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
282 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-
GIS and Environmental Justice Analysis 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.
284 Social Science Quarterly

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.
GIS and Environmental Justice Analysis 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
286 Social Science Quarterly

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
GIS and Environmental Justice Analysis 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-
288 Social Science Quarterly

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
GIS and Environmental Justice Analysis 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
290 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-
292 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.

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

TABLE 1

Bivariate OLS Regression of Distance to Hazardous Facility

Independent Variable Standardized Coefficient Adjusted R2

Population density (logged) –0.984 .967

Percentage Mmnority (squared) –0.831 .684

Percentage poverty (logged) –0.948 .897

Percentage degree 0.987 .975

NOTE: All results are significant at the 0.0005 level. N = 50.
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.
294 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.

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