Mapping Environmental Injustices: Pitfalls and Potential of Geographic Information Systems in Assessing Environmental Health and Equity

Abstract

Geographic Information Systems (GIS) have been used increasingly to map instances of environmental injustice, the disproportionate exposure of certain populations to environmental hazards. Some of the technical and analytic difficulties of mapping environmental injustice are outlined in this article, along with suggestions for using GIS to better assess and predict environmental health and equity. I examine 13 GIS-based environmental equity studies conducted within the past decade and use a study of noxious land use locations in the Bronx, New York, to illustrate and evaluate the differences in two common methods of determining exposure extent and the characteristics of proximate populations. Unresolved issues in mapping environmental equity and health include lack of comprehensive hazards databases; the inadequacy of current exposure indices; the need to develop realistic methodologies for determining the geographic extent of exposure and the characteristics of the affected populations; and the paucity and insufficiency of health assessment data. GIS have great potential to help us understand the spatial relationship between pollution and health. Refinements in exposure indices; the use of dispersion modeling and advanced proximity analysis; the application of neighborhood-scale analysis; and the consideration of other factors such as zoning and planning policies will enable more conclusive findings. The environmental equity studies reviewed in this article found a disproportionate environmental burden based on race and/or income. It is critical now to demonstrate correspondence between environmental burdens and adverse health impacts--to show the disproportionate effects of pollution rather than just the disproportionate distribution of pollution sources.

Full text

Environmental Health Perspectives
•
VOLUME 110 |SUPPLEMENT 2 |April 2002
161
Mapping Environmental
Injustices
Although the mainstream environmental
movement of the 1950s and 1960s alerted the
public to the dangers posed by pollution and
environmental degradation, these impacts on
people’s health and the environment were not
generally acknowledged (or thought) to be
spatially or socially differentiated: everyone
was presumed to be affected just about
equally. The understanding that environmen-
tal problems may impact certain locations and
people more than others (and in a predictable
pattern based on race and income) is a rela-
tively new concept that gained nationwide
attention in the late 1980s.
Environmental injustice can be defined
as the disproportionate exposure of commu-
nities of color and the poor to pollution,
and its concomitant effects on health and
environment, as well as the unequal envi-
ronmental protection and environmental
quality provided through laws, regulations,
governmental programs, enforcement, and
policies (1–3).
Within the past decade it has become
increasingly prevalent to try to map
instances of environmental injustice, usually
by geographically plotting facilities or land
uses suspected of posing an environmental
and human health hazard or risk, and then
trying to determine the racial, ethnic, and
economic characteristics of the potentially
affected populations compared with a refer-
ence population. This often results in dra-
matic maps showing toxic facilities
concentrated in areas with high proportions
of African Americans, Latinos, or Native
Americans (4–8).Mapping became a
favored method among researchers attempt-
ing to determine the existence of environ-
mental injustice. Additionally, the wealth of
environmental and demographic data now
available on the Internet, as well as the pro-
liferation of websites with interactive map-
ping applications available, have brought
environmental justice mapping within reach
of virtually anyone (9).
Although such maps can be unusually
effective in visually demonstrating the dis-
proportionate spatial distribution of noxious
or hazardous facilities, these maps have also
come under scrutiny and been criticized for
being misleading and inaccurate, and their
findings have often been contradicted by
other spatial analyses. Mapping a phenome-
non such as environmental injustice is not a
straightforward exercise, and the difficulties
encountered in producing such spatial
analyses leave the maps open to a variety of
interpretations and second-guessing. Just as
no map can be viewed as an objective
embodiment of the real world, maps depicting
environmental injustice are also social con-
structions, and therefore subjective and
based on assumptions (10,11).
A fundamental concern with mapping
environmental injustice is that it does not
yield definitive findings about differential
exposure levels or health outcomes for the
population in proximity to the noxious facil-
ities or land uses. This drawback makes these
studies less useful in conclusively demon-
strating (and measuring) the correspondence
between the location of potential environ-
mental burdens, exposures, and health
effects. However, it is feasible to develop
methods and tools for producing more
meaningful spatial analyses. Some of the
issues that are contested in mapping envi-
ronmental injustice, and the technical and
analytic difficulties encountered in such
mapping projects, are outlined below (12),
along with some suggestions for using
Geographical Information Systems (GIS) to
better assess and predict environmental and
health conditions.
The Findings of Environmental
Justice Spatial Analyses
The groundbreaking environmental justice
study, “Toxic Wastes and Race in the
United States: A National Report on the
Racial and Socio-Economic Characteristics
of Communities with Hazardous Waste
Sites,” was produced in 1987 under the aus-
pices of the United Church of Christ’s
Commission for Racial Justice (4). The
report presented maps of the locations of the
country’s hazardous waste facilities in
conjunction with the characteristics of the
nearest populations (by ZIP code), using
indicators such as race, ethnicity, and
income. Compared with the areas that were
This article is part of the monograph Advancing
Environmental Justice through Community-Based
Participatory Research.
Address correspondence to J. Maantay, Dept. of
Geology and Geography, 250 Bedford Park Blvd.
West, City University of New York, Lehman
College, Bronx, NY 10468 USA. Telephone: (718)
960-8574. Fax: (718) 960-8584. E-mail:
maantay@lehman.cuny.edu
This article is based on work supported (in part) by
a grant from the City University of New York PSC-
CUNY Research Award Program. The project title
was “Mapping Asthma Incidence and Environmental
Hazards in the Bronx in New York City.”
Received 13 August 2001; accepted 14 January
2002.
Environmental Justice
Geographic Information Systems (GIS) have been used increasingly to map instances of environ-
mental injustice, the disproportionate exposure of certain populations to environmental hazards.
Some of the technical and analytic difficulties of mapping environmental injustice are outlined in
this article, along with suggestions for using GIS to better assess and predict environmental
health and equity. I examine 13 GIS-based environmental equity studies conducted within the
past decade and use a study of noxious land use locations in the Bronx, New York, to illustrate
and evaluate the differences in two common methods of determining exposure extent and the
characteristics of proximate populations. Unresolved issues in mapping environmental equity and
health include lack of comprehensive hazards databases; the inadequacy of current exposure
indices; the need to develop realistic methodologies for determining the geographic extent of
exposure and the characteristics of the affected populations; and the paucity and insufficiency of
health assessment data. GIS have great potential to help us understand the spatial relationship
between pollution and health. Refinements in exposure indices; the use of dispersion modeling
and advanced proximity analysis; the application of neighborhood-scale analysis; and the consid-
eration of other factors such as zoning and planning policies will enable more conclusive findings.
The environmental equity studies reviewed in this article found a disproportionate environmental
burden based on race and/or income. It is critical now to demonstrate correspondence between
environmental burdens and adverse health impacts—to show the disproportionate effects of pol-
lution rather than just the disproportionate distribution of pollution sources. Key words: environ-
mental hazards, environmental health, environmental justice, exposure analysis, Geographic
Information Systems, GIS, risk assessment, spatial analysis.
Environ Health Perspect 110(suppl 2):161–171 (2002).
http://ehpnet1.niehs.nih.gov/docs/2002/suppl-2/161-171maantay/abstract.html
Mapping Environmental Injustices: Pitfalls and Potential of Geographic
Information Systems in Assessing Environmental Health and Equity
Juliana Maantay
Department of Geology and Geography, Lehman College, City University of New York, Bronx, New York, USA

not hosts to a hazardous waste facility, the
host areas showed an unmistakable statistical
and spatial correspondence to minority
populations (13).
If “Toxic Wastes and Race” was the semi-
nal study that helped propel the issue of envi-
ronmental justice to the forefront of the
public’s consciousness in the late 1980s and
early 1990s, it was certainly not the first envi-
ronmental justice study. These issues have
been researched extensively since at least the
late 1960s, and study after study throughout
three decades has shown the existence of dis-
proportionate environmental impacts based
on race and/or income (14,15). Since then,
many other researchers have used mapping
exercises to try to substantiate or refute the
existence of environmental inequities.
In this article I review 13 GIS-based
environmental equity studies conducted
within the past decade (Table 1). In evalu-
ating these studies, it is important to
understand exactly what is being mapped,
and how it is being measured.
Limitations of Mapping
Environmental Justice Issues
Many environmental justice mapping studies
conducted in the early to mid-1990s had def-
initional, conceptual, methodologic, and data
problems, which limited their usefulness and
raised questions as to the ability of GIS to
assess environmental health or equity. Some
of these concerns have since been at least
partially addressed; others have not.
Is Injustice Predicted by Race
or Class?
Some commentators have raised what
may be termed philosophic questions,
such as Can racism as a phenomenon be iso-
lated? and Can discriminatory intent be
proved? (16,17). Others have questioned
whether environmental injustices are merely
by-products of our market-based economy
and due more to differences in land values
than discrimination (18,19). We are not
likely to reach consensus about these issues
or effectively prove or disprove them.
Is income or race the deciding variable in
exposure to pollution (20)? The findings of
many environmental justice studies are in
conflict on this very point: some clearly
show race as the determining variable by
controlling for income and still finding dis-
proportionate burdens on minorities (5,21),
whereas other studies control for race and
find that income is the more statistically
significant variable determining dispropor-
tionate environmental burdens (22–24).
In exploring which variable, race or eco-
nomic status, is more important in predict-
ing environmental injustices, some
researchers have found that although it cer-
tainly is not the most affluent communities
that bear the burden of pollution, it is not
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Environmental Health Perspectives
Table 1. Summary of findings, methodologies, and data used in selected environmental equity studies, 1993–1999.
Type of Scale Resolution Spatial Proximity Dispersion
Name of environmental (geographic (geographic coincidence analysis Exposure modeling Disproportionate
study (year) hazard extent of study) unit of analysis) method used used index used used burdens found?
Burke, 1993 TRI Los Angeles Census tract Yes N/A N/A N/A Yes, race and income
(5)County, CA
Perlin et al., TRI United States County Yes N/A PEI, based on total N/A Yes, race, but income
1995 (23)pounds emissions/ inversely
population in county
Bowen et al., TRI State of Ohio County, with Geographic unit with, N/A Toxicity index based N/A Yes, race in county
1995 (24)Cuyahoga County without, or adjacent on TLV and total and statewide but
at tract level to hazardous facility pounds emissions not in Cuyahoga
County’s tracts
Pollack and TRI State of Florida Census block group N/A Natural log of N/A N/A Yes, race and income
Vittas, 1995 distance to
(21)hazardous facility
Glickman and TRI, EHS, Allegheny County, Municipality, Yes; tract, block group 0.5-half mile and Toxicity weights: RfD ALOHA, ISCLT2, Yes, income in all
Hersh, 1995 power plants PA census tract, block and municipality 1-mile buffers; and potency COMPLEX1 methods; race in all
(6)group, and block plume buffers carcinogens models buffers, not in all
spatial coincidence
Centner et al., TRI States of Georgia Census block group N/A One-mile buffers Percent of total N/A Yes, income and race
1996 (17)and Florida pounds released
based on areal
assignment
Been and TSDF Nationwide, 544 Census tract Yes N/A N/A N/A Yes, race, but income
Gupta, 1996 communities inversely
(22)
Boer et al., TSDF Los Angeles Census tract Yes, by range of One-mile buffers Tonnage per year N/A Yes, race, but income
1997 (26)County, CA tonnage per year for large capa- (facility capacity) nonlinear
city TSDFs relationship
McMaster TRI, Superfund, Minneapolis- Census tract, block Yes; block group 100, 200, and 500 Pratt Index (chemicals N/A Yes, income and race
et al., 1997 PetroFund, St. Paul, MN group and block yards (for TRI only) weighted by pounds for spatial co-
(54)land recycling (citywide and and toxicity index) incidence, proximity
neighborhood analysis, and expo-
scale) sure index
Chakraborty TRI Des Moines, IA Census block group N/A Circular buffers (0.5 N/A ALOHA model Yes, race and income
and Armstrong, and 1 mile) and plume in all buffers
1997 (61)buffers
Neumann et TRI State of Oregon Census block N/A Five buffers: 0.71, 1, CI N/A Yes, race and income
al., 1998 (56)1.41, 1.73, and 2 miles
(equal areas between
circles); and tract
centroid buffers
Stretesky and ACR Hillsborough Census tract N/A Distance from tract N/A N/A Yes, race and income
Lynch, 1999 County, FL centroid to ACR
(25)
Sheppard et TRI, Superfund, Minneapolis, MN Census block group Yes Three buffers: 100, 500, N/A N/A Yes, income and race
al., 1999 (59)PetroFund, and 1,000 yards; for both spatial co-
land recycling proximity ratio incidence and
proximity analysis
Abbreviations: ACR, accidental chemical release; ALOHA, areal location of hazardous atmospheres; EHS, extremely hazardous substance; ISCLT2, industrial source complex long-term
(model); PEI, population emissions index.

the poorest communities either (25,26). The
relationship between income and proximity
to environmental hazards is nonlinear,
according to these studies, with the working-
class locations more often hosting these facil-
ities. The authors speculate about why this
may be the case and suggest that the very
poorest communities have so little economic
activity that they are too poor to attract even
a noxious facility. The most affluent com-
munities have the economic and political
power to successfully oppose such facilities
from locating in their area. In these studies,
however, race is still strongly associated with
the locations of hazardous facilities.
Of course, the real issue is that minori-
ties are disproportionately represented in the
lowest economic subgroups. Race and low
income are inextricably linked, and therefore
it will be difficult to overcome the
race/income confound in the base data (27).
As concluded in the article “Environmental
Racism in Southern Arizona”:
This research into the geopolitics of pollution
finds that economics and race are inextricably
intertwined. Those scholars who attempt to iso-
late economics from racism as causal factors in
explaining environmental inequity, therefore, are
missing the point. In fact, such efforts to tease
out, for analytical purposes, the effects of each of
these discrete variables on pollution impacts can
itself be seen as a form of racism. Certainly, from
the perspective of people of color having to deal
with a dirtier environment, the effort to isolate
class and race makes very little sense. (8)
Another angle that has been explored
through GIS is that of which came first—the
nuisance or the people. Been and Gupta (22)
conducted a longitudinal study that looked
at the population characteristics of the areas
surrounding noxious facilities at the time the
facilities were sited, and then every 10 years
thereafter. The premise was if it could be
shown that the minority population came to
the area after the facility was in place, no dis-
criminatory intent could be established and
presumably there would be no environmen-
tal injustice in the siting. The problem with
this line of reasoning is that it does not take
into account that minorities are often very
constrained as to where they are able to live,
and is it not racism that restricts their
choices to such undesirable places from
which other people with more choice and
money have fled?
As to the issue of discriminatory intent,
We think it is irrelevant whether environmental
injustice represents conscious racism, or classism,
on the part of policy-makers, either now or in the
past. Attempting to prove intent is a fool’s errand,
particularly when there are so many variables in
the mix. What truly matters is how the problems
are addressed by policy-makers and business
interests in the present. In other words, who ben-
efits from current siting and pollution control
practices, and who pays the consequences?
Further, how are these impacts affected by
ongoing policies and enforcement practices? (28)
The concept of race operates on many
levels to influence the potential for exposure
to environmental hazards and disease. Race is
often a proxy for other conditions that pose
risks or exacerbate exposures. For instance,
minority children suffer disproportionately
from lead poisoning. Poor nutrition has been
recognized as a contributing factor to child-
hood lead poisoning—nutritional deficien-
cies, especially in iron and calcium, increase
children’s susceptibility to lead toxicity.
According to studies, minority children are
more likely to be at greater risk of marginal
nutritional status than White children (29).
This is in addition to the fact that minority
and low-income children are disproportion-
ately exposed to lead in lead-based paints in
older housing units as well as in high-traffic
inner city areas where the contamination
from lead-based gasoline still remains years
after lead has been banned from gasoline.
Similarly, approximately 75% of the
tuberculosis cases in the United States are
people of color (30). Poverty, residential seg-
regation by race, and housing overcrowding
have been found to drive tuberculosis rates,
and these factors disproportionately affect
minorities. The discrimination experienced
by minorities in the housing market may be
emblematic of a whole range of conditions
helping to make minorities more susceptible
to tuberculosis (31–33). Thus, the context of
race, rather than race itself, can be viewed as
a risk factor.
Race and income, though the most
prevalent indicators in the selection of disad-
vantaged populations for environmental jus-
tice research, should not be the only
variables of concern. There are vulnerable
populations other than the poor and minori-
ties who may also be disproportionately at
risk, such as the very young, very old, preg-
nant, immune compromised, infirm, and
future generations. This poses difficulties in
appropriately and comprehensively choosing
the populations to study.
African-American, Hispanic-, Native- and poor
Americans seem to be the focus of attention. The
young and the elderly should also be included.
But should recent immigrant groups also be
included? Or must they also be poor? In
addition, consideration should be given to future
generations. But how should future generations
be represented? One way is by including aquifers
and forest areas, salt-water swamps, and endan-
gered species, all of which may be extremely
important to future generations, as well as those
already living. (34)
Environmental justice, in this more
inclusive definition, would apply not only
intragenerationally (equity for all people
currently alive) but also intergenera-
tionally, taking into account equity for
future generations.
What Is Counted as a Hazard?
What types of facilities should be included
in determining the existence of dispropor-
tionate environmental burdens? Many of the
studies reviewed focus on only one set of
hazardous land uses, such as transfer, stor-
age, and disposal facilities for hazardous
waste (TSDFs), Superfund sites, Resource
Conservation and Recovery Act (RCRA)
facilities, or Toxic Release Inventory (TRI)
facilities (Table 1). This is done primarily
because these types of facilities are registered
and tracked on a national level, and consis-
tent information is available on each facility,
thus allowing valid comparisons to be made
at the national level.
However, studying the impacts of only
one set of facilities produces misleading and
incomplete results. In many communities,
the most egregious offenders are the small
electroplating plants, auto-body welding
shops, drycleaners, and waste transfer sta-
tions. These types of facilities are typically
not required to register with the federal gov-
ernment, as are TSDFs or TRI facilities. The
small polluters, which cumulatively may be
creating more of an environmental burden
than one large facility, are virtually unregu-
lated and undetected. This makes mapping
their impacts problematic. There is no data-
base available for small geographic areas, and
certainly none on a statewide or national
level, making statewide and nationwide com-
parisons impossible. Most environmental
equity mapping has been restricted to facili-
ties that are on federal lists, because these lists
are standardized and easily obtainable, but
this just touches the tip of the iceberg as far
as environmental burdens in many commu-
nities. Analyzing only the impacts of TSDFs
or TRI facilities diminishes the magnitude of
the total likely impact. Because of reporting
deficiencies and lack of comprehensive data,
total cumulative impacts from all noxious
land uses within a given geography cannot be
readily calculated.
An assumption that all noxious facilities
are equally noxious is another source of
problems in mapping environmental injus-
tices. Many of the spatial analyses assume
that one TRI facility, for instance, is equiva-
lent to any other, but amounts of toxic emis-
sions vary widely among TRI facilities, and
emission levels and toxicity are often not
mapped or factored into the analysis.
Facilities in communities of color are typi-
cally worse polluters than those in White
neighborhoods, receiving less regulatory
enforcement and more lenient fines if
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discovered (1,35). These factors of difference
are mapped more rarely, with many
researchers focusing on simple counts of
noxious facilities of one type or another or a
binary measure of “facility” or “no facility”
within a certain geography.
How Do We Determine Exposure
Potential?
Spatial studies of environmental justice ana-
lyze the characteristics of the population
potentially exposed to a hazardous land use.
Exposure is often determined simplistically
and defined as whether the population is in
the same ZIP code, census tract, county, or
municipal boundary as the noxious facility.
This has the obvious drawback that one
could live right across the county line from a
facility just yards away, but for the purposes
of the analysis would not be considered
impacted by it, whereas one could live on
the opposite side of the county miles away
and still be considered impacted because of
being within the same county as the facility.
This becomes less of a problem for the finer
geographic levels of analysis but is nonethe-
less not a very accurate way of characterizing
the potentially impacted population.
In other analyses actual proximity to the
facility is taken into account by constructing
buffer zones of specified distances around
the facility, capturing the demographic data
for the entire population within the buffer
regardless of what political or enumeration
district they are in. The buffer zones are
intended to act as surrogates for the areas of
impact and are usually established as circles
with a radius of one-half mile or 1 mile, or
other appropriate distance, from the noxious
land use.
Depending on which method is used,
there can be substantial differences in esti-
mating the magnitude and characteristics of
populations affected by noxious land uses.
Figures 1 and 2 illustrate the differences in
the findings using the spatial coincidence
method versus proximity analysis. The loca-
tions of permitted waste–related facilities in
the Bronx, New York City, have been
geocoded and overlaid on the census tract
database (36). Figure 1 indicates the location
of these facilities in relation to the percent-
age of population in each census tract that is
“minority” (37). Figure 2 shows a compari-
son between the spatial coincidence method
and proximity analysis in determining the
potentially affected population. Using the
demographic information for only those
tracts housing a waste-related facility (spatial
coincidence method) does not adequately
capture the potential for exposure, as can be
seen by the multitude of facilities on the
edge of tract boundaries. Additionally, some
tracts are very small, whereas some are very
large, leading to a misrepresentation of the
exposed population. Average household
income was also calculated using the two
methods, with similar results. When the
locations of the Bronx TRI facilities are
added to the waste-related facilities and pop-
ulation characteristics are calculated using
the two methods, we again can see the differ-
ences in the numbers obtained (Table 2).
Proximity analysis is a more useful
means of analysis, but it still does not defini-
tively determine the potential for exposure.
There is little known about the relationship
between distance from a pollution source, such as
a hazardous waste site, and actual health
risks.... Accurate estimation of human expo-
sures to hazardous air pollutants across all levels
of geographic aggregation is constrained by the
paucity of suitable monitoring methods, relevant
ambient measures, and validated models for pre-
dicting exposures to populations of interest. (23).
Assumptions are also made that exposure
risk is distributed equally within a given
geography. The studies that try to account
for risk based on distance assume that the
risk a facility poses bears some relationship
to proximity to the facility, an assumption
that may be inaccurate in many cases.
A better unit of analysis would be one that was
based upon the actual distribution of the risk of
the facility, which would depend on the type of
substances the facility handled, wind patterns, the
hydrology and geology of the site, transportation
routes to the facility, and many other factors. (22)
Not only are these factors very compli-
cated to assess, but data are often simply not
available, or are not available in a uniform
way for the entire study area and reference
comparison areas. Other methods for deter-
mining exposure potential, such as disper-
sion modeling, are discussed below.
How Do We Measure Exposure?
A critical issue in these environmental justice
studies is the lack of a reliable risk exposure
index or proxy.
[Previous studies] of environmental equity lack
both a valid measure of the sources of pollution
to which people may be exposed, and relatedly, a
model that describes the relationship between
proximity to those sources and the likelihood of
exposure. (21)
Actual risk from TRI facilities, for
instance, is dependent on many variables
such as type of facility, substances emitted,
quantities emitted, height of smokestack,
exit velocity, wind direction and speed, pol-
lution controls used, and topographic fac-
tors. Simple distance proximity equations are
inadequate for measuring exposure.
We found that risk-based evaluations can lead to
different conclusions about environmental equity
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Environmental Health Perspectives
Permitted waste-related facilities
Parks and public land
Percent minority population
0–21
22–42
43–64
65–87
88–100
miles
N
The Bronx
New York City
Manufacturing zones in the Bronx
Parks and public land
Manufacturing zones
1012
Figure 1. Distribution of waste-related facilities in relation to minority population, Bronx, New York. Data
from U.S. Bureau of the Census (85), New York City Department of Sanitation (86), New York State
Department of Environmental Conservation (87), and New York City Department of City Planning (88).

than proximity-based evaluations. The differences
can be attributed to two principal factors. One is
that the impact areas in risk-based evaluations are
strongly influenced by the direction of the wind.
The other is that the sizes of the impact areas in
risk-based evaluations vary and are generally
much larger than the circles used in proximity-
based evaluations. (6)
The Geographic Unit of Analysis
The findings of some environmental justice
mapping studies have been diametrically
opposed to those of others. For instance, the
nationwide study by Perlin et al. of TRI facili-
ties at the county level shows a positive corre-
lation between income and environmentally
burdensome land use (23), with household
income increasing in relation to the presence
of TRI facilities, whereas the statewide study
by Pollack and Vittas of TRI facilities in
Florida at the census block group level shows
a negative correlation (21), with household
income declining in relation to the presence
of TRI facilities. Many of these contradictions
and discrepancies can be traced to the geo-
graphic unit of analysis used in the study,
often referred to as the Modifiable Area Unit
Problem (MAUP). Glickman and Hersh (6)
show that altering the geographic boundaries
of the study area has dramatic implications for
the results of the analysis. In their study of
industrial hazards in Allegheny County,
Pennsylvania, they found that
The choice of unit of analysis will affect even the
most basic findings of an environmental equity
study. Had we used only block groups to define
‘community’ we would have found contrary to
expectations that in TRI communities the pro-
portion of blacks and minorities is slightly lower
than in non-TRI communities. Similar results
hold for census tracts. This pattern is reversed,
however, when we look at the proportions for the
combined half-mile radius circles around TRI
facilities vs. the areas beyond the circles. We also
see that the proportion of blacks and minorities is
substantially higher in municipalities with TRI
facilities than in those without such facilities. (6)
Generally speaking, data aggregated at
higher levels of governmental unit (county
or city, for instance) will be less reliable as
indicators of disproportionate burdens, and
less accurate in identifying the affected pop-
ulations, than data aggregated by smaller
units such as census block groups or blocks.
Because there is so much variation in demo-
graphics and facility location within the
larger geographic units, impact and burden
are impossible to determine, and compari-
son among geographic units becomes almost
meaningless. Unfortunately, the availability
of data is often what dictates the level of
aggregation.
To reflect a potential environmental health-based
concept of risk, the boundaries should relate to
exposure or risk from the site; however, a single
boundary reflecting all variations in toxicity and
contaminant fate and transport for each chemical
present plus variabilities in the duration of
human exposure and vulnerability would be vir-
tually impossible…The scale of analysis chosen is
often dictated by expediency, determined by how
existing data bases are aggregated. . . . (38)
Therefore, the selection of the unit of
analysis may, in fact, have little relationship
to the actual geographic extent of exposure
and risk, yet can shape the outcome of the
analysis.
The Potential of GIS for
Environmental Health
and Equity Research
An effective public health management
program demands an understanding of the
spatial relationships between pollution and
health.
GIS can significantly add value to environmental
and public health data in areas such as exploratory
data analysis, hypotheses generation, confirmatory
data analysis, and decision making. (39)
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Table 2. Comparison of spatial coincidence method with proximity analysis in determining characteristics
of the population affected by hazardous facilities, Bronx, New York.
Percent Mean
minority household
population income
Geographic unit of analysis (1990) (1990)
Reference population
New York City (2,218 census tracts) 56 $41,700
The Bronx (355 tracts) 76 $29,200
Bronx manufacturing zones (97 partial tracts) 87 $25,200
Spatial coincidence method
Tracts containing TRI facilities (16 tracts) 88 $24,600
Tracts containing waste-related facilities (14 tracts) 71 $35,400
Tracts containing TRI and/or waste facilities (24 tracts) 78 $31,500
Proximity analysis using buffers:
Tracts within 0.5-mile buffer of TRI facilities (67 partial tracts) 88 $25,800
Tracts within 0.5-mile buffer of waste facilities (39 partial tracts) 87 $26,200
Tracts within 0.5-mile buffer of TRI and/or waste facility (75 partial tracts) 88 $24,600
Figure 2. Comparison of spatial coincidence method and proximity analysis in determining the characteristics
of the population affected by waste-related facilities. Data from U.S. Bureau of the Census (85), New York
City Department of Sanitation (86), and New York State Department of Environmental Conservation (87).
Census tracts within 0.5-mile buffer
of waste-related facilities
Comparison between
spatial coincidence method
and proximity analysis
Percent minority
population in
Bronx County
Percent minority
population in
tracts containing
waste facilities
Percent minority
population within
0.5-mile buffer of
waste facilities
Census tracts containing waste-related facilities
100
95
90
85
80
75
70
65
60
55
Permitted waste-related facilities
Census tracts
0.5-mile buffer of waste facilities

For instance, mapping disease rates
geographically and temporally may shed
light on previously unrecognized patterns,
which will suggest answers, or at least pro-
vide a focus and direction for further study.
GIS can also be used to select case study
areas meeting specific criteria and to create
and test hypotheses relating to environmen-
tal risk factors. GIS can combine health out-
comes on the individual level with exposure
data aggregated at the geographic unit level
(census tract, health district, etc.) and then
model potential exposures, for use in over-
laying the disease incidence data (40).
Although health outcomes were not a
specific part of the studies reviewed, GIS-
based environmental equity research never-
theless provides a valuable tool for health
professionals. Decision making and policy
formulation are enhanced by spatial infor-
mation. Identifying the population likely to
be affected by environmental burdens allows
more effective educational intervention and
the planning of health care delivery systems
(41). It may also help point out the geo-
graphic areas where health assessments
should be a priority.
Integration of Modeling and
Statistical Software with GIS
Some of the problems in using GIS for envi-
ronmental health and equity research are due
to software deficiencies such as the lack of
complex environmental modeling functions
integrated within GIS programs. Having to
use external modeling applications is more
cumbersome for the researcher and limits
the role of GIS to primarily data organiza-
tion and storage, data exploration, and dis-
play (42). This situation could be improved,
however, if the software developers had a
demand to respond to. As more researchers
use GIS in their work, it will be easier to jus-
tify the costs of development to integrate
GIS and modeling software.
This integration has already occurred to
a substantial degree with GIS and complex
geostatistical functions. Many geostatistical
functions have been incorporated into a
number of major GIS software packages or
are available as extensions (separate add-ons
to mapping programs). For instance, the
Spatial Analyst extension is available for
ArcView 3.x software (43), and both Spatial
Analyst and ArcGIS Geostatistical Analyst
have resident spatiotemporal analytic tools.
These include Inverse Distance Weighting
and Spline methods of spatial interpolation
on point data, enabling estimates of data
values at unsampled locations (44). Point
data might represent air monitoring sites;
facilities emitting pollution; soil, air, or
water sample locations; or ZIP code cen-
troids with disease rates attached. Kriging, a
linear interpolation method, is available in
ArcView through an Avenue script (pre-
written applet in the programming language
native to ArcView 3.x). Kriging allows pre-
dictions of unknown values of a random
function from observations at known loca-
tions by using a model of the covariance of
the random function and accommodating
and estimating the underlying trend (45).
Spatial regression and geostatistical
models can also be employed by using spe-
cialized software such as S+ Spatial Stats,
which has been designed by a software devel-
oper (46) to interface almost seamlessly with
the ArcView 3.x software (47). Scripts writ-
ten by individuals and widely accessible via
user group web sites can also be used directly
with industry-standard mapping packages to
perform cluster analysis such as K-function
and Gi* statistics (48,49). The ease of surface
modeling using these built-in and loosely-
coupled local and global interpolators will
change the way we measure and predict envi-
ronmental burdens and greatly improve the
speed and efficiency of data exploration.
Building Better Databases
The real constraint in using GIS for health
and equity research is not software, how-
ever, but data deficiencies. Incomplete, inac-
curate, and nonexistent information does
not necessarily reflect our state of knowl-
edge about the issues but may be merely an
indication of our society’s informational
(and funding) priorities.
For instance, it is virtually impossible to
create a measure of exposure and risk with-
out more detailed and careful data on actual
emissions and ambient conditions. Analysis
must be able to take into account measured
quantities to estimate cumulative impacts
from multiple sources of pollution and syn-
ergistic impacts from combining pollutants.
Studies that investigate exposure to only one
type of hazard are not helpful in determin-
ing the full extent of the impacts. Many of
the databases relied upon by researchers,
such as TRI, are notoriously inadequate for
detailed modeling. TRI information is self-
reported by the facilities and is based on
estimated emissions not measured quantities
(50,51). Some researchers compound the
errors by aggregating releases to soil, water,
and land as one quantity of raw pounds,
although the effects on human health and
the environment differ markedly by media.
Reliable health assessments are necessary
for environmental health and equity research
to progress to the next level. Issues such as
patient confidentiality, lack of data sharing
among hospitals, private doctors, and other
health care providers, and few mandatory
reporting mechanisms all conspire against
comprehensive health databases. There is no
national registry database for chronic
diseases such as asthma, for example.
GIS analysis has been less successful (or
less well used) in addressing issues such as
population mobility, occupation, and
genetic predisposition, although these all
potentially play a role in the relationship
between exposure and health. An under-
standing of population mobility, for exam-
ple, is crucial to tracking the environmental
exposures over time of susceptible popula-
tions, but databases detailing population
movement have not been widely developed.
An index of residential neighborhood stabil-
ity could be created for community compari-
son purposes, but this would be of limited
value in monitoring spatial correspondence
of specific hazards and health outcomes.
Some of the same data deficiency problems
exist for attempts to spatially link occupation
and health hazards and to monitor specific
populations by residence location.
For relatively small geographic areas,
however, it is possible to develop something
approaching a comprehensive hazards data-
base and to perform a more complete health
and population survey. In the Greenpoint-
Williamsburg community of Brooklyn, New
York, publicly available environmental data-
bases were assembled into a GIS, then sup-
plemented with local knowledge bases, such
as a detailed lot-by-lot land use inventory,
and updated regularly to keep the data cur-
rent (52,53). Because many hazardous facili-
ties are not tracked at a national, statewide,
or municipal level, community-led invento-
ries and monitoring of local conditions are
essential in assessing environmental loads.
Development of an Exposure Index
Several environmental health and equity
studies have employed an exposure index to
reflect some quantification of a population’s
risk from environmental burdens beyond
simply proximity to or presence of a haz-
ardous facility. In earlier studies exposure
was often a measure of facility capacity,
such as tons per year of hazardous waste
handled (26) or total pounds of pollutants
released per year, divided proportional to
area in the geography of concern (17) or
divided among the potentially affected pop-
ulation [as in the Population Emission
Index of Perlin et al. (23)].
The total quantity of chemicals handled
or released by a facility does not directly cor-
respond to health impacts, however. Bowen
et al, in their study of TRI facilities in Ohio
(24), took into account not only the number
of raw pounds of chemicals released but also
pounds adjusted for toxicity, using
Threshold Limit Values (TLVs). Although
TLVs are available for many of the chemicals
on the TRI list, it remains a somewhat
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problematic index for health and equity
assessments, generally having been developed
and used to gauge occupational safety
among a healthy worker population. It is
unknown how well this index estimates haz-
ard for more vulnerable populations such as
children, the elderly, pregnant women, and
the immune compromised.
The study of hazardous facilities in
Minneapolis, Minnesota, by McMaster et al.
(54) refined the measurement of exposures.
After ranking and mapping facilities by pro-
portional symbols according to total chemical
poundage released per year, they applied the
Pratt Index, which compares chemicals based
on their environmental behavior and toxicity
by calculating a ratio of potential exposure to
toxicity (55). This study found that minority
groups and poor people were not only more
likely to live in proximity to hazardous land
uses, but were also burdened by a higher con-
centration of such facilities, with a higher
level of exposure to toxic substances.
In a study of TRI facility impacts in
Oregon, Neumann et al. (56) used a media-
specific Chronic Toxicity Index (CI), which
incorporates chronic oral toxicity factors for
carcinogens and noncarcinogens to estimate
and compare relative hazards from TRI
releases. Although not useful for identifying
population or individual risk, it is an excel-
lent preliminary screening method.
The ranking of TRI emissions using the CI
combined with knowledge about the demo-
graphics of the communities at risk of exposure
such as population density, race, ethnicity,
socioeconomic status, and age is an attempt to
help set priorities for future risk or public health
assessments, epidemiological studies, and basic
research on cellular mechanisms associated with
environmental health problems. (56)
The limitations of data restrict the
completeness of the CI as a measure of expo-
sure. The CI is based on oral toxicity
because inhalation reference doses for TRI
chemicals are not available. However, inhala-
tion reference concentrations would obvi-
ously be more useful in estimating hazard
from TRI air releases. The CI also does not
measure acute toxicity, which would be use-
ful in identifying populations at greatest risk
from industrial accidents.
To be more meaningful, an exposure
index should reflect an estimate of total envi-
ronmental loads resulting from all types of
pollutants, i.e., a cumulative load index.
Theoretically, any given location could be
assigned a number indicating the total envi-
ronmental load borne by people in that
geography. A weighting and ranking index
could be developed with a unit of measure-
ment based on toxicity and concentration of
each pollutant, weighted for severity of
potential impact from exposure. This
methodology, although useful in a planning
and policy context, would be of more lim-
ited use for regulatory and enforcement pur-
poses, given the structure of current laws, as
it is not based on existing legal standards.
However, the cumulative impact index
would take into account effects of pollutants
that individually may not exceed thresholds
but when considered together may consti-
tute an impact to human health (57).
Linking the cumulative load model to the
GIS would allow visual inspection of the
spatial distribution patterns and complex
spatial analyses to be performed and result in
a block-specific score of carcinogenic and
noncarcinogenic pollutant loads.
Development of an aggregate environ-
mental load index would be of value in
establishing baseline profiles of communities
for comparative purposes and documenting
the relative environmental loads of various
communities. The aggregate environmental
load index would also allow communities
with the highest environmental loads to be
targeted for pollution prevention and r-
emediation programs and enable examina-
tion of the correspondence between
incidence of environmentally linked diseases
and environmental loads. GIS could also
facilitate research into the synergistic effects
of toxic substances by pinpointing the geo-
graphies subjected to such environmental
loads and comparing them with known or
suspected health problems in these areas.
Clearly, further refinements in exposure
indices will help estimate potential for health
effects from hazardous facilities. An index
that incorporates not only a toxicity factor
but also information about persistence and
environmental fate of toxic chemicals such as
discussed by Jia and Di Guardo (58) will
advance GIS-based health and equity
research significantly.
Advanced Proximity Analysis,
Dispersion Modeling, and Fate
and Transport Simulation
In addition to exposure indices reflecting
toxicity and other measures of impact,
advances have been made in more precisely
identifying the geographic extent of the
exposure from hazardous facilities. These
methods include dispersion modeling of air-
borne pollution and flow and transport
modeling of contaminants in subsurface
media, as well as more advanced methods of
proximity analysis.
For instance, Sheppard et al. (59), in a
study of the distribution of hazardous land
uses in Minneapolis, Minnesota, developed a
Proximity Ratio that was used with both the
spatial coincidence and buffering methods to
determine affected populations. The ratio
was computed by dividing poverty rates for
specific population groups in geographies
containing a hazardous facility by poverty
rates for that group in geographies without
such a facility. A high proximity ratio means
that a particular group living near a haz-
ardous facility is more likely to be in poverty
than the equivalent nonproximate group.
They found that for all groups studied
(African Americans, Latinos, Native
Americans, non-Hispanic Whites, and chil-
dren under the age of 5 years) proximity
ratios exceeded 1, meaning that people
within certain distances of a hazardous facil-
ity are more likely to live in poverty than
their counterparts outside the buffers or cen-
sus tracts containing the facilities.
To make sure these ratios reflect a true
significance and do not occur just by chance,
a randomization routine was run on the TRI
location data. Through Monte Carlo simula-
tion, 1,500 possible locations for the TRI
facilities were generated to test whether the
pattern of higher poverty rates near haz-
ardous facilities was coincidence and if these
high poverty rates would be observed if TRI
sites had been located randomly within
Minneapolis. The proximity ratios were
found to be unusually high compared with
those that might have resulted by chance.
Another technique used to improve the
assessment of impacted areas and popula-
tions is detailed in the study by Neumann
et al. (56). In addition to buffering facilities
and estimating exposure of populations
within the TRI buffers using the CI, as
mentioned above, they also buffered the
census tract centroids. By doing this they
were able to capture information on popu-
lations exposed to multiple sources of pol-
lution by aggregating emissions from all
facilities within the centroid buffers. This is
an important consideration in dense urban
areas where hazardous facilities may be
close to other hazardous facilities and proxi-
mate populations are at risk from exposures
to emissions from multiple facilities. This
yields a more realistic estimation of actual
exposure.
Provided that detailed facility informa-
tion is available, dispersion modeling may
offer the best means of determining the geo-
graphic extent and severity of exposure. By
using mathematic models executed exter-
nally to GIS, the spatial patterns of the aver-
age annual concentration of each pollutant
emitted to the air by a hazardous facility can
be estimated. Glickman and Hersh used a
variety of models developed by the U.S.
Environmental Protection Agency (U.S.
EPA) and the National Oceanic and
Atmospheric Administration (NOAA) in
their study of hazardous facilities in
Pennsylvania (6). The Areal Locations of
Hazardous Atmospheres (ALOHA) model
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(60), for instance, was used to determine the
worst-case chemical in each facility, i.e, the
one with the longest plume. A probability
distribution of wind speeds and directions
was factored in to create a probable impact
area for each facility, creating a plume buffer
that was brought into the GIS and overlaid
on the census data (similar to circular buffers
used in other studies) to determine the char-
acteristics of the exposed populations. Plume
extent was combined with dose–response
rates to yield risk estimates (average individ-
ual risks) measured in terms of the per capita
expected number of premature deaths in a
lifetime. The analysis used two factors to
form a toxicity weight: a measure of potency
for carcinogens, and reference dose (RfD), a
measure for noncarcinogens. The total vol-
ume of emissions was multiplied by the toxi-
city weight to derive a hazard rating.
Dispersion modeling was also used in
research by Chakraborty and Armstrong in
Des Moines, Iowa (61). The racial and eco-
nomic characteristics of the populations
exposed to toxic releases from TRI facilities
based on various circular buffers were com-
pared with those within plume buffers
obtained through dispersion modeling. A
composite plume buffer was developed based
on the largest chemical release at each facility
and averaged weather conditions. Their
research found that a larger proportion of
minorities and people below the poverty line
live within the plume buffers compared with
the circular buffers.
A public health assessment study being
conducted by the Agency for Toxic
Substances and Disease Registry (ATSDR) is
using air dispersion modeling integrated
with GIS to determine the geographic extent
of exposure and the demographic character-
istics of the population affected by two phos-
phate-processing plants near an American
Indian reservation in Idaho (62). Using the
U.S. EPA industrial source complex model,
particulate matter (PM) emissions will be
modeled based on specific information about
area topography and meteorology. This will
produce concentration isopleths (contour
lines) of particles smaller than 2.5 µm in
diameter (PM
2.5
—those of greatest health
concern because they can penetrate the sen-
sitive areas of the respiratory tract). These
isopleths will be imported into the GIS and
transformed into concentration polygons,
which will be overlaid with census data. The
overlay analysis will clip the demographic
information of people who have been
exposed to PM
2.5
above the health-based
standard, reflecting the concentration poly-
gons predicted by the air dispersion model-
ing. The demographic data about total
population exposed, total susceptible popu-
lations exposed, and the socioeconomic sta-
tus of persons exposed will be obtained and
compared with address-geocoded mortality
data for respiratory and cardiopulmonary
deaths. By performing a point-in-polygon
analysis, it can be determined if the
geocoded addresses for the deaths are within
the polygons representing a geographic area
where people have been exposed at levels of
health concern.
The report concludes with a number of
cautions about the proposed methodology,
and the limitations of GIS and air dispersion
models in exposure assessments:
The problems of areal interpolation and the fal-
lacy of the homogeneous polygon must also be
considered carefully when evaluating the method
used to determine the demographics of the
exposed population defined by the air dispersion
model. The polygons that define the various
exposure levels predicted by the air dispersion
model will not correspond to the US Census
Bureau’s reporting units (e.g., census tracts or
block groups, etc.). Furthermore, the populations
within the census units are not evenly distrib-
uted. Therefore, an overlay analysis method that
does not provide some estimate of the population
densities within each census unit will likely pro-
duce much exposure misclassification. ATSDR
uses an area proportion program (a script written
in Avenue, the programming language of
ArcView GIS [ESRI, Redlands, CA]) that is good
for many applications; however, it assumes that a
population within a given census reporting unit
is evenly distributed. . . . Other estimates are
being evaluated that provide better estimates of
population densities within the census reporting
units. The two methods currently being evalu-
ated are the kernel density method and the cen-
sus control method. Both of these methods use
techniques that “disaggregate” the census report-
ing units, helping to alleviate the areal interpola-
tion problem and avoid the fallacy of the
homogeneous polygons. . . . An ecologic study
design based on a GIS analysis carries with it
unique methodological issues beyond those that
may be encountered in other epidemiologic
designs. Ecologic fallacy, disease and exposure
misclassification, and control for confounding
must be carefully considered when designing an
ecologic study and in interpreting its results. (62)
In “Establishing Links between Air
Quality and Health: Searching for the
Impossible?”, Dunn and Kingham outline
some of the problems associated with disper-
sion modeling (63). The models require
detailed inputs about emissions and facilities
that may not be available or accurate, and
they rely on assumptions about meteorologic
and topographic conditions that may not
reflect reality. Small differences in terrain
and building configurations can affect the
behavior of airborne contaminants, and
these fine differences are difficult to repre-
sent adequately in a model. Additionally,
most of the models are based on point-
source pollution (from a smokestack) and do
not take into account fugitive emissions
(from non–point sources) or pollution from
linear sources such as roads. Results of
dispersion modeling should therefore be
treated with caution.
GIS have also been used to assess fate
and transport of contaminants in the subsur-
face environment. As with air dispersion
modeling, groundwater flow models are gen-
erally executed outside the GIS environ-
ment, with results brought into the GIS in
the form of contaminant concentration iso-
pleths, which are then overlaid with the
demographic data to assess the extent and
characteristics of the exposed population. An
exposure assessment case study conducted by
ATSDR attempted to link contamination
from environmental sources with increased
health risk to humans (64). In “Exposure
Assessment of Populations, Using Environ-
mental Modeling, Demographic Analysis,
and GIS,” an external mathematic model
using a finite-element Galerkin procedure
provided the researchers with contours delin-
eating the geographic extent of groundwater
contamination from trichloroethylene
released from an industrial facility (64).
Various groundwater modeling and simula-
tion techniques are well established, and
those used in this study include steady lay-
ered aquifer model and contaminant trans-
port in layered aquifer media, which were
run to simulate different scenarios based on
various assumptions about contamination
levels and remediation plans. Because the
industrial plant had continued to contami-
nate the groundwater for more than 20 years,
and nearby residences were eventually con-
nected to town water supplies and thereafter
presumably no longer exposed to the conta-
minants in the groundwater, the study had a
temporal as well as a spatial component.
By integrating the results of the model-
ing with the GIS and demographic data-
bases, the researchers were able to obtain a
snapshot of the exposed populations.
However, because this was a longitudinal
study exploring exposures over time, popula-
tion mobility is a factor in assessing human
health impacts. Availability of additional
demographic information on the distribu-
tion and mobility of households would facil-
itate the generation of more precise spatial
and temporal exposure patterns that could
easily be accommodated by methodology
described by Maslia et al. (64). As with
many of the other studies discussed, the lack
of key data is a prime impediment to preci-
sion and accuracy in exposure assessments.
Conducting Neighborhood-Scale
Analyses
Most environmental health and equity
studies have been conducted at the national,
statewide, regional, or city level of analysis,
as evidenced by the majority of studies
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reviewed in this paper. Because of the
volume and type of data required to accu-
rately inventory and assess existing condi-
tions and to model future conditions, it is
likely that neighborhood- or community-
level analysis will be more feasible and useful
than studies of larger geographic extents. By
definition, studies covering larger geogra-
phies use coarser-resolution data and cannot
pinpoint as accurately the spatial patterns
and connections that may exist.
Neighborhood-scale studies also have the
advantage of being able to incorporate local
knowledge bases, which can be used to aug-
ment (and verify the accuracy of) publicly
available data sources on environment and
health. For instance, communities can
inventory the locations of hazardous facilities
that do not appear on any state or national
list, such as drycleaners, solid waste-related
facilities, junkyards, auto bodyshops, small
industrial facilities that fall beneath the
reporting thresholds for TRI, or coal-burn-
ing schools, as well as confirm and cross-
check the locations and types of facilities
listed by governmental permitting agencies.
It is also less complex to aggregate exposures
from multiple and varied sources of pollu-
tion at a neighborhood scale, and more
likely that necessary informational inputs
can be obtained in a comprehensive way for
modeling purposes. The locations of sensi-
tive populations such as schools, day care
centers, hospitals, and nursing homes can
also be more accurately mapped and quanti-
fied at the neighborhood scale. There is the
potential for using geodemographic data at a
finer resolution, as the census data can be
supplemented in many cases by data col-
lected by the city’s planning department or
by community data bases. McMaster et al.
discuss using GIS to identify, map, and
monitor potential environmental hazards in
four Minneapolis neighborhoods (54):
Neighborhood-scale analysis and mapping holds
great promise for assisting communities in identi-
fying risks posed by environmental hazards to dif-
ferent social groups in their neighborhood and for
the development of more detailed, complete, and
positionally accurate information by incorporat-
ing ‘local knowledge’ into the GIS database. (54)
Perhaps the most important benefit of
neighborhood-scale analysis is the potential
for direct involvement of the affected peo-
ple and the intimate knowledge of their
surroundings that they bring to the project,
along with the sense of ownership that their
involvement with the project brings to
them (65).
Community-based GIS projects have
been instrumental in advocacy work and
have proved effective in contributing to
community organizing, data collection and
documentation of land use, public health,
and environmental conditions of low-
income neighborhoods and communities of
color for purposes of influencing policy and
planning decisions. The New York City
Environmental Justice Alliance (NYCEJA) is
an example of a nonprofit organization using
GIS to promote environmental justice:
Our vision of technical assistance is not an end,
but a means for building our collective capacity to
fight against environmental discrimination. . . .
[D]isparate environmental conditions are often
disregarded by policy makers. Documenting the
environmental conditions in underserved commu-
nities is important because data is often out of
date, inaccessible, or not available for these areas.
NYCEJA strives to not only assist its members col-
lect and analyze data, but it also ensures that grass-
roots communities are directly involved in the
entire process and to advocate for themselves. (66)
Implications of Policy and Planning
Decisions on Environmental Health
and Equity
Most environmental equity studies are based
on the locations of specific hazardous facili-
ties. Not only must we prepare more com-
prehensive analyses based on multiple
sources of pollution, but we must also con-
sider other factors in the potential for expo-
sure and disproportionate burdens. We need
to be concerned not only with the location
of existing and past hazardous land uses and
the siting of such facilities disproportionately
in poorer neighborhoods and communities
of color but also with the distribution of
locations having the potential to house
hazardous facilities.
My own research on industrial zoning
changes and environmental justice in New
York City indicates that the reasons for dis-
proportionate concentrations of noxious
land uses go deep into underlying policies
and assumptions (67,68). In New York City,
as in many other places with zoning regula-
tions, noxious facilities can be located only
in areas zoned for manufacturing (M zones),
and M zones tend to be located primarily in
or near neighborhoods where residents are
poorer than average and have a higher-than-
average likelihood of being African
American, Latino, or other minority. This
obviates the principles of the city’s Fair
Share guidelines (69), which were intended
to ensure that the burdens of urban (post-)
industrial life be shared equally and not fall
disproportionately on any group or area.
This inequitable state of affairs in New
York City is perpetuated by city planning
practices and policies that continue to decrease
the areal extent of industrial zones in more
affluent and less heavily minority neighbor-
hoods while increasing the areas of industrial
zones in poorer and more heavily minority
neighborhoods. This serves to further concen-
trate noxious land uses within predominantly
poor and minority communities. Because this
situation likely is not unique to New York
City, zoning and other planning policies and
practices must be taken into account when
evaluating exposure and risk from hazardous
land uses.
Ultimately, any siting of hazardous activ-
ities may lead to unjust exposures. The idea
that we can solve the problem of dispropor-
tionate toxic exposures by spreading the pol-
lution around more equitably is absurd.
Many believe the real solution is to be found
in eliminating or sharply reducing the need
for many of these noxious facilities to exist
(70). This, of course, will require structural
changes in patterns of consumption, waste
production and disposal, transportation, and
community governance, planning, and
policy making. There have been instances,
however, where “Not In My Back Yard” has
become “Not In Anybody’s Back Yard,”
thereby forcing government and industry to
evaluate broader issues, including “the pro-
priety of a production system under private
control where, in the quest for profit, the
public is exposed to known risks” (71).
Another aspect of the long-term solution is
to make noxious facilities less harmful, by
pollution prevention techniques, source
reduction of toxic substances, strengthened
and evenly applied enforcement of environ-
mental regulations, and equitable remedia-
tion of hazardous conditions.
Making the Connection
between Environmental
Justice and Environmental
Health
Although showing environmental inequity
regarding the distribution of noxious facilities
is certainly of consequence, especially in com-
bating future inequities, it is probably more
critical at this point to demonstrate linkages
between environmental burdens and adverse
health impacts. Only when the spatial corre-
spondence is clear can public health and envi-
ronmental protection officials, the medical
research community, health care providers,
and pollution prevention scientists begin to
develop solutions to existing environmental
injustices and resulting health effects. People
within communities disproportionately bur-
dened with pollution are suffering adverse
physical and psychologic impacts, as well as
economic impacts, according to a wealth of
anecdotal reports and empirical research
(28,72–81). It is important to show the dis-
proportionate effects of pollution rather than
just the fact that disproportionate distribution
of pollution sources exists.
There are encouraging precedents for
positive results stemming from such research
and subsequent actions based on the
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research. For instance, studies from the
1970s suggested that the high rate of child-
hood lead poisoning in inner cities, dispro-
portionately affecting minority and
low-income children, was connected to the
high traffic volumes in these areas and the
concomitant exposure to lead-based gasoline
emissions (82). In large measure because of
these findings, lead in gasoline was phased
down by U.S. EPA regulation in the 1980s,
and thereafter the rate of childhood lead poi-
soning dropped dramatically, demonstrating
the potential for reducing unjust environ-
mental exposures (83).
A more recent example is given in the
paper “Impact of Changes in Transportation
and Commuting Behaviors during the 1996
Olympic Games in Atlanta on Air Quality
and Childhood Asthma,” which shows that
childhood asthma events were significantly
reduced in the 17-day period when vehicular
traffic was curtailed in the metropolitan area
because of the Olympic Games of Atlanta
(84). Concomitant changes in air quality
were also examined and compared with the
4 weeks preceding and following the games.
Peak daily ozone concentration decreased
nearly 28%, peak weekday morning traffic
counts dropped nearly 23%, and the num-
ber of asthma acute-care events decreased
44% during the Olympic Games. This indi-
cates that the decreased traffic density was
“associated with a prolonged reduction in
ozone pollution and significantly lower rates
of childhood asthma events”(84). However,
such clear-cut cases of spatial correspondence
and causality are unfortunately rare.
Tracing the evolution of GIS-based envi-
ronmental equity research has shown that
although refinements in methods and tech-
niques have been made, in most cases we are
still far from producing conclusive spatial
correlations. Nevertheless, the bulk of the
research, from the most basic binary studies
of presence or absence of a hazard to the
more sophisticated exposure indices, model-
ing, and statistical analyses, have all tended
to find a disproportionate environmental
burden upon the nation’s poor and minority
populations. Because of the lack of compre-
hensive data and the conservative nature of
the methods used in these studies, there is a
great likelihood that the disproportionate
burden has been vastly underestimated.
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