Childhood Obesity in the Greater Chicagoland Area: A Geographical & Social Analysis
By
Mei Yan Yuen
An Undergraduate Thesis
Submitted in Partial Fulfillment for the Requirements of
Bachelor of Arts
in
Geography and Earth Science
Advisor:
Professor Devonee Harshburger
Dr. Wayne Thompson
Carthage College
Kenosha, WI
April 2010
ABSTRACT
Obesity in young children is becoming a problem and it is growing rapidly. This thesis
focuses on childhood obesity in greater Chicagoland, which includes Cook and Lake County,
Illinois, and Kenosha County, Wisconsin. There have been significant changes to the lifestyles
and diets of all age groups, most importantly children. Childhood obesity is a factor that can be
tied to many other diseases later in life such as diabetes, heart disease and others. This study
explores how childhood obesity has been increasing and what factors contribute to its growth.
Data analysis includes demographics of the people within the Chicagoland study area and
application of social theories is used to explain why a certain area may be more affected by
unhealthy weight in their younger population. SPSS and GIS (Geographic Information Systems)
is utilized for data analysis. Hot spot analyses in GIS are to identify the most affected areas in
southeastern Wisconsin and northeastern Illinois and how these may correlate to
demographically at-risk populations. Through analysis and mapping it is concluded that
education is the most important factor, followed by race and income. The most vulnerable
areas for childhood obesity is centered around metropolitan areas of Chicago and Waukegan,
as they move farther away from the center of the city vulnerability is decreased. Social
disorganization of neighborhood that surrounds these cities is used to explain the high cases of
childhood obesity.
ii
Table of Contents
List of Figures
iv
List of Tables
v
Introduction
1
Methodology
15
Study Area
15
Sample Population
15
Data Acquisition
16
Statistical Analysis
19
Spatial Analysis
20
Results
23
Discussion
39
Future Research
41
Conclusion
42
Acknowledgements
43
Reference
45
iii
List of Figures
Figure 1: Body Mass Index (BMI) graph for boys, example for 10-year old boy (CDC.gov)
Figure 2: Body Mass Index (BMI) graph for boys, comparing same BMI with different ages
(CDC.gov)
Figure 3: Obesity through the years, changes from different studies (Ogden & Carroll, 2010).
Figure 4: Natural Areas and Urban Zones as Depicted by Faris & Dunham (Traub & Little, 1980).
Figure 5: Visualization of Moran’s I Cluster (ESRI (1) n.d.).
Figure 6: Visualization of Getis Ord Gi* (ArcGIS Help Menu).
Figure 7: Results from Moran’s I Spatial Correlation of Childhood obesity
Figure 8: Scatterplot to show age distribution of obese children population.
Figure 9: Race distribution of childhood obesity.
Figure 10: Scatterplot of percentage under poverty level (Census 2000) and percent population
with childhood obesity.
Figure 11: Scatterplot of percentage with low educational attainment, high school diploma
(equivalent) or less and childhood obesity.
iv
List of Tables
Table 1: Pearson’s Correlation test for all variables obtain from NHANES dataset, analyzed in
SPSS.
Table2: Age distribution of sample set and compared to obese children (BMI over 24) (NHANES
data)
Table 3: Age distribution regression test. (NHANES data)
Table 4: Race and childhood obesity distribution by raw number and percentage (NHANES
data)
Table 5: Race and childhood obesity regression analysis model summary (NNANES data)
Table 6: Race Regression Test (NHANES data)
Table 7: Income and childhood obesity regression analysis model summary (NHANES data)
Table 8: Food Security and BMI of children regression model summary (NHANES data)
Table 9: Regression model summary for all NHANES variables
Table 10: Regression analysis for all NHANES variables used
Table 11: Pearson Correlation for childhood obesity with poverty level, and education (Census
data by block group).
Table 12: Percentage of block group with poverty status and percent childhood obesity
regression model summary (Census)
Table 13: Percentage of block group with poverty status and percent childhood obesity
regression analysis.
Table 14: Percent with low educational attainment and childhood obesity regression model
summary (Census)
Table 15. Percent with low educational attainment and childhood obesity Regression analysis
v
CHILDHOOD OBESITY IN THE GREATER CHICAGOLAND AREA: A GEOGRAPHICAL & SOCIAL
ANALYSIS
Obesity is not a new problem nor has it been ignored. It has been on the minds of the
nation for many years and is a growing public health issue. Obesity in the United States has
been an increasing problem for all age groups, but most importantly the youth. The health of
the young is slowing spiraling downward. In the last twenty years, obesity rates among children
have doubled (Brown, et al. 2008). The statistics show that obesity will soon overtake cigarette
smoking as the number one cause for preventable deaths in the United States (Terrell 2006). In
order to have a strong youth that can contribute to a better future we must have a healthy
population. The epidemic may be stemming for changes in diets and activities. These both
derive from the social and spatial changes of our nation.
BACKGROUND
Measuring BMI: tool for identifying obesity
In the United States, obesity is measured by weight and height. The measurement is
then calculated to a Body Mass Index or BMI. This method is but one screening tool to identify
possible weight problems in adults and children. It is a relatively easy measure for a clinical
worker to begin the process of identifying obesity but is only determined with further
assessment from a healthcare provider. The definition of overweight and obese differs by age
and sex. For adults 20 and older, BMI under 18.5 is underweight, 18.5-24.9 is normal 25.0-29.9
overweight and 30.0 is obese.
1
For the younger population there is a slight difference between these measurements
since it accounts for variables of differing stages of childhood development. (Center for Disease
Control and Prevention (1) 2009). For children there is a growth chart and for both males and
female children. The BMI is then plotted on this chart to determine if the child is either
underweight, healthy weight, overweight or obese. They are ranked by percentiles and are
relative to the child’s age and sex. Underweight is classified as being less than the 5th
percentile, healthy weight is 5th percentile to less than the 85th percentile, overweight is 85th
percentile to less than the 95th percentile, and obese is calculated at equal to or greater than
the 95th percentile. This form of measuring BMI and percentile ranking begins at the age of two
and continues through twenty years of age (Center for Disease Control and Prevention (1)
2009).
Figure 1: BMI Graph for boys, showing example of how to measure
BMI for a 10-year-old boy. (CDC.gov)
The following figure is an
example of the graph that is used for
boys. Figure 1 shows the percentiles
and provides an example of how a 10year old-boy would be placed on the
chart according to the BMI he had at
that age. Figure 2 describes how
another boy with the same BMI but
older would vary from the first child.
This chart and measurement system
2
takes into account age which an adult BMI calculator doesn’t. This is very important because
children are constantly growing and changing which does not allow for one single measurement
to be applied to all ages. An example would
be a 15-year-old who is heavier, however
this Index understands that at this age
newly developed muscles and other factors
play a role in the weight gain (Center for
Disease Control and Prevention (1) 2009).
Again, BMI is not a diagnostic tool.
Instead it is directional and indicates the
child might need to be further evaluated.
So if a child has a high BMI for his or her age
Figure 2: Comparing same BMI with different ages. (CDC.gov)
and sex, excess fat still needs to determine. Health care providers have to perform further tests
such as skin-fold thickness measurements, evaluation of the child’s or teen’s diet, physical
activity, family history, and other appropriate screenings (Center for Disease Control and
Prevention (1) 2009).
3
Over the years, the United States has had a steady increase of people being categorized
as overweight or obese. According to the CDC there has been an increase in obesity in children
2-19 years of age through the years of 1976 to 2008. Data collected from years of 1976-1980
and 2007-2008 are compared and show that: in pre-school children age 2-5 there was an
increase from 5.0% to 10.4%, younger adolescents aged 6-11 went from 6.5% to 19.6% and
older adolescents aged 12-19 had an increase from 5.0% to 18.1%. Percentages are
respectively shown to the years listed above (Ogden & Carroll, 2010).
Figure 3: Obesity through the years (Ogden & Carroll, 2010).
4
Obesity’s Long-Term Affects
Being overweight or obese is one of the top ten health indicators according to the
Healthy People 2010 (Hedley, et al. 2004). Obesity at a young age can be a contributor to other
long-term illnesses and disease. It has been linked to degenerative diseases such as heart
disease, diabetes, sleep apnea, asthma and psycho-sociological risks (Nestle and Jacobson
2000).
Heart disease and diabetes have high medical and social costs. These disease cost
Americans millions of dollars in tax money to treat, which makes obesity not only a medical and
social problem but an a contributor to an economic problem (Nestle and Jacobson 2000). There
have been some efforts to halt the growing problems of obesity but many are often too drastic
and have proven inadequate because many viewpoints and behaviors of diet and foods are
rooted into the culture and lifestyles of people (Nestle and Jacobson 2000).
Built Environment & Crime
A drop in physical activity and increase in sedentary activities has been a major change
that has affected the youth. Technological changes make it more comfortable to stay indoors
with more things to do including and not limited to cable television, internet, and video games.
Since there are more indoor sedentary activities these results in less outdoor activities, this is
contributing to increased weight. Areas with neighborhood crime and road safety are
negatively correlated with physical activity. Access to safe play environments, such as at
schools, show a correlation to increased physical activity. This is also rooted into the parental
perceptions of crime in their neighborhood (Brown, et al. 2008).
5
Minority & Socio-Economic Status
Children are more likely to become obese if they have a background as an ethnic
minorities and low-income, than children of non-minority status (Kumanyika and Grier 2006).
Girls of lower socio-economic parents had a higher risk of become obese compared to the girls
of higher socio-economic status parents (Morgan, et al. 2010). There are many socio-economic
and demographic variables that contribute to this. Socioeconomic status is defined by the
social class, family income, parental education level, occupation and social status in their
community (North Central Regional Educational Laboratory n.d.).
As explained earlier, sedentary activity comes from changing lifestyle patterns and also
may be related to perception to crime rates which have a negative effect on outdoor activity
levels. People of lower socioeconomic status are more prone to live in areas with high crime
rates and poor built environment. There are fewer areas of safe play areas that located in a
community of low economic status. Parents may restrict their children’s outdoor activities for
safety reason. Along with this analysis other socio-economic variables must be taken into
account that is affecting physical activities. These variables could be rooted into the family’s
work schedule, money and car ownership which can be factors in children being involved with
recreational activities or sports to keep the young active (Kumanyika and Grier 2006). Other
factors can include the exposure of media and commercials to young children from greater
amounts of television watching. Ethnic minorities and low-income youth are exposed to a large
number of food advertisements due to the increased amount of television watching compared
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to white, non-poor children, which can affect the food preference and perception (Kumanyika
and Grier 2006).
Food Deserts
Although the United States is considered one of the most developed countries in the
world, food deserts still exist (Center for Disease Control and Prevention (2) 2010). There isn’t a
standard definition for a food desert, but according to the US Department of Agriculture there
are still pockets of population living in the United States that have limited ability to access to
affordable and nutritious food, such as, fruits, vegetables, whole-grains, low-fat milk and others
from a well-balanced diet. These populations live far from a supermarket or large grocery store
and have a little access to reliable transportation. Communities high in ethnic minority and
low-income children are affected by having fewer average supermarkets and convenience
stores that stock fresh, good quality, and affordable foods (Hedley, et al. 2004). It is also noted
by Kumanyika and Grier (2006) that neighborhoods where low-income and minority children
live have more fast food restaurants that contain high-fat and high-caloric foods and fewer
healthy food vendors than wealthier or predominately white communities.
Similar to “white-flight” where Caucasians fled areas that were homes to African
Americans and other minority groups, there was a “supermarket flight,” where supermarkets
fled the inner cities and left primarily low-income neighborhoods for high-income areas, leaving
the low-income neighborhoods with 30 percent less supermarkets than high-income
neighborhoods (Hedley, et al. 2004). It is suggested by many scientific studies that food
7
deserts negatively affect the health outcomes of the population (Center for Disease Control and
Prevention (2) 2010).
Weight-gain is caused by more calories ingested than burned; the calories from nonnutritious foods are often substituted for a well-balanced diet. There have been many studies
that demonstrate that food deserts are more frequently in areas with high poverty levels and
food insecurities. A study done in Pennsylvania, with an analysis done with Geographic
Information Systems was used to identify food deserts in school districts (Schafft, Jensen and
Hinrichs 2009). This analysis concluded that there are higher percentages of school districts
with populations located within food deserts are more likely to be socially and economically
disadvantaged. They also found that there is an increased rate of children being overweight
and obese that resides in a food desert (Schafft, Jensen and Hinrichs 2009).
Food Stamps
In the United States, people that need assistance with purchasing food are provided
funds through the USDA Food Stamp Program. The program provides low-income households
the funds to purchase foods at a grocery store. It began in the 1960’s and expanded in the 70’s
due to the under consumption of food and nutrients in America (Ploeg and Ralston 2008). This
provided the opportunity for people with low-income or poverty status to purchase healthier
food choices. However, this did not assist with transportation to grocery stores.
Today it is called SNAP or short for Supplemental Nutrition Assistance Program. SNAP is
available to assist those that fall 100 percent beneath the poverty line and for elderly or
disabled members. To qualify for SNAP a person must have a gross income that is below 130
8
percent the poverty line. Eligibility is based on both financial and non-financial factors. People
must complete and file a form, go through an interviewing process to verify facts in order to be
determine eligibility. Funds are supplied in the form of an electronic benefit transfer or EBT,
instead of coupons that were given out in the past (Food Reseach and Action Center 2010).
Things that can be bought with food stamps include food and for plants and seeds to grow food
for the household to eat. The funds are not able to be used on non-foods such as pet food,
soap, paper products, etc. They cannot be used for alcoholic beverages or tobacco, vitamins or
medicine, food eaten at the store or hot foods such as made and ready to eat meals (United
States Department of Agriculture 2010). However, this does not prohibit the purchase of foods
high in sugar and saturated fats. This can include items like soda, chips, and frozen foods,
which can contribute to higher caloric intake.
There have been numerous studies that link food stamps to obesity. White women
living in poverty are more likely to be obese compared to black or Hispanic males (Ploeg and
Ralston 2008). This could be because women are more likely to have sedentary activities than
men. Further, if they are unemployed, they are less likely to participate in high physical
activities. Women that are linked to long term use of food stamps, are found to have higher
chances of becoming obese upward of 20 to 50 percent increase in obesity rates (Ploeg and
Ralston 2008). Ploeg and Ralston did not find that adolescent children were linked to higher
rates of obesity and food stamps in either teenage boys or girls. In a different study, there
seems to be a correlation between long-term food stamps participants and overweight in lowincome young daughters and their mothers (Ploeg and Ralston 2008).
9
Perception & Education of Parental Figures
Perception and the role of education play an important role on parents and obese
children. Low-income mothers have a higher probability that they will not pursue higher
education. They have less understanding regarding the health of children and what is good for
them. They often perceive that their child is not obese unless they are made fun of for their
weight or are less active or obesity which affects their physical activity. Only 11 percent of
mothers without college education, thought their pre-schooler was overweight (Jain, et al.
2001). Mothers are critical players and mediators for preventing obesity in their children
because they have a large responsibility to shape the diet and activity pattern of their young
children (Jain, et al. 2001). Obesity also affects college enrollment for women (Crosnoe 2007).
Theoretical Considerations in the Study of Childhood Obesity
Conflict Theory
Childhood obesity has not been studied and put under a sociological perspective and
theory. This thesis will try to address this omission. Conflict theory might be one of the best
ways to analytically frame childhood obesity. The conflict perspective targets the issue of the
continuous power struggles for control of scarce resources (Kendall, 2007). Karl Marx was a key
player in using conflict theory to shape this perspective. He focused on the power struggle
between people with control of the means of production and those without it, and the
exploitation of the oppressed by the people holding power (Kendall, 2007). Conflict theory can
also be applied to things such as suicide or feminism. In this study, we will use it to apply and
explain childhood obesity that is being caused by low socioeconomic status.
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Social Disorganization
Robert E.L. Faris and H. Warren Dunham were theorists that incorporated social
disorganization and urbanism. Rural-urban comparisons of dependency, crime, divorce and
desertion and suicide are more prominent and severe in the cities. (Traub & Little, 1980). The
concepts of the city is that the farther one moved away from the center of the city or
concentric zones (distinct areas or circles that radiated from the central business district) the
fewer social problems were found. The basic idea was that the growth of the city, location of
areas, and social problems were not random but a part of a pattern (William & McShane, 1999).
The first zone is the central business
district, where businesses and factories were
located and very few residences exist. The next
zone was called zone of transition. This zone
was not a desirable living location. However it
was the cheapest location due to its
deterioration. Immigrants usually settled in
these areas because it was inexpensive and
near factories that they could find work. As
these members of the zone of transition moved
up on the social ladder they moved to the third
Figure 4: Natural Areas and Urban Zones as Depicted by
Faris & Dunham (Traub & Little, 1980).
zone called the zone of workingmen’s homes.
When they moved out another wave of
11
immigrants take their spot in the zone of transition (William & McShane, 1999).
Social Stratification
William Julius Wilson is a sociologist that emphasizes that race is not the main factor
when it comes to social problem but it is more importantly about social class. He says that
social problems of urban life measures the problems of racial inequality (Wilson, 1987). In
addition, he argues that when industrialized work disappears from inner cities, and work
disappears there is a urban unemployment increase. Which leads to people in the inner-city to
not have any social class movements, and they are immobilized in the stratification of class and
location. When there is disorganization within the neighborhood. This leads to crime, low
education, female-headed households and teenage pregnancy (Wilson, 1987). For Wilson, he
focus on how the economic change of a location plays a greater role in negative outcomes in an
area. Housing, employment and economic activity, education and other factors that relates to
negative outcomes.
Summary
A change in diet and lack of physical activities over time in children had been a
social and medical epidemic resulting in children to become more overweight and obese. There
are many negative socio-demographics and socio-economic factors that contribute to the
increased number of children being categorized as overweight or obese.
Minority status that affects financial difficulties plays an enormous role in higher rates
of childhood obesity. People that fall under the poverty line and those that are unemployed
12
are more likely to live in poorly built environments. They are also more likely to live in areas
with higher crime and drug use areas and may be more fearful of letting their children do much
needed outdoor activities. They are then more likely to keep their children safely inside the
home. Unfortunately this will lead to activities that are less physical and in turn have a higher
likelihood of television watching, video games, and internet among other sedentary activities.
Food stamps have shown mixed results in its ability to provide people that fall below the
poverty line a way to obtain healthy foods which they are unable to buy without it. It provides
families a chance to purchase a healthier diet, although food stamps have few exceptions on
what it can be used for. Unfortunately, there are still people in the United States that have
minimal means to visit a full line grocery store. It is still accepted to buy foods high in sugar,
saturated fats, sodium, and many others that contribute to unhealthy diets. So, although a
mother or responsible member has the ability to purchase fresh and healthier choices, old
habits and choices made prior to assistance through food stamps could affect their preference
of foods.
Significance of Study
Childhood obesity is not just what is consumed in the body but the behaviors that lead
to what is put in the body and what is done to maintain a healthy weight or not. There is little
social interpretation found on this issue. This study’s goal is to contribute to the sociological
world a start to understanding how social and geographical location is affecting childhood
obesity today. By understanding why and where, it can be more effort to provide these areas
13
with more assistance or further try to understand why some population are more at risk than
others.
Hypotheses
Null Hypothesis
Socio-demographic and socio-economic factors does not increase the rate of childhood obesity.
Alternative Hypothesis
Negative socio-demographics and socio-economical is associated with the increase number of
children categorized as overweight or obese.
Sub-Hypotheses
A. Minority race (mainly Hispanic and African American) is associated with higher
childhood obesity compared to non-minority races.
B. Low income is associated with higher rates of childhood obesity.
C. Households that are unable to afford a balanced meal are associated with higher rates
of childhood obesity.
D. Lower levels of education (high school diploma or less) are associated with higher rates
of childhood obesity.
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METHODOLOGY
Study Area
The study consists of Greater Chicagoland counties from Illinois and Wisconsin. Cook,
Lake, and Kenosha County are studied at the block group level, which is defined by the U.S.
Census Bureau as a cluster of
Map 1
census blocks that share the same
first digit of their four-digit
identification number within a
census tract. These block groups
never cross state, county, or other
similar boundaries (U.S. Census
Bureau, Geography Division
Cartographic Product
Management Branch 2005).
Within the three counties, there
are 4,748 block groups.
Sample Population
Childhood Obesity for this analysis is children ages 2 to 16 years of age. Ages 0 to 1 are
excluded because body mass index was not measured or insufficient for this age group.
15
Overweight and obese were defined at BMI’s over 24. This is the starting point for 16 year olds
to be considered overweight.
Data Acquisition
Two data sets from the Census Bureau and the Center for Disease Control (CDC) would
be utilized for examining childhood obesity. These data sets and its information will go through
extensive analysis and modeling. The data acquired from the U.S. Census Bureau will contain
demographics such as race and income level. From the CDC, the National Health and Nutrition
Examination Survey (NHANES) is used for health data and BMI measurements.
NHANES
NHANES program, which began in the early 1960’s, is designed to assess the health and
nutritional status of adults and children in the United States. It is a survey that combines the
use of interviews and physical examinations. This is done by surveying a nationally
representative sample of about 5,000 people each year. The interviews include demographics,
socioeconomic, dietary, and other health related questions. The physical examination uses the
combination of medical, dental, physiological measurements and laboratory tests that are
administered by the medical personal. NHANES is a major program that is part of the National
Center for Health Statistics, which is a part of the CDC. The CDC’s responsible to produce and
provide information to the nation about vitals and health statistics (Center for Disease Control
(3), 2009).
16
Data was aggregated from the data set. Variables that were used from NHANES:
1: Body Mass Index (BMXBMI)
Body mass index was calculated for immediate use. BMI will be utilized as our
dependent variable. At times, it would be used as is, when identifying childhood overweight
and obese population, this sample will be queried to BMI over 24. This is a number that is
average for most older age groups but is overweight or obese for any of the ages between 2 to
16.
2: Age at Screening Adjudicated (RIDAGEYR)
The NHANES provides a wide sample of ages to represent the United States. Since this
study is on the children population only, an age group of 2 thru 16 was queried. BMI starts
being measured at the age of two. There was insufficient data for age one and no data was
available for age group of zero. Age was measured every whole year.
3: Annual Household Income (INDHHIN2)
Annual Household Income was measured and coded in unequal groups. The original
data was coded: 1) $0-4,999; 2) $5,000-$9,999; 3) $10,000-14,000; 4) $15,000-19,999; 5)
$20,000-24,999; 6) $25,000-34,999; 7) $35,000-44,999; 8) $45,000-54,999; 9) $55,000-64,999;
10) $65,000-74,999; 12)Over $20,000; 13) under $20,000; 14) $75,000-99,000; 15) over
$100,000. This was re-coded to: 1) $0-14,999; 2) $15,999-24,999; 3) $25,000-34,999; 4)
$35,000-44,000; 5) $45,000-54,999; 6) $55,000-64,999; 7) $65,000-74,999; 8) $75,000-99,000;
9) Over $100,000.
17
4: Race/Ethnicity (RIDRETH1)
Race was nominally measured. It was coded by NHANES as 1) Mexican Americans; 2)
Other Hispanics; 3) Non-Hispanic White; 4) Non-Hispanic Black; 5) Other Race and Multiple
Race. The categories were re-issued to make a series of “dummy” variable. Each race was
categorized individually and coded 0 and 1. When the case was true for that race it was issued
a one and when false it was issued a 0. This was completed in SPSS by writing syntax.
5: HH (Household) Couldn’t Afford Balanced Meals (FSD032C)
This variable is useful for our study to measure food security/ food scarcity. Throughout
the report this variable will be mentioned as either food security or food scarcity. This variable
is associated and can provide a way to measure food stamps usage and the diets indirectly. But
information and results collected can be a key to understanding underlying issues. The
question to respondents was “Household couldn’t afford a balance meal”, their possible
responses were/coded 1) Often true; 2) Sometimes True; 3) Never True.
Census Data
Data from Census 2000 was utilized for block group level data. This is the most current
full census data that is available. Variables used at block group level: Sex by Educational
Attainment for the Population 25+ years (P37) and Poverty Status in 1999 by Age (P87).
Education was re-coded to population with a high school diploma/equivalent and less. This
variable was converted into a percent by dividing it by the total population of the block group.
The poverty status variable provided a total number of people that fell under the poverty level.
18
This was also converted into a percentage by dividing the number of people that fell below the
poverty level and the total sample size. This information was used for the spatial analysis and
also utilized in SPSS to measure correlation and regression test.
Statistical Analysis
Microsoft Excel and SPSS (Statistical Package for Social Sciences) were utilized for
statistical analysis for the various data. Microsoft Access was used as a helping tool to connect
the variables and aggregate the data. Access was also used to convert table data to databases
that are able to be joined and used in ArcGIS (spatial analysis tool).
Excel and SPSS were used to construct charts to showing relationships, correlations and
regressions. Correlation and regression test provide a statistical significant values or p-values
(when necessary and obtainable), test are only significant at p ≤ .05. Regressions are identified
with a strong correlation with R values over .7. However, the value is accepted to be lower
when examining human behavior. Using standardized regression coefficients (betas), we can
assess the relative importance of the independent variables in predicting the dependent
variable, in this study childhood obesity or BMI. The larger the beta values the higher the
importance.
Variables analyzed with regressions (with graphs) and correlations with Excel in this
study were: age & childhood obesity and income & food scarcity. Race and childhood obesity
was examined with percentages and bar chart to display data. Data analyzed in SPSS were
transported from with spatial data. This omitted the issues of pairing BMI’s with the population
weights provided from NHANES data set. The variables that were investigated and analyzed in
19
SPSS were: childhood obesity & poverty levels and childhood obesity and education level of
high school diploma (or equivalent) or less. These variables have statistical test of correlation
and regressions (with graphs) as well.
Spatial Analysis
Geographic Information System (GIS) will be utilized to perform spatial analysis of the
NHANES data. GIS is a mapping tool that integrates data and used to manage, analyze, and
display data spatially. GIS allows data to be visualized, understood spatially, questioned, and
interpreted. Explorative mapping will be done, including densities of childhood obesity.
Modeling Data
The counties will be broken down to block groups. Block groups are defined by the U.S.
Census Bureau, as clusters of census blocks that have the same first digit within a census tract
(U.S. Census Bureau, Geography Division Cartographic Product Management Branch 2005).
However, the NHANES data does not have location data, so the data had been modeled.
The modeling of the NHANES data is done by allocating obesity data out to census block
groups using the available weights in the data set. They are weights assigned to each record
that will be modeled out to the population by percentages of race and number of children in
those race categories. The data is then proportioned to the entire nation by block groups.
20
Moran’s I
Moran’s I and is a spatial statistic tools
to identify clustering of data. Moran’s I analysis
whether the given variable and attribute is
Figure 5: Moran's I (ESRI (1) n.d.)
clustered, dispersed or random (ESRI (1) n.d.). The test calculates a z-score to identify if the
findings are statistically significant or not, in addition with a Moran’s I Index, Expected Index,
Variance, and p-value. +1.0 z-score value will indicate high clustering whereas -1.0 z-score
value indicates dispersion. Moran’s I is displayed in an output window to summarize the
finishing data.
Moran’s I analysis is done on the percent of childhood obesity in greater Chicagoland.
This analysis will determine if the density map of values percentage of childhood obesity is
clustered and the statistical significance of the data.
Childhood Obesity Vulnerability Index (COVI)
A Childhood Obesity Vulnerability Index is created by giving each variable a percentage
that acts as normalization for the block group. The variables consist of childhood obesity (BMI
24 and over), low education (high school diploma (equivalent) or less) and people that fall
under the poverty level. The variables will be layered added together called “map algebra” to
determine which location is most vulnerable to high childhood obesity rates. This index begins
to allow us to identify the most at risk locations, give the ability to understand why these areas
may be affected and how we are able to target and provide more information to these areas.
21
Getis Ord Gi*
Getis-Ord Gi*, sometimes known as hotspot analysis, will be utilized in ArcGIS Spatial
Figure 6: Getis Ord Gi* (ArcGIS Help Menu)
Analyst extension. This tool identifies statistically
significant spatial clusters of high values (hot spots) and low values (cold spots). It returns with
the z-score and p-value, which measures statistical significance to determine whether or to
reject or fail to reject the null hypothesis. High z-score and low p-value signify a spatial
clustering of high values. A z-score very close to zero would indicate no apparent spatial
clustering (ArcGIS Help Menu).
Getis Ord Gi* analysis is performed on the Childhood Obesity Vulnerability Index (COVI).
This analysis will identify were the hot spots and cold spots are occurring to all three variables
combined together. This will further our understanding of patterns of vulnerability for children
to become obese.
22
RESULTS
Spatial View on Childhood Obesity
A density map with graduated colors was created in ArcGIS showing percentage of
children ages 2 thru 16 who had a body mass index of over 24 by block group (map 1). The
darker the block group indicates a higher percentage of children that are overweight or obese.
Moran’s I was executed for this map to find statistically significant clustering of childhood
obesity. The data is falling with a standard deviation of over 2.58. The z-score was 327.13 and
p-value was 0.000. This means there is less than 1% likelihood that this clustering pattern could
be the result of random chance.
Figure 7: Moran's I for Childhood Obesity Cluster Analysis
23
Map 2: Density of Childhood Obesity
24
Correlation for BMI and Variables from NHANES (Age, Race, Income, and Food Scarcity)
Correlation test was done on the NHANES data set. It includes children ages 2 to 16. All
measurements have been normalized for the weights of the population. Table 1 shows the
correlations between the variables. Significance test for all correlations with body mass index
equaled 0.000. This means the variables are statistically significant. Pearson Correlation
identifies if there is a positive or negative correlation between variables and increasing BMI.
For age and BMI, Pearson Correlation is .541 which indicates a positive correlation, meaning as
children get older, BMI increase. This positive correlation is occurring with people in the race
categories of Mexican Americans, other Hispanics and African Americans. A negative
relationship is occurring when BMI is correlated with the Caucasian race, household income (as
income increases, BMI decreases), and food security. Food Security is coded, 1 for often true, 2
for sometimes true, and 3 for never true. So this correlation is interpreted as food security is as
food security is troublesome there is a higher BMI.
25
Table 1: Correlations from NHANES Variables
Body Mass Pearson Corr.
Index
Body
Age of
Mass
Resp.
Mex.
Other
Index
(2-16 yrs)
Amer.
Hisp.
1
Sig. (2-tailed)
N
59308752
White
Black
Househol
Food
d Income
Security
.541**
.027**
.027**
-.036**
.061**
-.071**
-.072**
.000
.000
.000
.000
.000
.000
.000
59308752 59308752
59308752
59308752
59308752
-.070**
-.006**
.055**
.011**
.079**
-.019**
.000
.000
55694296 59046939
Age of
Pearson Corr.
.541**
Resp.
Sig. (2-tailed)
.000
.000
.000
.000
.000
(2-16 yrs)
N
59308752
67526623 67526623
67526623
67526623
67526623
-.108**
-.468**
-.171**
-.177**
-.107**
.000
.000
.000
.000
.000
67526623
67526623
67526623
1
-.306**
-.112**
-.126**
-.076**
.000
.000
.000
.000
67526623
67526623
67526623
1
-.484**
.254**
.061**
.000
.000
.000
Mexican
Pearson Corr.
.027**
American
Sig. (2-tailed)
.000
N
59308752
Other
Pearson Corr.
.027**
Hispanics
Sig. (2-tailed)
.000
N
White
.000
1
67526623 67526623
-.006**
-.108**
.000
.000
67526623 67526623
-.036**
.055**
-.468**
-.306**
Sig. (2-tailed)
.000
.000
.000
.000
67526623 67526623
67526623
67526623
67526623
1
59308752
Pearson Corr.
.061**
.011**
-.171**
-.112**
-.484**
Sig. (2-tailed)
.000
.000
.000
.000
.000
67526623 67526623
N
Household Pearson Corr.
Income
-.070**
Pearson Corr.
N
Black
59308752
1
Sig. (2-tailed)
N
59308752
63430737 67207540
63430737 67207540
63430737 67207540
63430737 67207540
-.140**
-.007**
.000
.000
67526623
67526623
67526623
-.071**
.079**
-.177**
-.126**
.254**
-.140**
.000
.000
.000
.000
.000
.000
.000
63430737 63430737
63430737 63430737
55694296
Food
Pearson Corr.
-.072**
Security
Sig. (2-tailed)
.000
(HH could
N
59046939
63430737 67207540
1
63430737
63430737
63430737
-.019**
-.107**
-.076**
.061**
-.007**
.223**
.000
.000
.000
.000
.000
.000
67207540 67207540
67207540
67207540
67207540
.223**
1
63430737 67207540
not afford
balanced
meal)
**. Correlation is significant at the 0.01 level (2-tailed).
26
Age distribution & Childhood Obesity
A frequency table was created to compare the sample (N) with the obese sample based
on age (table 2). When age is increasing there is an increase of children that have a BMI of 24
or over. This is not surprising since it is unnatural for children at toddler age to have a BMI of
greater than 24. Unfortunately, the numbers are not zero for this age group. The age with the
most case of childhood obesity is in the 15 year old population (37.8%).
A scatterplot (see graph 1) displays the distribution of age and population that is obese.
Table 3 shows that adjusted R2=.915, indicating that 91.5% of the variance is number of children
with a BMI of over 24 is accounted for by the independent variable, age.
Table 2: Age Distribution
Age
N
Population (2-16 Yrs)
Obese
%
2
4,378,124
11,061
0.3%
3
3,638,527
14,942
0.4%
4
4,651,961
31,844
0.7%
5
3,803,359
119,014
3.1%
6
3,531,059
127,705
3.6%
7
4,145,240
134,416
3.2%
8
4,280,578
330,820
7.7%
9
3,542,921
469,770
13.3%
10
3,797,123
690,981
18.2%
11
4,507,460
897,945
19.9%
12
3,976,810
1,086,447
27.3%
13
3,528,404
999,350
28.3%
14
4,930,447
1,545,393
31.3%
15
4,417,630
1,669,927
37.8%
16
4,748,544
1,532,567
32.3%
61,878,188
9,662,180
15.6%
Total
Table 3
Regression Statistics
(AGE & CHO)
Multiple R
0.960
R Square
0.921
Adjusted R Square
0.915
Standard Error
176303.9
Observations
15
27
Figure 8: Scatterplot of Age Distribution in Sample
Race and Childhood Obesity
A race distribution of the sample was also completed. The sample set reflects the
demographics of the United States. Table 4 displays the distribution of race of our raw sample
and our extracted obese (BMI over 24) sample. The sample contains 57.7% Caucasian, 14.9%
African American, 13.8% Mexican American, 7.0% other & multi race, and 6.5% other Hispanics.
Table 4: Race and Childhood Obesity Distribution
Population (2-16 Yrs)
Race
Mexican American
Other Hispanic
N
8,531,069
%
Obese
% (obese/N)
13.8%
1,476,863
17.3%
4,048,923
6.5%
745,552
18.4%
White
35,728,953
57.7%
5,366,030
15.0%
Black
9,212,045
14.9%
1,686,568
18.3%
Other & Multi Races
4,357,197
7.0%
387,167
8.9%
61,878,188
100.0%
9,662,180
15.6%
Total
28
From this a sample of children with a BMI of over 24 was extracted to compare to the total of
all the children in the NHANES sample. 15.6% of children ages 2 to 16 were classified with a
BMI of 24 and higher. 18.4% of the total other Hispanic group was considered to be obese.
Followed closely by African Americans, which had 18.3% of their children’s population
classified as overweight or obese. 17.3% of Mexican American children are overweight or
obese. Caucasians came in with 15% of the children are overweight or obese. The last category
is other and multi race people that would include populations such as Asians. This group had
8.9% classified in the childhood obesity range.
A regression was done in SPSS on racial groups. Table 5 shows that adjusted R2=.010.
This indicates that one percent of the variance in childhood obesity is accounted for by the
independent variables on race. According to Standardized Coefficients Beta scores in table 6,
the most important group to focus on is African American group (Beta=.21, p=.000). Mexican
Americans are the next most important (Beta=.10, p=.000), followed by Hispanic group
(Beta=0.052, p=.000) and lastly, Caucasians (Beta=.013, p=.000).
Table 5: Model Summary
Model
dimension0
1
R
R Square
.102a
Adjusted R Square
.010
Std. Error of the Estimate
.010
4.97228
a. Predictors: (Constant), race_dum_black, race_dum_othhisp, race_dum_mex, race_dum_white
29
Table 6: Race Regression
Coefficientsa
Model
Standardized
Unstandardized Coefficients
B
1
(Constant)
White
Mexican American
Other Hispanic
Black
Std. Error
27.859
.007
.113
.007
1.175
Coefficients
Beta
t
Sig.
4162.668
.000
.013
16.367
.000
.008
.100
156.278
.000
.838
.008
.052
101.300
.000
2.371
.007
.210
318.668
.000
a. Dependent Variable: BMI over 24
Figure 9: Race and % Childhood Obesity
Regression on Income and BMI (NHANES Variables)
Regression analysis was completed on NHANES variables of income that was recoded as
mentioned in the methodology section and BMI of over 24 (childhood obesity). Table 7 reports
30
R2=.007 with a beta of -.081. This indicates a weak negative relationship that lower income has
higher cases of childhood obesity. However, it is still statistically significant (p=.000).
Table 7: Income & Childhood Obesity (NHANES)
Model Summary
Model
dimension0
a.
1
R
R Square Adjusted R Square Std. Error of the Estimate
.081a
.007
.007
4.28171
Predictors: (Constant), Household Income Recoded
Food Scarcity: Household not able to afford balance meals
The final regression was computed on food scarcity and its impacts on Childhood
Obesity. Table 8 reports a R2 value of .000. This indicates no relationship between the
variables higher BMI and food scarcity issues.
Table 8: Food Security & BMI - Model Summary
Model
dimension0
1
R
.021a
R Square
Adjusted R Square
.000
.000
Std. Error of the Estimate
4.25423
a. Predictors: (Constant), Food Security (HH could not afford balanced meal)
Regression on All Hypothesized Variables within NHANES Data
Variables on race, income and food security were run in a multiple regression against
childhood obesity to compare which variables are the most important. Table 9 illustrates the
adjusted R2=.046, indicating that 4.6% of the variance in Childhood obesity is accounted for by
the independent variables included in this regression. Table 10 shows the beta levels which
determine which variables is the most essential, with the highest value being most important.
31
Most important is looking at the African American race (Beta=.220, p=.000). Next in
importance is Mexican American race (Beta=.101, p=.000). Followed in importance are
Household Income with a negative correlation (Beta=-.083, p=.000). Thereafter, Other
Hispanics (Beta=.057, p=.000), Food Scarcity (Beta=.050, p=.000), and lastly Caucasian
(Beta=.036, p=.000) came in order of importance.
Table 9. Model Summary
Model
dimension0
R
R Square
.215a
1
Adjusted R Square
.046
Std. Error of the Estimate
.046
4.19510
a. Predictors: (Constant), Food Scarcity recoded never true and sometimes/often true, race_dum_othhisp,
race_dum_mex, race_dum_black, Household Income Recoded, race_dum_white
Table 10. Regression Coefficientsa
Model
Standardized
Unstandardized Coefficients
B
1
(Constant)
Std. Error
27.483
.010
1.225
.008
Other Hispanics
.943
White
Coefficients
Beta
t
Sig.
2854.454
.000
.102
153.101
.000
.009
.058
106.880
.000
.311
.007
.036
42.009
.000
Black
2.540
.008
.222
320.931
.000
Household Income
-.110
.000
-.083
-233.082
.000
.625
.004
.055
156.700
.000
Mexican American
Food Scarcity
a. Dependent Variable: BMI over 24
32
Population of Obesity, Poverty Status, and Low Education (High School Diploma or Less)
Data on education and poverty status was collected from the Census website. This data
was integrated into the block group levels with the childhood obesity spatial model. The
education was regrouped to show percent of people that had high school diploma or less (low
education), and poverty was also converted into a percentage to normalized for size of block
group. This data was then analyzed in SPSS. Correlation and regression of these variables were
computed. Pearson Correlation was used to compare the percentage of low education and
poverty status with childhood obesity (table 10). Both test had a p-value = 0.000. This indicates
both variables are statistically significant and correlate with each other.
Table 11: Pearson's Correlation (Census data by Block Group)
Correlations: Obesity, Poverty Level, and Education
Percent of
Percent
Population
Population with
Overweight and
High School
Obese (BMI
Percent
Diploma (and
over 24) 2-16
Population Under
Equivalent) or
yrs of age
Poverty Level
less
.433**
.634**
.000
.000
4746
4746
4746
.433**
1
.508**
Percent of Population
Pearson Correlation
1
Overweight and Obese (BMI
Sig. (2-tailed)
over 24) 2-16 yrs of age
N
Percent Population Under
Pearson Correlation
Poverty Level
Sig. (2-tailed)
.000
N
4746
4746
4746
Percent Population with
Pearson Correlation
.634**
.508**
1
High School Diploma (and
Sig. (2-tailed)
.000
.000
Equivalent) or less
N
4746
4746
.000
4746
**. Correlation is significant at the 0.01 level (2-tailed).
33
Regressions and scatterplots were computed for each relationship. Poverty status and
childhood obesity has a R-squared value of .188 (table 12). The scatterplot (graph 3) and best
fit linear line shows a slight positive correlation. Beta equals .433, with a statistical significance
of .000 (table 13).
Figure 10: Scatterplot of Poverty Level and Childhood Obesity
Table 12: Regression Poverty Status and Childhood Obesity
Model Summary
Model
dimension0
R
1
.433a
R Square
Adjusted R Square
.188
Std. Error of the Estimate
.188
3.803
a. Predictors: (Constant), Percent Population Under Poverty Level
34
Table 13: Poverty Status and Childhood Obesity
Coefficientsa
Model
Unstandardized
Standardized
Coefficients
Coefficients
B
1 (Constant)
Std. Error
13.080
.074
.124
.004
Percent Population Under Poverty
Beta
t
Sig.
176.892 .000
.433
33.107 .000
Level
a. Dependent Variable: Percent of Population Overweight and Obese (BMI over 24) 2-16 yrs of age
Low education and childhood obesity has a R-squared value of .402 (table 12). The
scatterplot (graph 4) and best fit linear line shows a positive correlation. Beta equals .634, with
a statistical significance of .000 (table 13).
Figure 11: Low Education and Childhood Obesity
35
Table 14: Regression Low Education and Childhood Obesity
Model Summary
Model
dimension0
1
R
R Square
.634a
Adjusted R Square
.402
Std. Error of the Estimate
.402
3.263
a. Predictors: (Constant), Percent Population with High School Diploma (and Equivalent) or less
Table 15:
Coefficientsa
Model
Unstandardized
Standardized
Coefficients
Coefficients
B
1 (Constant)
Percent Population with High School Diploma
Std. Error
Beta
t
Sig.
8.922
.113
78.980 .000
.121
.002
.634 56.431 .000
(and Equivalent) or less
a. Dependent Variable: Percent of Population Overweight and Obese (BMI over 24) 2-16 yrs of age
Childhood Obesity Vulnerability Index Analysis (COVI)
These variables were combined to create a Childhood Obesity Vulnerability Index (COVI)
as explained in the methodology (map 2). This map indentifies the most-at risk locations in the
Greater Chicagoland area. They are categorized from low to high vulnerability. Dark blue
indicates a low concentration of the three variables combined where dark red indicates high
concentrations. Getis Ord Gi*, a hot spot analysis tool was also computed with the COVI map.
This shows spatial clustering of vulnerability. Dark red indicates hot values (hot spots) and dark
blue indicates low values (cold spots).
36
Map 3
37
Map 4
38
DISCUSSION
After the analysis of the NHANES data and Census data, there were several main issues
to focus on. We reject the null hypothesis that states that socio-demographics and socioeconomic factors do not increase the rate of childhood obesity. The graphs and maps show a
great impact of the variables used on childhood obesity. All variables were statistically
significant.
When age was examined, there is a noted increase in BMI as they got older. This is in
agreement with the BMI chart. However, there were some very young children that had high
BMI’s of over 24. Although they made a small percentage, there are still 11,061 2 year olds that
have a BMI of over 24.
It was hypothesized that race would have an impact on the numbers of children that are
obese or overweight. From the analysis we see a correlation that would indicate minority races
are an important variable when studying childhood obesity. Focus is on the African American
and Hispanic populations due to the higher correlation for childhood obesity compared to other
races. We fail to reject the sub-hypothesis (A) that minority race (specifically Hispanics and
African Americans) is associated with higher childhood obesity compared to non-minority race.
Although there was a higher percentages in the “other Hispanic” group, when the multiple
regression was ran it showed that African American and Hispanic group was most important
when examining race and high BMI.
Sub-hypothesis B predicted that low income was associated with higher rates of
childhood obesity. This variable was examined in two ways, through raw data on the national
39
level and on a spatial analysis using census data to compare the study area. After examining
the NHANES data of income and BMI, there was a very little correlation and relationship
between the variables (R2=.007). However, when comparing income of a block group and the
NHANES modeled childhood obesity data, there was a higher correlation (R2=.188). This
suggests that income characteristic of a location may have an impact on childhood obesity.
Sub-hypothesis C stated that households that are unable to afford a balanced meal is
associated with higher rates of childhood obesity. After analyzing the data, it seems to have a
very weak correlation. However, even though it was weak it was still statistically significant.
Since there was a negative but very weak correlation it would indicate that respondents with
higher BMI’s answered either often true or sometimes true. The conclusion is to reject the
sub-hypothesis C. There is not a significant correlation between food security and childhood
obesity. Due to incomplete data acquisition for this variable it could be theorized that this
variable did not correlate because of how the question was answered. In addition to there
being several versions of this question, it was asked as a negative which may have confused
some respondents. Food stamps may also be a benefitting contributor as well. Since lowerincome families are able to obtain food stamps they would be more likely to not answer often
true to the question.
Hypothesis D stated that lower levels of education are associated with higher rates of
childhood obesity. Since the education levels were taken from Census data it was joined
spatially to the block groups. The regression showed that education and childhood obesity
were correlated (R2=.402, p=.000). The data is interpreted if the block group has a high
40
percentage of people that have only a high school diploma or less they are more likely to be
living in an area with high childhood obesity.
Childhood obesity is highly clustered. It falls in certain patterns around major cities.
This pattern has been observed by sociologist, such as Robert E.L. Faris and Warren Dunham,
mentioned earlier. The Childhood Obesity Vulnerability Index (COVI) shows the pattern and
areas that are most greatly affected by childhood obesity, poverty, and low education
experience. Faris’s and Dunham’s theory depicted natural areas as circular zones. He used
crime to describe the areas that he designated. Crime was concentrated in the central part of
the city and eventually got better further away from the city center. This is the pattern that
childhood obesity is showing. There is a significant area around Chicago and Waukegan that
reflects this same idea. Not only does location affect criminal activity but it is also affecting the
physical body and health of the people and most importantly the children. Although their
theory may be dated, the idea and concepts are valid to understand the clustering of childhood
obesity and some socioeconomic variables.
Future Research
Childhood Obesity is an important topic which may have long-term impacts on our
society. This study only measures and examines a small amount of information. There are
many other variables that could be further studied to understand all the underlying issues of
childhood obesity and how socioeconomic status is correlated and related.
In addition to what this study examined, crime and sedentary activity would be a great
addition to this analysis. Also, further understanding where food deserts are located and fast
41
food locations are to study the relationship to access healthy and harmful foods and childhood
obesity. The use of 2010 Census data would be interesting to utilize as well, it was not available
at the time of the study, however a more comprehensive and complete version will be available
later in 2010. A linear study of history verses present childhood obesity would be a great way
to understand that changes in patterns of social and physical behavior of the people and
children.
In addition to using different methods and variables, a further study on the Chicago area
and looking at other economic issues would be viable. According to William Julius Wilson, his
theory that was discussed earlier is less about race but more about class. It would be
interesting to incorporate his ideas about class and how inner city segregation might be
affecting health, by examining the economy and racial profile of the area, housing values, and
other characteristics of the neighborhoods in Chicago or other metropolitan areas.
Conclusion
After the results of our analysis, we can conclude that some socioeconomic variables
play a role to affect the outcomes of childhood obesity. Out of the variables that were used in
this study, low education was the strongest predictor of childhood obesity. It was then
followed by race, and income. Childhood obesity and vulnerability is cluster around large
metropolitan cities, and from our study mainly Chicago and Waukegan are the most affect. This
would follow social disorganization theory which states that city centers bring more negative
and unwanted results, such as crime and childhood obesity.
42
ACKNOWLEDGEMENTS
I would like to extend my sincerest thank you to my advisor and mentor from the
Geography department , Professor Devonee Harshburger for guiding my and providing me with
an abundance of information and knowledge. Thank you for all the assistance with modeling
out and interpreting the NHANES data. Your comments and insight was greatly appreciated.
Your GIS skills are phenomenal and it was such an honor having you as an advisor, since it’s not
even part of your job description. Thank you to you and your family for inviting me to your
home to work on my thesis.
I would like to extend my greatest gratitude to Dr. Wayne Thompson, who was my
thesis advisor from the Sociology department. Thank you for sitting with me and working my
data and SPSS and providing me with useful insights. Thank you for lending me some of your
most treasured books. Your support throughout the last few years have been amazing and I
can never thank you enough for all the assistance and knowledge you have taught me.
In addition to my thesis advisors, I would like to thank my professors from Senior
Seminar, Dr. Joy Mast for Geography and Earth Science Seminar and Dr. Stephen Lyng. Thank
you for all the guidance and information. Your positive critiques and support has been greatly
appreciated.
I would also like to thank Dr. Julio Rivera for all the support through the last few years.
Thank you for getting me initially interested in Geography. I can’t remember the last time I
thought of an issue without asking “Where?”.
43
Lastly, I would like to thank my family and friends for the constant support and
encouragement. Thank you for keeping me sane and happy though the process.
44
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