Environmental Justice
Adam Dorr
GIS
3/1/2011
Adam Dorr – GIS Final Project Write Up – W11
Introduction
Project Goals
The topic of this project was environmental justice in Los Angeles county (shown in Figure 1
below) and my analysis was divided into two parts. The goal of Part 1 was to identify census tracts in Los
Angeles county that are at highest risk of exposure to environmental pollution, and therefore identifying
which Los Angeles county schools to target with environmental justice policies to improve indoor air
quality and thereby protect children’s health. The goal of Part 2 was to test whether the landmark
environmental justice findings of the 1987 United Church of Christ Report on Toxic Waste and Race in
the United States1 that the racial composition of a community is the best predictor of hazardous waste
sites can also be applied to predicting community composition based on air pollution risk.
Figure 1: Area of Analysis – Los Angeles County
The layout in Figure 1 above shows the project area of interest – central Los Angeles county – as
well as major highways, which are of particular importance given the role they play in contributing to
the area’s air pollution. An inset map of shows the location of the area of interest within the state of
California. This layout served as the template not only for much of my analysis, but also for my
presentation slides. Stylistically, I felt that it was highly effective to dissolve between one slide and the
next in a seamless transition so that only the salient features change while the background features
remain the same. To create this and the other project layouts I used both ArcMap and the Adobe
Creativity Suite of graphics applications. I attempted to direct the attention of the view to key features
of the layouts by making use of transparencies, subduing interior borders with soft grays, desaturating
the colors of background layers, and emphasizing areas of interest with heavier and darker border lines.
Data and Spatial Unit of Anlaysis
For both parts of the project I used air pollution risk data from the EPA National Air Toxics
Assessment (NATA) 20022. This dataset shows respiratory, cancer and neurological risk associated with
a range of different types of air pollution at the census tract level. Since this was the primary dataset for
both Part 1 and Part 2, the spatial unit of analysis I used for this project was the census tract. EPA also
maintains a national dataset of environmental “sites of concern”. Street addresses for each site are
Adam Dorr – GIS Final Project Write Up – W11
included in the dataset, and I was able to successfully geocode this dataset and use the more than 1400
sites in the Los Angeles county area for my analysis.
For state and county boundary layers along with point and polygon data for schools, airports
and waterbodies, I used data from UCLA’s mapshare website. For base layers, including shaded relief,
terrain and major highways I used data published by ESRI and Microsoft that is accessible directly
through ArcMap. I used imagery, 3D data and some placemarks for schools in the downtown Los
Angeles area from Google that were projected in Google Earth. Finally, I used data from the US Census
2000 and the American Community Survey 5-year estimates published by the US Census Bureau for
demographics such as income, racial composition and age composition at the census tract level.
Part 1
To identify census tracts in Los Angeles county that are hotspots for environmental pollution, I
began by mapping my NATA data for respiratory, cancer and neurological risk. In the NATA datasets risk
is delineated by more than a dozen specific environmental toxics (lead, mercury, formaldehyde, etc.)
and the dataset provides a consolidated category for “total risk”. The risk figure applies to a complex
algorithm intended to inform public health and epidemiological research as was therefore not
appropriate for the purposes of my project. I therefore chose to standardize, or normalize, the total risk
figure, yielding a score from 0 to 1. I applied this to each of the risk categories – respiratory, cancer and
neurological – to create three maps. I then combined these maps into a single map showing a
composite air pollution risk index by multiplying the index scores together, which again yielded a
standardized scale of 0 to 1. These maps are shown in Figure 2 and Figure 3 below.
Figure 2: Creating a Composite Air Pollution Risk Index Map
The majority of census tracts scored below 0.5 on this composite air pollution risk index, with
only a handful scoring 0.6 or higher. These hotspot census tracts are shown in black highlighted with a
yellow outline in Figure 3 below.
Adam Dorr – GIS Final Project Write Up – W11
Figure 3: Composite Air Pollution Risk Index
Next, I developed an index for environmental justice communities based on demographic
factors, following examples of the definitions for environmental justice communities used in
Massachusetts State Law.3 The logic behind creating a definition for an environmental justice
community is that these communities are particularly vulnerable to environmental harm, and therefore
lack the resources needed to be resilient in the face of such stressors. I used the following variables as
criteria to create an EJ Index:
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65% or more below median income
25% or more minority
25% or more foreign born
25% or more under age 10
25% or more age 65 or older
For median income, I used the county figure as the baseline, and I defined “minority” as those
who responded to any of the non-white categories reported in Census 2000. To compute a composite
environmental justice community index, I gave each census tract 1 point if it met any of the above
criteria, and added these points together for a final index score of 0 to 5. Calculations were performed
in an Excel spreadsheet by exporting the raw data from ArcMap to textfile, then converting to excel and
re-importing the computed data back into ArcMap. The resulting index is therefore scaled from 0 to 5.
The results are shown in Figure 4 below, and hotspot census tracts (with scores of 4; there were no
census tracts that scored 5) are identified in blocked highlighted by a yellow outline.
Adam Dorr – GIS Final Project Write Up – W11
Figure 4: Environmental Justice Demographics Hotspot Map
By multiplying the scores of the air pollution risk and environmental justice community indexes
together by joining the layer data and using ArcMap’s field calculator to compute data for a new field, I
was able produce a combined index of air pollution risk and environmental justice communities that
shows the key hotspot census tracts for Los Angeles county. The census tracts at highest risks are
shown in red, highlighted by a yellow outline, in Figure 5 below, and most are clustered downtown.
Figure 5: Combined Index of Air Pollution Risk and Environmental Justice
Adam Dorr – GIS Final Project Write Up – W11
To continue further refining my hotspot analysis, I attempted to incorporate EPA sites of
concern data into the combined air pollution and environmental justice map data. Since the EPA sites of
concern dataset is incomplete, most of the sites do not contain information about the quantity or nature
of pollutants that are being emitted. As a result, I was unfortunately forced to treat all EPA sites of
concern as if they were identical, and this certainly skewed the results of my analysis. Nevertheless, I
felt it important to try to incorporate point source pollution data (some of which was air pollution, but
some of which was likely water pollution, solid waste pollution, or noise pollution) into my analysis
because even without these details because polluting sites generally contribute to environmental blight
within environmental justice communities whatever their specific impacts may be.
I started by clipping the national EPA sites of concern database to the Los Angeles County area
and mapping those points to get a general sense of clustering and concentration. This is shown in Figure
6 below. A simple visual inspection showed corridors along highways and clusters around certain
communities, particularly in the downtown area.
Figure 6: EPA Sites of Concern
I used a combination of rasters, map algebra, computations in a series of exported and reimported Excel spreadsheets, the ArcMap spatial analyst Euclidian distance tools, buffers, dissolves and
ArcMap field geometry calculations to produce the maps show in Figure 7 below.
These maps show the sequence of progressives steps I took to combine together measurements
of the distances from EPA sites of concern, the spatial “density” of these sites, the presence of schools
within 500 meters of these sites, and the proportion of land area within a census tract lying with 500
meters of major highways.
Adam Dorr – GIS Final Project Write Up – W11
Figure 7: EPA Sites of Concern Maps
With this collection of measures I was able to compute a final composite index from 0 to
5, again by standardizing scales and multiplying them by the earlier 0 to 5 index, but the highest
score of which was less than 3.5 so only scores up to 3.5 are shown in the legend. This
combined both air pollution and environmental site data with environmental justice
Adam Dorr – GIS Final Project Write Up – W11
communities (Figure 8 below). Based on this final composite index of environmental risk, I
identified my hotspot cluster in downtown Los Angeles for further analysis of schools in the area
(shown inset).
Figure 8: Final Composite Environmental Risk Index
After identifying my hotspot in downtown Los Angeles, I created new layouts in ArcMap and
Google Earth to provide more detailed views of the area of concern. Figure 9 below shows the hotspot
area, with the 10 census tracts in Los Angeles county that score above 2.6 on my composite index scale
of 0 to 5 (only scores up to 3.5 are shown in the legend).
Figure 9: Hotspot Analysis - Downtown
Adam Dorr – GIS Final Project Write Up – W11
In addition to the ArcMap layout of the downtown hotspot, I also presented a 3D rendering of
the area generated in Google Earth, shown in Figure 10 below. This provides a much richer context and
sense of the spatial structure of the area than the 2D map from ArcMap alone.
Figure 10: Google Earth 3D Rendering of Downtown Los Angeles
To highlight key demographics from my census tract layers of the downtown hotspot area, I
computed the percentage of minority resident, percentage below the poverty line, percentage foreign
born, and percentage earning over $200,000 per year. These data are summarized in Figure 11 below.
Figure 11: Hotspot Demographics
With these stark demographics in mind, I returned to the downtown hotspot layout produced in
ArcMap and added layers to show the locations of schools, EPA sites of concern, and the major highways
in the area, as shown in Figure 12 below. Highways and EPA sites of concern were important to show
because they were key factors contributing to the construction of my environmental risk index. Since
environmental pollution does not respect census tract boundaries, I included schools that feel within a
Adam Dorr – GIS Final Project Write Up – W11
500 meter buffer around the 10 highest-risk tracts as well as the schools within the census tracts
themselves.
Figure 12: Downton Hotspot with Schools, Sites of Concern and Major Highways
I was then able to combine my ArcMap hotspot layout with my Google Earth layout using the
“overlay” function in Google Earth. This produced a compelling final hotspot analysis layout that
combines the best of both worlds, showing the graduate color symbology and buffer features of the
ArcMap layout with the 3D context and dynamics of the Google Earth layout. I produced two 3D layouts
from different perspectives, in order to provide a better view of the schools that are the target of my
policy intervention, shown in Figure 13and Figure 14 below.
Figure 13: ArcMap and Google Earth Combined Hotspot Layout
Adam Dorr – GIS Final Project Write Up – W11
Figure 14: ArcMap and Google Earth Combined Hotspot Layout
Policy Recommendations
According to my analysis, Downtown Los Angeles is the
area of highest environmental risk in Los Angeles county.
Unfortunately, because the area sees an extraordinarily broad mix
of uses, including high-rise and low-rise residential, high-rise and
low-rise commercial, and heavy industrial, there is no single local
policy that can be applied uniformly to all of these types of land use
and socioeconomic activity to reduce environmental pollution.
Furthermore, since the largest portion of environmental pollution in
the area is air pollution, the problem is diffuse and environmentally
destructive activity any one source (including millions of vehicles)
spreads out to affect a large spatial area.
For these reasons, my environmental justice policy recommendation for downtown Los Angeles
schools is not to attempt to improve downtown Los Angeles air quality but rather to improve the indoor
air quality of the schools themselves. During the academic year, children can spend up to one half of
their waking hours inside schools, and efforts to improve indoor air quality within schools themselves
therefore have the potential to significantly reduce total child exposure to environmental risk from air
pollution. I recommend four low-cost, easily-implemented methods for dramatically improving indoor
air quality that can be applied immediately in downtown schools:
1. Improved HVAC Air Filtration
Adam Dorr – GIS Final Project Write Up – W11
Heating, Ventilation and Air Conditioning (HVAC) systems process air
within a building to improve indoor comfort. With additional filtration,
they can also improve indoor safety by improving air quality.
Ion exchange and HEPA (High Efficiency
Particulate Air) filters can be fitted to existing HVAC
systems at modest cost and can dramatically reduce air
toxics.
2. Use low VOC paints and cleaning agents
Chemicals used indoors react over time with air, sunlight and heat to “offgas”,
or release, Volatile Organic Compounds (VOCs). Paints and cleaning agents
are particularly common sources of hazardous VOCs, as well as sources of
inorganic contaminants such as lead, mercury and arsenic. Low VOC paints
and environmentally friendly cleaning products are now readily available
and competitively priced in bulk with regular chemicals, and are
therefore another low-cost and easily implemented option for improving
indoor air quality in schools.
3. Reduction of pesticide application
Pesticides are applied both indoors (to kill insects and
rodents) and outdoors (to control weeds), and children
come into contact with these hazardous chemicals more
frequently and at higher concentrations per unit body
mass than adults because of their activity and frequent
contact with the ground. Eliminating pesticide application
and replacing pesticides with non-chemical alternatives
(such as rodent traps and replacing imported lawn grasses
with native plants) can reduce the presence of hazardous
chemicals inside schools.
4.
Bioremediation
Bioremediation is the use of plants to absorb
environmental toxics, and a number of species are
effective at removing air pollutants such as
formaldehyde and nitrous and sulfur oxides. Indoor
plants also improve the psychological environment
inside schools for added benefit.
Adam Dorr – GIS Final Project Write Up – W11
Part 2
In Part 2 of my final project I attempted to test
whether the findings of the 1987 United Church of Christ
report Toxic Waste and Race in the United States that race
is the best predictor of the location of hazardous waste
sites would also be true for air pollution. I suspected that
demographic factors would not explain all of the variation
observed, since air pollution is mobile and diffuse and is
therefore affected by weather and atmospheric conditions
in ways that ground sites are not. Nevertheless, I suspected
that there may still be some significant correlation between
the location of air pollution in a major city like Los Angeles
and the demographic composition of a given community.
To undertake this analysis, I began with a null model
that simply predicts that the air pollution in any census tract
will equal the average air pollution level of all census tracts.
I then progressively refined this analysis by adding variables to a multiple regression model.
Some of these variables best fit into the model in non-linear form. The models added these
variables as follows:
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Model 1: added percent of census tract within 500 meters of major highway
Model 2: added EPA site density by census tract area (log form)
Model 3: added percentage in poverty (quadratic form)
Model 4: added percentage foreign born (quadratic form)
Model 5: added percentage minority
I applied the results of the regression models to a successive series of maps, shown
below. The scheme of graduated color symbology is designed to indicate where the model
predicts the air to be more polluted than it actually is (red) or cleaner than it actually is (green).
Where the model neither over- nor under-predicts, the model shows white. These are
therefore maps of the residuals of the regression models, and so the more evenly distributed
and lighter the colors on the map, the more accurate the model is.
As Figure 15 below shows, the models become progressively more accurate. However,
the demographic variables only explain a small amount of variation compared to how much
variation in air pollution as measured by the NATA dataset is explained by the presence of
major highways and EPA sites of concern.
Adam Dorr – GIS Final Project Write Up – W11
Figure 15
The multiple regression model can be written as the following equation:
Y = 0.158 + 0.108*(near_highway) + 0.031*Ln(site_density) - 0.184*(in_poverty) + 0.339*(in_poverty)2
+ 0.218*(foreign_born) - 0.177*(foreign_born)2 + 0.073*(minority) + 0.004*(minority)2
Adam Dorr – GIS Final Project Write Up – W11
Despite the fact that the multiple regression modeling done here does not indicate that
demography has large predictive power for air pollution, this was nevertheless an extremely useful
exercise in using GIS to visualize multiple regression modeling data. Visually representing multiple
regression models by mapping their residuals is a very promising analytical technique and has been an
extraordinarily useful learning exercise. I fully intend to use this type of analysis again in the future.
SOURCES
1
Commission for Racial Justice. (1987). Toxic Waste and Race in the United States: a National Report on the Racial
and Socio-Economic Characteristics of Communities with Hazardous Waste Sites. United Church of Christ, 1987.
2
US EPA. 2002 National-Scale Air Toxics Assessment. http://www.epa.gov/nata2002/
3
Commonwealth of Massachusetts. The Environmental Justice (EJ) Policy of the Executive Office of Environmental
Affairs. http://www.mass.gov/Eoeea/docs/eea/ej/ej_factsheet_english.pdf