 1 0.31 AvgSlope 0.52082 0.0244 0.473 0.569 456.913 1 0 Closest * C1 -0.0018 0.0001 -0.002 -0.001 140.685 1 0 Closest * C2 -0.0064 0.0003 -0.007 -0.006 524.674 1 0 Closest * C3 -0.0126 0.0005 -0.014 -0.012 668.247 1 0 Closest * C4 -0.0049 0.0002 -0.005 -0.004 407.57 1 0 Health Ceners per 10000 -0.4757 0.0657 -0.604 -0.347 52.41 1 0 HouseEarthScrap 0.02094 0.0036 0.014 0.028 34.373 1 0 HouseUpperScale -0.03 0.0126 -0.055 -0.005 5.647 1 0.02 PctUrban 0.01489 0.0042 0.007 0.023 12.614 1 0 PctWomenWorkforce -0.0148 0.0072 -0.029 -0.001 4.191 1 0.04 Population 1.54E-06 9.17E-08 1.37E-06 1.73E-06 283.831 1 0 PovertyLevel 0.00916 0.0026 0.004 0.014 12.607 1 0 UnemploymentRate -0.0924 0.0088 -0.11 -0.075 111.132 1 0 Table 5 - Without Jeanne Poisson Regression Results

95% Wald Hypothesis Test Parameter B Std. Error Confidence Interval Lower Upper Wald Chi-Square df Sig. (Intercept) 20.1974 0.7176 18.791 21.604 792.084 1 0 AvgSlope 0.69637 0.0266 0.644 0.748 686.274 1 0 Closest * C1 -0.00594 0.0001 -0.006 -0.006 1964.319 1 0 Closest * C2 -0.01039 0.0003 -0.011 -0.01 1360.42 1 0 Closest * C3 -0.02162 0.0005 -0.023 -0.021 1545.74 1 0 Closest * C4 -0.00507 0.0002 -0.005 -0.005 1101.645 1 0 Density -0.00018 3.60E-05 0 0 24.858 1 0 Health Ceners per 10000 -0.60989 0.0967 -0.799 -0.42 39.782 1 0 HouseEarthScrap 0.05229 0.0032 0.046 0.059 260.668 1 0 HouseUpperScale 0.16913 0.0159 0.138 0.2 113.273 1 0 PctForest -0.03099 0.0016 -0.034 -0.028 353.131 1 0 PctUrban 0.06313 0.0057 0.052 0.074 122.027 1 0 PctVulnerableAge -0.83371 0.0246 -0.882 -0.785 1144.297 1 0 PctWomenWorkforce -0.13595 0.0065 -0.149 -0.123 439.231 1 0 Population 1.09E-06 1.01E-07 8.94E-07 1.29E-06 116.158 1 0 PovertyLevel 0.02022 0.0036 0.013 0.027 32.092 1 0 UnemploymentRate -0.27407 0.0094 -0.292 -0.256 853.751 1 0 Table 6 - With Jeanne Poisson Regression Results

Country District Actual Fatalities: WithJeanne Model Fatalities: WithJeanne Country District Actual Fatalities: Jeanne Without Model Fatalities: Jeanne Without Haiti L'Artibonite 3355 3006 Haiti Ouest 951 992 Haiti Ouest 951 1019 Haiti Sud-Est 651 514 Haiti Sud-Est 651 541 Haiti L'Artibonite 529 270 Haiti Nord-Ouest 109 161 Haiti Sud 109 153 Haiti Sud 110 158 Haiti Grand Anse/Nippes 95 149 D.R. San Cristobal 71 0 D.R. San Cristobal 71 19 Haiti Grand Anse/Nippes 95 90 D.R. San Juan 42 20 D.R. San Juan 42 7 D.R. Santo Domingo 30 24 D.R. Azua 29 49 D.R. Azua 29 16 D.R. Monsenor Nouel 29 15 D.R. Monsenor Nouel 29 22 D.R. Barahona 28 0 D.R. Barahona 28 19 D.R. Santo Domingo 32 25 D.R. Bahoruco 25 21 D.R. Bahoruco 25 56 D.R. Santiago 25 107 D.R. Santiago 25 111 D.R. Independencia 19 0 Haiti Nord 30 133 D.R. La Altagracia 17 0 D.R. La Altagracia 25 0 Haiti Nord 13 70 D.R. Independencia 19 0 D.R. Distrito Nacional 13 14 D.R. La Romana 15 13 D.R. Monte Cristi 12 0 D.R. Distrito Nacional 13 7 D.R. Peravia 11 0 D.R. Monte Cristi 12 0 Haiti Nord-Ouest 9 51 D.R. El Seibo 12 0 D.R. El Seibo 9 0 D.R. Peravia 11 1 D.R. Monte Plata 9 0 D.R. Monte Plata 9 0 D.R. Puerto Plata 9 5 D.R. Puerto Plata 9 0 Haiti Centre 9 169 Haiti Centre 9 215 D.R. La Romana 8 0 D.R. Duarte 6 0 D.R. Duarte 6 0 D.R. Sanchez Ramirez 6 0 D.R. Sanchez Ramirez 6 0 D.R. Samana 6 0 D.R. Samana 5 0 D.R. La Vega 4 16 D.R. San Pedro de Macoris 5 0 D.R. San Pedro de Macoris 5 1 D.R. La Vega 4 27 D.R. Maria Trinidad Sanchez 4 0 D.R. Valverde 3 2 D.R. Valverde 3 8 D.R. Maria Trinidad Sanchez 2 0 D.R. Espaillat 2 51 D.R. Espaillat 2 0 D.R. Hato Mayor 1 0 D.R. Hato Mayor 1 0 Haiti Nord-Est 1 14 Haiti Nord-Est 1 6 D.R. San Jose de Ocoa 1 2 D.R. San Jose de Ocoa 1 13 D.R. Dajabon 0 0 D.R. Dajabon 0 0 D.R. Elias Pina 0 2 D.R. Elias Pina 0 3 D.R. Hermanas Mirabal 0 0 D.R. Hermanas Mirabal 0 0 D.R. Pedernales 0 0 D.R. Pedernales 0 0 D.R. Santiago Rodriguez 0 0 D.R. Santiago Rodriguez 0 0 Table 7 - Model Predicted and Actual Fatalities by District With and Without Jeanne

The model clearly allowed for a representation of what locations had the highest death toll given the storms that actually occurred but it did not illustrate a predicted number based on equal circumstances. Since several locations were either hit more frequently or by stronger storms it would be difficult to determine the true vulnerability of an arbitrary district. So to assess the vulnerability of the districts on a more equal basis, four “phantom” storms were created.

These phantom storms would impact every province and department equally for each of the four storm clusters. The phantom storms were each set at one hundred kilometers from the center of every province and department. The output in the model for the predicted number of fatalities was collected for all four phantom storms and for both the models with and without Jeanne as will be discussed in Chapter 5.1. These phantom storms allowed for an equal comparison to be made based on the predicted number of fatalities experienced for each location based on that district’s level of vulnerability. The higher number of predicted fatalities the more impactful the certain storm cluster classification is as well as indicating a potential increased vulnerabilities to tropical cyclone fatalities in that district.

5 DISCUSSION

The discussion of the results will, unless specified, only pertain to analysis conducted on the model without Hurricane Jeanne. Since this tropical cyclone is considered an outlier, the results of the model with Hurricane Jeanne are considered to be not as precise. They will therefore be discussed separately.

5.1 Tropical Cyclone Findings

There were fifty tropical cyclones, including Hurricane Jeanne that impacted Hispaniola during the study period of 1994-2013. The most common cluster type (Figure 16) that impacted the island was cluster 3, which impacted the districts individually a total of 637 times. This was followed by cluster 1, 4, and 2. The deadliest clusters varied from cluster 4 (3,928 deaths) in the model with Jeanne to cluster 1 (1150 deaths) in the model without Jeanne. This was followed by cluster 1 (1150), 2 (348), and 3 (228) in the model with Jeanne and cluster 4 (1062), 2 (348), and

3 (228) in the model without Jeanne.

Since Hispaniola was not impacted equally by each tropical cyclone, it is difficult to compare the different clusters impact on each district. The phantom storms allowed for the equal comparison to takes place. The phantom storms when run for the model containing Jeanne also had cluster 4 being the deadliest storm type followed by cluster 1, 2, and 3. The largest predicted fatalities were departments within Haiti particularly L’Artibonite where the massive death toll from Hurricane Jeanne occurred. This can be seen in Figure 19. Also noticeable in the data is the large number of Haitian departments with high predicted death totals. This clearly shows that

many of the Haitian departments are more vulnerable than the majority of provinces in the Do- minican Republic to sustaining potential tropical cyclone related mortalities.

With such a large number of deaths sustained in Hurricane Jeanne, the phantom storms for the model without Hurricane Jeanne may give a more commonly expected predicted number of mortalities. With the removal of Hurricane Jeanne (a cluster 4 storm), cluster 1 now becomes the deadliest class followed by clusters 4, 2, and 3. Again however, Haiti still has the majority of districts with the highest predicted mortalities compared to the Dominican Republic.

The phantom storms give a good indication of the full contribution from each model variables to the predicted number of mortalities. Based upon the vulnerabilities experienced, through the data values collected, each district will illustrate through the model the level of vulnerability to tropical cyclone related mortalities. Table 8 thus contains the ranking of districts based vulnerability. While fatality estimates may change with the cluster classification, the ranking is static. Based upon the results of the phantom storms in Table 8, Ouest would be considered the most vulnerable district on Hispaniola with Monte Cristi being the least vulnerable. Haiti has all of its departments near the top of the list showing that it has higher vulnerability as a nation than the Dominican Republic. This data allows each nation to understand which provinces or departments are the most vulnerable within the nation and which are the least as will be discussed.

Country Districts Jeanne C4100Kmw/o Jeanne C3100Kmw/o Jeanne C2100Kmw/o Jeanne C1100Kmw/o Country Districts WithC4Jeanne 100 Km WithC3Jeanne 100 Km WithC2Jeanne 100 Km WithC1Jeanne 100 Km Haiti Ouest 41.704 19.290 35.895 57.089 Haiti L'Artibonite 213.783 40.852 125.582 195.970 Haiti Sud-Est 22.058 10.203 18.985 30.194 Haiti Ouest 69.675 13.314 40.929 63.870 Haiti L'Artibonite 11.646 5.387 10.024 15.943 Haiti Sud-Est 36.910 7.053 21.682 33.834 Haiti Centre 7.551 3.493 6.499 10.337 Haiti Centre 15.429 2.948 9.064 14.144 Haiti Sud 7.414 3.429 6.381 10.148 Haiti Sud 12.281 2.347 7.214 11.258 Haiti Grand Anse/Nippes 6.944 3.212 5.976 9.505 Haiti Nord-Ouest 12.176 2.327 7.153 11.162 D.R. Santiago 5.245 2.426 4.514 7.180 Haiti Nord 10.093 1.929 5.929 9.252 Haiti Nord 3.416 1.580 2.940 4.677 D.R. Santiago 9.954 1.902 5.847 9.125 Haiti Nord-Ouest 2.338 1.081 2.012 3.200 Haiti Grand Anse/Nippes 6.589 1.259 3.870 6.040 D.R. Santo Domingo 1.396 0.646 1.202 1.911 D.R. Espaillat 4.647 0.888 2.730 4.260 D.R. La Vega 1.376 0.636 1.184 1.883 D.R. Bahoruco 4.568 0.873 2.683 4.187 D.R. Monsenor Nouel 1.213 0.561 1.044 1.660 D.R. Azua 4.488 0.858 2.637 4.114 D.R. San Cristobal 1.179 0.546 1.015 1.614 D.R. Santo Domingo 2.265 0.433 1.330 2.076 D.R. Bahoruco 1.126 0.521 0.969 1.542 D.R. La Romana 1.472 0.281 0.865 1.350 D.R. Barahona 1.126 0.521 0.969 1.541 D.R. Monsenor Nouel 1.422 0.272 0.836 1.304 D.R. San Juan 0.942 0.436 0.811 1.290 D.R. La Vega 1.344 0.257 0.789 1.232 D.R. Azua 0.760 0.351 0.654 1.040 Haiti Nord-Est 1.287 0.246 0.756 1.180 D.R. San Jose de Ocoa 0.670 0.310 0.577 0.918 D.R. Distrito Nacional 0.982 0.188 0.577 0.900 D.R. Distrito Nacional 0.668 0.309 0.575 0.914 D.R. Valverde 0.771 0.147 0.453 0.707 D.R. Puerto Plata 0.559 0.259 0.481 0.765 D.R. San Juan 0.735 0.140 0.431 0.673 Haiti Nord-Est 0.518 0.240 0.446 0.709 D.R. San Jose de Ocoa 0.523 0.100 0.307 0.479 D.R. Elias Pina 0.416 0.192 0.358 0.569 D.R. Peravia 0.448 0.086 0.263 0.411 D.R. Valverde 0.354 0.164 0.305 0.485 D.R. Elias Pina 0.415 0.079 0.244 0.380 D.R. Peravia 0.318 0.147 0.274 0.436 D.R. San Pedro de Macoris 0.410 0.078 0.241 0.376 D.R. Santiago Rodriguez 0.235 0.108 0.202 0.321 D.R. Barahona 0.355 0.068 0.209 0.326 D.R. Samana 0.208 0.096 0.179 0.284 D.R. Santiago Rodriguez 0.193 0.037 0.114 0.177 D.R. Espaillat 0.188 0.087 0.162 0.258 D.R. San Cristobal 0.137 0.026 0.081 0.126 D.R. La Romana 0.187 0.087 0.161 0.256 D.R. Puerto Plata 0.109 0.021 0.064 0.100 D.R. Independencia 0.183 0.084 0.157 0.250 D.R. Independencia 0.085 0.016 0.050 0.078 D.R. El Seibo 0.147 0.068 0.127 0.202 D.R. Pedernales 0.052 0.010 0.031 0.048 D.R. Hermanas Mirabal 0.147 0.068 0.126 0.201 D.R. Monte Cristi 0.052 0.010 0.030 0.047 D.R. San Pedro de Macoris 0.130 0.060 0.112 0.178 D.R. Dajabon 0.040 0.008 0.023 0.036 D.R. Pedernales 0.127 0.059 0.109 0.174 D.R. Hato Mayor 0.029 0.005 0.017 0.026 D.R. Maria Trinidad Sanchez 0.125 0.058 0.108 0.171 D.R. Maria Trinidad Sanchez 0.026 0.005 0.015 0.024 D.R. Dajabon 0.115 0.053 0.099 0.157 D.R. Duarte 0.022 0.004 0.013 0.020 D.R. Duarte 0.105 0.048 0.090 0.143 D.R. Samana 0.018 0.003 0.010 0.016 D.R. Monte Plata 0.078 0.036 0.067 0.107 D.R. Sanchez Ramirez 0.013 0.003 0.008 0.012 D.R. Sanchez Ramirez 0.072 0.033 0.062 0.098 D.R. La Altagracia 0.012 0.002 0.007 0.011 D.R. La Altagracia 0.064 0.030 0.055 0.088 D.R. El Seibo 0.008 0.002 0.005 0.008 D.R. Hato Mayor 0.060 0.028 0.052 0.083 D.R. Hermanas Mirabal 0.004 0.001 0.003 0.004 D.R. Monte Cristi 0.060 0.028 0.052 0.082 D.R. Monte Plata 0.003 0.001 0.002 0.003

Table 8 - - Phantom Storm Fatalities by District

For Haiti, most of the departments are near the top of the list. Nord-Est, stands out as having a much lower predicted number of mortalities. Nord-Est does not even reach a single

predicted mortality with 0.71 persons with cluster 1, the most impactful classification. The other classifications are even lower, down to 0.24 persons. The Dominican Republic does not have as dramatic of a difference between provinces, having more of a gradual change but there remain provinces that have several predicted mortalities in comparison to most that are below a single predicted fatality.

The phantom storms allowed for equal comparison across Hispaniola but also allowed for equal comparison between the different storm classifications. Common perception is that stronger storms will cause more deaths but the phantom storms demonstrate that this is not necessarily true for Hispaniola. The most impactful storm classification were moderate to quick moving storms with a weak to moderate windspeed/pressure component in cluster 1. This was closely followed by cluster 4 which contained slow moving storms with a moderate windspeed/pressure component. Fewer predicted mortalities for cluster 3 illustrates that the weakest storms do not pose as great of a threat to Hispaniola. The most revealing fact that the phantom storms illustrate is that the most powerful storms classified in cluster 2 do not pose as great of a risk to predicted mortalities than cluster 1 and 4. This shows that an incredibly strong windspeed/pressure component is not more important in determining tropical cyclone related mortalities than the forward progression component is. Though cluster 3 has a very low forward progression component it is apparent with such low predicted mortalities that the storms are simply too weak to cause as many fatalities as the other classifications. It is ultimately the more the mid- range storms that have the highest potential to cause tropical cyclone related mortalities. As

Figure 17 illustrates however, these conclusions are made with the understanding that only one cluster 2 storm ever made a direct landfall on Hispaniola.

5.2 Vulnerability Variables

5.2.1 Physical Variables

The average slope of each province and department was the only remaining significant physical variable in the model. Landslides and mudslides are very prominent issues that Hispan- iola deals with. The model shows that slope has one the strongest relationships to fatalities with the third highest Wald Chi square among all the variables and the highest after two storm clusters as seen in Table 5. In the following maps (Chapter 5.2) the classification method for displaying the data was set to standard deviation to illustrate those districts that were on either side of the mean in order to visually present the distribution of the variable values.

5.2.1.1 Slope

The influence that slope has on mudslide and landslides is an important aspect for a mountainous island such as Hispaniola. The positive beta values in the model (Table 5) indicates that the greater the increase in average slope the greater the impact on predicted mortalities. As

Figure 21 illustrates, the contribution to the model is greatest in central and southeast Hispaniola, average in the North, and minor in the East. This figure demonstrates the large potential impact the average slope can have on Hispaniola during a tropical cyclone event. While the Dominican

Republic numerically has more provinces with high average slope variable values, all of Haiti’s departments are above average for the island. The Dominican Republic has more locations that are susceptible to an increase in their tropical cyclone related mortalities due to slope, but this is mitigated by the lower population level in these areas. As seen in Figure 21, the population of

Western Dominican Republic is much lower than the Eastern provinces with lower average slope

model variable values. Haiti, on the other hand, has most of its population in locations where slope could increase their potential level of tropical cyclone related vulnerability. It is for this reason that Haitians have the potential to be at a greater risk from the average slope of their departments than the Dominican Republic.

Figure 21 – Contribution to model prediction from average slope

Within Haiti, the department with the highest contribution from average slope to their predicted mortalities was Sud-Est. This location also has the second highest number of actual mortalities on Hispaniola. The highest number of fatalities, which occurred in Ouest, lays just one

standard deviation below that of Sud-Est. In fact, the only department that has a high number of fatalities and an average slope is L’Artibonite.

Nord is an exception among the provinces in that it has a lower number of fatalities than its average slope would predict but even so it does have more predicted fatalities than those with average slope, other than L’Artibonite. Figure 21 also illustrates that the impact from the average slope on predicted tropical cyclone mortalities does vary within Haiti with the south having a much higher influence from average slope than the North.

The Dominican Republic’s range of predicted model variable values for average slope is much greater than Haiti’s. As seen in Figure 21, San Jose de Ocoa has the highest contribution from average slope while Distrito Nacional, San Pedro de Macoris, and La Romana have the lowest contribution. While San Jose de Ocoa surprisingly has only one actual total fatality, a majority of the province’s with high mortalities are those locations with higher contributions from average slope outside of San Cristobal (average slope) and Santo Domingo (below average slope). This shows that there is an influence within the Dominican Republic that is present between those locations with higher contributions to the model’s prediction from average slope and those with a lower contribution.

5.2.2 Social Variables

5.2.2.1 Population

The Dominican Republic and Haiti have a similar total population. Population was the most significant factor within the social variables. The beta values in the model for population

were positive (Table 5) indicating that the more population a district has, the greater the contribution to the number of predicted mortalities. As seen in Figure 22, the predicted contribution from population for Haiti and the Dominican are similar although several of the Dominican Re- public’s provinces have the lowest contribution from population. This coincides with the fact that while the two nations have almost the same total population, the Dominican Republic accounts for over half of the island. This means that Haiti’s population is located within less land area and thus generally have higher per district population than the Dominican Republic.

Figure 22 – Contribution to model prediction from Population

The greatest contribution to predicted fatalities from the population model variable for

Haiti occurs in Ouest followed by L’Artibonite. These locations are also both within the top three districts in actual fatalities. Having large populations from cities like Port au Prince and Gonaives, these two departments have more people susceptible to tropical cyclones. While population is an important variable, particularly for Ouest and L’Artibonite, it does not play as large of a role for most other departments.

Within the Dominican Republic, Santo Domingo, Distrito Nacional, and Santiago all have high contributions from population as seen in Figure 22. The highest is located in Santo Domingo which also has the third highest predicted death toll in the Dominican Republic. Santiago also has a higher total number but a lot of provinces with high total mortality rates do not have as high of a contribution from population. This shows, as with Haiti, that while population is an important variable in impacting predicted fatalities, especially in highly populated provinces, it is not as important for the other less populated districts.

5.2.2.2 Poverty

It is clear in Figure 23 that Haiti has a far greater contribution from poverty than does the

Dominican Republic. Poverty level has a positive beta value (Table 5) indicating that the greater the poverty level, the greater the contribution towards a higher predicted mortality. Haiti clearly has an increased vulnerability to tropical cyclone induced mortalities from poverty level. Surpris- ingly, poverty level does not have as strong of a significance as many other variables. This can be attributed to the fact that department Ouest has the least contribution from poverty level in Haiti but has the highest total mortalities. Several other departments have a higher contribution from

poverty level but much lower total mortalities than other departments and even some of the

Dominican Republic’s provinces that have a very low contribution from poverty level.

Figure 23 – Contribution to model prediction from poverty level

Haiti clearly has a very strong impact from poverty level. While Ouest has the lowest contribution within Haiti, it is still very significant. While several of the northern departments have low total mortalities, the contribution from poverty towards those deaths are quite high. It is clear that Haiti has extreme poverty which increases the contribution to potential mortalities from a tropical cyclone impact but it does not indicate that extreme poverty leads to high actual

mortalities as illustrated by Northern Haiti. There are other factors involved in those locations towards increasing their total mortality risk.

The Dominican Republic, as seen in Figure 23, does not have the level of poverty experienced in Haiti. While the Dominican Republic is poor and does have influence from poverty level towards its potential mortalities, it is difficult to say how that influence is exerted. Many provinces with the highest total fatalities have the least contribution from poverty level. There are several provinces with slightly higher contribution mixed within those that have a high mortality rate. This mixture of results does not give a clear indication of poverty level’s contribution much beyond it playing a medium to minor role.

It is well understood that Hispaniola is an extremely poor island but to gain a better understanding about the influences on poverty level the correlation matrix can be used to illustrate those variables that have high correlations to poverty level. The percentage of drinking water obtained from a river, stream, or spring, infant mortality, percentage of people working in agriculture, fishing, or forestry, and wholesale and retail all have a strong positive correlation to poverty level. Districts with high percentages in some or all of these variables will have an increase their poverty level. The simple correlation matrix, as seen in (Red Indicates Strong Correlation)

Table 4, can give an indication as to the positive and negative relationships to poverty level and the interrelationships between those variables and others. It is important to illustrate the under- lying influences as they can allow for a better understanding as to why a variable such as poverty level contributes to a given district the way it does.

5.2.2.3 Percent of Urban Population

Figure 24 – Contribution to model prediction from Percent Urban The percent of urban population had a positive beta value in the model (Table 5) indicating that the higher the percentage of urban population the greater the contribution to their predicted mortalities. As Figure 24 illustrates, the Dominican Republic has a much higher percentage of urban population than Haiti. This figure gives a clear indication that the Dominican Republic has an increased vulnerability to potential mortalities in a tropical cyclone impact due to its increased urban population.

Figure 24 shows that within Haiti only Ouest has a significant contribution from its percentage of urban population, most likely influenced from the capital city of Port au Prince. Many of the other departments within Haiti that have high actual mortalities have a low contribution

from their urban populations. It is interesting to see in Figure 24 that the Southern departments and L’Artibonite have higher total actual mortalities but have fairly low contributions from their urban population model values.

Within the Dominican Republic, most of the locations with high actual mortalities (nine of the top ten) have a medium (Azua, San Cristobal, San Juan, and Bahoruca) to high (Santo Do- mingo, Santiago, Monsenor Noel, Independencia, and Barahona) contribution from percentage of urban population model values. It is surprising to see the east with such high contributions and yet low total actual mortalities besides La Altagracia. So while not suffering from as many fatalities, the eastern provinces have a large contribution from their urban populations to their lower mortality risk.

5.2.2.4 Unemployment Rate

Figure 25 - Contribution to model prediction from Unemployment Rate

The percentage of population unemployed is perhaps the most difficult variable in terms of how it fits within the model. Common perception would be that an increase in unemployment would result in an increase in potential vulnerability but this is not the case within the model.

With a negative beta value (Table 5) the greater the unemployment the smaller the contribution to predicted mortalities. As seen in Figure 25, Ouest would have the highest unemployment rate though this means in the model that its contribution to potential deaths is lowest on Hispaniola

for this variable. This perplexing result may be explained via examining the districts and their unemployment rates.

Within Haiti, the departments with the lowest unemployment rates, besides Centre, are located within the departments with the highest actual total mortality rate outside of Ouest. The departments with the highest rate of unemployment, besides Centre, are locations with the lowest actual mortalities. This aspect could cause the model to interpret unemployment rate as a variable more often associated with departments with lower total mortalities and thus a negative beta factor.

The Dominican Republic offers up a similar illustration to Haiti. Seven out of the top ten provinces with the highest total mortalities are located near the average or above. With only three provinces having higher total mortalities and higher unemployment rates, the model may have interpreted unemployment rate as a lower contributor to potential mortality risk. This could also be exacerbated by the fact that Elias Pina was a province with a higher unemployment rate but did not record a single total mortality.

5.2.2.5 Percent of Women in the Workforce

Figure 26 – Contribution to model prediction for Percent Women in the Workforce

The percent of women in the workplace tends to coincide with a reduction in vulnerability

(Cutter 2003). This is true for the model as the beta values were negative (Table 5) indicating the higher the percentage of women working, the lower the contribution to predicted mortalities.

Figure 26 shows that Hispaniola has strong divides as to the level of contribution women have in the workforce. With more than half of its departments having a lower to very lower contribution from women in the workforce, Haiti seems to lack the benefit that women could bring in reducing potential mortalities more so than the Dominican Republic.

The results shown in Figure 26 for Haiti are difficult to correlate with the fact that the variable reduces the predicted number of mortalities (having a negative beta value). The top two departments in actual mortalities (Ouest and Sud-Est) have some of the highest contributions from this model value. The contribution to predicted mortalities from the variable’s value is highest in the northern departments, which are three of the four lowest departments in Haiti in actual mortalities. Figure 26 illustrates that the northern departments have an increase in their potential mortality risk due to the lack of women in the workforce.

The Dominican Republic’s contribution varies more than Haiti’s as seen in Figure 26. A majority of the nation lies near the average and those with above average levels are counterbal- anced by those lower levels. The top ten provinces in total mortalities have five provinces with below average and five near the average. This may contribute to the low Wald Chi Square score the variable received as it is not as highly significant to predicted mortalities as other variables.

5.2.2.6 Housing Materials: Earthen or Scrap Materials

Figure 27 – Contribution to model prediction from Housing: Earthen/Scrap Materials

The model variable values for houses made from earthen or scrap materials increased a districts predicted mortalities. It is clear from Figure 27 that Haiti has a much higher contribution from these variable values than the Dominican Republic.

Not many departments within Haiti are immune from the negative effects from this factor. Housing appears to be an issue for most of Haiti though Nord-Ouest, L’Artibonite, and Centre are particularly affected. Of particular interest is the impact to L’Artibonite. This department has

devastating impacts from Jeanne and even without Jeanne has the third highest total actual mortalities. Right below L’Artibonite on the actual mortality count is Sud. This department, as seen in Figure 27, has the lowest contribution from this housing variable compared with the rest of

Haiti.

The Dominican Republic does not have nearly the impact from this factor as seen in Figure

27. A majority of the provinces with higher actual total mortalities are found at or below the average. The only two provinces within the top ten highest actual total mortalities that are above the mean are Independencia and Bahoruca. Besides these two provinces, this is not an issue that greatly increases the potential tropical cyclone mortality risk.

5.2.2.7 Housing Materials: Upper Scale

Figure 28 – Contribution to model prediction from Housing: Upper Scale

Although the percentage of homes considered upper scale is very small, it does contribute towards lowering the predicted mortality risk as it has a negative beta factor (Table 5). Both nations do not have an overwhelming trend in either direction though the Dominican Republic does have a large group of provinces in the north which are above the mean. This may indicate that while not a vast difference, the Dominican Republic does not benefit from having as many provinces with low upper scale housing variable values.

Haiti varies widely with at least one department in each grouping. Ouest stands out as having a strong contribution to decreasing potential mortalities as Port au Prince, the capital and largest city, is located here. The contribution from this city may influence the higher number of upper scale homes. Nord-Est also has this contribution like Ouest yet stands out due to the fact that there is not any obvious reason as to why this would be. Nord-Est is one of the poorest departments and has the smallest population in Haiti. It is interesting to consider, as seen in Fig- ure 28, that the southern departments, Sud and Sud-Est have the lowest contribution from upper scale housing and are two of the top four departments in actual total mortalities.

The Dominican Republic has many of its northern provinces above average, though only three of the top ten provinces in total actual mortalities are in this group. The Dominican Republic is once again spread out with the provinces that have high total actual mortalities and their respective contribution from upper scale housing. As seen in Figure 28, it is interesting to see that the capital and completely urbanized capital city of Distrito Nacional has the lowest contribution from the variable values. In contrast, two of the highest provinces with contribution from the variable values, Elias Pina and Pedernales, have some of the lowest population in the nation with the third highest province San Juan and Pedernales having some of the highest poverty levels in the nation.

5.2.2.8 Health Centers Per 10,000

Figure 29 – Contribution to model prediction from Health Centers Per 10,000

The beta factor used within the model for health centers per 10,000 people had a negative value (Table 5), indicating that the more health centers per 10,000 people the greater the decrease in predicted mortalities. It is quite apparent in Figure 29 that Haiti has the least number of health centers per 10,000 people on Hispaniola. As each nation has approximately ten million inhabitants, Haiti clearly lacks the necessary health infrastructure.

The contribution from every Haitian department from the health centers variable towards their predicted mortalities is nearly equal for all departments. While departments such as Ouest 81

and L’Artibonite have much higher populations, the number of respective health centers per

10,000 people is the same as those with less population.

The Dominican Republic varies more than Haiti. The contribution from health centers towards the models predicted mortalities seems to be more population dependent rather than an unequal disbursement of health centers in the Dominican Republic. Many of the provinces in the lowest have the highest population totals. Several also have some of the highest actual mortalities. While not a nationwide issue, the lack of health care available to provinces with higher populations and total actual mortalities is something important to consider in a tropical cyclone impact.

5.3 With Hurricane Jeanne

Hurricane Jeanne was considered an outlier for the primary analysis though as an actual event, it is important to take note of its impact. The primary analysis was conducted through the model variable values without Hurricane Jeanne. The separate model run with Hurricane Jeanne provided much of the same results with three exceptions. These included: percent of forestation,

Density, and Percent of Vulnerable age. These will be discussed as to what they revealed though with the understanding they are associated with Hurricane Jeanne being an outlier.

5.3.1 Percent of Forest Coverage

Figure 30 – Contribution to model prediction from Percent of Forestation

Forest coverage was identified as being a significant factor only in the model containing

Jeanne. This factor had a negative beta value (Table 6) which corresponds to the idea that the less forest coverage the more vulnerable a place will be. Those districts which have high forest coverage had larger negative values as seen in Figure 30. Haiti notoriously suffers from deforestation and has the lowest percentages of forest coverage on Hispaniola. The model values illustrate this even more as most of Haiti’s provinces are near the top grouping, having the highest contribution to the model’s predicted fatalities. With five out of the top eight, Haiti is clearly

impacted more by the percent of forestation influencing model predicted fatalities than the Do- minican Republic. Although Haiti commands attention to its desertification, not all its departments are impacted by deforestation. The Dominican Republic is not immune to this problem.

Several of its Provinces rank quite high within the produced model values.

Within Haiti seven of its nine departments have the highest contribution from forest coverage to model predicted mortalities. These are also contained the top six districts in total fatalities. L’Artibonite and Ouest have the lowest percentage of forest coverage of any district on

Hispaniola. L’Artibonite was also the most impacted by Hurricane Jeanne, sustaining over eighty percent of its total fatalities and just over fifty percent of all fatalities occurring on Hispaniola.

This may help to help to explain the discrepancy between the two models in percent of forestation being significant. It is of note that the last of the top three departments, Centre, sustained only nine total fatalities yet had three hundred and thirty-nine model predicted fatalities, illustrating that while an important variable, percent of forestation does not always increase the number of tropical cyclone related mortalities. A couple departments, Grand ‘Anse and Nord, have a much lower contribution to the model predicted mortalities than the rest of Haiti as seen in Figure 30. So while Haiti suffers from deforestation, there are locations where the impact is much less.

The Dominican Republic has a much lower contribution from percent of forestation than

Haiti. There are however some provinces that do have a high contribution similar to those of some Haitian departments. Some of the most significant contributions to the model include

Monte Cristi and Elias Pina. These locations, however, do not consistently have an increase in

actual or predicted mortalities over other provinces with the least contributions such as Samana and Puerto Plata as seen in Figure 30.

5.3.2 Density

Figure 31 – Contribution to model prediction from Density

Density has the lowest Wald Chi Square score of any variable in the model (Table 6) so while significant, it is less so than the other variables. It also has an extremely low negative beta factor (Table 6). The low value makes it have a much smaller impact on the model variable values.

Unless there are already dramatic differences in the density of a location, the beta value will not cause any dramatic changes to the variable values. It is for this reason Figure 31 illustrates a very

minor impact. The Dominican Republic has the only locations with any larger contribution from density’s variable value.

While there are differences in Haiti between the departments in regards to their individual density measure, there is not any noticeable department that has a higher contribution towards predicted mortalities. Haiti has little contribution from density’s variable values which is surprising it is significant only in the model with Hurricane Jeanne as it sustained catastrophic losses from Hurricane Jeanne.

The only two locations on Hispaniola with any contribution from density’s variable values was the Dominican Republic. Even within the Dominican Republic only two provinces have any contribution, Distrito Nacional and Santo Domingo. While these provinces are very dense, particularly Distrito Nacional, they did not sustain a large impact from Hurricane Jeanne and thus it is intriguing again as to why density would be significant in the with Hurricane Jeanne model.

5.3.3 Percent of Population with Vulnerable Age

Figure 32 – Contribution to model prediction from Population with Vulnerable Age

The percent of population within a vulnerable age group was another confusing variable.

Within the model the variable had a negative beta factor (Table 6). This is not only contrary to the standard understanding of this variable within hazard literature but is also difficult to interpret based on Figure 32. The Dominican Republic appears based on Figure 32 to have more provinces with greater contributions than Haiti which would make it have less of a contribution to predicted mortalities than higher as normally assumed.

The departments with the lowest contribution to the model and the highest actual percentages from percent of population with vulnerable age were Ouest and L’Artibonite. These two departments also sustained the highest actual mortalities from Hurricane Jeanne, with L’Arti- bonite accounting for almost three thousand alone. This would lead one to think that this variable would have an influence on increasing predicted mortalities and not decrease the prediction particularly in the model that contains Hurricane Jeanne.

The Dominican Republic is similar with several of its provinces with higher total actual mortalities having higher to average deviations. Again one would assume that these locations would have an increase in potential mortalities instead of a decrease. The provinces again did not sustain as catastrophic impact from Hurricane Jeanne as Haiti did making it intriguing that this variable would be significant within the model with Hurricane Jeanne and not without Hur- ricane Jeanne.

5.4 Vulnerability to Tropical Cyclone Related Mortalities Temporal Analysis

As previously mentioned, 1000 Pielke Jr. et al. (2003), stated that if the 800 Caribbean or Central America were 600 struck by a tropical cyclone with a com- 400 parable intensity of Hurricane Mitch 200 and older devastating hurricanes (cat-

2002 2004 2005 2006 2007 2008 2008 2008 2011 2012 egory five Hurricanes and cluster type 1994

Figure 33 - Actual fatalities in Haiti since 1994 two) the death tolls would be just as

high if not higher. Since the devastating impacts of Hurricane Mitch in 1998, which caused over

10,000 deaths in the Caribbean, North America, and Central America, there has been only one

tropical cyclone that has made landfall as a category five or cluster type two in Hurricane

Georges. This tropical cyclone also struck in 1998 and caused 461 fatalities. The nearest death

toll to Hurricane Mitch and the older normalized hurricanes came in 2004 with Hurricane Jeanne

which, although not as intense of a storm as these hurricanes, caused 2,967 fatalities on Hispan-

iola. Since 1998 Hispaniola has experienced a total of 4,616 fatalities from tropical cyclone im-

pacts. This is with an increase in both nations population since Hurricane Mitch impacted in 1998

which is the year Pielke Jr. et al. (2003) used population counts to normalize older tropical cy-

clones death tolls to. A total of 4,616 fatalities since 1998 makes it apparent that Hispaniola has

by no means drastically improved in reducing death tolls from tropical cyclones but does show

signs of an overall reduction in mortalities (Figure 33 and Figure 34). This could be a result of

better forecasting skills and enhanced warnings as well as global contributions and perhaps a

decrease in vulnerability. These assertions, however, are again being made with the understand-

250 ing that Hurricane Mitch would have been

200 a cluster 2 storm for which Hispaniola has 150 only experienced one direct landfall from 100 a cluster 2 storm in Hurricane Georges 50 since 1994.

1996 2001 2004 2005 2007 2008 2008 2012 1994 Graphs of actual fatality data over

Figure 34 - Actual Fatalities in Dominican Republic since 1994 time for Haiti and the Dominican Republic

do show a somewhat reduction in overall

mortalities. The extent that this is due to changes in vulnerability or due to lack of cluster two storm impacts is an area that would be worth further study.

6 APPLICATIONS OF FINDINGS

With an understanding of the statistical results of the storm data and the physical and social data it is important to understand the potential application the analysis can have in mitigating the future impacts tropical cyclones may have on Hispaniola in the future. Hispaniola has, as clearly illustrated, suffered from high number of mortalities from tropical cyclones for many years. While very poor and lacking sufficient funds and infrastructure to make a complete shift immediately, having a comprehensive knowledge can lead to improvements in the mitigation efforts.

The applications of the statistical results will be discussed for only those variable values that were from the model without Hurricane Jeanne. Hurricane Jeanne is an important event, however, because it is an outlier within the data, applying the findings from the model with Hur- ricane Jeanne to potential considerations on decreasing vulnerability would have too much influence from an outlier event.

6.1 Dangerous Tropical Cyclones

From the analysis of the phantom storms and associated maps of Hispaniola it is apparent that Haiti has departments that are far more susceptible to sustaining a high number of mortalities than the Dominican Republic, though both nations increase in vulnerability to the storm classifications. Cluster 1 and 4 storms need to have the highest priority in preparing for an impact as they have the highest predicted mortalities. While cluster 2 storms rank as the third deadliest storm it does have some of the strongest winds coupled with slower forward progression. As seen in Figure 17 a possible reason for not having as high predicted mortalities may be due to the

fact that only one cluster 2 storm ever directly impacted Hispaniola during the study period before quickly changing to a cluster 4. For this reason Hispaniola should not discredit cluster 2 storms as being a lot less impactful than cluster 1 and 4. Cluster 3 storms present the least danger of sustaining high mortalities.

An understanding of the different classifications potential impacts allows for mitigation efforts to be greater for some storms but Hispaniola does not have the same equal impacts spa- tially. There are certain districts within each nation that may require more resources and mitigation efforts to reduce potential mortalities from tropical cyclones regardless of classification.

Within Haiti most of the departments besides the northern three of Nord, Nord-Ouest, and Nord-Est, all have high predicted mortalities with all three clusters. These six departments

(Ouest, Sud-Est, L’Artibonite, Sud, Centre, and Grand’ Anse) range from 3-19 mortalities with the weakest cluster to 10-57 mortalities with cluster 3. The northern three range from just 0.2-2 mortalities with the weakest cluster to 0.7-5 mortalities. The more vulnerable departments should require more attention in mitigation efforts to reduce these high predicted mortalities.

While not as severe and more gradual, the Dominican Republic does have similar characteristics within its provinces. As seen in Table 7, the top provinces with high predicted mortalities should have more resources available if there is the potential for a tropical cyclone impact. San- tiago stands out as the most vulnerable province with most all the remaining provinces gradually declining in predicted mortalities. For the Dominican Republic, mitigation efforts should be allo- cated towards Santiago initially with the remaining focus gradually towards rest of the nation.

6.2 Vulnerability Variable Findings

6.2.1 Physical Variable Findings

The only physical variable from the model was average slope. As Figure 21 illustrates, much of Hispaniola has some degree of terrain with higher slope. As landslides and mudslides are a major cause of fatalities in the nations, the districts with high average slope need to be acutely aware of this. Creating a threshold where once a slope reaches a certain degree homes cannot be built on it, reducing the number of homes built in areas prone to landslides and mudslides, and evacuation plans in districts with higher average slopes in the case of probable tropical cyclone impact are ways to mitigate against future fatalities. Deforestation is a prominent problem on Hispaniola and particularly Haiti. This deforestation has reduced the stability of the soil on the slopes and needs to be addressed in conjunction with the other mitigation efforts. These districts also need to have the resources available to deal with potential landslides and mudslides in the case they occur.

6.2.2 Social Variable Findings

As most of the variables were social variables, the vulnerability and mitigating strategies can be very dynamic and challenging. These variables all impact in different ways and all need to be dealt with in different ways. Understanding how each of these variables can have an impact, whether it be negative or positive, can help authorities make decisions that address their specific mitigation strategies.

The amount and location of population can influence the level of vulnerability of a district.

With higher populations, there is more need for resources both before and after a tropical cy-

clone impact. There is a greater need to provide enough shelters and emergency care post disaster. The type of housing takes on more of a role in the approach when addressing the population if the districts are more urbanized. A reduction in population and urbanization are not feasible options and thus emergency and urban planners need to understand the population size and vulnerable housing in mitigating against tropical cyclones. This is also true for the location and number of health centers. If there is a large concentration of population officials need to make sure there are more health centers available. They need to be located where they can care for the highest number of people as possible. Health centers are vital in the moments after a disaster has occurred and with poor placement and too few, the vulnerability to tropical cyclone induced mortalities will only increase.

The lack of preparedness in a highly urbanized are with a big population was seen in the

2010 Haitian earthquake which impacted Port au Prince in the Ouest Department. The results were a massive death toll and dispersion of many people seen in the Lu et al. (2012) study track- ing cell phone movement. Each nation needs to assess where there is high population counts in order to allocate the necessary resources and understand the dynamics of these districts as far as the built environment and its added needs in order to reduce vulnerability to tropical cyclone related mortalities.

Economic variables such as unemployment rate, the percent of women in the workforce, and poverty level have tremendous influences on the vulnerability of these nations and their districts to potential mortalities. These variables are part of a very complex issue that requires complex solutions. While there are no applications that could make an immediate impact, it is

important for these nations to consider some approaches that may increase some understanding of the issue. By examining the simple correlation matrix as seen in (Red Indicates Strong

Correlation)

Table 4, some of the interrelationships between economic variables and other variables may illuminate some unknown influences on the economic variables.

There should be a focus on encouraging and empowering women to have a place within the workforce. Allocation of resources that increase the ability for women to attain jobs should have a focus more in rural districts as there is greater correlation between urban districts and higher percentages of women in the workforce as seen in (Red Indicates Strong Correlation)

Table 4. This table also illustrates those variables that have correlation to poverty. Having the infrastructure to supply clean drinking water and proper building materials and health care can improve the vulnerability of a district. The type of occupation one has can also influence the level of poverty experienced and this should be carefully observed by officials. Occupations such as fishing, agriculture, forestry, wholesale, and retail are highly correlated with an increased poverty level. Focusing resources on people in these lower income occupations, can help to reduce the mortality risk.

Poor housing can lead to an increase in vulnerability both during and recovering from a tropical cyclone impact. The districts which have substandard housing should be recognized and given resources which allow for officials to decrease the total number of homes using poor construction materials. As seen in the correlation matrix an increase in housing material quality can

reduce the correlation to poverty level. The location of poor housing materials is also an important aspect. Officials need to also take notice of those districts with high average slopes to make sure housing, particularly substandard housing is not being built on these slopes.

7 CONCLUSION

7.1 Summary

Hispaniola is one of the most vulnerable islands in the Caribbean to the impacts of tropical cyclones. This is illustrated through the high mortality rate associated with these tropical cyclones. The island’s vulnerability is influenced by the number and type of impacting tropical cyclones and the physical, and social characteristics of the island and its people.

Data on the tropical cyclones that impacted Hispaniola were collected in a database from

NOAA and statistically analyzed through factor analysis, cluster analysis, and a Poisson Regres- sion. Phantom storms were used to evaluate equal levels of storm impact on each department and province. It was determined that Hispaniola should be keenly aware of cluster 1 and cluster

4 type storms as they caused the highest predicted mortality rate.

Fatality data at the province and department scale was collected for each storm. This was placed in a database that was used as the dependent variable in the Poisson regression analysis to understand the independent variables relationship to these mortalities. Based on the excep- tional number of deaths experienced, Hurricane Jeanne was generally considered an outlier.

Several physical variables were collected for Hispaniola though the only remaining variable significant in the Poisson regression for the model without Hurricane Jeanne was that of the average slope of a district. This illustrated the importance of areas susceptible to the deadly landslides and mudslides too often experienced on Hispaniola during a tropical cyclone event.

There were many social variables that were collected through each nation’s census and survey data. These data were placed in a created database where they were reduced based on respected hazard literature and literature specific to that of Hispaniola vulnerability. These variables were further reduced through a simple correlation matrix and VIF. Once this was completed the remaining variables were analyzed in the Poisson regression. Population, poverty level, percent of urban population, percent of women in the workforce, the unemployment rate, the number of health center per 10,000 people, earthen and scrap housing materials, and upper scale housing materials. These variables proved to be important in understanding the social vulnerability of the people of Hispaniola.

7.2 Research Questions

At the beginning of the thesis several questions were proposed in an effort to examine the relationship between vulnerability factors and cyclone-related mortality on Hispaniola. What makes the island so vulnerable to tropical cyclone-related mortality? Does this vulnerability differ between the two nations? Does this vulnerability vary within the nations? Are fatality rates associated with tropical cyclones related to the level of vulnerability of each nation?

The first question was answered through analyzing the most impactful storm classifications (C1 and C4 as seen in Table 8) and the resulting nine variables (Total Population, Average

Slope, Poverty Level, Health Centers per 10,000, Earthen/Scrap Housing, Upper Scale Housing,

Percent Urban, Percent of Women in the Workforce, and Unemployment Rate) within the model that are related to predicted mortalities as seen in Table 5. Based on their Beta values in Table 5, an increase in Average slope, Earthen/scrap housing, the percent urban, total population and

poverty will increase the vulnerability to tropical cyclone related mortality on Hispaniola while an increase in Health centers per 10,000, upper scale housing, women in the workforce, and unemployment rate are related to decreased vulnerability to tropical cyclone related mortalities

(unemployment rate’s unusual result is discussed in 5.2.2.4). Less contribution from variables with negative model values that contribute towards lower predicted mortalities and more in the five variables with positive model values that contribute towards higher predicted mortalities and storm classifications cause much of Hispaniola to have a high vulnerability to tropical cyclone related mortalities.

The second and third questions can be understood through the Poisson regression analysis in the figures and data table of the model variable values. The figures in Chapter 5.2 clearly illustrate a difference between the two nations and within the nations as to the contribution from the model variable values. The analysis for many of the variables indicates Haiti as a whole is more vulnerable than the Dominican Republic, continually having its districts with higher standard deviations in many of the figures in Chapter 5.2. This analysis is supported through the predicted total mortalities for each district as well as the predicted number of mortalities for each phantom storm. Haiti has eight of the top ten districts in highest predicted mortalities with phantom storms (Table 8) due to its districts having higher values for variables that contribute towards greater predicted mortalities. Haiti is predicted to have a greater mortality rate than the Domin- ican Republic and as Table 7 and Table 8 illustrate, the differences experienced between each nation can be large. These tables also indicate that there are differences in vulnerability within each nation. In Table 8 the departments in Haiti range in the deadliest cluster classification (C1) from 57.1 to 0.71 while the provinces in the Dominican Republic range from 7.2 to 0.08. Table 7

offers a similar picture with a range within Haiti’s departments from 992 predicted total mortalities to 6. The Dominican Republic’s range is from 107 to 0. As was discussed in Chapter 5 the factors that predicted vulnerability have clear difference between each nation and with each nation as demonstrated by Table 7 and Table 8.

The fourth question is answered through observing the results in Table 7 and Table 8 the number of actual and model predicted, and phantom storm predicted fatalities are almost always higher within the Haitian Departments than in the Dominican Republic’s Provinces. With the selected vulnerability variables that were found to be significant (Table 5) Haiti tends have higher values for the variables that are increase predicted mortalities and lower values in the variables that decrease predicted mortalities as seen in Chapter 5.2. This is further illustrated in Table 8 with the phantom storms where the storm distance and classification was equal and where the only difference in influencing predicted mortalities was each districts variables. Haiti’s departments again were predicted to have much higher predicted mortalities than the Dominican Re- public’s Provinces. This clearly illustrates that the fatality rates with tropical cyclones in Haiti are higher than the Dominican Republic because the level of vulnerability, while still high in the Do- minican Republic, is higher in Haiti.

7.3 Relationship to Literature

The results of the study coincide with many of the assertions made from the literature cited. The conclusion that greater the population, higher average slope, poverty level, and urbanization all lead towards an increase in vulnerability corresponds to several of the findings in the study by Pielke Jr. et al. (2003). The analysis of housing type, women in the workforce, and health

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centers also corresponded to the findings of the literature as mentioned in List of Social and

Physical Variables. The only exception was that of unemployment rate which statistical result was inverse to that of the literature, though this is assumed to be due to the coincidence of a few low unemployment districts being among the most severely impacted by storm related fatalities. Ul- timately, the results of both the study and the literature illustrate a relationship between increased vulnerability to tropical cyclone related mortalities based on their relationship to these variables.

7.4 Actions for Officials

These nation’s governments, emergency planners, and individuals themselves need to be aware of how and why they are at an increased risk if emergency preparedness and mitigation measures are to be properly realized. Through the model variables, officials can gain an understanding how each factor influences the vulnerability of the populations and which districts are the most vulnerable. The allocation of resources and knowledge both pre and post tropical cyclone impact can reduce the potential for tropical cyclone induced mortalities:

Developing an understanding of the correlations poverty has with other variables including occupations, and recognize the level of preparedness for the impending tropical cyclones classification type are some of the things officials can do to decrease the level of vulnerability to tropical cyclone induced mortalities. A better understanding of their coutry’s vulnerability should increase their ability to reduce this vulnerability and mitigate against future fatalities.

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There are two primary areas officials can focus on in creating and employing mitigation measures: measures for the immediate impact of a tropical cyclone event, and more long term measures to reduce overall vulnerability.

Based on this study, some steps that officials on Hispaniola can take in mitigating against an immediate impact would be to:

 Identify the classification of the impacting tropical cyclone, taking the greatest pre- cautions for cluster 1 and 4 storms.  Provide an adequate public warning system that is efficient in reaching the largest number of people as well as educating the populations how to react to these warning and where the shelter and health centers are located. These should be focused on populated urban areas initially in order to reach the largest number of people.  Evacuate populations located in areas that have high slope angles to shelters, reducing the potential loss of life from landslide and mudslides.  Focus on evacuating populations living in dwellings made from poor building materials, particularly those who are at a vulnerable age, to shelters.

Mitigation measures that focus on a longer term decrease in vulnerability, officials can:

 Enact zoning laws that prohibit the building of new communities on land that has a high slope angle.  Increase the number of shelters and emergency aid in areas with high population  Create incentives to help regrow the forests on the areas of high slope to further mitigate against landslide and mudslide fatalities.  Create programs and incentives to help improve, over time, the materials homes are constructed from.

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 Invest in both the education of women and their increasing roles in the workforce. In addition, investing in educating people on emergency preparedness plans. This information should include the location of emergency shelters and health care centers and should be focused on highly populated urban areas initially.  Further invest in the health infrastructure. Increase the number and accessibility of health care centers, again focusing initially in populated urban centers.

7.5 Limitations of the Study

This is a comprehensive look at the dynamics of vulnerability of Hispaniola and how it plays into their high number of mortalities from tropical cyclone impacts. It does, however, have its limitations. The spatial scale of the study does not allow for a comprehensive look into why or where certain aspects of the variables are within certain districts. Using the centroid to select districts for the inclusion criteria also has its spatial limitations. Because the size of each district is not the same this method may not be the foremost technique in capturing the spatial differences. The cause of deaths from each tropical cyclone would have also greatly increased the understanding as to what influences tropical cyclone mortalities on Hispaniola. The temporal scale of tropical cyclones does not provide the study with as much data from tropical cyclones themselves as well as any potential fatalities they may have caused. This temporal scale is also an issue with the social data. Being able to only collect data from Haiti for the year 2003 greatly took away from any change in society during the study period. Since social data is dynamic and not static this limited the study. The social data from the Dominican Republic was from 2004 and thus may not have been the same as it was at the same time as Haiti’s in 2003. As poor nations it was understood that the reporting of mortalities may not have been as thorough as a developed

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country and thus the count may not be as precise. An example would be in Hurricanes Fay, Gus- tav, Hanna, and Ike which had to have a level of estimation put in as Haiti was unable to collect all the data properly while being impacted by four storms in a single month.

7.6 Future Considerations

Based on the study conducted, there are a number of potential future considerations.

Using this approach, data from strong rain storms could be substituted for tropical cyclones to understand the impacts from the rain events as Hispaniola is vulnerable to a large number of mortalities from these types of weather events. This approach could also be used but on a larger spatial scale. This could allow for a more comprehensive look at a district and why the variables are the way they are. This study could serve as a stepping stone into other emergency manage- ment and hazards and disasters projects in the future.

Natural disasters are going to occur regardless of vulnerability. It is the reduction of the level of vulnerability that can prevent the high death tolls. This makes understanding physical and social vulnerability key to reducing mortality risk. By illustrating the factors that cause His- paniola to be vulnerable, potential mitigation efforts can be made to reduce the mortality risk level associated with tropical cyclones.

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Appendix A- Hurricane Mortality Sources Cited

Haiti:

1994 - Gordon

U.N. Department of Humanitarian Affairs. “Haiti Tropical Storm Nov 1994 UN DHA Situation Re-

ports 1-7.” ReliefWeb. November 15, 1994. URL: http://reliefweb.int/report/haiti/haiti-

tropical-storm-nov-1994-un-dha-situation-reports-1-7

1998 - Georges

Ponticq, Francoise. “Hurricane Georges in Haiti.” Pan American Health Organization

Representation in Haiti. November 22, 1999. URL: http://translate.googleusercon-

tent.com/translate_c?depth=1&hl=en&prev=/search%3Fq%3Dde-

sastres%2Busac%2Bdocumen-

tos%2B12139%2Bhaiti%26biw%3D1242%26bih%3D573&rurl=trans-

late.google.com&sl=es&u=http://ns.bvs.hn/ri-hn/pdf/spa/doc12139/doc12139-con-

tenido.pdf&usg=ALkJrhj-b0UujUhfCrPh8SbZlWeV_xBvHw

2002 – Lili

Associated Press. “Lili Killed 4 in Haiti; Deaths Unreported for a Week.” USA Today. October 05,

2002. URL: http://usatoday30.usatoday.com/weather/news/2002/2002-10-05-lili-

haiti.htm

2004 - Ivan

112

Agence France-Presse. “Hurricane Ivan Kills Three in Haiti.” ReliefWeb. September 13, 2004

URL: http://reliefweb.int/report/haiti/hurricane-ivan-kills-three-haiti

2004 - Jeanne

U.N. Office for the Coordination of Humanitarian Affairs. “Tropical Storm Jeanne – Haiti OCHA

Situation Report No. 16.” ReliefWeb. October 06, 2004. URL: http://reliefweb.int/re-

port/haiti/tropical-storm-jeanne-haiti-ocha-situation-report-no-16

U.N. Commision Economique Pour L’Amerique Latine et les Caraibes – CEPALC. “Le Cyclone

Jeanne en Haiti: Degats et Effets sur les Departments Du Nord-Ouest et de L’Artibonite:

Approfondissement de la Vulnerabilte.” March 17, 2005. URL:

http://www.eclac.org/publicaciones/xml/1/20971/L648-1.pdf

U.S. Agency for International Development. “USAID Assistance Continues Long After Hurricane

Winds Subside.” ReliefWeb. December 27, 2004. URL: http://reliefweb.int/report/do-

minican-republic/usaid-assistance-continues-long-after-hurricane-winds-subside

2005 - Dennis

Agence France-Presse. “Caribbean: Hurricane Dennis’s Death Toll Mounts as Emily Gathers

Steam.” ReliefWeb. July 12, 2005. URL: http://reliefweb.int/report/cuba/caribbean-hur-

ricane-denniss-death-toll-mounts-emily-gathers-steam

2005 - Wilma

113

Le Nouvelliste. “11 Morts, 6 Blesses et 2300 Familles Sinistrees.” LeNouvelliste.com. October

19, 2005.URL: http://lenouvelliste.com/lenouvelliste/article/21754/11-morts-6-blesses-

et-2300-familles-sinistrees.html

2005 - Alpha

Radio Kiskeya. “Net Alourdissement des Pertes Causees par la Tempete Alpha en Haiti: 13

Morts, 5 Disparus Provisoire.” ReliefWeb. October 25, 2005. URL: http://re-

liefweb.int/report/haiti/net-alourdissement-des-pertes-caus%C3%A9es-par-la-

temp%C3%AAte-alpha-en-ha%C3%AFti-13-morts-5-disparus

2006 - Ernesto

Jacobs, Steve. “Ernesto Pounds Haiti, Heads for Cuba.” The Washington Post. August 28, 2006.

URL: http://www.washingtonpost.com/wp-dyn/content/arti-

cle/2006/08/27/AR2006082700357.html

International Federation of Red Cross and Red Crescent Societies. “Haiti: Flood and Tropical

Storm Ernesto DREF Bulletin MDRFT001 Final Report.” ReliefWeb. May 23, 2007. URL:

haiti-floods-and-tropical-storm-ernesto-dref-bulletin-mdrht001-final-report

2007 - Dean

MetropoleHaiti.com. “Le Cyclone Dean Fait 4 Morts, 21 Blesses et 6 Disparus.”

MetropoleHaiti.com. August 21, 2007. URL:

http://www.metropolehaiti.com/metropole/archive.php?action=full&key-

word=Dean&sid=1&critere=2&id=12951&p=1

114

Alter Presse. “Haiti-Ouragan: Nuef Morts et 1 Disparu a la Suite du Passage de Dean, Selon un

Nouveau Bilan Partiel.” AlterPresse.org. August 22, 2007. URL: http://www.alter-

presse.org/spip.php?article6328#.UzXP2knD-P8

2007 - Noel

U.N. Office for the Coordination of Humanitarian Affairs. “Dominican Republic: Tropical Storm

Noel OCHA Situation Report No. 2.” ReliefWeb. November 02, 2007. URL:

http://www.static.reliefweb.int/report/bahamas/dominican-republic-tropical-storm-

noel-ocha-situation-report-no-2

2007 - Olga

MetropoleHaiti.com. “2 Morts et 2 Blesses, Bilan Partiel de la Tempete Olga.”

MetropoleHaiti.com. December 13, 2007. URL:

http://www.metropolehaiti.com/metropole/archive.php?action=full&key-

word=+noel&sid=1&critere=2&id=13316&p=7

2008 – Fay, Gustav, Hanna, Ike

U.N. OCHA. “Haiti Flash Appeal Revision: 2008 Consolidated Appeal Process.” UNOCHA.org.

2008. URL: https://docs.unocha.org/sites/dms/CAP/Revision_2008_Haiti.pdf

Direction de la Protection Civile. “Systeme National de Gestion des Risques et des Desastres

Centre d’Operations d’Urgence national Ministere de L’Interieur et des Collectiveites

Territoriales Direction de la Protection Civile: Bilan Concolide de l’Impact de Fay, Gustav,

115

Hanna, et Ike.” ReliefWeb. October 01, 2008. URL: http://reliefweb.int/sites/re-

liefweb.int/files/resources/91CD071EA4149607852574DA0068D457-

Rapport_complet.pdf

USA Today. “2 Babies Drown on Haiti Bus, Fay Death Toll Rises to 14.” USAToday.com. August

18, 2008. URL: http://usatoday30.usatoday.com/weather/storms/hurricanes/2008-08-

18-fay-haiti-bus_N.htm

AlterPresse. “Haiti/Tempete Tropical Fay: 7 Morts et 3 Disparus, d’apres un Nouveau Bilan Pro-

visoire Officiel.” ReliefWeb. August 19, 2008. URL: http://reliefweb.int/re-

port/haiti/ha%C3%AFtitemp%C3%AAte-tropicale-fay-7-morts-et-3-disparus-

dapr%C3%A8s-un-nouveau-bilan-provisoire

AlterPresse. “Haiti/Gustav: Une Cinqantaine de Morts.” AlterPresse.org. August 28, 2008. URL:

http://www.alterpresse.org/spip.php?article7623#.UzXVy0nD-P9

U.N. Stabilization Mission in Haiti. “Haiti: Ouragan Gustav – La Liste des Victimes s’Allonge.” Re-

liefWeb. August 28, 2008. URL: http://reliefweb.int/report/haiti/ha%C3%AFti-ouragan-

gustav-la-liste-des-victimes-sallonge

2010 - Tomas

AlterPresse. “Haiti-Tomas: Bilan a la Hausse – 21 Morts et 9 Disaprus – Le Sud Meurtri.” Alter-

Presse.org. November 09, 2010. URL: http://www.alterpresse.org/spip.php?arti-

cle10229#.UzXXM0nD-P8

2011 - Emily

116

Agence France-Presse. “Haiti: La Tempete Emily a Fait un Mort et de Nombreuses Familles Si-

nistrees.” LAPresse.ca. August 05, 2011. URL: http://www.lapresse.ca/interna-

tional/amerique-latine/201108/05/01-4423938-haiti-la-tempete-emily-a-fait-un-mort-

et-de-nombreuses-familles-sinistrees.php

2011 - Irene

AlterPresse. “Haiti-Irene: Alerte Levee – Deux Morts et Plusieurs Blesses.” AlterPresse.org. Au-

gust 25, 2011. URL: http://www.alterpresse.org/spip.php?article11440#.UzXYdknD-P8

U.N. Stabilization Mission in Haiti. “Le Passage d’ Irene Entraine la Mort de Deux Personnes.”

ReliefWeb. August 26, 2011. URL: http://reliefweb.int/report/haiti/le-passage-

d%E2%80%99ir%C3%A8ne-entra%C3%AEne-la-mort-de-deux-personnes

2012 - Isaac

U.N. Office for the Coordination of Humanitarian Affairs. “Bulletin Humanitaire Haiti.” Re-

liefWeb. August 31, 2012. URL: http://reliefweb.int/report/haiti/haiti-bulletin-humani-

taire-num%C3%A9ro-21-1er-au-31-ao%C3%BBt-2012

2012 - Sandy

U.N. Office for the Coordination of Humanitarian Affairs. “Haiti: TS Sandy’s Impact (Update of

23-29 October 2012).” ReliefWeb. October 29, 2012. URL: http://re-

liefweb.int/map/haiti/haiti-ts-sandy%E2%80%99s-impact-update-23-29-october-2012

Dominican Republic:

117

1994 - Debby

Associated Press. “Debby Begins to Come Apart in Caribbean.” The Tuscaloosa News. Septem-

ber 11, 1994. URL: http://news.google.com/newspapers?id=CEggAAAAIBAJ&sjid=vaU-

EAAAAIBAJ&pg=3751,3560906&dq=tropical+debby&hl=en

Avila, Lixion., and Rappaport, Edward. 1996. “Annual Summaries Atlantic Hurricane Season of

1994.” Monthly Weather Review 124: 1558-1578

1994 - Gordon

El Tiempo. “Gordon se Volvio Huracan.” Tiempo.com. November 18, 1994. URL: http://www.el-

tiempo.com/archivo/documento/MAM-252309

1996 - Hortense

Mejia, Odalis.”Empieza Hoy una Activa Temporada Ciclonica.” Hoy.com. June 01, 2010. URL:

http://hoy.com.do/empieza-hoy-una-activa-temporada-ciclonica/

1998 - Georges

U.N. Comision Economica Para America Latina Y el Caribe – CEPAL. “Republica Dominicana: Eva-

luacion de los Danos Ocasionados por el Huracan Georges, 1998.” CEPAL. URL:

http://www.eclac.cl/publicaciones/xml/6/40886/Huracn_Georges_Repblica_Domini-

cana_1998.pdf

2001 - Iris

118

Agence France-Presse. “Hurricane Iris Kills 3 in Dominican Republic, Barrels Towards Jamaica.”

ReliefWeb. October 07, 2001. URL: http://reliefweb.int/report/dominican-republic/hur-

ricane-iris-kills-3-dominican-republic-barrels-towards-jamaica

2004 – Ivan

ChinaDaily. “Hurricane Ivan Nears Jamaica, Kills 23.” Chinadaily.com. September 10, 2004. URL:

http://www.chinadaily.com.cn/english/doc/2004-09/10/content_373341.htm

2004 - Jeanne

Bracken, Amy. “Rain Lashes Haitian Storm Survivors.” High Beam Research. September 25,

2004. URL: http://www.highbeam.com/doc/1P1-99437022.html

Mercedes, Elizabeth. “One Dead, Two Missing and 5,000 Homeless in El Seibo.” Hoy.com. Sep-

tember 19, 2004. URL: http://hoy.com.do/un-muerto-dos-desaparecidos-y-5-mil-dam-

nificados-en-el-seibo/

Quiroz, Fernando. “Up to 23 Toll From Cyclone Jeanne.” Hoy.com. September 20, 2004. URL:

http://hoy.com.do/sube-a-23-cifra-de-muertos-por-ciclon-jeanne/

2005 - Alpha

BBC News. “Storm Alpha’s Death Toll Hits 26.” BBCNews.co.uk. October 27, 2005. URL:

http://news.bbc.co.uk/2/hi/americas/4381864.stm

2007 - Dean

119

Listin Diario. “El Huracan Dean Deja Republica Dominicana Tras Causar Muerte de Una Per-

sona.” Listin.com. August 20, 2007. URL: http://www.listin.com.do/la-repub-

lica/2007/8/19/25172/El-huracan-Dean-deja-Republica-Dominicana-tras-causar-

muerte-de-una

2007 - Noel

U.N. Comision Economica Para America Latina Y el Caribe – CEPAL. “Evolucion Del Impacto de la

Tormenta Noel en Republica Dominicana.” March 07, 2008. CEPAL. URL:

http://www.eclac.cl/publicaciones/xml/8/32458/L853-1.pdf

2007 - Olga

Reuters. “Tropical Storm Olga Kills 38 in Caribbean.” ReliefWeb. December 14, 2007. URL:

http://reliefweb.int/report/dominican-republic/tropical-storm-olga-kills-38-caribbean

Dominican Today. “Dominican Government Commission: No Fault in Tavera Dam Drain Which

Killed 30.” Dominicantoday.com. March 19, 2008. URL: http://www.dominican-

today.com/dr/local/2008/3/19/27374/Dominican-Government-commission-no-fault-in-

Tavera-dam-drain-which-killed

Agence France-Presse. “25 Morts, dont 22 en Republique Dominicaine Suit a Une Tempete.” Ya-

hoo.fr. December 13, 2007. URL: http://fr.newsyahoo.com/afp/2--71213/tsc-domini-

caine-intemperies-caraobes-c2ff8aa_2.html

2008 - Fay

120

RTVE. “La Tormenta Tropical Fay ya ha Causado una Muerte en Republica Dominicana.” Rtve.es.

August 16, 2008. URL: http://www.rtve.es/noticias/20080816/tormenta-tropical-fay-

causado-una-muerte-republica-dominicana/137570.shtml

2008 - Gustav

International Federation of Red Cross and Red Crescent Societies. “Caribbean: Hurricane Gustav

Emergency Appeal No. MDR49003.” ReliefWeb. September 02, 2008. URL: http://re-

liefweb.int/report/united-states-america/caribbean-hurricane-gustav-emergency-ap-

peal-no-mdr49003

2008 - Ike

U.N. Office for the Coordination of Humanitarian Affairs. “Situation Report 10 – Caribbean Hur-

ricane Season.” ReliefWeb. September 08, 2008. URL: http://reliefweb.int/sites/re-

liefweb.int/files/resources/A587DB33CC55C164492574BF00050476-Full_Report.pdf

2011 - Irene

American Broadcasting Corporation. “Hurricane Irene Blamed for at Least 40 Deaths.”

WJLA.com. September 1, 2011. URL: http://www.wjla.com/articles/2011/08/nine-re-

ported-deaths-from-hurricane-irene-65730.html

2012 - Isaac

Sistema Bolivariano de Comunicacion e Informacion. “Storm Isaac Leaves Five Dead in Domini-

can Republic.” August 28, 2012. URL: http://laradiodelsur.com/?p=110642

121

The World Post. “Hurricane Isaac 2012: Death Toll in Haiti Rises to 10.” Huffingtonpost.com. Au-

gust 27, 2012. URL: http://www.huffingtonpost.com/2012/08/26/hurricane-isaac-2012-

haiti-death-toll-7_n_1831449.html

2012 - Sandy

BiobioChile. “Two Drowned and 26,000 Displaced in Dominican Effect of Hurricane Sandy.” Bio-

biochile.cl.com. October 26, 2012. URL: http://www.biobiochile.cl/2012/10/26/dos-

ahogados-y-26-000-desplazados-en-dominicana-por-efecto-del-huracan-sandy.shtml

122

Social and Physical Sources Cited

Institut Haitien de Statistique et d’Informatique. “Enquete sur les Conditions de Vie en Haiti.”

2003. URL: http://www.ihsi.ht/pdf/ecvh/ecvh_volume_I_(juillet2003).pdf

Oficina Nacional de Estadistica. “Perfiles Sociodemograficos Provinciales y Municipales 2008.”

2008. URL: http://www.one.gob.do/index.php?module=articles&func=view&catid=217

NASA Jet Propulsion Laboratory. “Photojournal” January 14, 2010. NASA. URL: http://photo-

journal.jpl.nasa.gov/mission/SRTM?order=Xdim*Ydim*Zdim&sort=ASC&start=200

IFPRI (International Food Policy Research Institute).”Global Agricultural Extent v 2.0. Reinterpre-

tation of Global Land Cover Characteristics Database (GLCCD v. 2.0).” 2002. URL:

www.asb.cgiar.org/BNPP/phase2/bnpp Phase 2 datasets

Organizacion Panamerican de la Salud. “Republique D’Haiti: Profil Epidemiologique.” February

2004. URL: http://fmp.ueh.edu.ht/PDF/ProfilEpidemilogiqueHaiti.pdf

Ministere de la Sante Publique et de la Population. “Enquete Mortalite, Morbidite et Utilisation

des Services EMMUS-IV.” January 2007. URL: http://dhspro-

gram.com/pubs/pdf/FR192/FR192.pdf

123

Hurricane Data

National Hurricane Center. “NHC Data Archive.” March 28, 2014. National Weather Service.

URL: http://www.nhc.noaa.gov/data/#hurdat

124