# 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 makeassessments of environmental injustice problematic. Geographic information systems (GIS) can be used to facilitate multiscale analysis through the generation ofstatistical 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’sCommission 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 havebeen 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 tothank 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 Association282 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 justiceanalysis, 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 thesemethodological 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 ofenvironmental injustice. The purpose of this article is to describe the methodological issues and problems associated with using GIS in environmentaljustice 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 ananalysis 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 theracial 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 onthis 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 usingU.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 aggregationboundaries 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 ortracts, 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 withina 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 ofwithin-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

N OTE : 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 hazardousfacility, 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 containmentmethod (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 isconsidered 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 particularareal 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 moreexploratory approach toward investigating the distribution of socioeconomic character and its spatial relationship to the locations of hazardousfacilities. 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 modelin 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 unitcounterparts, 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 approachtherefore 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 thestatistical 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 was286 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

N OTE : 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 betallied 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 atGIS and Environmental Justice Analysis 287

the block level. In addition, the sheer number of blocks, even within a singlemetropolitan 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 etal. (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 EPAdatabase indicates how each hazardous facility was geocoded (digitally assigned to a coordinate location in the GIS). In order to improve upon thelocational 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 unitdemographic 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, theurban 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 characterin 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 byexamining 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 variablewas 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

pop ucb =( f

ucb * p b) / n ub,

where

pop ucb : Population assigned to one grid cell with urban density classification u , in county c, and in block group b f

ucb : Fraction of the population of block group b assigned to urban density classification uin county c(calculated from the empirically derived ratio of population density among the urban density classifications)

p b : Population of block group b

n ub : Number of grid cells of urban density classification uin 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 assignedGIS 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 arealunit. 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 theoriginal block groups and by the statistical surface generated from the dasymetric mapping.

FIGURE 3

Detail of Boxed Area in Figure 2

N OTE : 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 km 2 (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 areawithin 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 of25 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 buffer290 Social Science Quarterly

FIGURE 4

Relationship between Distance to Hazardous Facility and Other Characteristics

N OTE : 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 thecentroid 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 approximately10,000 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. Thegreater 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 approximately500 meters, then declines at a regular rate with increasing distance to hazardous facility before plateauing at a distance of five kilometers. Graphs thatdescribe 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. Thesegraphs 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 personswith 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 facilityof 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. Therewas 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 minorityand 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 R 2

Population density (logged)

– 0.984 .967 Percentage Mmnority (squared)

– 0.831 .684 Percentage poverty (logged)

– 0.948 .897 Percentage degree 0.987 .975

N OTE : 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 livenearby 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 aproxy 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 spatiallyclustered. 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 2.5kilometer radius. Although somewhat arbitrary, this radius captures the general area of proximity around a hazardous facility. Percentages for each ofthe 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 anddensity 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 andpoverty 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

N OTE : 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 inGIS 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 innovativeways 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 intostatistical 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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