Using Geographic Information Systems to
Create and Analyze Statistical Surfaces of
Population and Risk for Environmental
Justice Analysis*
Jeremy Mennis, University of Colorado
Objective. Methodological issues associated with the conventional statistical approach to environmental justice research, such as scale of analysis, continue to make
assessments of environmental injustice problematic. Geographic information systems (GIS) can be used to facilitate multiscale analysis through the generation of
statistical surface representations of both socioeconomic character and environmental risk. Methods. As a case study, U.S. Bureau of the Census and U.S. Environmental Protection Agency data sets were used to generate statistical surfaces of
socioeconomic character and environmental risk for the southeast Pennsylvania
region. Results. Analysis of these statistical surfaces reveals that socioeconomic status
decreases with proximity to, and density of, hazardous facilities. Conclusions. Further research calls for incorporating other relevant information, such as amount and
toxicity of toxic release, into GIS-based statistical surface representations of risk.
Research in environmental justice investigates whether certain disadvantaged segments of the population, typically minorities and/or the poor, bear
a disproportionate burden of environmental risk. The recent attention on
environmental justice can be traced to the release of studies by the U.S.
General Accounting Office (GAO; 1983) and the United Church of Christ’s
Commission for Racial Justice (CRJ; 1987) that reported evidence of racially based discrimination in the locational distribution of certain environmentally hazardous facilities. The U.S. Environmental Protection
Agency (EPA) has also recognized the issue of environmental justice in
forming federal environmental policy (EPA, 1992). Subsequently, there have
been many empirical studies that have found evidence for racially based inequity in the siting of hazardous facilities on the national and regional scales
(e.g., Bullard, 1990; Mohai and Bryant, 1992; Ringquist, 1997). Other
*Direct all correspondence to Jeremy Mennis, Department of Geography, CB 260, University of Colorado, Boulder, CO 80309-0260 <jeremy@colorado.edu>. The author will
share all data and coding materials upon request for purposes of replication. I would like to
thank Brian Lipsett of the Environmental Background Information Center for his contributions to this research and Colin Flint and the anonymous reviewers for their helpful comments on a previous version of this article.
SOCIAL SCIENCE QUARTERLY, Volume 83, Number 1, March 2002
©2002 by the Southwestern Social Science Association
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Social Science Quarterly
studies, however, have presented results that counter the claim that race is a
primary determinant in the siting of hazardous facilities (e.g. Anderton et
al., 1994; Bowen et al., 1995).
A number of researchers, including those on both “sides” of the environmental justice debate (i.e., over whether environmental injustice even exists
and/or is pervasive), have used geographic information systems (GIS) to
manage and structure environmental justice analyses (e.g., Glickman,
Golding, and Hersh, 1995; Boer et al., 1997; McMaster, Leitner, and Sheppard, 1997). The benefits of using GIS for environmental justice research
are relatively straightforward: environmental justice is an inherently spatial
issue (i.e., what is the spatial relationship between the distribution of people
and environmental risk), and GIS provides an efficient environment for the
management, analysis, and display of spatial environmental justice data.
However, the “conventional” statistical approach to environmental justice
analysis, and the GIS software and data that are used to support this approach, adhere to particular models of the real world that impose representational and methodological constraints and assumptions on the way
environmental justice is understood and therefore analyzed. Many of these
methodological issues lie at the foundation of the dispute over the interpretation of statistical evidence of environmental injustice.
Unfortunately, the methodological choices made by environmental justice
researchers often go unacknowledged in the interpretation of evidence of
environmental injustice. The purpose of this article is to describe the methodological issues and problems associated with using GIS in environmental
justice research. In addition, I demonstrate how GIS-based techniques associated with the generation and analysis of statistical surfaces can mitigate
some of these methodological problems. These techniques are applied to an
analysis of environmental justice in the southeast Pennsylvania region, including the city of Philadelphia, as a case study.
GIS and the “Conventional” Statistical Approach to
Environmental Justice
The conventional statistical approach to investigating environmental justice generally entails identifying those communities (however “community”
may be defined) that host environmentally hazardous facilities, tallying the
racial and/or socioeconomic character of those host communities, and comparing that socioeconomic character to those communities in the region
that do not host environmentally hazardous facilities or to the character of
the region at large (e.g., CRJ, 1987; Anderton et al., 1994). A variation on
this approach uses aspects of socioeconomic character to predict the probability of the presence of a hazardous facility in a given community (e.g.,
Boer et al., 1997; Ringquist, 1997). Evidence of injustice is then defined as
when communities that host environmentally hazardous facilities have sig-
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283
nificantly higher rates of minorities and/or other indications of socioeconomic disadvantage as compared to nonhost communities. This methodology of environmental justice analysis is easily implemented in a GIS using
U.S. Bureau of the Census demographic and boundary data, hazardous facility data derived from publicly available EPA databases, and basic statistical functions found in commercial GIS packages or in separate statistical
software packages.
There are two problematic issues associated with the conventional statistical approach that are particularly relevant to its implementation in a GIS:
the scale of analysis (Cutter, Holm, and Clark, 1995; Sui, 1999) and the
ability of census areal units to capture the notion of “community” in a
meaningful way (Zimmerman, 1994; Williams, 1999). Scale of analysis
concerns both the scope of analysis (the region that the study covers) and
the resolution of analysis (which generally refers to the choice of areal unit
at which demographic data are represented and tallied, e.g., zip code versus
census tract). However, this definition of resolution is problematic, because
census tracts (and nearly all census- or other organization-based geographic
zonation schemes) vary widely in their areal extent: they are typically much
smaller in urban areas than in rural areas. Despite this issue, the choice of
areal unit often serves as the definition of “community” and is then used to
determine whether a community does or does not host an environmentally
hazardous facility.
This issue of choice of resolution in environmental justice analysis is associated with what is referred to as the modifiable areal unit problem (MAUP;
Openshaw, 1983). The MAUP concerns the fact that varying the scale of
data aggregation, and/or aggregating data using different aggregation
boundaries at a single scale, may affect the results of spatial statistical analysis. It has been shown that because of the MAUP, results from the statistical
analysis of census data may be manipulated by using different census areal
units (Fotheringham and Wong, 1991).
As an example of the impact of the MAUP on environmental justice research, consider the work of Glickman, Golding, and Hersh (1995), who
used GIS to examine the demographic character of communities that host
Toxic Release Inventory (TRI) facilities in Allegheny County, Pennsylvania.
The EPA-maintained TRI database is composed of manufacturers that are
required by law to report to the EPA any annual release of greater than
25,000 pounds of certain toxic chemicals. Glickman, Golding, and Hersh
(1995) reported mixed, sometimes contrary, results concerning evidence of
injustice depending on which areal unit was used to define “community.”
For instance, when “communities” are defined by census block groups or
tracts, the percentage of minorities in TRI-host communities is not significantly different than that in nonhost communities. However, when municipalities, a generally larger areal unit than block groups or census tracts, form
the basis for defining “community,” TRI-host communities have significantly higher proportions of minorities than nonhost communities.
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A number of researchers have also used GIS to measure proximity to a
hazardous facility using “distance buffers” as a means to define community
or exposure to risk (e.g., Glickman, Golding, and Hersh, 1995; Boer et al.,
1997). In this case, the issue becomes that of how to identify the population
that is within a given distance of a hazardous facility. In some GIS packages,
population data that are assigned to a given areal unit are considered within
a proximity buffer if any portion of the unit overlaps with the buffer. Chakraborty and Armstrong (1997) refer to this method as the polygon containment method (Figure 1). This method may lead to misleading calculation of
within-buffer population character, since the people living within the overlapping areal unit may in actuality be concentrated in a particular portion of
the areal unit that is not actually within the distance buffer.
FIGURE 1
Three Different Methods of Measuring Demographic Character within
a Given Proximity of a Hazardous Facility
NOTE: Panel (a) depicts polygon containment, panel (b) represents buffer containment, and
panel (c) shows centroid containment. The triangle in each panel represents the location of a
hazardous facility, the bold circle represents an arbitrary buffer distance from that hazardous
facility, and the other linework represents block group boundaries. Those block groups designated as “within” the distance buffer using each of the three different methods are shaded a
darker gray.
Zimmerman (1994) notes that GIS can be used to partition the population data assigned to an areal unit that is only partially within a distance
buffer into “inside-the-buffer” and “outside-the-buffer” portions based on
the percentage of the areal unit that lies within and without the distance
buffer, respectively. Chakraborty and Armstrong (1997) refer to this method
as the buffer containment method (Figure 1). However, this approach assumes a homogeneous distribution of population throughout the areal unit.
An alternative, called the centroid containment method (Chakraborty and
Armstrong, 1997; Figure 1), is to represent population data as assigned to
an areal unit centroid point. If the centroid falls within the distance buffer,
the population data for the entire areal unit represented by that centroid is
considered within the buffer. Again, however, error may occur if the centroid falls within the buffer but the actual population is concentrated in a
portion of the areal unit outside the buffer.
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285
A number of authors have argued that there exists an “appropriate” areal
unit of analysis for environmental justice studies (e.g., Anderton et al.,
1994; Yandle and Burton, 1996). Sui (1999) notes, however, that an environmental justice study done at only one scale or based on one particular
areal unit cannot, by definition, produce a reliable indication of environmental justice or injustice, because one can never tell how the analytical
results were affected by the nature of the data aggregation. As an approach
to this problem, GIS has been suggested as a means to support multiscale
environmental justice analysis (McMaster, Leitner, and Sheppard, 1997; Sui,
1999). I argue that the purpose of multiscale analysis is not to find the
“best” scale of analysis but to investigate how socioeconomic character and
its spatial relationship with environmentally hazardous facilities varies across
scales. This information may indicate the subtle and complex demographic
patterns that lie at the root of the environmental justice debate.
Environmental Justice in Southeast Pennsylvania
I propose a GIS-based multiscale environmental justice analysis methodology that aims to mitigate the impact of the MAUP and facilitate a more
exploratory approach toward investigating the distribution of socioeconomic character and its spatial relationship to the locations of hazardous
facilities. This approach uses remotely sensed imagery and dasymetric mapping techniques to transform demographic data from a representation based
on areal units (e.g., census tracts), called the vector model in GIS, to a representation based on a statistical “surface” of demographic distribution. Statistical surfaces are typically represented in GIS using the raster model, an
exhaustive tessellation of space into square grid cells that each contain a
value for a particular variable. It is important to note that both the vector
and raster models are geometric representations of the real world and
therefore simplify and generalize the spatial distribution of a demographic
variable according to the nature of that geometry. However, because raster
grid cells are typically significantly smaller than their vector areal unit
counterparts, the statistical surface approach to representing population allows for data aggregation to a variety of (larger) areal units, facilitates the
exploration of how demographic character varies across scales, and provides
the means to create more informative visualizations of the distribution of
demographic character (Bracken, 1993; Martin, 1996). This approach
therefore provides the means to mitigate the MAUP in environmental justice research by facilitating multiscale analysis and aiding in the creation of
more accurate maps for cartographic analysis. As a demonstration of the
statistical surface approach, I present a case study investigation of environmental justice in southeast Pennsylvania, encompassing Philadelphia, Bucks,
Montgomery, Chester, and Delaware counties (Figure 2). This analysis was
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performed using ArcView GIS by Environmental Systems Research Institute,
Inc. (Redlands, California).
FIGURE 2
Distribution of Hazardous Facilities and Percentage Minority by Block Group,
Southeast Pennsylvania Study Area
NOTE: Figure depicts Philadelphia, Bucks, Montgomery, Chester, and Delaware counties. The
small box in the center of the figure refers to a detailed view shown in Figure 3.
I hypothesize that in the southeast Pennsylvania region, disadvantaged
socioeconomic status decreases as distance to hazardous facility increases. By
modeling population as a surface, as opposed to an aggregation of areal
units, demographic variables that indicate socioeconomic character can be
tallied within a series of distance buffers generated from the hazardous facility locations. Three demographic variables are used to indicate socioeconomic character in this case study: number of minorities, number of people
living below the poverty line, and number of people over the age of 25 with
a college degree. These population data (1990 figures) were acquired from
the U.S. Bureau of the Census at the block group level. Although block
level data are available, they are rarely used in environmental justice analyses
because many of the U.S. census’s demographic variables are not available at
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287
the block level. In addition, the sheer number of blocks, even within a single
metropolitan area, can prohibit efficient data handling. Data on environmentally hazardous facilities in the Philadelphia region were acquired from
1995 EPA databases including 42 treatment, storage, and disposal facilities
listed in the Biennial Reporting System (BRS) and 368 additional hazardous
facilities listed in the TRI database.
Scott et al. (1997) describe a framework for mitigating positional error in
EPA hazardous facility databases that I followed as time constraints allowed.
Although I did not call or visit each individual hazardous facility, as Scott et
al. (1997) did, I checked for logical inconsistencies in the locations of hazardous facilities to ensure that no hazardous facilities were located in obviously incorrect locations, such as the middle of a water body. The EPA
database indicates how each hazardous facility was geocoded (digitally assigned to a coordinate location in the GIS). In order to improve upon the
locational accuracy of the hazardous facility data, I geocoded by addressmatching many of the hazardous facilities that the EPA had previously geocoded only by census tract centroid. I did not alter those hazardous facility
locations that the EPA had already geocoded by address-matching. I also
manually went through both the TRI and BRS databases to eliminate all
redundant hazardous facility listings.
A variety of procedures for generating statistical surfaces from areal unit
demographic data have been proposed, including areal weighting (Flowerdew, Green, and Kehris, 1991), interpolation from areal unit centroids
(Bracken, 1993), and the use of remotely sensed imagery and dasymetric
mapping (Langford and Unwin, 1994). Dasymetric mapping is a technique
that uses ancillary data to redistribute spatial data in a more accurate and
logical way. It is used here to improve upon the methods of population data
representation that are typically used in environmental justice research. The
dasymetric mapping/statistical surface generation method described here is a
variation on the method described by Langford and Unwin (1994) and uses
urban density classification data derived from satellite remote sensing to
redistribute population within the original block group data boundaries.
Pennsylvania urban density data for 1996 were acquired from the Environmental Resources Research Institute (ERRI) at the Pennsylvania State
University. These data were generated by ERRI through photointerpretation
of classified Landsat Thematic Mapper (TM) imagery that was overlaid
with road network data to produce a polygon coverage that partitions the
state into areas of high-density urban, low-density urban, and nonurban.
Note that “density” in this case refers to the degree of urbanization (i.e., development), not population density. Although degree of urbanization is by
no means a perfect proxy for population distribution, its utility in modeling
population has been demonstrated in a variety of contexts (Jensen and
Cowen, 1999).
Note that although the urban density polygons are typically larger than
an individual block group in an urban setting, they allow for the identifica-
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tion of parks, cemeteries, and other urban areas that are often included as
part of a larger block group but within which few people actually live. Block
groups in suburban and rural settings are typically much larger than those in
urban areas and therefore often partially overlap with, or are bisected by, the
urban density polygons. Population in these block groups is also often concentrated in sub-block-group-sized areas. The urban density polygons
therefore allow for the partitioning of suburban and rural block groups into
populated and unpopulated regions.
The urban density classification data were converted from a representation based on areal units to a statistical surface representation with a grid
cell resolution (length) of 100 meters. This resolution was chosen because it
is fine enough to capture the spatial heterogeneity of population character
in an urban setting yet is not so fine that it interferes unduly with processing time. Each grid cell was assigned a population value according to three
factors: the population of its host block group, the ratio of the population
density of its urban density classification as compared to the other urban
density classifications, and the percentage of the area of the host block
group occupied by its urban density classification. The ratio of population
density among the urban density classifications was found empirically by
examining those block groups that were wholly contained within each urban density classification. This empirical measurement was carried out for
each individual county to acknowledge the fact that the ratio of population
density among the three urban density classifications may vary from county
to county. Approximately 20 percent of the block groups for each county
were selected in this manner to indicate the ratio of population density
among the three urban density classifications. Each demographic variable
was distributed to each grid cell in proportion to the distribution of the total population. The statistical surface generation calculations were carried
out primarily in the ArcView GIS Tables module and can be expressed as
popucb = (fucb * pb) / nub,
where
popucb : Population assigned to one grid cell with urban density classification u, in county c, and in block group b
fucb :
Fraction of the population of block group b assigned to urban
density classification u in county c (calculated from the empirically derived ratio of population density among the urban
density classifications)
pb :
Population of block group b
nub :
Number of grid cells of urban density classification u in block
group b
This procedure preserves what Tobler (1979) referred to as the pycnophylactic property: summing the population for all the grid cells within any
block group produces the same population figure as that originally assigned
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289
to that block group. Therefore, any error introduced by the dasymetric
mapping is limited to within the boundary of each original, individual areal
unit. The results of the dasymetric mapping were a series of statistical surfaces that described the number of minorities, persons living below the poverty line, and persons over the age of 25 with a college degree associated
with each grid cell in the surface. As an example, Figure 3 shows a detail of
the study area comparing the density of minorities as represented by the
original block groups and by the statistical surface generated from the dasymetric mapping.
FIGURE 3
Detail of Boxed Area in Figure 2
NOTE: Figure compares the distribution of the density of minorities by (a) the block group and
(b) the raster statistical surface generated by the dasymetric mapping procedure. Note that a
grid cell in the statistical surface has an area of 0.01 km2 (resolution = 100 m).
Statistical surfaces of percents for each variable were created by dividing
the above “count” surfaces by a statistical surface that described the total
population associated with each grid cell. A series of distance buffers around
the hazardous facility locations were then created that described the area
within 100 meters of a hazardous facility, within between 100 and 200 meters, within between 200 and 300 meters, and so on up to 10,000 meters,
which encompasses 99.9 percent of the total population. Percentage minority, percentage living below the poverty line, and percentage over the age of
25 with a college degree were then tallied within each of these distance buffers. These calculations are not averages of the values of the grid cells within
each buffer but, rather, reflect the character of the entire population within
each buffer.
Note that the hazardous facilities are not necessarily located in the middle
of a grid cell and that ArcView GIS considers a grid cell to be inside a buffer
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Social Science Quarterly
FIGURE 4
Relationship between Distance to Hazardous Facility and Other Characteristics
NOTE: Figure depicts relationship between distance to hazardous facility and (a) total population, (b) socioeconomic character by percentage, and (c) socioeconomic character by density.
GIS and Environmental Justice Analysis
291
if the centroid of the grid cell falls within the buffer. Each buffer therefore
captures a “ring” of grid cells that are a particular distance from the nearest
hazardous facility. Because the grid cell resolution is 100 meters, this ring is
only one or two cells in “width.” Although this approach is similar to the
centroid containment method described above, it differs because of the difference in size between the block groups and grid cells: a 100-meter-resolution grid cell may be considered homogeneous with respect to demographic
character, whereas block groups, which range in size from approximately
10,000 to 70,000,000 square meters throughout the study area, clearly cannot be considered as such.
The graphs presented in Figure 4 show how various population characteristics behave as a function of distance to hazardous facility. Figure 4a, for
example, describes how the total population within each distance buffer
varies as distance to hazardous facility increases. Note that the population of
each buffer zone, given as a percentage of the total population of the region,
peaks at a distance of approximately one kilometer and then rapidly declines
before plateauing at a distance of five kilometers. This is partly explained by
the decrease in area of each buffer zone, also expressed as a percentage of the
total area of the region, with increasing distance to hazardous facility. The
greater rate of postpeak decline of percentage of total population with distance to hazardous facility, as compared to percentage of total area, is explained by the change in population density as a function of distance to
hazardous facility. Population density peaks at a distance of approximately
500 meters, then declines at a regular rate with increasing distance to hazardous facility before plateauing at a distance of five kilometers. Graphs that
describe the cumulative value of each of the percentage variables over distance to hazardous facility—that is, percentage minority within a distance
to hazardous facility of 100 meters, within 200 meters, within 300 meters,
and so on (not shown)—demonstrate that all percentage variables plateau at
a distance of five kilometers to become equal to their value for the region as
a whole. For this reason, the following analysis focuses on the area within
five kilometers of a hazardous facility.
The relationship between each of the demographic variables and distance
to hazardous facility is presented graphically in Figures 4b and 4c. These
graphs show that, generally, as distance to hazardous facility increases, density of minorities, density of persons living below the poverty line, and percentage of persons living below the poverty line all decrease, but at a
decreasing rate—that is, it is a curvilinear relationship. Percentage minority
also decreases in a curvilinear manner with increasing distance to hazardous
facility, but at an increasing, not a decreasing, rate. The graphs also reveal
notable deviations from these general trends. Note that density of persons
with a college degree actually increases as a function of distance to hazardous facility up to a distance of 1,300 meters before declining, but at a much
lesser rate than that of density of minorities and persons living in poverty.
Interestingly, percentage minority and percentage poverty peak, and per-
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Social Science Quarterly
centage degree has nearly its lowest value, at a distance to hazardous facility
of 500 meters. This suggests that the greatest degree of socioeconomic disadvantage is found not at the exact location of a hazardous facility, but
rather in the immediately surrounding area.
TABLE 1
Bivariate OLS Regression of Distance to Hazardous Facility
Independent Variable
Population density (logged)
Percentage Mmnority (squared)
Percentage poverty (logged)
Percentage degree
Standardized Coefficient
–0.984
–0.831
–0.948
0.987
Adjusted R2
.967
.684
.897
.975
NOTE: All results are significant at the 0.0005 level. N = 50.
Regression was used to explore the relationships between each of the
demographic variables and distance to hazardous facility. Three of the
demographic variables were transformed prior to the regression to account
for the curvilinear relationships shown in Figure 4 and thus better approach
a normal distribution of the residuals. A logarithmic transform was applied
to percentage poverty and population density, and the percentage minority
variable was transformed by squaring each percentage minority value. There
was no need to transform the percentage degree variable. Because the presence of multicollinearity among transformed (and untransformed) demographic variables (r > .8 for each variable pair) prohibited the use of
multiple regression, bivariate ordinary least squares regression was used. I
also regressed distance to hazardous facility on both percentage minority
and percentage minority squared, but this equation showed no improvement in the model over the use of percentage minority squared alone. Results of the regression tests are reported in Table 1. Clearly, each of the
demographic variables is strongly related to distance to hazardous facility:
percentage minority, percentage poverty, and population density all have a
negative relationship with distance to hazardous facility, and percentage
with degree has a positive relationship.
These statistical results may be partially explained by the dual development of industry and urbanization in the Philadelphia region. Note that
there are various clusters of hazardous facilities spatially coincident with
traditional centers of industry and population along the Delaware River,
such as the Philadelphia river bank and the city of Chester (Figure 2). These
two cities contain the highest population densities in the region as well the
highest densities of minorities, persons living below the poverty line, and
GIS and Environmental Justice Analysis
293
those without a college degree. Note that whereas the region’s population in
general is clustered around Philadelphia, the greatest concentrations of
nonminorities and higher educational attainment are typically found in the
inner-ring suburbs, not the urban core. Although there are a number of
hazardous facilities located throughout the suburban and rural parts of the
region (Figure 2), these facilities are situated in areas of significantly lower
population density than those in the urban areas. The statistical results,
therefore, reflect the fact that although hazardous facilities do occur
throughout the region, nearly all those of lower socioeconomic status live
nearby a few large clusters of hazardous facilities that are proximal to traditional urban and industry centers.
Thus far in the analysis, distance to hazardous facility has been used as a
proxy for environmental risk via the use of distance buffers. Statistical surfaces offer other ways of modeling the distribution of risk, for instance, by
density of hazardous facilities. A statistical surface of density of hazardous
facilities indicates where in the region hazardous facilities may be spatially
clustered. In addition, tallying the demographic variables by classes of hazardous facility density indicates how demographic character varies according
to the degree of hazardous facility clustering.
A statistical surface of hazardous facility density was created in which each
surface grid cell contained the number of hazardous facilities within a 2.5
kilometer radius. Although somewhat arbitrary, this radius captures the general area of proximity around a hazardous facility. Percentages for each of
the demographic variables were then tallied for each hazardous facility density class, just as they were tallied for each of the distance to hazardous facility buffers. This yielded a series of graphs, analogous to those of Figure 4,
describing the relationship between each of these demographic variables and
density of hazardous facilities (Figure 5). Figure 5a shows that over 60 percent of the area of the region, and 30 percent of the population, has no hazardous facility located within 2.5 kilometers. Exponentially decreasing
amounts of the region’s area correspond to incremental increases in hazardous facility density. Percentage of total population also decreases with increasing density of hazardous facilities, but at a lesser rate than that of the
percentage of the region’s total area, because of the positive relationship
between population density and density of hazardous facilities.
Figures 5b and 5c show that as the density of hazardous facilities increases
up to a value of nine, density and percentage for both the minority and
poverty variables increase. Consider, for example, that whereas only approximately 5 percent of the total population lives within 2.5 kilometers of
six hazardous facilities (Figure 5a), over 40 percent of those 5 percent are
minorities (Figure 5b). Interestingly, as hazardous facility density increases
past a value of nine, percentage minority decreases abruptly, and percentage
living below the poverty line also decreases. Note, however, that there is
hardly any population in the southeast Pennsylvania region living in areas
with a hazardous facility density of greater than nine, as shown in Figure 5a.
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Social Science Quarterly
FIGURE 5
Relationship between Density of Hazardous Facilities and Other Characteristics
NOTE: Figure depicts relationship between density of hazardous facilities and (a) total population, (b) socioeconomic character by percentage, and (c) socioeconomic character by density.
GIS and Environmental Justice Analysis
295
Conclusion
This article is intended as both a caution and an encouragement for the
use of GIS in environmental justice research. On the caution side, the data
representations that are embedded within the conventional approach to the
statistical analysis of environmental justice, and often tacitly accepted in
GIS implementations of the conventional approach, present potential pitfalls to researchers who do not explicitly acknowledge how data and methods of analysis can control analytical results. Although the issue of making
explicit an investigation’s analytical assumptions exists for nearly any analysis, the ease of use of many GIS often serves to make this issue transparent
to the casual user.
More importantly, however, GIS packages provide new and innovative
ways of investigating environmental justice. Dasymetric mapping techniques produce more accurate models of the distribution of demographic
character than conventional U.S. Bureau of the Census areal units, whereas
statistical surface representations of population facilitate multiscale analysis.
In combination, these approaches offer a way to mitigate the MAUP, one of
the primary problems in the statistical assessment of environmental justice.
Other statistical surface GIS functions, such as the calculation of hazardous
facility density, can also aid in modeling the distribution of environmental
risk. Further research calls for incorporating other relevant information into
statistical surface representations of population and risk, such as other placebased indicators of socioeconomic character and the amount and toxicity of
toxic release from a hazardous facility.
REFERENCES
Anderton, Douglas L., Andy B. Anderson, John Michael Oakes, and Michael R. Fraser.
1994. “Environmental Equity: The Demographics of Dumping.” Demography 31(2):229–48.
Boer, J. Tom, Manuel Pastor, Jr., James L. Sadd, and Lori D. Snyder. 1997. “Is There
Environmental Racism? The Demographics of Hazardous Waste in Los Angeles County.”
Social Science Quarterly 78(4):793–810.
Bowen, William M., Mark J. Salling, Kingsley Haynes, and Ellen J. Cyran. 1995. “Toward
Environmental Justice: Spatial Equity in Ohio and Cleveland.” Annals of the Association of
American Geographers 85(4):641–63.
Bracken, Ian. 1993. “An Extensive Surface Model Database for Population-Related
Information: Concept and Application.” Environment and Planning B: Planning and Design
20:13–27.
Bullard, Robert. 1990. Dumping in Dixie: Race, Class and Environmental Quality. Boulder,
Colo.: Westview.
Chakraborty, Jayait, and Marc P. Armstrong. 1997. “Exploring the Use of Buffer Analysis for
the Identification of Impacted Areas in Environmental Equity Assessment.” Cartography and
Geographic Information Systems 24(3):145–57.
296
Social Science Quarterly
Commission for Racial Justice (CRJ). 1987. Toxic Wastes and Race in the United States: A
National Report on the Racial and Socioeconomic Characteristics of Communities with
Hazardous Waste Sites. New York: United Church of Christ Commission for Racial Justice.
Cutter, Susan L., Danika Holm, and Lloyd Clark. 1995. “The Role of Geographic Scale in
Monitoring Environmental Justice.” Risk Analysis 16(4):517–25.
Environmental Protection Agency (EPA). 1992. Environmental Equity: Reducing Risk for All
Communities. Washington, D.C.: U.S. Environmental Protection Agency.
Flowerdew, Robin, Mick Green, and Evangelos Kehris. 1991. “Using Areal Interpolation
Methods in Geographic Information Systems.” Papers in Regional Science: The Journal of the
RSAI 70(3):303–15.
Fotheringham, A. Stewart, and David W. S. Wong. 1991. “The Modifiable Areal Unit
Problem in Multivariate Statistical Analysis.” Environment and Planning A 23:1025–44.
Glickman, Theodore S., Dominic Golding, and Robert Hersh. 1995. “GIS-Based
Environmental Equity Analysis: A Case Study of TRI Facilities in the Pittsburgh Area.” Pp.
95–114 in G. E. G. Beroggi and W. A. Wallace, eds., Computer Supported Risk Management.
Dordrecht, Netherlands: Kluwer Academic Publishers.
General Accounting Office (GAO). 1983. Siting of Hazardous Waste Landfills and Their
Correlation with Racial and Economic Status of Surrounding Communities. Washington, D.C.:
U.S. General Accounting Office.
Jensen, John R., and Dave C. Cowen. 1999. “Remote Sensing of Urban/Suburban
Infrastructure and Socio-Economic Attributes.” Photogrammetric Engineering and Remote
Sensing 65(5):611–22.
Langford, Mitchel, and David J. Unwin. 1994. “Generating and Mapping Population
Density Surfaces within a Geographical Information System.” Cartographic Journal 31:21–
26.
Martin, David. 1996. “An Assessment of Surface and Zonal Models of Population.”
International Journal of Geographical Information Systems 10(8):973–89.
McMaster, Robert B., Helga Leitner, and Eric Sheppard. 1997. “GIS-Based Environmental
Equity and Risk Assessment: Methodological Problems and Prospects.” Cartography and
Geographic Information Systems 24(3):172–89.
Mohai, Paul, and Bunyan Bryant. 1992. “Environmental Racism: Reviewing the Evidence.”
Pp. 163–76 in Bunyan Bryant and Paul Mohai, eds., Race and the Incidence of Environmental
Hazards: A Time for Discourse. Boulder, Colo.: Westview.
Openshaw, Stan. 1983. The Modifiable Areal Unit Problem. Concepts and Techniques in
Modern Geography No. 38. Norwich, U.K.: Geo.
Ringquist, Evan J. 1997. “Equity and the Distribution of Environmental Risk: The Case of
TRI Facilities.” Social Science Quarterly 78(4):811–29.
Scott, Michael, Susan L. Cutter, Charmel Menzel, Minhe Ji, and Daniel Wagner. 1997.
“Spatial Accuracy of the EPA’s Environmental Hazards Databases and Their Use in
Environmental Equity Analyses.” Applied Geographic Studies 1(1):45–61.
Sui, Daniel. 1999. “GIS, Environmental Equity Analysis, and the Modifiable Areal Unit
Problem (MAUP).” Pp. 41–54 in M. Craglia and H. Onsrud, eds., Geographic Information
Research: Trans-Atlantic Perspectives. London: Taylor and Francis.
Tobler, Waldo R. 1979. “Smooth Pycnophylactic Interpolation for Geographic Regions.”
Journal of the American Statistical Association 74:519–30.
GIS and Environmental Justice Analysis
297
Williams, Robert W. 1999. “The Contested Terrain of Environmental Justice Research:
Community as a Unit of Analysis.” Social Science Journal 36(2):313–28.
Yandle, Tracey, and Dudley Burton. 1996. “Methodological Approaches to Environmental
Justice: A Rejoinder.” Social Science Quarterly 77(3):520–27.
Zimmerman, Rae. 1994. “Issues of Classification in Environmental Equity: How We
Manage Is How We Measure.” Fordham Urban Law Journal 21(3):633–70.