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A spatiotemporal analysis of racial disparity in the distribution of superfund sites within Santa Clara County, California

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A spatiotemporal analysis of racial disparity in the distribution of superfund sites within Santa Clara County, California

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A Spatiotemporal Analysis of Racial Disparity in the Distribution of Superfund Sites within
Santa Clara County, California

by

Chelsea Mana-ay Valenzuela

A Thesis Presented to the
FACULTY OF THE USC DORNSIFE COLLEGE OF LETTERS, ARTS AND SCIENCES
University of Southern California
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(GEOGRAPHIC INFORMATION SCIENCE AND TECHNOLOGY)

May 2022

Copyright © 2022 Chelsea Mana-ay Valenzuela

ii

To my family who provided me with endless encouragement, comfort, and snacks throughout
my endeavors.
iii
Acknowledgements
I would like to thank my committee members, Dr. Duan and Dr. Wu, for taking the time to
provide invaluable feedback. I would also like to thank my advisor, Dr. Bernstein, for her
genuinely vital support, insight, and encouragement during the completion of this project.

iv
Table of Contents
Dedication ....................................................................................................................................... ii
Acknowledgements ........................................................................................................................ iii
List of Tables ................................................................................................................................. vi
List of Figures ............................................................................................................................... vii
Abbreviations ............................................................................................................................... viii
Abstract .......................................................................................................................................... ix
Chapter 1 Introduction .................................................................................................................... 1
1.1. The National Priority List and Superfund Site Program .....................................................2
1.2. Study Area: Santa Clara County, California .......................................................................3
1.2.1. SCC Superfund Sites..................................................................................................4
1.3. Objectives and Methods Overview .....................................................................................5
1.4. Thesis Organization ............................................................................................................6
Chapter 2 Related Work.................................................................................................................. 7
2.1. Quantifying and Measuring Disparities ..............................................................................7
2.2. Effects of Historic Practices ................................................................................................8
2.2.1. Redlining ....................................................................................................................9
2.2.2. Executive Order 12898 ..............................................................................................9
2.3. Spatial Spillover and Spatial Scale ...................................................................................10
2.4. Modeling Populations and Hazards Longitudinally .........................................................11
Chapter 3 Methods ........................................................................................................................ 13
3.1. Data Scope and Preparation ..............................................................................................13
3.1.1. Temporal Scope .......................................................................................................14
3.1.2. Demographic Scope .................................................................................................16
3.1.3. Data Preparation.......................................................................................................16
v
3.2. Superfund Site Data ..........................................................................................................17
3.2.1. Site Attributes ..........................................................................................................17
3.2.2. Site Polygons ...........................................................................................................19
3.3. Area Weighting .................................................................................................................19
3.3.1. Buffers – Communities Around Sites ......................................................................20
3.3.2. Tabulate Intersection Tool .......................................................................................21
3.3.3. Summary Statistics...................................................................................................23
3.4. Assessing Disparate Distribution and Demographic Change ...........................................26
3.4.1. Assessing Demographic Change..............................................................................27
Chapter 4 Results .......................................................................................................................... 29
4.1. Condensed and Partially Condensed Race Categories......................................................29
4.2. Disparate Site Distribution Results ...................................................................................34
4.2.1. Demographic Breakdown ........................................................................................34
4.2.2. Location Quotient ....................................................................................................37
Chapter 5 Discussion and Conclusions ......................................................................................... 39
5.1. Discussion .........................................................................................................................39
5.1.1. Assessing Disparate Siting and Post-Siting Demographic Change .........................39
5.2. Limitations ........................................................................................................................40
5.3. Future Research and Implications .....................................................................................41
References ..................................................................................................................................... 43
Appendix A Location Quotient Results ........................................................................................ 47
Appendix B NPL Site Types, Counts, and Percent Change ......................................................... 51

vi
List of Tables
Table 1 SCC NPL Site by Type ...................................................................................................... 5
Table 2 Datasets and Sources ....................................................................................................... 15
Table 3 SCC Demographic Proportions from 1950-2010 ............................................................ 32
Table 4 SCC Demographics for areas within a half-mile of NPL Sites ....................................... 33
Table 5 SCC Demographics for areas within 1-mile of NPL Sites .............................................. 34
Table 6 SCC Demographics for areas within 2-miles of NPL Sites ............................................. 34
Table 7 County-Wide Percent Change 1950-2010 ....................................................................... 38

vii
List of Figures
Figure 1 Map of Santa Clara County, California study area ........................................................... 3
Figure 2 Summary of early project workflow stages ................................................................... 13
Figure 3 Chart of SCC NPL sites' initial operation years ............................................................. 14
Figure 4 NPL Site Polygons and Buffers ...................................................................................... 20
Figure 5 Closer view of NPL Site Polygons and Buffers ............................................................. 21
Figure 6 Tabulate Intersection Tool Parameter setup for 2010, half-mile polygon buffer zone .. 22
Figure 7 Resulting table for Tabulate Intersection tool for 2010 census data and half-mile
polygon buffers ...................................................................................................................... 24
Figure 8 Resulting table for Summary Statistic for 2010 census data and half-mile polygon
buffers .................................................................................................................................... 24
Figure 9 Partially Condensed Race Category Predominance 1960 - 2010 ................................... 28
Figure 10 Population Density by Census Tract 1960 - 2010 ........................................................ 30
Figure 11 NPL Site Boundaries and % Non-White ...................................................................... 31
Figure 12 Location Quotient for .5-mile, dissolved buffer zone 1960 - 2010 .............................. 35
Figure 13 Location Quotient for 1-mile, dissolved buffer zone, 1960 – 2010 ............................. 36
Figure 14 Location Quotient for 2-mile, dissolved buffer zone 1960 - 2010 ............................... 36

viii
Abbreviations
CA California
CERCLA Comprehensive Environmental Response, Compensation, and Liability Act
CERCLIS Comprehensive Environmental Response, Compensation, and Liability
Information System
EO Executive Order
EPA Environmental Protection Agency
GIS Geographic Information System
GISci Geographic Information Science
HOLC Home Owner’s Loan Corporation
MAUP Modifiable Areal Unit Problem
OLEM Office of Land and Emergency Management
NASA National Aeronautics and Space Administration
NHGIS National Historical Geographic Information System
NPL National Priority List
SCC Santa Clara County
SEDAC SocioEconomic Data and Applications Center
SEMS Superfund Enterprise Management System
SSI Spatial Sciences Institute
US United States
USC University of Southern California

ix
Abstract
Sites listed on the Environmental Protection Agency’s National Priority List (NPL) are some of
the most polluted or contaminated locations in the United States. Only locations that have been
evaluated as posing the greatest widespread and imminent threat to human health and/or the
biophysical environment make it onto the NPL, and Santa Clara County (SCC) in California is
home to twenty-three of them. Since the creation of the NPL and associated Superfund program
in the 1980s, hundreds of studies in the field of environmental justice have provided evidence
that the burdens of environmental hazards, like Superfund sites, are not distributed equally across
racial, ethnic, or economic groups. Thus, in an effort to better understand the extent of this idea
this project seeks to ascertain if a spatial disparity in the distribution of Superfund site locations
within SCC exists today and whether post-siting demographic change occurred around sites
within the county. This project maps the locations of active and historic Superfund sites in
addition to completing a longitudinal, area-weighted analysis of the surrounding communities
and study area. By spatiotemporally assessing theories associated with hazardous waste sites and
disparities, this project ultimately seeks to provide a clearer understanding of how environmental
hazards and disparities can affect and shape the communities in which they are found.

1

Chapter 1 Introduction
Sites listed on the Environmental Protection Agency’s (EPA) National Priority List (NPL) are
some of the most polluted or contaminated locations in the entire United States. Colloquially
known as Superfund sites, only locations evaluated as posing the greatest widespread and
imminent threat to human health and/or the biophysical environment make it onto the NPL.
Unbeknownst to many, Santa Clara County (SCC), right in the heart of California’s Silicon
Valley, is home to twenty-three NPL sites—more than any other county in the United States (US
EPA OLEM 2021).
Since the inception of the NPL and associated Superfund site program in the 1980s,
hundreds of papers have been published within the field of environmental justice confirming that
the burdens of environmental hazards, like Superfund sites, are not distributed equally across
racial, ethnic, or economic groups (Bullard 1983; UCC 1987; Mohai & Saha 2015). The
detrimental health and quality of life caused by contaminants leaching or being emitted from
these sites is concerning on its own (citation). However, the additional factor that certain groups
of people are disproportionally burdened by these environmental hazards has spurred many to
search for explanations as to why and how these disparities came to be in order to remedy and
eliminate environmental injustices.
Studies point to two main theoretical processes through which environmental disparities
can occur— disparate siting and post-siting demographic change. With both processes, historical,
systemic factors and practices in-part contribute to the proliferation and persistence of
environmental disparities. To better understand the extent of these theories, this project utilizes
census tract level census data to complete a longitudinal, area-weighted analysis of the SCC
communities around NPL sites from 1960-2010. Through this spatiotemporal analysis, this

2

project seeks to ascertain if a spatial disparity in the distribution of Superfund site locations
within SCC exists today and whether demographic change occurred around sites within the
county.
1.1. The National Priority List and Superfund Site Program
The NPL was created by the U.S. Environmental Protection Agency (EPA) after the
Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) was
passed in 1980 (US EPA, OLEM 2015a). Through CERCLA, polluted sites across the United
States were documented and evaluated by the EPA to ascertain how dangerous they were to
surrounding populations and ecosystems. The sites which posed the greatest widespread,
imminent threat to human health and/or the environment were added to the NPL (US EPA,
OLEM 2015b). Once listed on the NPL these sites— also referred to as Superfund sites— are
further assessed, have remediation plans created for them, be thoroughly cleaned, and finally
undergo long-term monitoring (US EPA, OLEM 2015c.)
On initial inspection, CERCLA and establishment of the NPL seem to be wholly positive
measures taken by the U.S. federal government to safeguard all its citizens from environmental
hazards promptly. However, after these measures were enacted, two pivotal studies published by
Bullard in 1983 and the United Church of Christ in 1987 presented evidence of environmental
racism, spurred by or in part contributed to by the country’s systemic practices and issues. Since
then, hundreds of subsequent studies have confirmed that environmental hazards are not
distributed equally across racial, ethnic, or economic groups as well as that historical, systemic
factors and practices have contributed to disparities existing today (Mohai and Bryant 1992;
Lester et al. 2001; Ringquist 2005; Anderton, Oaks, and Egan 1997; O’Neil 2007).

3

Figure 1 Map of Santa Clara County, California
1.2. Study Area: Santa Clara County, California
Prior to the Fortune-50 companies and start-up incubators, the majority of land in what is
now Santa Clara County (SCC) was primarily used for agricultural purposes (City of Santa Clara
2021). Starting around the 1940s, industry shifted towards industrial manufacturing. By the

4

1950s, the area was known for being a major silicon transistor manufacturing hub. The county’s
embrace of and reliance on this particular industry during the mid-twentieth century earned the
general region its now widely recognized nickname of “Silicon Valley.” While the county’s
current identity as a “global tech-hub” was built upon its legacy of industrial manufacturing in
the region, this same legacy paved the way for SCC becoming home to more active, polluted,
and contaminated sites than any other county in the entire United States. Of the 1,322 highly
polluted or contaminated sites on the National Priority List (NPL) as of 2021, twenty-three are
located in SCC (US EPA 2021).
Santa Clara encompasses approximately 1,300 square miles of land and is situated in the
South Bay area of Northern California (Figure 1) (County of Santa Clara 2016). As of 2020,
SCC is the sixth most populated county in the state with over 1.9 million residents is the 5
th

fastest-growing county in California with its population seeing an 8.7% increase from 2010 to
2020 (United States Census Bureau 2021a). Population wise the county is dense and diverse with
approximately 1,499 people per square mile and a 70.1% on the Census’ Diversity Index ranking
8
th
in the state and 37
th
in the county (out of 58 CA counties and 3,143 US counties) (United
States Census Bureau 2021b).
1.2.1. SCC Superfund Sites
Since 1980, twenty-three superfund sites have been identified in SCC and added to the
NPL, but only two of them (Intel Corp. - Santa Clara III and Jasco Chemical Corp.) have
successfully met the EPA’s remediation criteria and been subsequently removed or “delisted”
from the NPL (US EPA 2021). Notably, all of the county’s site entries on the NPL are still
considered active Comprehensive Environmental Response, Compensation, and Liability
Information System (CERCLIS) sites due to ongoing remediation or monitoring (US EPA 2021).

5

While the county’s active CERCLIS sites are the focus of this research, it is important to
note that there are other sites in the study area with “known or potential contamination” along
with facilities which are permitted to “treat, store, or dispose of hazardous waste” (EnviroStor
2021). Specifically, of the twenty-three sites listed, the majority are categorized as
“manufacturing/ processing/ maintenance” (Table 1 and Appendix B). The earliest Superfund
sites in SCC are Moffett Field and Lorentz Barrel and Drum Co., built in 1933 and 1946
respectively, while the remaining 21 superfund sites were built after 1950 (US EPA 2021).
Site Type Count
Manufacturing/Processing/Maintenance 19
Waste Management 1
Recycling 1
Other 2
Grand Total 23
Table 1 SCC NPL Site by Type (Source: US EPA 2021)
1.3. Objectives and Methods Overview
This study’s two main objectives are to determine if there is a disparity in the distribution
of Superfund sites and how SCC’s demographics have changed across time. Based on similar
studies and key papers highlighted in Chapter 2, the demographic/racial composition of
populations living closest to Superfund sites is not expected to be proportionate with the
demographics of SCC as a whole. Therefore, to ascertain the validity and extent of the study’s
main objectives, this project mapped the locations of active and historic Superfund sites,
completed an area-weighted analysis of the sites’ surrounding communities and study area, and
calculated the percent change in demographic composition between years.

6

1.4. Thesis Organization
Chapter 1 touched upon the main topic of this project along with its goals, study area, and
scope. Chapter 2 discusses the background research on topics that informed this project. The
findings and methods laid out in those works, assisted in the selection of the area-weighted
approach that is used for this project in addition to the project’s scope. Chapter 3 details the
methodology as to the study was conducted. It describes the scope of the project, which sources
were used, the attributes associated with important datasets, and the steps that have or will need
to be taken for each portion of the spatial analysis. Chapter 4 explains the project’s results and
discusses whether the results align with the hypothesis. Chapter 5 discusses the significance of
the analysis’ results, along with limitations of the study, and avenues for further research.

7

Chapter 2 Related Work
In the 1980s, some of the first papers presenting evidence that ethnic, racial, and economic
minority groups experienced pollution and environmental hazard exposure more often than their
white or more affluent counterparts within the United States were published (Bullard 1983; UCC
1987). These seminal works gave rise to the field of disparity and environmental justice research
and hundreds of subsequent studies have been published to date. Since then, numerous
systematic reviews on this body of work have confirmed that environmental risk is not
distributed equally across racial, ethnic, or economic groups (Mohai and Bryant 1992; Lester et
al. 2001; Ringquist 2005). The following sections review related research exploring how
environmental disparities come to be, why the risks associated with environmental hazards have
affected certain communities disproportionately, and what some of the conceptual or procedural
challenges associated with this research are.
2.1. Quantifying and Measuring Disparities
The exact definitions of inequalities, inequities, and disparities have greatly changed over
the years, and the parameters for qualifying what factors or actions play into them are continually
evolving. As a result, there is a subset of research which specifically evaluates the variation in
definitions and indicators, or measures used for these interrelated groups of terms.
The lack of a clear consensus on how exactly the aforementioned concepts manifest
makes the objective of conclusively quantifying their presence and consequences complex. One
systematic review identified six different measures of structural racism - “residential
neighborhood/housing, perceived racism in social institutions, socioeconomic status, criminal
justice, immigration and border enforcement, political participation, and workplace
environment” from just 20 articles meeting fairly limiting inclusion criteria (Groos et al. 2018).

8

Notably, the study limited its scope to only articles that were quantitative, specifically evaluated
the concept in association with a health outcome, and explicitly mentioned the terms
institutional, institutionalized, structural, or systemic racism within the title or abstract.
Another article assessed how conclusions drawn from several environmental studies
varied considerably based on what definition of environmental inequality was being employed
(Downey 2007). Using just five definitions, the author then assessed eight studies and explained
how conclusions drawn from each study varied significantly depending on the definition used.
These articles highlight the complex nature of this topic and group of concepts by demonstrating
how critical chosen definitions and measures are within fields evaluating justice and inequality.
In addition to the complexities and pitfalls of working on research that utilizes such qualitative
and contextually driven data. The complex nature of this subject matter is further supported
when accounting for the conceptual and methodological challenges associated with spatial data
and analysis in this field.
2.2. Effects of Historic Practices
Historic practices influenced the socioeconomic and physical make-up of many
neighborhoods and cities in the United States. Numerous studies have highlighted a relationship
between previously redlined neighborhoods, lower home values, and increased proximity to
undesirable land uses such as waste sites. Alternatively, there are systematic and structural
practices that many today would view positively due to their original intent of increasing equality
and equity. One such example is the growth of public environmental concern and recognition of
pollutants and hazards as a national issue that was also fraught with disparity. This movement
brought about programs and legislation aimed at specifically mitigating the disparate harm those
contaminants could cause, but studies have shown that these “positive” practices ultimately did

9

little or nothing in that regard (O’Neil 2007; Murphy-Green and Leip 2002). The impacts historic
practices had upon people and places across various sectors within the US is hard to refute, but
not necessarily easy to quantify and validate with complete certainty.
2.2.1. Redlining
Researchers studying areas where redlining occurred have provided evidence of the
practice’s ongoing influence even over 80 years later (Donovan and Fischer 2020). Several
studies have shown a relationship between comparatively lower property values than average for
a region and being located within a historically, redlined neighborhood as ranked by the Home
Owner’s Loan Corporation (HOLC) (Appel and Nickerson 2016; Charles 2018). For example,
one study states that median home values within a historically redlined Los Angeles
neighborhood were only 7.2% higher than prices during previous housing market booms while
median values in historically high ranking HOLC neighborhoods was 45.6% higher (Kau and
Munneke 2019; Mikhitarian 2018). Additionally, there are several studies highlighting a
relationship between redlined neighborhoods and increased proximity to superfund, brownfield,
or toxic waste disposal sites (Bullard 1983; Bullard et al. 2007; Maranville, Ting, and Zhang
2009; Moxley and Fischer 2020).
2.2.2. Executive Order 12898
Issued in 1994 by President Bill Clinton, Executive Order (EO) 12898 requires federal
agencies to ensure that environmental justice in minority and low-income populations is central
to their programs and policies (US EPA 2013). Despite its enactment, a socio-economic and race
driven disparity in the designation, listing, and remediation of highly hazardous sites as
Superfund sites on the NPL still seems to persist at all stages of the process (Daley and Layton
2004; O’Neil 2007).

10

One such study supporting this conclusion is an event history analysis evaluating
Executive Order 12898’s impact at addressing concerns regarding the Superfund program’s
equitability. Specifically assessing siting step of the process, the study found that sites
discovered after the executive order had a smaller chance of being added to the Superfund list if
located in areas with “marginalized and poor populations” (O’Neil 2007). This aligns with Daley
and Layton’s (2004) study exploring why some sites would be more likely to be remediated
through survival analysis. One of their findings indicated remedial action is more likely to occur
when political oversight is present, which is notable because marginalized and poor populations
tend to be overlooked in favor of areas and people with more political “importance” or weight to
leverage (Daley and Layton 2004).
2.3. Spatial Spillover and Spatial Scale
Many studies examine how a neighborhood’s features can affect social, economic, and
health outcomes using spatial data and analysis, but fail to adequately consider whether the
aggregation method or type of geographic unit being used is appropriate for the outcome being
assessed (Root 2012). Since a study’s chosen spatial scale and boundaries directly impacts the
precision, accuracy, and significance of the analysis and conclusions there is also an entire subset
of research focused on assessing the methodological appropriateness of chosen aggregation
methods as they relate to the modifiable areal unit problem (MAUP) and effects of spatial
spillover (Fisher, Kelly, and Room 2006; Jelinski and Wu 1996; Dark and Bram 2007).
The use of area-based measures of neighborhood or population characteristics that are
solely derived from enumeration units can lead to the underestimation of a factor’s effect upon
an area or people (Root 2012; Oka and Wong 2016). This underestimation occurs because the
boundaries of enumeration units, such as census tracts, are artificial demarcations upon

11

geographic space. In the field of environmental justice, these enumeration unit boundaries are
arbitrary as they do not realistically reflect the modern and historic effects of social/political
practices and influences or the behaviors of a population when it comes to assessing exposure to
a pollutant/hazardous substance (Oka and Wong 2016).
2.4. Modeling Populations and Hazards Longitudinally
Analyzing temporal patterns can necessitate the aggregation of data from a few years to
several decades and aggregating socio-economic/demographic data spanning decades can be
challenging due to the continually changing factors comprising and influencing this data. This
challenge’s complexity is further compounded when attempting to model and examine the causes
of environmental disparities which had only started being explored in the late 1970s within the
US. Much of the existing, quantitative research on this topic are cross-section/snapshot studies
which look at a hazardous site and the study population's characteristics during just one point in
time. However, longitudinal analyses looking at the demographic makeup of an environmentally
hazardous site before and after it is built/recognized are necessary to fully understand how or
why disparate site distributions came to be (Mohai and Saha 2015).
Existing, quantitative environmental justice studies have typically used a “unit-hazard
coincidence” or “distance-based” approach when examining the effect of a hazardous source on
different groups within a study area (Mohai and Saha 2015). The unit-hazard coincidence
method was used by earlier quantitative studies in the field (Mohai and Saha 2006; Chakraborty
et al. 2011). This approach compares the demographic characteristics of a geographic unit (such
as a county or zip-code area) that has a hazard located within its boundaries against units that do
not. These units’ relative distances from the exact location of a hazard are disregarded and they
are referred to as “host” and “non-host” units, respectively. Conversely, distance-based methods

12

account for the precise location of a hazard by aggregating units/their demographic
characteristics within a certain distance from the hazard together. This method of grouping is
also known as the areal containment or appropriation method and can be utilized to further
specify exactly how much of a unit’s population should be included within a grouping.

13

Chapter 3 Methods
This study aims to determine if there is a spatial disparity in the distribution of Superfund site
locations within Santa Clara County (SCC) and explore whether these sites affected the
demographic composition of their surrounding communities. Based on similar studies and key
papers highlighted in the previous chapter, the demographic, racial composition of populations
living closest to Superfund sites is not expected to proportionally follow the demographic
composition presented by SCC as a whole. Therefore, to ascertain the validity and extent of the
aforementioned objectives, this project will map the locations of active and historic Superfund
sites in addition to completing a longitudinal, area-weighted, location quotient analysis of the
surrounding communities and entire study area. Figure 2 outlines the general research workflow
for the earliest project stages while subsequent sections of this chapter discuss later stages such
as project scope, data sources, data preparation, area-weighting, and location quotient methods
ultimately chosen for this project.

Figure 2 Summary of Early Project Workflow Stages
3.1. Data Scope and Preparation
This project utilizes spatial and categorical data derived from authoritative sources such
as the Environmental Protection Agency and United States Census Bureau. The project’s spatial
scope is limited to Santa Clara County (SCC), California which was discussed in more detail
within Chapter 1. Regarding the project’s temporal scope, the preliminary data exploration step
was critical because understanding what historic demographic and geographic data was available
Preliminary Data
Exploration
Data Acquisition Data Preparation Data Exploration

14

for SCC consequently shaped the project’s scope and limitations. Table 2 provides details on all
the datasets and sources that were utilized during this project’s process. Of the data presented in
Table 2, the most critical datasets are “Superfund Sites” and any sets sourced from IPUMS
National Historical Geographic Information System (NHGIS). These sources will be further
discussed in dedicated sections of this chapter.
3.1.1. Temporal Scope
The temporal scope for the project spans from 1930-2010, however the demographic
analysis will only utilize data from 1960-2010 since population counts at the census tract level
prior to 1960 were not readily available. The data pulled from IPUMS NHGIS for 1950-2010
would hypothetically be sufficient to complete both main goals mentioned earlier this chapter,
but 1930 through 1949 is also included in the project’s temporal scope. As shown in Figure 3,
there are at least two SCC NPL (Superfund) sites that started operating within this block of years
and could therefore serve as important, baseline cases for evaluating the project’s second, main
objective.

Figure 3 Chart of SCC NPL sites' initial operation years (Source: US EPA 2021)

15
Table 2 Datasets and Sources
Dataset Description Format Data Type Spatial Scale Time Period Source
Superfund
Sites
Location and
attributes of
superfund sites within
SCC study area
.csv
Text and number
fields
Sites within
SCC
1930-2010
U.S. EPA and the
California
Department of Toxic
Substances Control
Site Boundaries .shp
Vector data -
polygon
Sites within
SCC
2010
NASA’s
SocioEconomic Data
and Applications
Center (SEDAC)
Santa Clara
Administrative
Boundaries
Recent boundaries for
county, surrounding
counties, and census
tract
.shp
Vector data -
polygon
SCC and
census tracts
of various
areal sizes
2010 U.S. Census Bureau
Historic boundaries
for county, cities, and
census tracts
.shp
Vector data -
polygon
City
boundaries and
census tracts
of various
areal sizes
1960-2000
IPUMS National
Historical
Geographic
Information System
(NHGIS)
Race/Ethnicity
Dataset reporting
race/ethnicity
population estimates
.csv
Aggregated
census tract
population
estimates – text
field
Tracts of
various areal
sizes
1960-2010
U.S. Census Bureau
and
IPUMS NHGIS

16

3.1.2. Demographic Scope
In the US, the concept of race has changed drastically over time and these shifting
notions of it are reflected across the US Census. In 2010 there were six race categories available
for selection while the 1960 census included just three race categories – white, black, and other
(Pratt et al. 2015; Brown 2020). Thus, two demographic grouping methods were utilized to
address the inconsistencies present in US Census data aggregated by race – the fully condensed
grouping (FCG) with two broad categories, and partially condensed grouping (PCG) with five,
less broad categories. For the FCG, White includes individuals who self-identified as “White,
alone” while Non-White includes those self-identifying as any group other than “White, alone.”
The PCG includes White (White, alone), Black (Black, alone), American Indian/Alaskan Native
(AI/AN, alone), Asian (Asian, alone), and Other (Other race, alone; Two or more races).
As an important note, ethnicity was not included in this project’s scope or analyses due to
the even greater number of changes in category options this question has undergone across the
decennial census surveys when compared to race. Therefore, when a particular race category is
referenced or discussed in this project it includes all individuals who have reported/self-
identified as that race/race group on their census questionnaires – regardless of what ethnicities
they have reported or how they responded to questions regarding Hispanic or Latino origin.
3.1.3. Data Preparation
The majority of data preparation for the project was completed during the secondary data
exploration stage. After acquiring all of the necessary data listed in Table 2, basic clean-up was
performed on .csv files as needed using Excel. Basic .csv clean-up included deleting any features
outside the SCC study area and ensuring that table header/field names did not contain any

17

characters that could prevent the table for importing into ArcGIS Pro properly. Then, all tables
and shapefiles were added to ArcGIS Pro.
In ArcGIS Pro several built-in tools were utilized to further prepare and visualize the
data. First, the XY Table to Point was used to convert the imported EPA .csv containing the
Superfund site attribute information into a point layer. Next, the NHGIS census tract layers for
2010, 2000, 1990, 1980, 1970, and 1960 were joined with their respective NHGIS population
tables using the “GISJOIN” field common to all. All shapefiles were then clipped to the SCC
study area boundary to streamline the data for faster loading, viewing, processing, and analysis.
Finally, all the shapefiles were transformed from their given projections to NAD 1983 (2011)
State Plane California III FIPS 0403 (Meters) using the Project tool.
3.2. Superfund Site Data
As discussed in section 1.1.1, twenty-three hazardous waste sites located in SCC have
been listed on the NPL since the program’s inception. Of those sites, also commonly referred to
as Superfund sites, only two (Intel Corp. - Santa Clara III and Jasco Chemical Corp.) have
successfully met the EPA’s remediation criteria and been subsequently removed from the NPL.
Notably, all twenty-three of the sites listed on the NPL are still considered active Comprehensive
Environmental Response, Compensation, and Liability Information System (CERCLIS) sites due
to ongoing remediation or monitoring (US EPA 2021).
3.2.1. Site Attributes
Attribute information for Superfund Sites is available from the EPA website’s various
pages and databases and three separate webpages were used to locate necessary site information
(US EPA 2021). The “Search Superfund Site Information” webpage allows users to search for
active and archived sites within the EPA’s Superfund Enterprise Management System (SEMS)

18

public user database. Users can narrow down the results pulled from the SEMS database by
typing in or selecting search criteria information for fields such as Site Name, County, Region, or
NPL Status. For this project the search criteria used was as follows – County: Santa Clara, State:
California, Region: 9, NPL Status: Proposed, Current, and Deleted NPL Sites. The webpage
returned twenty-three sites matching the given search criteria along with the option to download
the sites and their associated attributes as a comma separated file (.csv). The .csv contained
unique, 12-character EPA IDs for each site along with useful information for each feature like
Site Name, City, Street Address, NPL Status, whether the site is ready for specific uses, site
CERCLIS status, type, type subcategory, Hazard Ranking System (HRS) score, aliases, and a
link to the site’s unique EPA profile page.
The .csv downloaded from this initial webpage did not include coordinates, so the
“SEMS Search” EPA webpage was then filled out, using similar search criteria, to obtain the
coordinates for each of the sites from SEMS. These point coordinates were manually added to
the site .csv downloaded from the first webpage using Excel.
The individual site profile pages included in the .csv from the first EPA webpage were
next visited to collect important milestone dates for each of the sites. These dates can be found
under the “Cleanup Activities” tab on the left side of each profile and by next clicking on the
related sub-tab named “Cleanup Progress.” Each profile page for the project sites had milestone
dates for: Initial Assessment Completed, Proposed to NPL, Finalized on NPL, Remedial
Investigation Started, Final Remedy Selected, Remedial Action Started and/or Final Remedial
Action Started. These dates were manually copied and pasted from the site profile page into the
.csv obtained from the first webpage using Excel. Finally, the Operation Start Year (date) for

19

each site, was pulled from information presented in the “Background” section located on each
site’s Superfund Site Profile Home Page and pasted into the site .csv with Excel.
3.2.2. Site Polygons
The polygon boundaries for each Superfund site were available from NASA’s
SocioEconomic Data and Applications Center (SEDAC) website. The site polygons for all
Superfund sites within the United States was located in a single shapefile in a downloadable
folder titled “ATSDR Hazardous Waste Site Polygon Data with CIESIN Modifications, v2
(2010).” For this project, the boundaries of each Superfund site are important to include because
their areas vary greatly, with the smallest being approximately .01 square kilometers (5,691.86
square meters) to the largest at roughly 30.52 square kilometers. Each sites’ boundaries also
directly affect the area covered by the buffer zone that will be created and used for the area-
weighting analysis component.
3.3. Area Weighting
A number of spatial analysis methods were considered for this project, but area-
weighting – specifically the area-apportionment method, was chosen for this project after the
preliminary data exploration process. Distance-based methods, like area-weighting, account for
the precise location of hazards by aggregating the demographic characteristics of geographic
units within a certain distance from the hazard together. Area-apportionment goes further by
accounting for the proportion of each geographic unit falling within a particular distance of a
hazard to determine what proportion of each geographic units’ population is considered as being
within the hazard zone.
As previously mentioned, the area-weighting method that will be used for this project is
based off the methodology presented in "Locations of licensed and unlicensed cannabis retailers

20

in California: A threat to health equity?" (Unger et al. 2020). There are notable differences
between this and the aforementioned paper’s study area (state of California vs SCC), topic
(cannabis retailers vs superfund sites), and temporal scope (2018 vs 1960-2010). There are also
differences in the approaches used for area-weighting specifically such as this project’s use of
geodesic buffers instead of service areas, decennial census data instead American Community
Survey (ACS) data, and the tabulate intersection tool to calculate and associate site buffer/zone
data with demographic data.

Figure 4 NPL Site Polygons and Buffers
3.3.1. Buffers – Communities Around Sites
During the secondary data exploration stage of the project, half-mile,1-mile, and 2-mile
geodesic buffers were created using the ATSDR_NPL polygon layer (Figure 4 and Figure 5) for

21

a total of three buffer polygon layers. The half-mile polygon buffer was used during this stage to
test and confirm that the tabulate intersection tool could successfully link the site-zone buffers
with project’s demographic data and accurately calculate the proportion of the population within
a half-mile of each superfund site. During this time, it was decided that the EPA_NPL point
buffers, which were also created for testing, would be shelved in favor of the polygon buffers.
The point buffers did not realistically account for the variations in boundary size across all the
superfund sites which meant that using them for area-weighting would lead to an
underestimation of people and census tracts that have been affected by the sites.

Figure 5 Closer view of NPL Site Polygons and Buffers
3.3.2. Tabulate Intersection Tool
The parameters used for the initial test of the tabulate intersection tool are visible in
Figure 6 and are as follows: The half-mile buffer polygon layer set as the input zone feature

22

while the 2010 demographic data is set as the input class feature. The class fields include
tract_2010 (full census tract number), tract_name (shortened tract number), and Shape_Area
(tract area in square meters). The Sum Fields included all race groups (for 2010 there are five
groups) and total population count per tract. Square meters were selected as the Output Unit for
the subsequent table and the tool was then run.

Figure 6 Tabulate Intersection Tool Parameter setup for 2010, half-mile polygon buffer zone
The test of the tabulate intersection tool ran successfully, providing counts for each
census tract that intersected with or fell within every half-mile buffer zone boundary in a table.
Part of that resulting table can be seen in Figure 7. Taking row 1 as an example, the table shows

23

that its tract_name is 5087.04. The tract_area_intersection column shows what percent of the
tract intersects or was as within the half-mile buffer zone for a given site. For row 1, tract
5087.04 it is .4235 or 42.35% which means that 42.35% of the tract fell within the boundaries of
the half-mile buffer zone. The total column shows the amount of the population that was
included based on the intersection percentage (tract_area_intersection) – approximately 2,213
people for tract 5087.04 (42.35% of tract 5087.04’s normal 5,225.35-person population).
Lastly, the race fields display the proportional number of people from each race that were
included in the total column. Taking Asian as an example, this group originally made up 42.47%
(2,219.20 people) of tract 5087.04’s original total population of 5,225,35 people. By listing
Asian as one of the Sum fields (along with the other race group), the tabulate intersection tool
considered that this group comprised 42.47% of the total population and should still make up
42.47% of the proportional population (2,213 people, 43.35% of the tract/population) considered
as being within the half-mile zone buffer. This process was repeated for each year and buffer
zone distance.
3.3.3. Summary Statistics
The summary statistics tool was then used to calculate the total number of individuals
within a half-mile, 1-mile, and 2-mile of each site by race. As seen in Figure 7, the tabulate
intersection tool returned one row for each census tract that fell within a site’s half-mile buffer
zone. While that is useful for tracking changes across individual tracts, knowing the total number
of people within a particular site’s buffer zone by race makes comparing changes across several
years easier and more digestible. The tabulate intersection results table using the 2010, half mile
buffer zone data was chosen as the Input Table; the asian, black, white, other, and total fields
were added as Statistics Fields with Statistic Type set to Sum; and site_id and site_name were

24

added as Case fields. Figure 8 shows part of the resulting table and now each site has only one
row and count associated with for total population, the total number of people within each racial
group, and the number of census tracts that fall within/intersect their respective half-mile zones.
As with the tabulate intersection tool, this process was also completed for each year and each
buffer distance.

25

Figure 7 Resulting table for Tabulate Intersection tool for 2010 census data and half-mile polygon buffers

Figure 8 Resulting table for Summary Statistics calculated for 2010 census data and half-mile polygon buffer

26

3.4. Assessing Disparate Distribution and Demographic Change
In order to determine whether there is a disparity in the distribution of the twenty-three
SCC NPL sites, the difference in racial composition of the population within the buffer areas and
outside the buffer areas/within the study area as a whole will be compared. Two key
expectations/assertions for this study are that race-related environmental inequality exists and
one manifestation of this inequality is the disproportional concentration of certain demographic
groups (Non-White) within the superfund site zone boundaries compared to other groups
(White). Notably, a race (non-white to white) ratio for individuals inside the boundary does not
take into account the racial composition for the greater area (SCC). For example, in 2000 there
were 62,509 White individuals within a half-mile of a NPL site compared to just 29,983 Non-
white individuals. This ratio ends up being 2.08 or 208 White people for every 100 Non-White
(alternatively 47 Non-White for every 100 White individuals). Thus, to better assess and
demonstrate the existence of this expectation of environmental inequality, a location quotient
comparing the ratios for a demographic group inside and outside of a site-zone boundary to the
ratio of the population within and outside those boundaries as a whole is utilized.
𝐿𝑜𝑐𝑎𝑡𝑖𝑜𝑛 𝑄𝑢𝑜𝑡𝑖𝑒𝑛𝑡 =
(𝑋 /𝛴𝑋 )
(𝑁 /𝛴𝑁 )
=
[𝐷𝑒𝑚𝑜𝑔𝑟𝑎𝑝 ℎ𝑖𝑐 𝑔𝑟𝑜𝑢𝑝 ] 𝑤𝑖𝑡 ℎ𝑖𝑛 𝑏𝑜𝑢𝑛𝑑𝑎𝑟𝑦 𝑅𝑎𝑡𝑖𝑜
[𝐷𝑒𝑚𝑜𝑔𝑟𝑎𝑝 ℎ𝑖𝑐 𝑔𝑟𝑜𝑢𝑝 ] 𝑤𝑖𝑡 ℎ𝑖𝑛 𝑐 𝑜𝑢𝑛𝑡𝑦 𝑅𝑎𝑡𝑖𝑜
X = [Demographic group] population in site-zone boundary
ΣX = Total population in site-zone boundary
N = [Demographic group] population in county
ΣN = Total population in county
Ideally, across all years studied, there would be little to no difference between the in-zone
and outside-of-zone population ratios meaning that no demographic group is disproportionally
located near superfund sites. For example, if the county’s overall population is observed to be
60% White, 40% Non-White and the population within the site-zone boundary is also 60%

27

White, 40% Non-White, then the resulting LQ values for each demographic group would equal 1
(LQ = 1). However, it is expected that some demographic groups will be disproportionally
concentrated within site zones compared to other groups. In these cases, An LQ > 1 indicates a
higher spatial concentration or overrepresentation for the selected demographic group within a
site-zone compared to the expected population proportions for the greater region. Conversely, an
LQ < 1 indicates that the observed counts for a particular demographic group is lower than
expected within a site-zone when compared to the greater region’s expected population
proportions. For this study the “greater region” is Santa Clara County and the expected
population proportions have been collected for each decennial year from 1960 through 2010.
3.4.1. Assessing Demographic Change
The secondary aim of this project entails assessing how the demographic makeup around
sites changed over time. Key environmental justice (EJ) studies referenced in Chapter 2 involved
researchers attempting to determine whether the disparate siting of polluted locations occurred
due to the existing and historical population makeup of an area, or if post-siting demographic
changes occurred in surrounding area after the site was established. Ideally, this project’s
analysis would be similar; ascertaining whether the makeup of the community influenced the
establishment and persistence of SCC’s superfund sites or if the establishment of a site/official
listing of a site on the NPL affected the subsequent demographic composition of the surrounding
community. However, given the complex nature and variability of socio-economic factors across
time and place, the findings of this project’s analysis are unlikely to provide a concrete answer
one of the longest standing questions in the EJ field. In spite of this, the results gleaned from this
portion of the analysis can still provide additional context regarding how NPL sites seem to have
affected SCC historically and today.

28

The county’s demographic composition changes will be assessed using percent-change
between years for the two FCGs (White and Non-White) across each buffer zone distance for
each site. If post-siting demographic change is not at all a factor, then the percent change among
the populations found within each buffer distance and site should mirror the percent change
found across the greater, county study area.

29

Chapter 4 Results
Longitudinally assessing the spatial distribution of Superfund/National Priority Listed sites in
SCC was a multi-step process. While the findings of this particular project are unlikely to answer
one of the longest standing questions regarding environmental disparities and race, the results
can still assist in providing a more complete picture of the superfund site situation in Santa Clara
County historically and today.
4.1. Condensed and Partially Condensed Race Categories
As previously mentioned in Chapter 3, two race grouping methods – “Condensed” and
“Partially Condensed” were utilized to address the decade-to-decade inconsistencies present in
US Census data aggregated by race. The Condensed method only has two categories: White and
Non-White, while Partially Condensed includes: White, Black, American Indian/Native
American, Asian, and Other. For the “Condensed” grouping the “White” category includes
individuals who self-identified as “White, alone” while “Non-White” includes those self-
identifying as any group other than “White, alone.” Then, for the partially Condensed group,
Asian combines counts for Asian and Native Hawaiian and Other Pacific Islander while the
“Other” subgroup combines counts for Other and Two or More Races/Multiracial.
Figure 9 utilizes the five categories from the Partially Condensed grouping method and
shows which race category was predominant in each census tract for a given decennial census
year. The map also visualizes the largest race category’s relative predominance with variable
transparency – the lighter the color, the less a predominant race category comprises of a tract’s
overall population. From 1960 to 2010, White is the predominant race category for most census
tracts but starting in 1980 the relative predominance for White markedly begins decreasing.

30

Figure 9 Partially Condensed Race Category Predominance 1960 - 2010

31

In 1990 7.9% of census tracts have Other or Asian as the predominant category which increased
to 27.9% in 2000 and 36.6% in 2010. Even in the census tracts where White remained the largest
group proportionally the predominance relative to the other categories markedly decreased.
Interestingly, the areas where relative predominance first began shifting in the county,
specifically the north-central portion of the county, is where the City of San Jose is located.
The decade-to-decade shifts seen in Figure 9 regarding predominant race categories
spatially aligns with changes seen in the county’s population density over the same 1960-2010
period (Figure 10). The entire county saw an increase in population and subsequently population
density across those years, but Figure 10 shows that the census tracts with the highest population
densities were also spatially located in the same areas where relative predominance for White
deceased starting in 1980. Since changing boundaries, numbers, and names complicates the
direct one-to-one comparison of a specific tracts across decennial censuses, the additional spatial
and temporal context Figures 9 and 10 provide about the county’s demographic change over a
fifty-year period is valuable.
Figure 11 uses data from the Condensed grouping method to visualize the Non-White
category from 1960 – 2010 in conjunction with NPL site points, boundaries, and a 2-mile buffer.
The NPL sites are mainly clustered around the north-west portion of the county, specifically in
the cities of Santa Clara, Sunnyvale, and Mountain View. In 1960 and 1970, the majority of
census tracts in the county and within 2 miles of an NPL site boundary was comprised of White
individuals and Non-White made up less than 30.7% of a tract’s population. In 1980 this begins
to shift when 23.5% or 33 of the 140 tracts partially within the 2-mile-buffer have a Non-White
proportion of at least 30.8%. The number of sites has also increased in the north-west portion of
the county, filling-in the aforementioned cluster. In 2000, 40.65% of tracts (69 of 170) partially

32

within 2 miles have Non-White proportions of at least 50.7%, and by 2010 this amount has
grown to 58.4% (104 of 178) of those tracts at least partially within 2-miles.

Figure 10 Population Density by Census Tract 1960 - 2010

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Figure 11 NPL Site Boundaries and % Non-White

34

4.2. Disparate Site Distribution Results
Area-apportionment, an area-weighting method, along with summary statistics and
location quotient were used to analyze the distribution of Superfund/NPL sites within Santa
Clara County. The demographic makeup of areas within .5, 1, or 2-miles of a site boundary were
determined using the methods outlined in Chapter 3. The resulting in-zone demographic ratios
were compared against the greater study area’s population proportions as a whole in order to
provide insight on the phenomena of disparate, hazardous site distribution within the county. The
county-wide, demographic proportions (presented in Table 3) and area-weighted results (Tables
4-6) were critical components of the project analysis.
Table 3 SCC Demographic Proportions from 1950-2010
4.2.1. Demographic Breakdown
Three “in-site-zones” were determined for each of the NPL sites for every decennial
census year from 1960 through 2010. These zones included individuals living within a half-mile,
1-mile, and 2-miles from an NPL site boundary. In addition to providing a more realistic area of
effect regarding the sites’ pollutants, the area-apportionment method was used in tandem with
the Tabulate Intersection tool to circumvent issues arising from census tract boundaries changing
Santa Clara County, County-Wide Demographic Breakdown
1950
(n = 95,280)
1960
(n = 642,315)
1970
(n = 1,064,714)
1980
(n = 1,295,071)
1990
(n = 1,497,577)
2000
(n = 1,682,585)
2010
(n = 1,781,642)
% White 97.85% 96.78% 94.29% 78.59% 68.92% 53.83% 46.96%
% Non-White 2.15% 3.22% 5.71% 21.41% 31.08% 46.17% 53.04%
% American Indian/
Native Alaskan
- - 0.38% 0.66% 0.62% 0.67% 0.73%
% Asian - - 3.06% 7.72% 17.46% 25.56% 32.02%
% Black 0.62% 0.65% 1.70% 3.38% 3.75% 2.80% 2.61%
% Other 1.53% 2.57% 0.57% 9.66% 9.24% 16.79% 17.29%
n = Total County Population

35

over time. Tables 4, 5, and 6 display the percentage of the county’s population residing within
each “boundary zone” distance along with the respective zones’ demographic breakdown across
every year of interest for both Condensed and Non-Condensed sub-groupings. Since historical
population counts for the study area at the census tract level were not readily available for years
between the decennial survey years, the operation start year for an NPL site was not considered
when determining the general “in-zone” and “outside-of-zone” counts.
For example, in 2010, Santa Clara County had over 1.7 million (1,7891,642) inhabitants
with 46.96% self-identifying as “White, alone” and the other 53.04% self-identifying as a group
other than “White, alone” – hereafter referred to as Non-White. Following the Partially
Condensed subgroupings, the Non-White group’s demographic breakdown was .73% American
Indian/Alaskan Native, 32.03% Asian (Condensed), 2.61% Black, and 17.29% Other
(Condensed). Around 7.17% of the county’s population or approximately 127,671people were
living within a half-mile of a superfund site. Of this half-mile group, 43.4% self-identified as
White, alone while the remaining 56.6% were Non-White. Of the 279,460 people living within
Table 4 SCC Demographics for areas within a half-mile of NPL Sites
Demographic Breakdown for areas within a half-mile of sites by Percentage
1960 1970 1980 1990 2000 2010
Of total County
Population
8.15% 6.56% 6.25% 6.18% 6.45% 7.17%
White 95.80% 93.15% 73.59% 67.58% 51.81% 42.54%
Non-White 4.20% 6.85% 26.41% 32.42% 48.19% 57.46%
American Indian/
Native Alaskan
0.00% 0.43% 0.82% 0.70% 0.65% 0.62%
Asian 0.00% 4.25% 11.84% 18.95% 28.84% 37.71%
Black 0.82% 1.61% 3.97% 4.41% 3.13% 2.93%
Other 3.38% 0.56% 9.78% 8.36% 15.57% 16.20%

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1-mile, 42.54% were White while 57.46 were Non-white. Then of the 636,435 people (35.72%
of county population) within 2-miles, 44.11% were White and 55.89% were Non-White.
Demographic Breakdown for areas within 1-mile of sites by Percentage
1960 1970 1980 1990 2000 2010
Of total County
Population
18.77% 15.97% 15.14% 15.06% 15.16% 15.69%
White 96.34% 93.56% 76.06% 68.64% 52.50% 43.74%
Non-White 3.66% 6.44% 23.94% 31.36% 47.50% 56.26%
American Indian/
Native Alaskan
0.00% 0.38% 0.72% 0.64% 0.63% 0.63%
Asian 0.00% 4.00% 10.63% 18.35% 28.35% 36.78%
Black 0.62% 1.51% 3.64% 4.12% 2.91% 2.72%
Other 3.04% 0.55% 8.95% 8.25% 15.61% 16.13%
Table 5 SCC Demographics for areas within 1-mile of NPL Sites
Demographic Breakdown for areas within 2-miles of sites by Percentage
1960 1970 1980 1990 2000 2010
Of total County
Population
44.88% 39.10% 35.55% 35.16% 34.90% 35.72%
White 96.73% 93.43% 77.22% 68.28% 51.98% 44.11%
Non-White 3.27% 6.57% 22.78% 31.72% 48.02% 55.89%
American Indian/
Native Alaskan
0.00% .40% 0.67% 0.60% 0.61% 0.63%
Asian 0.00% 3.67% 9.27% 18.15% 28.37% 36.57%
Black 0.68% 1.89% 3.56% 3.85% 2.75% 2.68%
Other
2.59% 0.61% 9.28% 9.12% 16.29% 16.01%
Table 6 SCC Demographics for areas within 2-miles of NPL Sites

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4.2.2. Location Quotient
The calculated location quotients (LQ) for the White and Non-White Condensed groups
across the three site-zone distances have been visualized in Figures 12, 13, and 14 (full tables can
be found in Appendix A). The LQ results show that across all years and “in-site-zone” distances
there is a spatial disparity present for one of the Condensed Demographic groups. The Non-
White demographic group is shown to have an LQ value of greater than 1 for every study year
which indicates that there is a higher spatial concentration or over-representation of individuals
of this particular group within the areas 2 miles and closer to NPL site boundaries. The White,
Condensed Group consistently had LQ values less than one (or nearly equal) to one. LQ values
less than 1 indicate a lower spatial concentration or under-representation of the demographic
group within the three site-zone-distances and an LQ value of 1 meaning that the spatial
concentration is in-line with the greater, county population breakdown (e.g., 40% of the county is
White and 40% of individuals within the given site-zone-distance is also White).

Figure 12 Location Quotient for .5-mile, dissolved buffer zone 1960 - 2010
0.8
0.9
1
1.1
1.2
1.3
1950 1960 1970 1980 1990 2000 2010
LOCATION QUOTIENT: HALF-MILE
LQ_WHITE LQ_NON_WHITE
Linear (LQ_WHITE) Linear (LQ_NON_WHITE)

38

Figure 13 Location Quotient for 1-mile, dissolved buffer zone, 1960 – 2010

Figure 14 Location Quotient for 2-mile, dissolved buffer zone 1960 - 2010
0.8
0.9
1
1.1
1.2
1.3
1950 1960 1970 1980 1990 2000 2010
LOCATION QUOTIENT: 1-MILE
LQ_WHITE LQ_NON_WHITE
Linear (LQ_WHITE) Linear (LQ_NON_WHITE)
0.8
0.9
1
1.1
1.2
1.3
1950 1960 1970 1980 1990 2000 2010
LOCATION QUOTIENT: 2-MILE
LQ_WHITE LQ_NON_WHITE
Linear (LQ_WHITE) Linear (LQ_NON_WHITE)

39

Chapter 5 Discussion and Conclusions
This study assessed the spatial distribution of National Priority Listed (Superfund) sites in Santa
Clara County (SCC) and examined how the demographic makeup of the areas surrounding those
sites changed from 1960 through 2010. This concluding chapter provides a deeper discussion of
the results, limitations of the study, overall implications, as well as suggestions for further
research.
5.1. Discussion
The results of this project’s analyses are in-line with the generally accepted trend that the
burden of environmental hazards has historically, and currently still is, disproportionately located
within non-white, low-income communities. While this pattern is especially apparent in areas
where racial and socio-economic stratification is prevalent, it does still occur in areas that many
would consider to be racially/ethnically diverse and have generally progressive environmental
policies – such as Santa Clara County.
5.1.1. Assessing Disparate Siting and Post-Siting Demographic Change
Despite Whites making up the majority of SCC’s population from 1960-2010, individuals
within the non-white groups were disproportionately represented/found within the communities
immediately surrounding NPL listed sites. In terms of percent change, SCC experienced
continued population growth across study years (US Census Bureau 2021a). However, when
looking at condensed race groupings (Table 7), Non-White grew from across all study years with
a positive percent change while White only experienced growth between 1950-1990 then saw a
decline from 1990 onward.

40

When looking at the percent change for the areas surrounding individual NPL sites,
(Appendix B) it is apparent that the areas closest to these sites does not align with the county-
wide percentage changes for the coinciding decades. With the exception of a handful of sites, the
presence of Non-White individuals residing within 2-miles increased across all study years while
the presence of White individuals typically saw a decline/negative percent change across the
majority of years. Interestingly, for 10 out of 23 sites, there seemed to be an increase in the
number of white individuals within 2-miles of sites between 1990 and 2000 which does not
follow the county-wide trend for percent change during that time period. Regardless of if a
specific site followed the same general growth/decline county trend for the given time period, the
magnitude of the percent changes between site and county were wildly different.

Table 7 County-Wide Percent Change 1950-2010
5.2. Limitations
Historical census data and shapefiles for the study area were limited with census tracts
being the smallest unit available and readily accessible for the entire study period. The use of
census block groups or blocks would have likely provided a clearer view of the communities
surrounding Superfund sites. This data availability limitation extended to zoning data prior to
County-Wide Percent Change
1950-1960 1960-1970 1970-1980 1980-1990 1990-2000 2000-2010
White (%) 5.668 0.615 0.014 0.014 -0.123 -0.076
Non-White (%) 9.098 1.939 3.558 0.679 0.669 0.216
American Indian/
Native Alaskan (%)
- - 1.101 0.090 0.225 0.142
Asian (%) - - 2.063 1.616 0.645 0.327
Black (%) 6.085 3.321 1.417 0.286 -0.161 -0.016
Other (%) 10.319 -0.633 19.651 0.107 1.041 0.090

41

2010, where the use of zoning layers could have increased the accuracy of the county’s actual
residential land area during the area-weighting and tabulate intersection process.
Similarly, more detailed data regarding census respondent ethnicity was not readily
accessible across all study years and thus not included. Even barring the intrinsic issues and
complexities surrounding/related to race and ethnicity with their continually changing
definitions, the inclusion of this data would have provided a clearer understanding of the
communities surrounding SCC’s NPL sites prior to and after their construction.
Furthermore, while the project’s results do align with the generally accepted patterns
between disparate environmental burdens and race, there are a myriad of other factors affecting
when, where, and why people choose to move into and away from an area. This is particularly
important in the case of SCC where a boom in technology/software related jobs occurred
alongside and contributed to some of the highest costs of living in the country and issues related
to housing availability/affordability which have only continued to spiral in the past two decades
and cannot be addressed quickly enough. Thus, the inclusion of additional sociodemographic
variables such as socioeconomic status, education level, home ownership, and median
housing/rent costs in this project’s analysis, across the study year, would have provided valuable,
additional context and which in turn could have increased the robustness of this project’s
findings.
5.3. Future Research and Implications
While the findings of this particular project are unlikely to answer one of the longest
standing questions regarding environmental disparities and race, the results can still assist in
providing a more complete picture of the superfund site situation in Santa Clara County
historically and today.

42

This project’s findings can serve as a starting point for further, target analysis of the NPL
sites and demographic changes that have occurred in Santa Clara. A closer analysis centered on
individual NPL site trends across time would be a logical next step since it can directly build
upon the findings from this project. Then, the inclusion of additional sociodemographic variables
such as socioeconomic status, education level, home ownership, and median housing/rent costs
would provide additional context and increase the robustness of the findings. The use of
historical data such as redlining maps, which came into use around the 1930s, could also provide
further social context or reasoning behind why white and non-white individuals were located
where they were prior to the majority of SCC’s sites being built. Additionally, utilizing zoning
layers could increase the accuracy of the county’s residential land area during the area-weighting
and tabulate intersection process. Thus, if the previously mentioned limitations were addressed
and additional datasets and analyses were incorporated, an even better understanding of
environmental disparities and their effect upon SCC and its residents across the years could be
gleaned.

43

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47

Appendix A Location Quotient Results
Location Quotient for Non-White 1960-2010
Location Relative
to Site Boundary
Non-
White
Count
Non-White
Percentage
Region
Population
Region
Population
Percentage
Location
Quotient (%Non-
White/ %Region)
2010
Within Half-Mile

73354 0.077621145 127671 0.071659177 1.083198939
Outside Half-Mile 871672 0.922378855 1653971 0.928340823 0.993577825
Within 1-Mile 157231 0.166377433 279460 0.156855305 1.060706443
Outside 1-Mile 787795 0.833622567 1502182 0.843144695 0.988706413
Within 2-Mile 355734 0.376427738 636435 0.357218229 1.053775278
Outside 2-Mile 589292 0.623572262 1145207 0.642781771 0.970115037
2000
Within Half-Mile 52318 0.067339833 108562 0.06452096 1.04368926
Outside Half-Mile 724607 0.932660167 1574023 0.93547904 0.996986706
Within 1-Mile 133906 0.147854603 255057 0.151586398 0.975381729
Outside 1-Mile 771754 0.852145397 1427528 0.848413602 1.004398556
Within 2-Mile 281939 0.362890884 587170 0.348968997 1.039894337
Outside 2-Mile 494986 0.637109116 1095415 0.651031003 0.978615632
1990
Within Half-Mile 29983 0.064425951 92492 0.061761098 1.043147756
Outside Half-Mile 435404 0.935574049 1405085 0.938238902 0.997159729
Within 1-Mile 70752 0.152028312 225597 0.150641336 1.009207141
Outside 1-Mile 394635 0.847971688 1271980 0.849358664 0.998367031
Within 2-Mile 167020 0.358884112 526508 0.351573241 1.020794729
Outside 2-Mile 298367 0.641115888 971069 0.648426759 0.988725218
1980
Within Half-Mile 21366 0.077073473 80908 0.062474 1.23369284

48

Location Quotient Results Table for Non-White.

Location Quotient for White 1960-2010
Outside Half-Mile 255850 0.922926527 1214163 0.937526 0.984427445
Within 1-Mile 46941 0.169330053 196047 0.151379 1.118580959
Outside 1-Mile 230275 0.830669947 1099024 0.848621 0.978847194
Within 2-Mile 104906 0.378426931 460427 0.355523 1.064424422
Outside 2-Mile 172310 0.621573069 834644 0.644477 0.964460604
1970
Within Half-Mile 4782 0.078630624 69842 0.065597 1.198693132
Outside Half-Mile 56034 0.921369376 994872 0.934403 0.986051346
Within 1-Mile 10952 0.180084188 169992 0.15966 1.127924588
Outside 1-Mile 49864 0.819915812 894722 0.84034 0.975695069
Within 2-Mile 27347 0.449667851 416284 0.390982 1.150098625
Outside 2-Mile 33469 0.550332149 648430 0.609018 0.903638549
1960
Within Half-Mile 2199 0.106283229 52339 0.081485 1.304329697
Outside Half-Mile 18491 0.893716771 589976 0.918515 0.973001763
Within 1-Mile 4410 0.213146448 120562 0.187699 1.135574729
Outside 1-Mile 16280 0.786853552 521753 0.812301 0.968672609
Within 2-Mile 9419 0.455244079 288274 0.448805 1.014347811
Outside 2-Mile 11271 0.544755921 354041 0.551195 0.988317453
Location Relative
to Site Boundary
White White
Percentage
Region
Population
Region
Population
Percentage
Location
Quotient
(%White/
%Region)
2010
Within Half-Mile 54317 0.064924649 127671 0.071659177 0.906020014
Outside Half-Mile 782299 0.935075351 1653971 0.928340823 1.007254371

49

Within 1-Mile 122229 0.146099286 279460 0.156855305 0.931427122
Outside 1-Mile 714387 0.853900714 1502182 0.843144695 1.012757027
Within 2-Mile 280701 0.335519522 636435 0.357218229 0.939256438
Outside 2-Mile 555915 0.664480478 1145207 0.642781771 1.033757503
2000
Within Half-Mile 56244 0.062102776 108562 0.06452096 0.962520948
Outside Half-Mile 849416 0.937897224 1574023 0.93547904 1.002584969
Within 1-Mile 133906 0.147854603 255057 0.151586398 0.975381729
Outside 1-Mile 771754 0.852145397 1427528 0.848413602 1.004398556
Within 2-Mile 305231 0.337026036 587170 0.348968997 0.965776442
Outside 2-Mile 600429 0.662973964 1095415 0.651031003 1.018344688
1990
Within Half-Mile 62509 0.060559587 92492 0.061761098 0.980545825
Outside Half-Mile 969681 0.939440413 1405085 0.938238902 1.001280603
Within 1-Mile 154845 0.150015985 225597 0.150641336 0.995848745
Outside 1-Mile 877345 0.849984015 1271980 0.849358664 1.000736262
Within 2-Mile 359488 0.348276965 526508 0.351573241 0.99062421
Outside 2-Mile 672702 0.651723035 971069 0.648426759 1.005083499
1980
Within Half-Mile 59542 0.058497527 80908 0.062474 0.936353023
Outside Half-Mile 958313 0.941502473 1214163 0.937526 1.004241234
Within 1-Mile 149106 0.146490414 196047 0.151379 0.967704104
Outside 1-Mile 868749 0.853509586 1099024 0.848621 1.005761033
Within 2-Mile 355521 0.349284525 460427 0.355523 0.982453807
Outside 2-Mile 662334 0.650715475 834644 0.644477 1.009679266
1970
Within Half-Mile 65060 0.064807381 69842 0.065597 0.987963198

50

Location Quotient Results for White.

Outside Half-Mile 938838 0.935192619 994872 0.934403 1.000845008
Within 1-Mile 159040 0.158422469 169992 0.15966 0.992250346
Outside 1-Mile 844858 0.841577531 894722 0.84034 1.001472389
Within 2-Mile 388937 0.38742681 416284 0.390982 0.990907046
Outside 2-Mile 614961 0.61257319 648430 0.609018 1.005837563
1960
Within Half-Mile 50140 0.080659562 52339 0.081485 0.989870772
Outside Half-Mile 571485 0.919340438 589976 0.918515 1.000898602
Within 1-Mile 116152 0.186852202 120562 0.187699 0.995487567
Outside 1-Mile 505473 0.813147798 521753 0.812301 1.001042693
Within 2-Mile 278855 0.448590388 288274 0.448805 0.999522451
Outside 2-Mile 342770 0.551409612 354041 0.551195 1.000388839

51

Appendix B NPL Site Types, Counts, and Percent Change
Site Name Site Type and
Sub-Type
Year Count
White
Percent
Change
White
Count
Non-White
Percent
Change Non-
White
Advanced
Micro Devices,
Inc.
Manufacturing/
Processing/Mai
ntenance;
Electronic/elect
rical equipment
1960 3081

73

1970 2167 -29.67% 106 45.21%
1980 1397 -35.53% 765 621.70%
1990 1301 -6.87% 1128 47.45%
2000 1722 32.36% 2305 104.34%
2010 1592 -7.55% 2593 12.49%
Advanced
Micro Devices,
Inc. (Building
915)
Manufacturing/
Processing/Mai
ntenance;
Electronic/elect
rical equipment
1960 6139

103

1970 6218 1.29% 316 206.80%
1980 3859 -37.94% 2048 548.10%
1990 3373 -12.59% 3145 53.56%
2000 3468 2.82% 5348 70.05%
2010 3253 -6.20% 5663 5.89%
Applied
Materials
Manufacturing/
Processing/Mai
ntenance;
Electronic/elect
rical equipment
1960 1556

45

1970 1052 -32.39% 77 71.11%
1980 442 -57.98% 191 148.05%
1990 370 -16.29% 243 27.23%
2000 1155 212.16% 1375 465.84%
2010 1107 -4.16% 1586 15.35%
CTS Printex,
Inc.
Manufacturing/
Processing/Mai
ntenance;
Electronic/elect
rical equipment
1960 2252

84

1970 3004 33.39% 319 279.76%
1980 2816 -6.26% 1125 252.66%
1990 2575 -8.56% 1218 8.27%
2000 2343 -9.01% 1453 19.29%
2010 2046 -12.68% 1702 17.14%
Fairchild
Semiconductor
Corp.
(Mountain
View Plant)
Manufacturing/
Processing/Mai
ntenance;
Electronic/elect
rical equipment
1960 2636

282

1970 5077 92.60% 506 79.43%
1980 4372 -13.89% 1877 270.95%
1990 5035 15.16% 2482 32.23%
2000 4478 -11.06% 3553 43.15%
2010 4123 -7.93% 4307 21.22%
Fairchild
Semiconductor
Corp. (South
San Jose
Plant)
Manufacturing/
Processing/Mai
ntenance;
Electronic/elect
rical equipment
1960 89

15

1970 1291 1350.56% 61 306.67%
1980 3580 177.30% 753 1134.43%
1990 4112 14.86% 1382 83.53%
2000 4009 -2.50% 2292 65.85%
2010 3392 -15.39% 2551 11.30%
Hewlett-
Packard (620-
640 Page Mill
Manufacturing/
Processing/Mai
ntenance;
1960 3018

305

1970 3343 10.77% 352 15.41%
1980 3239 -3.11% 605 71.88%

52

Road) Electronic/elect
rical equipment
1990 3657 12.91% 1024 69.26%
2000 2436 -33.39% 966 -5.66%
2010 2233 -8.33% 1264 30.85%
Intel Corp.
(Mountain
View Plant)
Manufacturing/
Processing/Mai
ntenance;
Electronic/elect
rical equipment
1960 2636

282

1970 5077 92.60% 506 79.43%
1980 4372 -13.89% 1877 270.95%
1990 5035 15.16% 2482 32.23%
2000 4478 -11.06% 3553 43.15%
2010 4123 -7.93% 4307 21.22%
Intel Corp.
(Santa Clara
III)
Other;
Research,
development,
and testing
facility
1960 1675

40

1970 1382 -17.49% 83 107.50%
1980 826 -40.23% 272 227.71%
1990 715 -13.44% 360 32.35%
2000 1142 59.72% 1198 232.78%
2010 1099 -3.77% 1364 13.86%
Intel
Magnetics
Manufacturing/
Processing/Mai
ntenance;
Electronic/elect
rical
equipment,
Multiple, Other
1960 1572

38

1970 1437 -8.59% 85 123.68%
1980 759 -47.18% 355 317.65%
1990 666 -12.25% 442 24.51%
2000 1135 70.42% 1406 218.10%
2010 1081 -4.76% 1625 15.58%
Intersil
Inc./Siemens
Components
Manufacturing/
Processing/Mai
ntenance;
Electronic/elect
rical equipment
1960 3650

88

1970 7168 96.38% 275 212.50%
1980 6165 -13.99% 821 198.55%
1990 5393 -12.52% 1368 66.63%
2000 4429 -17.88% 3078 125.00%
2010 3541 -20.05% 4424 43.73%
Jasco
Chemical
Corp.
Manufacturing/
Processing/Mai
ntenance;
Chemicals and
allied products
1960 5077

277

1970 7319 44.16% 645 132.85%
1980 5967 -18.47% 2183 238.45%
1990 7057 18.27% 3665 67.89%
2000 7241 2.61% 5172 41.12%
2010 6867 -5.17% 6145 18.81%
Lorentz Barrel
& Drum Co.
Recycling;
Drums/tanks
1960 1315

33

1970 3188 142.43% 181 448.48%
1980 2507 -21.36% 1954 979.56%
1990 2816 12.33% 2053 5.07%
2000 1890 -32.88% 2908 41.65%
2010 1725 -8.73% 3264 12.24%
Moffett Naval
Air Station
Other;
Military/Other
Ordinance
1960 3824

297

1970 5073 32.66% 524 76.43%
1980 3956 -22.02% 1536 193.13%
1990 5077 28.34% 2012 30.99%
2000 3732 -26.49% 3099 54.03%

53

2010 3204 -14.15% 3731 20.39%
Monolithic
Memories
Manufacturing/
Processing/Mai
ntenance;
Electronic/elect
rical equipment
1960 1394

43

1970 833 -40.24% 52 20.93%
1980 387 -53.54% 221 325.00%
1990 338 -12.66% 357 61.54%
2000 1129 234.02% 1405 293.56%
2010 989 -12.40% 1662 18.29%
National
Semiconductor
Corp.
Manufacturing/
Processing/Mai
ntenance;
Electronic/elect
rical equipment
1960 2002

68

1970 2216 10.69% 177 160.29%
1980 2257 1.85% 817 361.58%
1990 2476 9.70% 1407 72.22%
2000 2761 11.51% 3422 143.21%
2010 2395 -13.26% 4486 31.09%
Raytheon
Corp.
Manufacturing/
Processing/Mai
ntenance;
Electronic/elect
rical equipment
1960 1270

157

1970 2978 134.49% 291 85.35%
1980 2603 -12.59% 1151 295.53%
1990 2929 12.52% 1530 32.93%
2000 2791 -4.71% 2245 46.73%
2010 2522 -9.64% 2640 17.59%
South Bay
Asbestos Area
Waste
Management;
Co-disposal
landfill
(municipal and
industrial)
1960 12489

526

1970 12648 1.27% 893 69.77%
1980 13823 9.29% 5147 476.37%
1990 13258 -4.09% 7039 36.76%
2000 13215 -0.32% 15001 113.11%
2010 15335 16.04% 28708 91.37%
Spectra-
Physics, Inc.
Manufacturing/
Processing/Mai
ntenance;
Electronic/elect
rical equipment
1960 2776

142

1970 5223 88.15% 734 416.90%
1980 4538 -13.12% 1879 155.99%
1990 5361 18.14% 2312 23.04%
2000 4666 -12.96% 3037 31.36%
2010 4363 -6.49% 3431 12.97%
Synertek, Inc.
(Building 1)
Manufacturing/
Processing/Mai
ntenance;
Electronic/elect
rical equipment
1960 917

28

1970 484 -47.22% 42 50.00%
1980 95 -80.37% 30 -28.57%
1990 79 -16.84% 45 50.00%
2000 693 777.22% 805 1688.89%
2010 652 -5.92% 859 6.71%
Teledyne
Semiconductor
Manufacturing/
Processing/Mai
ntenance;
Electronic/elect
rical equipment
1960 1764

90

1970 3398 92.63% 482 435.56%
1980 2946 -13.30% 1270 163.49%
1990 3339 13.34% 1491 17.40%
2000 2919 -12.58% 1905 27.77%
2010 2644 -9.42% 2071 8.71%
TRW Manufacturing/ 1960 4019

76

54

Microwave,
Inc (Building
825)
Processing/Mai
ntenance;
Electronic/elect
rical equipment
1970 3438 -14.46% 173 127.63%
1980 2028 -41.01% 1152 565.90%
1990 1808 -10.85% 1754 52.26%
2000 2148 18.81% 3049 73.83%
2010 2016 -6.15% 3293 8.00%
Westinghouse
Electric Corp.
(Sunnyvale
Plant)
Manufacturing/
Processing/Mai
ntenance;
Electronic/elect
rical equipment
1960 6759

163

1970 7188 6.35% 311 90.80%
1980 6587 -8.36% 2381 665.59%
1990 7297 10.78% 3219 35.20%
2000 5123 -29.79% 5567 72.94%
2010 4869 -4.96% 6430 15.50%

Abstract (if available)

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University of Southern California Dissertations and Theses

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Asset Metadata

Creator Valenzuela, Chelsea Mana-ay (author)

Core Title A spatiotemporal analysis of racial disparity in the distribution of superfund sites within Santa Clara County, California

School College of Letters, Arts and Sciences

Degree Master of Science

Degree Program Geographic Information Science and Technology

Degree Conferral Date 2022-05

Publication Date 02/07/2022

Defense Date 01/11/2022

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag area-apportionment,area-weighting,California,environmental disparity,environmental justice,geospatial,GIS,HGIS,historical GIS,mapping,OAI-PMH Harvest,race disparity,Santa Clara,spatial analysis,spatiotemporal,Superfund

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Bernstein, Jennifer (committee chair), Duan, Leilei (committee member), Wu, An-Min (committee member)

Creator Email chelseamvalenzuela@gmail.com,cmvalenz@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC110618319

Unique identifier UC110618319

Legacy Identifier etd-Valenzuela-10380

Document Type Thesis

Format application/pdf (imt)

Rights Valenzuela, Chelsea Mana-ay

Type texts

Source 20220207-usctheses-batch-911 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given.

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu