Masters Project Report
On
Analyzing Environmental Justice in
the Dallas/Forth Worth Area
Advisor: Dr. Ronald Briggs
Acknowledgement: Dr. Michael Tiefelsdorf
Course: Masters Project (GISC 6389)
Prepared by
Rumana Reaz Arifin
Fall 2007
Table of Contents
1. Introduction
4
2. Project Objective
5
3. Literature Review
6
3.1 General Studies of Environmental
3.2 Methodological Issues
Justice
3.2.1 Variable Selection
3.2.2 Classification of Pollutant Zones
3.2.3 Analysis in Different Geographic Unit
6
8
8
9
10
4. Project Hypotheses
11
5. Data and Data Sources
12
5.1 Spatial Data
5.2 Attribute Data
6. Analysis and Methodology
6.1 Classifying Zones for Pollutant Exposure
6.1.1 Standard Deviational Elliposoidal Distribution of Pollutant Sites
6.1.2 Classification of Pollutant Zones
6.1.2.1 Spatial Join
6.1.2.2 Hot Spot Analysis
6.1.2.3 Results of Classifying Pollutant Zones
6.2 Analysis of Socio-Economic Variables
6.2.1
6.2.2
6.2.3
6.2.4
Analysis of Variance (ANOVA)
Kruskal-Wallis Chi-Squared Test
Pearson’s Product Moment Correlation Coefficient
Multinomial Regression
7. Results and Discussion
7.1 Results of Graphical Exploration
7.1.1
7.1.2
7.1.3
7.1.4
7.1.5
7.1.6
Population of Racial Group as % of Total Population
Percentage of Geographic Unite where Minority Ratio > 1
Differences in Income Level
Percentage of Population Below and Above Poverty Level
Percentage of Population at Different Education Level
Summary of Graphical Exploration
7.2 Results of Statistical Analysis
7.2.1 Results of ANOVA and Kruskal-Wallis Chi-Squared Test
7.2.2 Results of Correlation Matrix
7.2.3 Results of Multinomial Regression
8. Conclusion
8.1 Future Work
9. References
12
13
14
15
16
17
17
18
22
22
23
23
24
24
25
25
25
26
27
28
29
30
30
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34
38
40
41
41
2
List of Figures and Tables
Figures
1. Standard Deviational Ellipsoidal Distribution of Pollutant Sites at Census
Tract Level
2. Standard Deviational Ellipsoidal Distribution of Pollutant Sites at Zipcode
Level
3. Clean Tracts
4. Result of Hot Spot Analysis
5. Zoomed In View of Hot Spot Analysis Overlaid with Kernel Density
6. Kernel Density Analysis
7. Classified Pollutant Zones at Census Tract Level
8. Classified Pollutant Zones at Zipcode Level
9. Racial Comparison at Census Tract
10.Racial Comparison at Zipcode
11.Minority Ratio Comparison at Census Tract
12.Minority Ratio Comparison at Zipcode
13.Comparison of Income Level at Census Tract
14.Comparison of Income Level at Zipcode
15.Comparison of Poverty Level at Census Tract
16.Comparison of Poverty Level at Zipcode
17.Comparison of Education Level Attained at Census Tract
18.Comparison of Education Level Attained at Zipcode
16
16
18
20
20
20
21
21
25
26
26
27
27
27
28
28
29
29
Tables
1. Some Selected Studies of Racial and Income Disparities in the
distribution of Environmental Hazards (1967-1993).
2. Result of ANOVA at Census Tract
3. Result of Kruskal-Wallis Chi-Squared Test at Census Tract
4. Result of ANOVA at Zipcode
5. Result of Kruskal-Wallis Chi-Squared Test at Zipcode
6. Pearson’s Product Moment Correlation Matrix for Census Tract
7. Pearson’s Product Moment Correlation Matrix for Zipcode
8. Summary of Multinomial Regression Result
6
30
31
32
33
34
36
39
3
1. Introduction
The issue of equal and fair distribution of pollution hazards and their impact
on
surroundings
is
a
well
exercised
topic
of
debate.
Basically,
when
the
disproportionate impact of environmental hazards relates with people of color and
economically –disadvantaged groups, it leads to the formation of environmental
justice issue. Environmental justice in general can be depicted as the right of having
equal justice for any environmental phenomena without discrimination based on race
and socioeconomic status. A book definition can be like this, “pursuit of equal justice
and equal protection under the law for all environmental statutes and regulations
without discrimination based on race, ethnicity, and /or socioeconomic status. This
concept applies to governmental actions at all levels—local, state and federal—as
well as private industry activities.” (Frokenbrok and Sheeley 2004). But the reality is
different from what is expected. It has been historically observed that minority group
populations (for example, populations of specific races such as African Americans,
Hispanics, etc, and/or economically disadvantaged groups) live in closer proximity to
pollutant sites compared to the Anglo population and/or populations with higher
income levels. Hence it can be said that environmental justice is violated in the
proximity of pollutant sites. There are a number of examples supporting this burning
issue. A study based on modeling total air toxic concentration in 44 large
metropolitan areas reveals that, in large metropolitan area of USA, blacks are more
likely than whites to be living in census tracts with higher total modeled air toxic
concentrations (Lopez 2002). Location of pollutant sites plays a key role in assessing
the environmental justice issue as it has been seen that the people of color and/or
low income group tend to live near the toxic sites or in other words, toxic sites are
located where people of specific race and income groups are residing. This is again a
issue of debate that whether the fact is true or not but a general assumption is like
4
that, owner of pollutant sources selects such location for the pollutant site where
there are more minority groups as they will not go for pushing that site elsewhere.
Mohai and Bryant in a study suggest that, the minority population is subjected to
disproportionate pollution exposure because of their lesser ability to defend
themselves due to poverty and political powerlessness (Mohai and Bryant 1992). So
anyhow, there is a connection between the location of pollutant sites and people
living near it and in most cases those people are found to be people of color and/or
of low income group. Here, GIS plays a dominant role in assessing the pollution
exposure in surrounding population. To asses the environmental justice issue it is
critical to determine the location of pollutant sites which are hazard prone and also
the population group living in the proximity of hazard prone area needs to be
identified correctly. So, classifying pollutant site locations is a major issue and where
GIS can play a vital role. There are several indexes of determining nearness to
location of pollutant sites like zoning and proximity indices (Huang and Batterman
2000). However, this study has taken approach to asses the pollution exposure
based on new method called Hot spot analysis which will be discussed in detail later
on.
2. Project Objective
Based on the discussion above my research objective is set to analyze the
environmental justice issue in the five counties of the Dallas/Forth Worth region to
determine if people in
specific racial and socio-economic groups are differentially
exposed to environmental pollution. This region has a large metropolitan area also
with sub-urban and rural area. There are a number of pollutant site locations in this
region and also a good number of people of color living in this region. Hence, my
research will try to determine if there exists any relation between them. As
5
mentioned above the project will show a new method for classifying pollutant sites
and after the literature review more specific hypotheses will have been set to
accomplish the project objective.
3. Literature Review
As the environmental justice is a well-exercised issue, extensive literature exists
on this topic. Hence, the literature review here will mainly focus on two major
aspects:
1. General studies of environmental justice
2. Methodological issues
The relevant discussions from different articles on the topics stated above are
reviewed as follow:
3.1 General studies of Environmental Justice
Historically environmental justice issues are related to location of pollutant zones and
pollution exposure. Many researches have found a connection between poverty,
minority racial status and exposure to environmental toxicity. Below a chronological
review of a number of literatures (1967-1993) addressing pollution in correlation
with race and income is shown (White 1998):
Table1: Some Selected Studies of Racial and Income Disparities in the Distribution
of Environmental Hazards (1967—1993).
Year
Author
1967
1972
Hoffman et.al.
Freeman
1977
Berry et al.
1978
1983
Asch and Seneca
U.S. GAO
1987
UCC and PDA
Type
of
Hazards
Pesticides
Air pollution
Pollution/pes
ticides, etc.
Air pollution
Hazardous
waste
Hazardous
Geographic
Focus
Chicago, Ill.
Kansas City/
St. Louis/D.C.
Urban areas
Disparity
Race
Income
Yes
Yes
Yes
Yes
Yes
Urban areas
Southeast
Yes
Yes
Yes
National
Yes
Yes
6
1989
Belliveau et al.
1991
1992
Conner
Thornton
Goldman
1992
Mohai and Bryant
1993
Bowen et al.
1993
Hamilton
1993
Zimmerman
and
waste
Toxic
releases
Hazardous
waste
Toxic
air/waste
Hazardous
waste Toxic
Toxic
releases
Hazardous
waste siting
Hazardous
waste
Richmond,
Calif.
National
Yes
Yes
Yes
Yes
National
Yes
No
Detroit,
Michigan
Ohio
Yes
Yes
Yes
No
National
Yes
Yes
National
Yes
No
Source: Derived from Benjamin A. Goldman, Not Just Prosperity: Achieving Sustainability with
Environmental Justice (Washington, D.C.: National Wildlife Federation, 1993).
The review shows that most of the research have a found a correlation between
pollution exposure and race. So, comprehensively race is dominant factor in
analyzing environmental justice issue. Berry et al.(1977) and Asch and Senneca
(1978) conducted study for urban areas all over USA and found both race and
income as dominant factor. The first national level study conducted by UCC and PDA
in 1987 found disparity for both race and income. This study reveals that 60% of
African-Americans live in communities with one or more abandoned toxic waste sites
(Hockman and Morris 1998). However another national level study conducted by
Goldman in 1992 found the correlation for race but not for income. Similarly
Hamilton (!993) found adverse effect for both race and income whereas in the same
year Zimmerman (1993) found evidence only for race. Results are subject to
variation as different methodology in assessing pollution exposure has been used.
However, some recent studies continue to show the evidence for the violation of
environmental justice in hazard prone areas. A study conducted in Michigan in 1998
found that, minority group in terms of race and low income are more exposed to
environmental pollution. African American population is more exposed compared to
White population. Racial discrimination shows stronger association compared to
7
income (Hockman and Morris 1998). In Southern California Morello-Frosch et al. in
2002found that census tracts with a pollutant site or within 1 mile distance of a
pollutant site have significantly higher percentage of residents of color (particularly
Hispanic), lower per capita and household income. The results remained consistent
even when the percentages of African American and Hispanic residents were
evaluated as separate groupings (Morello-Frosch et al. 2002). A recent national level
study by Mohai and Saha reveals that over 40% of population living within 1 mile of
hazardous waste TSDFs (TRI facilities and treatment, storage and disposal facilities)
were people of color (using distance-based method) (Mohai and Saha 2007). Hence,
the evidence for racial discrimination continues up to present time and sometimes
income disparity is also evident. Hence, this research will do the analysis to
determine the racial and socio-economical discrimination in D/FW area.
3.2. Methodological Issues
Several methodological issues have been reviewed from previous articles to get the
proper guidance about the analysis. The prime issues are discussed below:
1. Variable selection
2. Classification of Pollutant Zones
3. Analysis in different Geographic units.
3.2.1. Variable Selection
To do the analysis selection of variable is an important criterion. The result depends
much on which and how much relevant variables are analyzed. From the review of
general studies on environmental justice it is evident demographic variables of race
are major variables for analysis. Along with that socio-economic variables for
determining income disparity and other social status are also analyzed. Variables for
measure of education is also an important issue as it sometimes assumed that
8
people who are living near the pollutant sites are less solvent and being less solvent
not reaching towards higher education level and so becoming less conscious about
the adverse affect of pollution exposure. The studies conducted upon environmental
justice usually analyze different socio-economic variables. Examples from the
literature review are as follow: (Bowen et al. 1995) and (Clutter et al. 1995).

Population density

Minority (Black and/or Hispanic) proportion of the total population

Minority Ratio (Black/White and/or Hispanic/White)

Median household income

Poverty Status

Median value of owner-occupied housing

Education level attained


% of at least High School level education

% of College Degree
% of Employment and Unemployment
Based on this review the socio-economic variables for my project have been
selected.
3.2.2 Classification of Pollutant Zones
As mentioned earlier, assessing pollution exposure is a major concern and results
vary depending on the methodology adopted for classifying pollutant sites for
different exposure level. Several methods are used for classifying the pollutant sites
based on their location. However two common indicators are Zonal and Proximity
indicators (Huang and Batterman 2002).

Zonal indicators: which classify locations using ‘zones’ with the assumption
that individuals within each zone will have the same exposure level.
9

Proximity indicators: which determine the distance from the pollutant source
with the assumption that exposure to pollution increases as the distance
decreases.
These two approaches may be combined for classifying the pollutant zones. For
example, the population in a polluted area may be identified by proximity to sources
and subsequently divided in to subgroups using zones. Alternatively high pollution
areas, may be identified by the location of pollution sources and subgroups within
this areas demarcated by distance to the source. Considering the location of a
pollutant site, zoning can be done based on the count and adjacency of site
locations.
So, considering the location of a pollutant site, zoning can be done based on the
count and proximity of site locations. A study conducted by Bowen et al. had taken
an approach to classify the census tracts of Cleveland, Ohio combining zonal and
proximity indicators (Bowen et al. 1995)

Clean Tract: No TRI site.

Potentially Exposed Tract: In proximity of a TRI site.

Dirty Tract: Have one or more sites in the tract
In my study, I adopted similar approach and identified pollutant zones based on site
density and distance to pollutant sites using Hot Spot analysis.
3.2.3 Analysis in Different Geographic Unit
Another major reason for varying result of different studies of environmental justice
is selected geographic unit for analysis. In general it is assumed that the smaller the
area coverage, the more variability is induced and results are more dependable. For
larger geographic area, variability decreases due to averaging of variables values.
But after reviewing the literature, it is found that there is no anonymous choice
about geographic unit. Different researchers have used different geographic units
10
and had spoken in favor of it. Arguments have been advanced in favor of both
Zipcodes and Census tracts. Clutter, Holm and Clarke suggest Census tracts and
Block groups may provide better approximation of neighborhood because of their
smaller aerial coverage, compared to Zipcodes (Clutter et al. 1995). However,
Hockman and Morris argue that, neighboring Census tracts are too similar in socioeconomic characteristics and suggest Zip codes as a good compromise between
Counties and Census tracts (Hockman and Morris 1998).
Hence, my approach is to do the analysis for both geographic units and compare
results.
4. Project Hypotheses
Based on the literature review, the following hypotheses are made, to specify the
project objective more clearly:

Hispanic and African American populations will be positively correlated with
high pollution exposure.

Low income groups will be positively correlated with high pollution exposure.

Lower education levels will be positively correlated with high pollution
exposure.

Minority (racial) population will have a positive correlation with lower income
and lower education level.

Results for Census tract should produce stronger association than those of
Zipcode because, as the geographic unit gets larger, less variability is induced
due to averaging of variables.
Hence, the aim of the analysis will be to determine how far the assumed hypotheses
can be proved.
11
5. Data and Data Sources
5.1. Spatial Data
Counties: The project is carried out at five counties in Dallas/Forth Worth region.
The counties are:

Dallas

Denton

Collin

Rockwall

Tarrant
Total Census tract count within this region: 945
Total Zipcode tabulation area count within this region: 225
As mentioned earlier, the project is carried out at both Census Tract and Zipcode
level.
Pollutant Sites: Five types of Pollutant sites are included for analysis. They are as
follow:
1. TRI Sites
Tri sites are all Toxic Release Inventory sites in Texas. TRI contains information
about more than 650 toxic chemicals that are being used, manufactured, treated,
transported, or released into the environment. The locations of such activities are
marked as TRI sites.
2. Solid Waste Sites
Registered landfills and associated municipal solid waste (MSW) facilities for
Texas. The dataset contains both closed and open landfills.
3. Water Pollution Sites
12
The
waprt
pollution
sites
include
all
permitted
municipal
and
industrial
wastewater quality outfalls.
4. Hazardous Sites
These are all permitted industrial and hazardous waste locations in Texas.
Facilities which store, process, or dispose of hazardous waste are listed as
hazardous sites
5.
Superfund Sites
These are all superfund clean-up sites in Texas.
Total 999 sites in Census tract area
Total 1078 sites in Zipcode tabulation area
The reason for varying number of sites for Census tract and Zipcode as Zipcode
tabulation areas in some cases extend slightly beyond County boundaries.
Data Source

NCTCOG: http://www.dfwmaps.com/clearinghouse/

Texas Commission on Environmental Quality:
http://www.tceq.state.tx.us/gis/sites.html

Environmental Protection Agency :
http://www.epa.gov/enviro/html/tris/ez.html
5.2 Attribute Data
Socio-Economic Variables: Based on the literature review the following socioeconomic variables are selected for analysis of environmental justice issues for race,
income and education level.
 Measures for Minority Population groups:

White

African American

Hispanic
13

Others (Asian, Alaskan Native, Pacific Islander)

Percentage of total population

Minority Ratio (AfAm/White & Hispanic/White)
 Measures for Income level

Median Household Income

Per Capita Income
 Measures for Poverty Status

% of population above or below poverty level
 Measures for Education Level Attained

% of at least High School level education

% of College graduate
Data Source

NCTCOG (Census 2000) http://www.nctcog.org/ris/census/download.asp
6. Analysis and Methodology
The analysis is carried out to determine the correlation between environmental
justice issues and pollution exposure at both Census tract and Zipcode level. The
analysis is done with the aim to see how far the results go with the hypotheses and
where and how it deviates from assumed hypothese. The analysis is focused on two
major sections:
1. Classifying Zones for Pollutant Exposure: Geographic area is classified into
three pollution exposure zones based on density and proximity of pollutant sites. The
process involves:

Spatial Join

Hot Spot Analysis
14
Difference with others’ work is the use of GIS based Hot Spot analysis to classify
pollutant zone. So far, this method has not been used for classifying pollutant zones
or pollution exposure. In most previous works, GIS is used only for mapping purpose
for showing results. Hence this project has taken the initiative to use GIS more
effectively for classifying pollutant zones by using hot spot analysis. The applicability
and the reason for using this method will be described later on.
2. Analyzing Socio-Economic Variables: The socio-economic variables are
analyzed for three classified pollutant zone to determine the formulated hypotheses
go with the real world or not. The process involves:

Graphical Exploration

Statistical Analysis
Each section is described in detail in following part.
6.1. Classifying Zones for Pollutant Exposure
Before doing the classification, the following steps needed to be done:

All data are stored in geodatabase in NAD 83 State Plane Texas North Central
FIPS 4202 Feet.

TRI sites geo-coded using Business Analyst extension as data were in csv file
format.

Standard Deviational Ellipse for each site is created to show the distribution of
pollutant sites and to get a general idea about the concentrated zone.

Kernel density for all pollutant sites are determined to show concentrated
zone of pollutant sites. This is done as a check for the result of Hot Spot
analysis.
The distribution of pollutant sites and classification of pollutant zones is described
in following sub-sections:
15
6.1.1. Standard Deviational Ellipsoidal Distribution of Pollutant Sites
Figure 1: Standard Deviational Ellipsoidal Distribution of Pollutant Sites at Census Tract Level
Figure 2: Standard Deviational Ellipsoidal Distribution of Pollutant Sites at Zipcode Level
16
The maps give a general idea about the concentrated area of pollutant sites where
the ellipsoidal distribution of each type of site is overlaid one upon another. At
Census tract level Superfund sites and Water pollution sites have a more dispersed
distribution than the others. At Zipcode level Superfund sites, Water pollution sites
and Hazard sites have more dispersed distribution than the others. The directional
distribution of sites’ are similar at Census tract and Zipcode levels except for
hazardous sites and solid waste sites. For these two types distributions are different
probably due to slightly varying number of pollutant sites at Census tract and
Zipcode level.
6.1.2. Classification of Pollutant Zones
To compare exposure among different population groups, geographic areas (Census
tract and Zipcode) are classified into three pollution exposure zones: (from low to
high exposure)

Clean Zone

Exposed Zone

Hazard Zone
The process outlined here is for Census tracts. A similar methodology was adopted
for Zipcodes.
Clean Tract: No site in it nor within 0.25 mile distance of tract boundary.
Exposed Tract: Low site density or within 0.25 mile of a site location.
Hazard Tract: Have highly clustered site density within.
6.1.2.1. Spatial Join: Clean tracts are determined by spatial join of sites with
Census tract. By spatial join I got:

Count of sites per tract

Distance of closest site to each Census tract
17
So based on the count of sites per tract and the distance from the boundary line of
Census tract to closest site, Clean tracts are obtained as those have no site and nor
within 0.25 mile distance of any site. Figure 3 shows that Clean tracts, in Hot spot
analysis these tracts are excluded.
Figure 3: Clean Tracts
6.1.2.2. Hot Spot Analysis: Hot Spot analysis is carried out at the tracts which are
left after excluding Clean tracts. Hazard and Exposed tracts are classified based on it.

The Hot Spot Analysis calculates the Getis-Ord Gi* statistic for each feature,
which indicates whether features with high values or features with low values
tend to cluster in a study area. It compares the value with the mean value of
all features. If the value of a feature is higher than the mean value than it has
high value and if lower, it has low value.
18

The Gi* statistic is actually a Z score. The larger the positive Z score, the
more intense the clustering of high values. The smaller the negative Z Score,
the more intense the clustering of low values.

If a feature's value is high, and the values for all of it's neighboring features is
also high, it is a part of a hot spot. [1]
So, in Hot Spot analysis any feature cannot be called a hot spot by random chance of
having a high value. If its neighboring features also get a high a value then it will be
a hot spot. So when we are determining pollutant zone for highly clustered site
density, it will take into account the density of a particular tract and will compare it
with the other neighboring tracts. If the neighboring tracts also have similar higher
site density than that particular tract will be a hot spot. This is good for locating
pollutant zone, as in the zoning concept the exposure level should be similar within a
zone. So, when we are getting a hot spot tract, we know that its neighboring tracts
also have similar high density of sites, hence satisfies the zoning concept. Also it is
comparing each tract’s site density with the mean value so, we are getting that
tracts as hot spot which have substantially higher site density than others.

Based on the Hot spot analysis the Hazard tracts are determined which have Gi*
statistic >= 0.0 which indicates highly clustered site density.

Remaining tracts are named as Exposed tracts that have low site density or are
within 0.25 mile of site location.
[1] ArcGIS Desktop help
19
Figure 4: Result of Hot Spot Analysis
Figure 6: Kernel Density
Analysis
Figure 5: Zoomed In View of Hot Spot Analysis Overlaid
With Kernel Density
The maps show the result of Hot spot analysis. The red areas in map are the Hazard
tracts. Kernel Density result is overlaid with the result of Hot spot analysis to check if
the results are coinciding or not. The two layers have almost overlaid for similar
area. Also the standard deviation ellipsoidal distribution in figure 6 is showing the
concentrated pollutant zone.
20
Similarly the Zipcodes were classified. So the final classification of Census tracts and
Zipcodes are shown as below:
Figure 7: Classified Pollutant Zones at Census tract Level
Figure 8: Classified Pollutant Zones at Zipcode Level
21
6.1.2.3. Result of Classifying Pollutant Zones: The count and area percentage
for Census tract and Zipcode after classification is compared below:
Census tract
(Count & Area Percentage)
Clean Tract: 525 (22.0% area)
Exposed Tract: 331 (73.85% area)
Hazard Tract: 89 (4.2% area)
Zipcode Area
(Count & Area Percentage)
Clean Zipcode: 37 (5.6% area)
Exposed Zipcode: 134 (85.43% area)
Hazard Zipcode: 54 (8.97% area)
From the result it can be seen that, Clean Zipcode percentage area (5.6%) is
considerably lower than clean Census tract percentage area (22.0%).This is probably
due to as the Zipcode area unite gets larger it pulls more pollutant site in within
units. As a result the hazard area for Zipcode is higher that that of Census tract.
Hazard Zipcode percentage area (8.97%) is almost twice that of hazard Census
tracts percentage area (4.2%).
These differences may affect the results of analysis of pollution exposure and
indicate the wisdom of using different geographic areas.
6.2 Analysis of Socio-Economic Variables
Socio-economic variables were analyzed by graphical and statistical analysis and
results were compared for three pollutant zones and also between Census tract and
Zipcode. Here, only the methodology for statistical analysis will be discussed. Results
of graphical exploration are shown in results section.
22
Statistical analysis involves:
1. Analysis of Variance (ANOVA)
2. Kruskal-Wallis Chi-squared Test
3. Pearson’s Product Moment Correlation Coefficient
4. Multinomial Regression
The analysis methods are described below:
6.2.1. Analysis of Variance (ANOVA)

Purpose: To do the significance testing on the socio-economic data.

Tests the hypothesis that means for socio-economic variables differ among
the three pollutant zones. This test was done to determine if in reality the
population groups of three pollutant exposure zone differ significantly or not.

Null hypothesis: there is no difference in the group means of three pollutant
zones. H0: µ1= µ2= µ3=…..= µg.
6.2.2. Kruskal-Wallis Chi-squared Test

Purpose: To do the significance test and compare the results with those of
ANOVA. This test is done as in some cases the ANOVA result may not be
accurate as the distribution for all sample populations are not normal and not
of equal variance.

Tests the hypothesis that, mean ranks for socio economic variables differ
among three pollutant zones.

Null hypothesis: there is no difference in the group mean ranks of three
pollutant zones.
Key Difference with ANOVA

Non-parametric test; so does not require normal distribution of population
and equal variance of samples like ANOVA.
23

Observations are converted to rank in the overall dataset, and variances of
ranks among groups are calculated.

So this test is done as a check for ANOVA results.
6.2.3. Pearson’s Product Moment Correlation Coefficient

Purpose: To explore the correlations among socio-economic variables, and
with site density or in other words pollution exposure.

Tests the positive or negative correlation between two variables and gives the
value measuring strength of the correlation.

Null hypothesis: there is no correlation in the population among socio
economic variables, and with site density. H0: ρ= 0.

Significance test: t-test to identify significant correlations.
6.2.4. Multinomial Regression

Purpose: To predict the relationships of pollutant zones with socio-economic
variables.

Key features of Multinomial Regression
o
It is the extension for logistic regression when the dependent variable
is categorical and has more than two groups. The independent
variables can be of any type.
o
Among the categorical groups, one is chosen as reference group, and
other groups are analyzed in comparison with it.

Here, the dependent variable is Pollutant Zone which has three categorical
groups: Clean, Exposed and Hazard. Clean zone is chosen as reference group.

Independent variables are: % of AfAm population, % of Hispanic population,
Median Income and % of High School population. The other variables are
excluded as their variation inflation factor was high so may lead to less
24
accurate regression model. Also to lessen the effect of multicollinearity some
variables are dropped

Significance Test: ANOVA Type II (Likelihood-Ratio Chi-squared Test.)
7. Results and Discussions
The results and discussions can be classified into two sub-sections:
1. Result of Graphical Exploration
2. Result of Statistical Analysis
7.1. Result of Graphical Exploration
Data were graphically explored to compare the socio-economic variables among
three pollutant zone and also between Census tract and Zipcode. The results are
shown and discussed below:
7.1.1 Population of Each Racial Group as Percentage of Total Population
Racial Comparison by Zones
70.00%
60.00%
50.00%
White
40.00%
AfAm
30.00%
Hispanic
20.00%
Other
10.00%
0.00%
Clean Tract
Exposed Tract
Hazard Tract
Figure 9: Racial Comparison at Census tract
Figure 9 shows, at census tract level, % of White population decreases towards
hazard zone whereas % of Hispanic population increases towards hazard zone which
supports hypotheses. But % of AfAm population decreases towards hazard zone
which does not go in hypothesized direction.
25
Racial Comparison by Zones
90.00%
80.00%
70.00%
60.00%
50.00%
40.00%
30.00%
20.00%
10.00%
0.00%
White
AfAm
Hispanic
Other
Clean ZCTA
Exposed ZCTA
Hazard ZCTA
Figure 10: Racial Comparison at Zipcode
Figure 10 shows, % of White population decreases whereas % of Hispanic population
increases towards hazard zone in Census tract which supports hypothese. Also at
Zipcode level % of AfAm population increases towards hazard zone which also
supports hypotheses and differs from the results obtained for Census tract.
7.1.2 Percentage of Geographic Unit where Minority Ratio > 1
% of Tracts for Minority Ratio>1
Minority Ratio Comparison by Zones
40.00%
35.00%
30.00%
25.00%
20.00%
15.00%
AfAm/White
Hisp/White
AfAmORHisp
AfAmANDHisp
10.00%
5.00%
0.00%
Clean Tract
Exposed Tract
Hazard Tract
Figure 11: Minority Ratio Comparison at Census tract
Figure 11 shows, % of tracts for Minority ratio > 1 increases for Hispanic/White and
also for Hispanic/White or AfAm/White. But it decreases for AfAm/White ratio and
also for Hispanic/White and AfAm/White ratio. The results are in harmony with the
previous result for Census tract as we have seen AfAm population decreases towards
hazard zone for Census tract.
26
% of ZCTA for Minority Ratio>1
Minority Ratio Comparison by Zones
35.00%
30.00%
25.00%
AfAm/White
20.00%
Hisp/White
15.00%
AfAmORHisp
10.00%
AfAmANDHisp
5.00%
0.00%
Clean ZCTA
Exposed ZCTA
Hazard ZCTA
Figure 12: Minority Ratio Comparison at Zipcode
Figure 12 shows, % of zipcodes for Minority ratio > 1 increases for both
Hispanic/White and AfAm/White ratio. As seen earlier, at Zipcode level, AfAm
population increases towards hazard zone like Hispanic population group.
7.1.3 Differences in Income Level
Comparison of Income Level by Zones
60000.00
50000.00
40000.00
Median Income
30000.00
PerCapita Income
20000.00
10000.00
0.00
Clean Tract
Exposed Tract
Hazard Tract
Figure 13: Comparison of Income level at Census tract
Comparison of Income Level by Zones
70000.00
60000.00
50000.00
40000.00
Median Income
30000.00
PerCapita Income
20000.00
10000.00
0.00
Clean ZCTA
Exposed ZCTA
Hazard ZCTA
Figure 14: Comparison of Income level at Zipcode
27
Figure 13 and 14 show that both for zipcode and census tract, Median Household
Income and Per Capita Income decreases towards hazard zone which supports
hypotheses that people of low income level live in high pollution exposure zone.
7.1.4 Percentage of Population Below and Above Poverty Level
Comparison of Poverty Level by Zones
120.00%
100.00%
80.00%
Below Poverty
60.00%
Above Poverty
40.00%
20.00%
0.00%
Clean Tract
Exposed Tract
Hazard Tract
Figure 15: Comparison of Poverty level at Census tract
Comparison of Poverty Level by Zones
120.00%
100.00%
80.00%
Below Poverty
60.00%
Above Poverty
40.00%
20.00%
0.00%
Clean ZCTA
Exposed ZCTA
Hazard ZCTA
Figure 16: Comparison of Poverty level at Zipcode
Figure 15 and 16 show that both for zipcode and census tract, % of population below
poverty level increases towards hazard zone which supports hypotheses. It implies
people of low income group are more exposed to pollution exposure hence, income
disparity exists in D/FW area.
28
7.1.5 Percentage of Population at Different Education Level
% of Population at each education
level
Comparison of Education Level Attained by Zones
25.00%
20.00%
15.00%
HighSchool
10.00%
Bachelor
5.00%
0.00%
Clean Tract
Exposed Tract
Hazard Tract
Figure 17: Comparison of Education Level Attained at Census tract
% of Population at each education
level
Comparison of Education Level Attained by Zones
30.00%
25.00%
20.00%
HighSchool
15.00%
Bachelor
10.00%
5.00%
0.00%
Clean ZCTA
Exposed ZCTA
Hazard ZCTA
Figure 18: Comparison of Education level attained at Zipcode
Figure 17 and 18 show that both for zipcode and census tract, % of High School
population increases from clean to hazard zone which contradicts hypotheses
whereas % of Bachelors population decreases towards hazard zone which supports
hypotheses. My assumption was people living in high pollution zone should have
lower level of education. It is clearly evident that people living in high pollution zone
are of lower education level as % of Bachelors population is lower than % of High
school population for both Census tract and Zipcode. But % of High School
population was supposed to be lower for hazard zone compared with clean zone but
it increases towards hazard zone and so it contradicts hypotheses for both Census
tract and Zipcode level.
29
7.1.6 Summary of Graphical Exploration
A summarized review of results graphical exploration is stated below:

% of Hispanic population and Minority ratio Hispanic/White increase from clean to
hazard zone (same for CT and Zipcode) which supports hypotheses.

% of AfAm population and Minority ratio AfAm/White decrease towards hazard
zone for CT but increase for Zipcode which supports hypotheses for zipcode but
not for Census tract.

Median household income and Per Capita income both decrease from clean to
hazard zone (same for CT and Zipcode) which supports hypotheses..

% of population below Poverty level increases for hazard zone compared to clean
and exposed zone (same for CT and Zipcode) which supports hypotheses.

% of High School population increases toward clean to hazard zone (same for CT
and Zipcode) which contradicts hypotheses.

%of Bachelors population decreases towards clean to hazard zone (same for CT
and Zipcode) which supports hypotheses.
7.2 Result of Statistical Analysis
The results obtained by statistical analysis are shown and discussed below:
7.2.1 Result of ANOVA and Kruskal-Wallis Chi-squared Test
Table 2: Result of ANOVA at Census Tract
Group
(Mean)
Clean
%
White
61.68
Exposed 62.9
Hazard
52.44
F Stat.
Prob>F
%
AfAm
%
Hisp
AfAm/
White
Hisp/
White
Median
Income
16.25
17.51
52.42
47.78
547512.12 26690.06 11.16
20.77
21.4
13.73
19.5
47.1
51.02
49836.09 22866.44 12.25
23.15
17.8
13.70
30.03
48.4
81.94
39890.71 19004.3 17.76
22.50
13.11
6.75
1.67
0.0012 0.1900
23.5
0.2015
<0.0001 0.8175
S ig .
**
Not
***
Level
Comment Support Contradicts Support
Hyp.
Hyp.
Hyp.
Not
Per
Capita
%Below %High
Poverty School
%Bachelor
10.45
13.14
13.0
13.32
8.41
<0.0001 <0.0001 <0.0000 <0.0001 0.00024
19.52
<0.0001
***
***
***
Contardict Support Support
Hyp.
Hyp.
Hyp.
***
***
***
Support Support Contradict Support
Hyp.
Hyp.
Hyp.
Hyp.
30
The results show that mean % of White population is significantly different for three
pollutant zone and it decreases towards hazard zones. Similarly % Hispanic
population and Hispanic/White ratio, mean differs significantly and increases towards
hazard zone. For AfAM population and AfAm/White ratio difference in mean is not
significant and does not go in hypothesized direction. Median income and Per Capita
income both decreases towards hazard zone and difference of mean for three
pollutant zones are significant. The results for % of Bachelors and % of High School
population are significant but for High School levels mean is higher for hazard zone
than clean zone which contradicts hypotheses. The results are consistent with
graphical exploration result.
Table 3: Result of Kruskal-Wallis Test at Census Tract
Group
%
(Median) White
%
AfAm
%
Hisp
AfAm/ Hisp/
White White
Median Per
Income Capita
Clean
6.7
12.13
10.4
22.5
46781.5 22153.0 7.75
21.46
20.5
6.07
13.17
9.5
20.6
46043.0 20918.0 8.65
23.55
15.53
6.8
23.97
13.5
62.2
37436.0 16426.0 16.57
21.77
66.45
Exposed 68.14
Hazard
50.94
%Below %High
Poverty School
%Bachelor
10.33
2
Chi
17.48
2.275
41.05
4.54
30.05
25.26
22.56
28.4
16.45
36.4
Prob>
<0.0001 0.3206
<0.0001 0.1032 <0.0001 <0.0001 <0.0000 <0.0001 0.00026 <0.0001
Chi2
S ig .
***
Not
***
Not
***
***
***
***
***
***
Level
Comment Support Contradicts Support Support Support Support Support Support Contradict Support
Hyp.
Hyp.
Hyp.
Hyp.
Hyp.
Hyp.
Hyp.
Hyp.
Hyp.
Hyp.
The results show that mean rank value % of White population is significantly
different for three pollutant zone and it decreases towards hazard zones. % Hispanic
population and Hispanic/White ratio, mean rank value differs significantly and
increases towards hazard zone. For AfAM population and AfAm/White ratio difference
in mean rank value is not significant. For % of AfAm population it does not go in
hypothesized direction but for AfAm/White ratio it goes in hypothesized direction.
Here the result varies from that of ANOVA. Median income and Per Capita income
both decreases towards hazard zone and difference of mean rank value for three
pollutant zones are significant. The results for % of Bachelors and % of High School
31
population are significant but for High School levels mean rank value is higher for
hazard zone than clean zone which contradicts hypotheses. The results are
consistent with ANOVA result except for AfAm/White ratio.
Summary

At Census tract level, results of ANOVA and Kruskal-Wallis test say that,
population groups for three zones are different for all variables except % of
African American population and AfAm/White minority ratio.

At Census tract level, mean % of population increases towards Hazard zone for
African American population, AfAm/White minority ratio and High School
population which contradicts hypotheses.

At Census tract level, mean % of population for White, Hispanic, Hispanic/White
ratio, Bachelors, Median income, Per Capita Income and % of Below Poverty
population goes in hypothesized direction which supports hypotheses.
Table 4: Result of ANOVA at Zipcode
Group
(Mean)
Clean
%
White
78.31
Exposed 70.11
Hazard
45.32
F Stat.
Prob>F
%
AfAm
%
Hisp
AfAm/ Hisp/
White White
Median Per
Income Capita
10.16
11.48
28.93
20.02
57165.65 29112.70 7.52
21.71
24.32
11.11
15.03
26.5
27.95
54500.41 24873.71 8.95
24.34
17.32
20.64
31.7
47.71
69.40
40334.46 19451.75 17.2
23.55
12.07
20.7
6.33
<0.0001 0.0021
S ig .
***
**
Level
Comment Support Support
Hyp.
Hyp.
%Below %High
Poverty School
%Bachelor
30.4
1.44
21.65
13.14
13.0
18.85
1.30
<0.0001 0.2388 <0.0001 <0.0001 <0.0000 <0.0001 0.2746
9.42
0.00012
***
***
Not
***
***
***
***
Not
Support Support Support Support Support Support Contradict Support
Hyp.
Hyp.
Hyp.
Hyp.
Hyp.
Hyp.
s Hyp.
Hyp.
The results show that mean % of White population is significantly different for three
pollutant zone and it decreases towards hazard zones. Similarly % Hispanic
population and Hispanic/White ratio, mean differs significantly and increases towards
hazard zone. At Zipcode level, % of AfAM population and AfAm/White ratio also
increases towards hazard zone which supports hypotheses and the results vary from
those of Census tracts but difference in mean for AfAm/White ratio is not significant
32
Median income and Per Capita income both decreases towards hazard zone and
difference of mean for three pollutant zones are significant. The results for % of
Bachelors are significant and go in hypothesized direction. For % of High School
levels mean is higher for hazard zone than clean zone which contradicts hypotheses
and differences in mean are not significant which vary with the results of Census
tract. The results are consistent with graphical exploration result.
Table 5: Result of Kruskal-Wallis Test at Zipcode
Group
%
(Median) White
%
AfAm
%
Hisp
AfAm/
White
Hisp/
White
Median Per
Income Capita
Clean
5.2
10.24
6.7
11.60
54038.0 26041.0 5.74
19.21
24.36
4.56
11.33
5.5
14.95
51235.5 22590.0 6.86
25.22
17.32
11.28
27.14
19.15
49.25
39406.5 18384.0 12.78
23.80
12.07
82.2
Exposed 81.04
Hazard
58.15
Chi2
Prob>
Chi2
40.25
20.83
<0.0001 <0.0001
%Below %High
Poverty School
%Bachelor
44.37
25.00
49.03
28.30
21.60
31.03
3.8
<0.0001 <0.0001 <0.0001 <0.0001 <0.0000 <0.0001 0.1496
14.75
0.00062
S ig .
***
***
***
***
***
***
***
***
Not
***
Level
Comment Support SupportHyp. Support Support Support Support Support Support Contradicts Support
Hyp.
Hyp.
Hyp.
Hyp.
Hyp.
Hyp.
Hyp.
Hyp.
Hyp.
The results show that mean rank value % of White population is significantly
different for three pollutant zone and it decreases towards hazard zones. % Hispanic
population and Hispanic/White ratio, mean rank value differs significantly and
increases towards hazard zone. At Zipcode level, mean rank value for % of AfAM
population and AfAm/White ratio also increases towards hazard zone which supports
hypotheses and the results vary from those of Census tracts and are significant.
Median income and Per Capita income both decreases towards hazard zone and
difference of mean rank value for three pollutant zones are significant. The results
for % of Bachelors are significant and go in hypothesized direction. For % of High
School levels mean rank value is higher for hazard zone than clean zone which
contradicts hypotheses and differences in mean rank value are not significant which
vary with the results of Census tract. The results are consistent with ANOVA result.
33
Summary

At Zipcode level, results of ANOVA say that, population groups for three zones
are different for all variables except AfAm/White minority ratio and % High
School population. Kruskal-Wallis test say that, population groups are different
for AfAm/White ratio also along with other variables except for % High School
population.

At Zipcode level, mean % of population increases towards Hazard zone for High
School population which contradicts hypotheses.

At both Census Zipcode level, mean % of population for
White, Hispanic,
Hispanic/White ratio, Bachelors, Median income, Per Capita Income and % of
Below Poverty population goes in hypothesized direction
which supports
hypotheses.
7.2.2 Result of Correlation Matrix
Table 6: Pearson’s Product-Moment Correlation Matrix for Census Tract
Census
Tract
AfAm
Hispanic
White
AfAm
1.00***
-0.12**
-0.76*** 0.50*** 0.39***
0.38***
-0.36*** -0.01
1.00***
-0.54*** 0.48*** 0.15***
0.52***
1.00*** 0.64*** -0.39***
0.71***
-0.63*** 0.14***
0.67***
-0.09**
-0.09**
Hispanic
White
MedInc
MedInc Bl_Pov High_Sch_Pop Bach_Pop Site_Den
Support
Hyp.
Blw_Pov
Support
Hyp.
High_Sch_Pop Contradict
Hyp
Support
Hyp.
Support
Hyp.
Contradict
Hyp
Support
1.00*** -0.56***
Hyp.
0.63***
Support
1.00*** 0.26***
Hyp.
Contradict
1.00***
Hyp
0.76***
Bach_Pop
Support
Hyp.
Support
Hyp.
Support
Hyp.
1.00***
-0.08*
Site_Den
Contradicts Support
Hyp.
Hyp.
Support
Hyp.
Support
Hyp.
1.00***
Support Support Support Hyp.
Hyp.
Hyp.
-0.61*** 0.07*
-0.76*** -0.002
Significance codes: *** p< 0.001, ** p< 0.01 ,* p< 0.05
Green= Significant Positive Correlation; Blue= Significant Negative Correlation; Red=Not Significant
Correlation
34
The results at Census tract level show that, AfAm population has negative correlation
with Site Density which contradicts hypothesis but the result is not significant.
Hispanic population has significant positive correlation and White population has
significant negative correlation. For racial groups at Census tract level, results are
consistent with previous results. Median income has significant negative correlation
and % of population below poverty level has significant positive correlation. Both the
results go in hypothesized direction and consistent with previous results. % of
Bachelors and % of High School population both have negative correlation with site
density which supports hypotheses but results for High school population is not
consistent with previous results and not significant.
Minority population that is AfAm and Hispanic groups have significant negative
correlation with median income and significant positive correlation with poverty level.
Hence minority population is of lower income group which supports hypotheses.
Minority population has significant negative correlation with % of Bachelors which
supports hypotheses but significant positive correlation with % of High school
population which contradicts hypotheses that minority population is deprived of
education. However, it can be said that minority population is of lower education
level as correlation with % bachelors (which is higher education level than high
school) is negative. The results are consistent with previous results except for the
correlation of % of High school population with site density but that result is not
significant. So far, we have seen Hispanic population is more exposed that AfAm
population at Census tract level, so racial disparity exists for Hispanic population.
Along with income disparity exists and consistent with pervious results.
35
Table 7: Pearson’s Product-Moment Correlation Matrix for Zipcode
Zipcode
AfAm
Hispanic
White
MedInc Bl_Pov High_Sch_Pop Bach_Pop Site_Den
AfAm
1.00***
0.16*
-0.89***
0.54*** 0.13
0.44***
-0.31*** 0.23***
1.00***
-0.54***
0.56*** -0.06
0.53***
0.55*** -0.21**
0.65***
-0.42*** 0.53***
Hispanic
White
1.00***
MedInc
0.35***
-0.41***
-0.37***
Support
Hyp.
Blw_Pov
Support
Hyp.
High_Sch_Pop Contradicts
Hyp.
Support
Hyp.
Support
Hyp.
Support
Hyp.
Support
1.00*** -0.47***
Hyp.
0.66***
Support
1.00*** 0.16*
Hyp.
Contradicts
1.00***
Hyp.
0.74***
Bach_Pop
Support
Hyp.
Support
Hyp.
Support
Hyp.
1.00***
-0.25***
Site_Den
Support
Hyp.
Support
Hyp.
Support
Hyp.
Support
Hyp.
1.00***
Support Support Support Hyp.
Hyp.
Hyp.
-0.51*** 0.54***
-0.82*** -0.06
Significance codes: *** p< 0.001, ** p< 0.01 ,* p< 0.05
Green= Significant Positive Correlation; Blue= Significant Negative Correlation; Red=Not Significant
Correlation
The results at Zipcode level show that, AfAm population has significant positive
correlation with Site Density which differs from Census tract result, supports
hypothesis and consistent with pervious results. Hispanic population has significant
positive correlation and White population has significant negative correlation which
support hypothese and consistent with previous results. So, for racial groups at
Zipcode level, results are consistent with previous results. Median income has
significant negative correlation and % of population below poverty level has
significant positive correlation. Both the results go in hypothesized direction and
consistent with previous results. % of Bachelors and % of High School population
both have negative correlation with site density which supports hypotheses but
results for High school population is not consistent with previous results and not
significant.
Minority population that is AfAm and Hispanic groups have significant negative
correlation with median income and significant positive correlation with poverty level.
36
Hence minority population is of lower income group which supports hypotheses.
Minority population has significant negative correlation with % of Bachelors which
supports hypotheses. At Zipcode level, Hispanic population has negative correlation
with % of High School population also which supports hypothesis but AfAm
population has positive correlation but both the results are not significant. However,
it can be said that minority population is of lower education level as correlation with
% bachelors (which is higher education level than high school) is negative.
The
results are consistent with previous results except for the correlation of % of High
school population with site density but that result is not significant.
Summary Relative to Site Density

Site Density has significant positive correlation with AfAm population for
Zipcode and negative correlation for Census tract which is a weak correlation
and not significant. It supports hypotheses for Zipcode but not for Census tract.

Site Density has positive correlation with Hispanic population for both Census
tract and Zipcode which supports hypotheses and the correlation is stronger
than AfAm population.

Site Density has negative correlation with Median Income for both Census tract
and Zipcode which supports hypotheses.

Site Density has positive correlation with % of Population below Poverty level
for both Census tract and Zipcode which supports hypotheses.

Site Density has negative correlation with % of Bachelors and % of High School
population for both Census tract and Zipcode, though the correlation for % of
High School is weak and not significant. It supports hypotheses for % of
Bachelors but not for % of High School.

For each type of correlation with Site density, Zipcode produces stronger
correlation than Census tract which contradicts hypotheses.
37
7.2.3 Result of Multinomial Regression
Census Tract
Coefficients:
(Intercept)
Exposed -0.8426098
Hazard
Hisp_Pop
AfAm_Pop
-0.1338936
-0.6930193
-1.433429
1.9092155
MedIncome
High_Sch_Pop
-4.752875e-06
3.9829025
-1.378699
-2.736089e-05
-0.2434503
Std. Errors:
(Intercept)
Hisp_Pop
AfAm_Pop
MedIncome
High_Sch_Pop
Exposed 1.950399e-11
3.863262e-12 2.087521e-12 1.250480e-06 4.006589e-12
Hazard
1.313767e-11
5.452539e-11
6.283066e-12 2.625053e-06 1.159580e-11
Residual Deviance: 1654.684
AIC: 1674.684
Anova Table (Type II tests)
Response: Site
LR Chisq Df
Hisp_Pop
AfAm_Pop
MedIncome
Pr(>Chisq)
5.8145
2
0.0546253 *
13.8662
2
0.0009750 ***
9.0643 2
High_Sch_Pop 15.4373
2
0.0107573 *
0.0004445 ***
Significance Codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Zipcode
Coefficients:
(Intercept) Hisp_Pop AfAm_Pop MedIncome
High_Sch_Pop
Exposed -1.776600 5.141167
0.3424879
2.018719e-05
5.122521
Hazard
2.1234811
-1.584511e-06
2.799972
-2.368417 9.694381
Std. Errors:
(Intercept)
Hisp_Pop
AfAm_Pop
MedIncome High_Sch_Pop
Exposed 4.429172e-11 7.498141e-12 5.132482e-12 3.174424e-06 8.862092e-12
Hazard
8.987643e-11 1.733637e-11 1.321051e-11
4.587719e-06 2.075645e-11
Residual Deviance: 356.5254
AIC: 376.5254
38
Anova Table (Type II tests)
Response: Site
LR Chisq Df
Pr(>Chisq)
Hisp_Pop
19.4408
2
6.005e-05 ***
AfAm_Pop
3.0754
2
0.2149
MedIncome
2.8197
2
High_Sch_Pop
4.0290
2
0.2442
0.1334
Significance Codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Table 8: Summary of Multinomial Regression Result
Hispanic_Pop
AfAm_Pop
Med.Income
High_Sch_Pop
Clean-Exposed
-*
-*
-*
+*
Clean-Hazard
+*
-*
-*
-*
Clean-Exposed
+*
+
+
+
Clean-Hazard
+*
+
-
+
+
-
-
Census tract
Zipcode
* Significant
Hypothesized Sign +

Hispanic population increases for Hazard Tract which supports hypotheses and is
consistent with previous results. It contradicts for Exposed Tract but is
inconsistent with previous results.

Hispanic population increases for both Hazard and Exposed Zipcode which
supports hypotheses and it is the only significant result for Zipcode.

AfAm population decreases for both Hazard and Exposed Tract, which contradicts
hypotheses but is consistent with previous results.

Median Income decreases for both Hazard and Exposed Tract which support
hypotheses and is consistent with previous results.
39

High School population decreases for Hazard Tract which supports hypotheses
but contradicts for Exposed Tract. Results for High School population are
inconsistent with previous results.

The results for Zipcode are not significant except for Hispanic population hence,
not considered for drawing conclusions.
8. Conclusions
To summarize, the results of graphical exploration and statistical analysis show that
Hispanic population is positively correlated with high pollution exposure which
supports hypotheses. Whereas African American population is negatively correlated
with high pollution exposure which contradicts hypotheses (I am excluding the
results for Zipcode level here, as the result for regression is not significant). So, it
can be said that, racial disparity for Hispanic population exists in D/FW area. Low
income group is positively correlated with high pollution exposure which supports
hypotheses. Hence, poor community is living in high pollution zone in D/FW area.
Population for Bachelors level is negatively correlated with high pollution exposure
which supports hypotheses so it can be said people living in high pollution zone is of
lower education level with respect to Bachelor’s level. Correlation with Population for
High School level is not consistent through out different analytical steps, hence no
clear conclusion could be made for High School population. Minority population has
positive correlation with lower income level and lower education (Bachelors) level
which supports hypotheses that in general minority population groups are of lower
income and education level.
For Pearson’s Correlation Coefficient analysis, Zipcode produces stronger correlations
than those of Census tract whereas for Regression analysis, Census tract produces
40
more significant results than Zipcode. Hence, no clear conclusion could be made
about the preferable geographic unit.
And finally, this project has shown a new method for classifying pollutant zones
involving Hot spot analysis and has applied use of GIS more effectively.
8.1. Future Work
In addition to this existing work I plan to extend the scope of the project and to dig
deeper in methodology for getting more dependable results. Methodology for
classifying pollutant zone can be improved by involving:
o
Rural vs. Urban area
o
Residential vs. Non-residential area
The results may vary at this approach as demographic and socio-economic
composition is different for rural and urban area and also for residential and nonresidential area. Also density of pollutant sites varies for rural and urban area.
I also want to do the analysis at Block-group level and compare the results with this
project to get a clear picture about the variation of results depending on geographic
unit.
The existing project has shown us proof for racial and income disparity but it does
not tell us which factor is more dominant hence more analysis is needed to
determine the dominant factor between race and income. Finally I intend to do the
analysis for State of Texas.
9. References
1. Frokenbrok, D.J and J. Sheeley. 2004. Effective Methods of Environmental
Justice Assessment. NCHRP Report 532. Transportation Research Board.
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2. Mohai, P. and B. Bryant (eds). 1992. Race and the Incidence of Environmental
Hazards: A Time for Discourse. Boulder,
CO: Westview Press.
3. White, H. L. 1998. Race, class and environmental hazards. In D.E Camacho
(Ed.), Environmental injustices, political struggles (61-81). Durham, NC: Duke
University Press.
4. Hockman, E.M. and C. M. Morris. 1998. Progress towards Environmental
Justice: A Five-year Perspective of Toxicity, Race and Poverty in Michigan,
1990-1995. Journal of Environmental Planning and Management 41(2): 157176.
5. Morello-Frosch, R, M. Pastor, Jr., C. Porras and J. Sadd. 2002. Environmental
Justice and Regional Inequality in Southern California: Implications for Future
Research. Environmental Health Perspectives 110(2): 149-154.
6. Mohai, P and R. Saha. 2007. Racial Inequality in the Distribution of Hazardous
Waste : A National Level Reassessment. Social Problems 54(3): 343-370.
7. Bowen, W.M, M.J Salling, K.E Haynes and E.J Cyran.1995. Toward
Environmental Justice: Spatial Equity in Ohio and Cleveland. Annals of the
Association of American Geographers 85(4): 641-663.
8. Clutter, S.L, D Holm and L Clarke.1995.Role of Geographic Scale in Monitoring
Environmental Justice. Risk Analysis 16(4): 517-526.
9. Huang, Y. and S. Batterman. 2000. Residence location as a measure of
environmental exposure: a review of air pollution epidemiology studies. Journal
of Exposure Analysis and Environmental Epidemiology 10 (1): 66-85.
10. Lopez, R. 2002. Segregation and Black/White differences in exposure to Air
Toxics in 1990. Environmental Health Perspectives 110(2): 289-295.
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