AN ENVIRONMENTAL EQUITY STUDY FOR INACTIVE HAZARDOUS WASTE
SITES
Superfund Program for Inactive Hazardous Waste Sites on the NPL
U.S. Environmental Protection Agency
Region 2
Revised Draft Final Report
February 9, 1994
Prepared by Rae Zimmerman
for the
Emergency and Remedial Response Division
Program Support Branch
Pre-Remedial & Technical Support Section
U.S. Environmental Protection Agency
Region 2
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TABLE OF CONTENTS
Executive Summary
INTRODUCTION 1
Background 2
Organization of the Report 4
Synopsis of Methodology 4
PART I. LOCATION AND EQUITY ISSUES 6
Introduction 6
General Locational Findings 7
Rural/Urban Location
HRS Scoring Procedures as a Factor in Site Distribution
Socioeconomic Characteristics of Areas
Surrounding NPL Sites 14
Racial and Ethnic Characteristics
Age and Family Status
Interrelationships Among Socioeconomic Characteristics
Housing Characteristics 34
Housing Tenure, Condition and Value '
Interrelationships between Population and Housing
Characteristics
Site Distribution by Municipality and County 44
Summary: Site Location 47
PART H. CLEANUP AND EQUITY ISSUES 49
Introduction 49
Indicators of Cleanup Activity
Highlights of Regulatory Characteristics of NPL Sites
Modeling the Relationships: From Site Proposal to
the Record of Decision 56
STEP 1 - The Basic Model of Regulatory Parameters
STEP 2 - The Basic Model of Regulatory Parameters
Plus Socioeconomic Characteristics
Selected Population Subgroup Clusters and Cleanup
Remedial Design (RD) and Remedial Action (RA) 83
Emergency Removal 84
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Summary: Site Cleanup 85
SUMMARY AND CONCLUSION 86
Basic Findings 86
Data Limitations and Qualifications 87
Future Research Needs 88
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EXECUTIVE SUMMARY
Introduction
Environmental equity has become an important issue at a number of different kinds of waste sites.
This report explores environmental equity issues associated with inactive hazardous waste sites on
the National Priority List (NPL) in Region 2. Equity issues are discussed in terms of site location
and site cleanup. The study's objectives are: (1) to determine if any sectors of the population live
in proximity to these sites in disproportionate numbers, and (2) to determine if certain sectors of
the population are disproportionately served by any of the processes and procedures for
identifying and conducting remedial and removal actions at NPL sites.
The general findings with respect to NPL site location were that'areas within about one mile of
the sites had, on average, lower house values and rents than was typical of the states within which
the sites were located. Other socioeconomic characteristics that were studied ~ including racial
demographics -- differed little, on average, from statewide characteristics. The distribution of site
characteristics showed a very high standard deviation, however, with respect to the characteristics
studied; that is, the sites exhibited a high degree of variability or diversity from one another with
respect to these characteristics. Also, some geographic clustering of sites did appear, with a
significant number being concentrated in a relatively small number of counties and municipalities.
Regarding site cleanup, the study found that the timing of each step in the cleanup process, from
site discovery to remedial action, appeared to be driven by prescribed timetables, as well as the
timing of the previous step. The socioeconomic characteristics of the population near the site
showed no apparent influence upon the timing of the cleanup process.
The analysis of socioeconomic characteristics around existing NPL sites in Region 2 was
conducted primarily for populations residing within approximately one mile of the site. A set of
seven distances ranging from 0.25 miles to 4 miles was evaluated for the relative stability of
socioeconomic characteristics in order to select a given distance which would be representative of
the areas around the sites. These distances were also used for some selected analyses of racial and
ethnic characteristics around the sites. One mile was selected as the distance that would be the
most representative of characteristics of the population residing around the sites. The one mile
distance represents an approximation to a circle with a one mile radius, because the geographic
areas of analysis follow the boundaries of the Census blocks rather than the boundary of a circle.
Information on socioeconomic characteristics was obtained from 1990 Census data aggregated at
the Census Block level (which is the smallest geographic unit for which population data from the
Census are available). Data on the characteristics of the cleanup process were primarily obtained
from the U.S. EPA WasteLAN files (a computerized data base of site cleanup information), and
supplemented by other agency records where necessary.
The NPL data base consisted primarily of two hundred sites which had been finalized for inclusion
on the NPL as of September 1993 in New Jersey, New York and Puerto Rico (the Virgin Island
site had not been finalized), although proposed and deleted sites are included for certain analyses.
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Puerto Rico and Virgin Island sites are not always included, since the Census data for those two
areas are not comparable to data for the continental U.S.
An equity study based in Region 2 has a particular significance since New Jersey and New York
have high proportions of Blacks and Hispanics relative to other states. Outside of the South, New
York ranks the highest among states in the proportion of its population that is Black, and New
Jersey ranks fourth. Additionally, New York has the highest number of Blacks of any state in the
country (even including the South). New York ranks third and New Jersey ranks sixth, relative to
other states, in the number of Hispanic residents.
Site Location and Equity
Demographic Patterns Around NPL Sites
The following demographic patterns were found for populations living within about one mile of
the defined centers of NPL sites in 1990:
The population density averaged 1,892 persons per square mile (with a Standard Deviation, or
S.D., of 3,331), which was above the statewide number for either New Jersey or New York,
but below what normally typifies urban areas in these states.
Associated with density is the fact that the Hazard Ranking System (HRS) score assigned to
the sites as a basis for listing sites for cleanup has, on average, a bias toward the groundwater
route of exposure. Groundwater route scores exist for 82% of the NPL sites. The mean value
of the groundwater score of 61.8 is higher than the means of the other route scores, thereby
constituting, on average, the largest component of the total score. This may explain why fewer
sites are located in large, dense urban areas dependent on surface water supplies. The
dominance of sites on the NPL with high groundwater scores does not necessarily imply,
however, that all sites with high groundwater scores are in low density areas with low
minority populations, since minority populations live in areas of low density as well as high
density. Moreover, some NPL sites with high groundwater scores are in urbanized areas that
have relatively high population densities and high minority populations, such as certain sites
on Long Island and in Bergen County, New Jersey.
The population within about one mile of the sites was, on average, 7% Black (S.D. 14%), 1%
Native American (S.D. 8%), 2% Asian (S.D. 4%) and 5% Hispanic (S.D. 9%). These means
were unweighted by the size of the population, and were calculated using the assumption that
areas around the sites were equal to one another, regardless of the number of people in the
area. When the means were weighted by the size of the population within about one mile of
the sites, the averages rose to 12% for Blacks, declined to 0.2% for Native Americans, and
about doubled to 4% for Asians and 10% for Hispanics. Median percentages were far below
both the weighted and unweighted means, reflecting the skewed distribution of the sites across
racial and ethnic categories also apparent from the averages weighted by population. That is, a
few large areas with relatively large minority populations caused the mean to be higher than
the median.
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Weighted and unweighted mean and median percentages for Blacks, Native Americans,
Asians and Hispanics were below or about the same as the state proportions, regardless of
which of the seven distances from the site were evaluated.
The average proportion of elderly (persons 65 years or older) and young children generally
considered dependents (under 18 years of age) residing within about one mile of NPL sites
was about equal to the proportion for the states, whether weighted or unweighted means or
medians were used.
Characteristics of the High End of the Statistical Distributions
Because of the high degree of variability of demographic distributions around NPL sites, as
indicated by the large standard deviations around the means, it is useful to look beyond overall
averages and examine the high end of statistical distributions for various socioeconomic
categories.
If we look at the percentage of Blacks around NPL sites, for example (Blacks comprising the
largest component of the minority population), we find that, although for most NPL sites, %
Black was at or below the state proportions, the following characteristics appeared at the high end
of the distributions:
The average % Black population around 30 sites, which constituted about 15% of the total set
of NPL sites in New Jersey and New York, exceeded the state proportions for the Black
population in New Jersey and New York (13.4% in New Jersey and 15.9% in New York).
The average % Black population around 78 sites, which constituted about 40% of the total set
of NPL sites in New Jersey and New York, exceeded the proportion in the municipality in
which the sites are located.
The average % Black population around 19 sites, which constituted about 10% of the total set
of sites in New Jersey and New York, exceeded both the state and municipality proportions of
Blacks.
Other findings at the high end of the distribution of sites (i.e., greater than the NPL average or the
state proportions) for some of the other population characteristics within about one mile of the
sites were as follows:
Although the majority of the sites were at or below the average percentage for Hispanic,
Asian and Native American populations at NPL sites, about a tenth of the sites exceeded the
average percentage for Hispanics, a third exceeded the average for the percentage Native
Americans, and 14 percent exceeded the average for Asian populations.
Although about three-quarters of the sites were at or below the average population and
population density of all of the NPL sites, about a quarter of the sites (fifty five sites)
exceeded the average (for the NPL sites) of 5,000 people residing within about one mile of the
site as well as the average density of 2,000 persons per square mile. Although the areas
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around these sites had slightly higher racial and ethnic populations relative to the average for
all NPL sites, the differences were small.
Although about two-thirds of the sites had at or below 10% of their housing units occupied by
single householders with one or more persons under 18, for the other third of the sites, the
average percentage of such housing units exceeded 10% (10% is approximately the upper end
of the state proportions of such households: the New Jersey proportion is 8.4% and the New
York proportion is 10.2%).
Although about two-thirds of the sites had populations at or below 15% elderly (13.4% and
13.1% are the statewide proportions for New Jersey and New York respectively), for the
other third of the sites, the average percentage of the population that was elderly was greater
than 15%.
Housing and Demographic Patterns Around NPL Sites
Housing characteristics, in combination with population characteristics, provide another
dimension for portraying subpopulations around NPL sites. These characteristics were measured
selectively in terms of the extent of owner-occupancy, house values and rents, and crowded
housing conditions.
The average percentage of occupied housing units within about one mile of NPL sites that
were owner-occupied was well above the state proportions.
House values and rents within about one mile of the NPL sites were lower than the
comparable state figures. This was generally true whether or not mean or median figures were
used. The disparity was less pronounced for New Jersey sites than New York sites. (House
value data have to be disaggregated by state, since the two states differ substantially in the
average value of housing.)
For housing within about a one mile area around NPL sites, overcrowding measured as the
number of persons per room in a housing unit -- did not exceed state proportions.
Site Concentrations
Another dimension of equity as it pertains to NPL sites, is whether a given community is bearing
more than its share of such sites. One way to measure this is in terms of whether NPL sites are
clustered geographically (that is, two or more NPL sites are located in a single municipality or
county).
Over one quarter of the sites in New Jersey and New York are in municipalities that have two
or more NPL sites. This quarter of the total set of sites is concentrated in 23 municipalities,
which is only 0.2% of the total number of 1,186 municipalities in the two states.
84% of the sites are located in counties with more than one NPL site, little more than a third
of the total number of counties in New Jersey and New York combined (32 out 83).
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Cleanup Characteristics and Equity
There are two types of cleanup activity that are the subject of this analysis of NPL sites. One is
long-term remediation and the other is emergency removal. The sequence of the major steps in the
cleanup or long-term remediation of an NPL site that is used in the equity analysis for sites
already on the NPL is:
Site Discovery
HRS Scoring
Proposed for NPL
Finalized for NPL
Initiation and Completion of a Remedial Investigation and Feasibility
Study (RI/FS )
Initiation and Completion of a Record of Decision (ROD)
Remedial Design
Remedial Action
Deletion from the NPL
The first three steps prior to final NPL listing are included for the NPL sites to capture any
socioeconomic characteristics that might be associated with differences in the manner in which
they were placed on the NPL.
Emergency removals constitute separate actions and require different indicators. For emergency
removals (examined at NPL sites only in this report), removal investigations and removal actions
are used as key milestones.
The criteria for choosing these indicators were that:
they represented major cleanup milestones
one step was not wholly contained within another
complete data for each indicator were available for most of the sites
For the purpose of the equity analysis, the scheduling or timing of each of these steps, as well as
the number of such steps for each site, were the central indicators of site cleanup. The time frames
for the RI/FS (an independent site analysis) and ROD (a cleanup plan) were defined in terms of
start and completion dales (calculated as months prior to September 1993). Where there were
multiple events of the same type for a given site, the earliest and latest start dates and the earliest
and latest completion dates were used as end points. The number or count of such events that
occur for a site was applicable only to RI/FSs, RODs, Remedial Designs and Remedial Actions.
A series of regression equations was developed to reflect the sequence of steps in the cleanup
process for the entire set of NPL sites. Once this model was established, socioeconomic variables
were added to determine whether they had an effect on these relationships. Then, the effect of
socioeconomic variables alone on the timing of each one of the steps was evaluated. These
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evaluations were followed by more detailed analyses of selected minority subpopulations to
determine if the relationships found for the total set of NPL sites also held for the subpopulations.
The results of multivariate analyses of NPL sites showed that once an inactive hazardous waste
site is placed on the NPL, by far the strongest associations existed among time frames of the
individual steps themselves for both remediation and removals. That is, the time a given step was
initiated is most closely associated with the time the step just preceding it was initiated. For
example, the date a site was proposed for the NPL is related to when it was discovered, the start
date for the RI/FS is related to when the site was proposed, and the signing of the first ROD is
related to when the RI/FS was completed. This pattern appeared regardless of the statistical tests
used (simple averages and descriptive statistics, correlation, regression, and various coefficients of
association). Thus, the timing of steps within the remediation process is largely driven by the
sequence that has arisen, over time, in the cleanup process itself.
Other factors were evaluated as well, such as the relationship between the number of operable
units (which are separate cleanup activities) at a site and the timing of various steps. The number
of operable units tended to be highly related to the number of RI/FSs and RODs that were
conducted for a site. In addition, the evaluation of the magnitude of the initial Hazard Ranking
System (HRS) scores found that sites with higher scores were generally discovered, proposed and
finalized for the NPL earlier than sites with lower scores. The magnitude of the HRS score also
was positively associated with the first RI/FS start date, the first RI/FS completion date, and the
date the RODs were signed. The HRS score was one of the few parameters that had a continuing
relationship with the timing of the steps in the cleanup process. Under the old HRS (predating
1990 revisions), however, the magnitude of the HRS score was not intended to be a quantitative
measure of the level of threat or hazard at a site. In fact, one of the reasons the HRS was revised
was to increase the extent to which it reflected site risk. Most of the NPL sites in this data set
have scores assigned under the old HRS, and have not been rescored since they were placed on
the NPL.
The introduction of socioeconomic characteristics (within about one mile of the NPL sites) into
multivariate analyses had little effect. Little relationship between these socioeconomic
characteristics and the timing or number of steps in the cleanup process appeared. A similar set of
regression equations was run for minority subgroups of the total NPL set. The equation structure
that resulted was similar to that for the total NPL set. That is, the parameters that were
significantly associated with stages in cleanup for the entire NPL set were also significant for the
subsets. The only exception was that the HRS score did not seem to have much of an association
with the timing of the various steps as it had for the total set of NPL sites. Since no relationship
between the magnitude of the old HRS score and site risk exists, and since most of the sites
studied were scored under the old system, the significance of this is uncertain.
Another way of looking at equity is to look at two groups of sites - those with and without
cleanup plans (RODs). 58 NPL sites did not have RODs, while 142 did. Since the signing of a
ROD is a major milestone in the cleanup of a Superfund site, the socioeconomic profiles of sites
with RODs and those without RODs were compared extensively with respect to the timing of the
various stages in the cleanup process. Some of the key findings were:
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The date the site was proposed was a strong determinant of whether a site had a ROD or not,
that is, sites with RODs had been proposed for the NPL earlier and thus, had more time in the
cleanup process.
With respect to socioeconomic characteristics, the only relationship found was that sites with
RODs tended to have, on average, a slightly higher percentage Black population than sites
without RODs.
Sites with RODs and sites without RODs were disaggregated into time periods before and
after the Superfund Amendments and Reauthorization Act (SARA) was passed to explore the
socioeconomic characteristics of these two sets of sites. Areas around sites with RODs signed
after January 1987 (SARA's effective date) had much larger populations and population
densities than those with RODs that were signed prior to January 1987. Otherwise, virtually
no difference in socioeconomic characteristics around sites appeared between sites whose
RODs were signed before SARA and those whose RODs were signed after. No explanation
for the difference is apparent.
The remedial design and remedial action phases are the culmination of the cleanup process. The
duration of these steps was to some extent related to the duration of the steps preceding them, but
were unrelated to socioeconomic characteristics of the areas around the sites.
The study also revealed that practically no correlations existed between any of the socioeconomic
characteristics and the timing or number of emergency removal investigations or actions. Given
the short timing of the removal investigations and actions this is not unexpected.
Summary and Conclusions
In summary, although the location of NPL sites appears to show some patterns with respect to the
social and economic makeup of the area within about one mile of the sites, the timing of the
cleanup process showed no association with these socioeconomic characteristics.
The area within about one mile of an NPL site had, on average, lower house values and rents than
what is typical for the states within which it was located. Few other socioeconomic characteristics
appeared to differ, on average, between the areas surrounding these sites and what was typical of
the state, though there was considerable variability in site characteristics. Also, some clustering of
NPL sites appeared geographically. Over one quarter of the NPL sites in New Jersey and New
York were found to be concentrated in only 23 municipalities, less than 1% of the municipalities
in the two states. 84% of the sites were found to be concentrated in a little more than a third of
the counties in New Jersey and New York.
Multivariate analyses of NPL sites showed that the timing of the steps in the cleanup process was
largely internally driven by prescribed timetables in the Superfund program. The introduction of
socioeconomic characteristics had only a negligible effect on regression equations, which were
used to portray relationships between cleanup and site characteristics.
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INTRODUCTION
Environmental equity has become an important issue at waste sites throughout the nation. This
report focuses on environmental equity issues associated with inactive hazardous waste sites on
the National Priority List (NPL) under the Superfund program in Region 2. Equity issues are
discussed in terms of site location and site cleanup. The study's objectives are: (1) to
determine if any sectors of the population are disproportionately living in proximity to these
sites, and (2) to determine if certain sectors of the population are disproportionately served by
any of the processes and procedures for identifying and conducting remedial and removal
actions at hazardous waste sites under the Superfund program.
By September 30, 1993, the number of sites finalized for the NPL in the region was 200 sites,
and an additional four had been proposed for designation. Six others had been deleted because
no further action was needed. * A subsequent report will address equity issues associated with
non-NPL inactive hazardous waste sites on the Comprehensive Environmental Response
Compensation and Liability Inventory System (CERCLIS). The non-NPL sites constitute a
separate data base because information on their status is often not comparable to that for NPL
sites.
The first step in exploring environmental equity is to arrive at an approach to its
quantification. Measures of equity are often based on the distribution of risks and benefits over
different individuals or groups. This approach is most applicable to very specific activities
whose impacts are reasonably known and can be expressed in terms of a geographic area or
population affected. Conceptualizing risks and benefits for hazardous waste sites is more
1. A more extended discussion of the NPL list used for this study is contained in Appendix H.I. Two different
sets of sites are used in this analysis. One is a set of NPL sites (200) that had been finalized, and the other
includes both finalized, proposed and deleted sites (approximately 210, depending on the time period).
Regression analyses performed later in this report show that the two data sets hardly vary at all from one
another with respect to cleanup characteristics. Sample sizes identified throughout the report vary from the
total of 200 or 210 due to missing data.
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difficult, since sources of wastes from these sites can be very diffused. The contaminant
exposure areas of hazardous waste sites in general (i.e., potential populations at risk) and
service areas (i.e., alleged beneficiaries) are not as easily defined in spatial terms as they are
for sources of waste discharges from discrete points. Moreover, the delineation of exposure
areas for these sites often varies for different routes of exposure, different chemicals within a
given exposure route, and different environmental conditions. The service areas (in the case of
NPL sites these are historic service areas) often encompassed sources of wastes many miles
from the site, whose location changed over time.
Given these conceptual problems in defining the nature and extent of exposure for hazardous
waste sites as a basis for an equity analysis, the population components of the equity measures
used in this report were related to the resident population drawn at selected, approximately
radial distances from the sites rather than in terms of a specific exposure criterion.
Furthermore, once the geographic boundaries were set, the availability of socioeconomic data
to describe populations within the defined geographic areas posed somewhat of a constraint on
how these populations were characterized. In this report, the database selected to portray
socioeconomic characteristics is comprised of population and housing characteristics either
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obtained directly from the Census or based on Census data.
Background
An environmental equity study within EPA's Region 2 is particularly important since the
region has very high minority populations compared to the rest of the country. For example,
outside of the South, NY ranks the highest among states in the proportion of its population that
is Black, and NJ ranks fourth. Additionally, NY has the highest number of Blacks of any state
in the country as well as having the largest share of the Black population of any single state in
2. A full list and description of these parameters is contained in Appendix A.
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the country (9.5%), even including the states in the South. NJ and NY also have relatively
large Hispanic populations. NY ranks third among states in the number of Hispanics, and
seventh in terms of the proportion of the state population that is Hispanic. NJ ranks sixth
among states in the number of Hispanics, and ninth in its proportion.
A number of studies of environmental equity have been undertaken in connection with inactive
hazardous waste sites, and reflect different approaches to defining the geographic scope of the
equity issue. The geographic coverage of these studies, the level at which socioeconomic data
are aggregated, and the indicators of equity vary considerably. A recent review of the variety
of geographic units used in equity studies of hazardous waste sites-* pointed out that counties4,
municipalities^, and Zip Codes** have all been used to aggregate socioeconomic data around
such sites. Census tracts have recently joined this group especially in court cases.7 The use of
the more finely aggregated block groups and blocks is becoming more popular with the
availability of Census data at that level on CD-Rom databases.
A review of ongoing studies within the U.S. EPA and a few other federal agencies was
conducted in March 1993.^ Most of the EPA studies employ Geographic Information Systems
3. R. Zimmerman, "Issues of Classification in Environmental Equity: How We Manage is How We Measure,"
Fordham Urban Law Journal, forthcoming 1994.
4. J.A. Hird, "Environmental Policy and Equity: The Case of Superfund," Journal of Policy Analysis and
Management. Vol. 12, No. 2 (1993), pp. 323-343.
5. R. Zimmerman, "Risk and Public Controversy at Hazardous Waste Sites", Final Report to the U.S. EPA,
OSWER, January 15, 1992 (revised, February 1992); R. Zimmerman, "Social Equity and Environmental
Risk," Risk Analysis: An International Journal. Vol. 13, No. 6 (December 1993), pp. 649-666.
6. United Church of Christ, Commission on Racial Justice. Toxic Wastes and Race in the United States. New
York, NY: United Church of Christ, 1987; M. Lavelle and M. Coyle, "Unequal Protection. The Racial Divide
in Environmental Law," The National Law Journal. Special Investigation (September 21, 1992), 12 pp.
7. Court cases that have used census tracts or smaller units on a case basis include: Bean v. Southwestern Waste
Management Corp., 482 F. Supp. 673 (S.D. Tex. 1979); East Bibb Twiggs Neighborhood Assn. v. Macon-Bibb
County Planning & Zoning Commn., 706 F. Supp. 880 (M.D. GA.), affd, 896 F.2d 1264 (llth Cir. 1989);
R.I.S.E., Inc. v. Kay, 768 F. Supp. and 1141 and 1144 (E.D. VA. 1991).
8. The study entitled, "Summary of Environmental Equity Studies in the U.S. EPA and Selected Other Federal
Agencies" (New York: U.S. EPA, Region 2, ERRD, March 16, 1993) by R. Zimmerman summarizes these
studies and their outcomes.
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(CIS) as a means of aggregating socioeconomic information, however, they differ in the way
in which basic census data are transformed and aggregated. An agency wide conference entitled
a National EPA CIS/Environmental Justice Forum held in October 26-29, 1993 began to
address these differences.
Organization of the Report
This report is divided into two sections. Part I addresses the present location of the NPL sites
and equity in terms of the proximity of residential populations and selected characteristics of
these populations. Part II addresses site cleanup status and equity in terms of the relationship
of the timing of steps in the cleanup process to one another and to the population
characteristics generated in the first part of the report.
Synopsis of Methodology
Each NPL site included in this analysis was assigned a longitude and latitude using data
provided by the U.S. EPA Office of Policy and Management, supplemented with Region 2
locational data for new sites. This point is referred to throughout the report as the "center" of
the site, although it may vary from a geometric center. Seven radii within 5 miles of each site
were used to define the distance between the centers of the Census Blocks and the longitude
and latitude assigned to the NPL site. The socioeconomic data for each Block selected for each
radius were extracted and aggregated (cumulatively, that is, the data at closer distances was
part of the data aggregated at further distances). Thus, using this method, the area of the
Census Blocks extracted for a given radius from the site's center actually only approximates
the area of a circle for that distance rather than being exactly equal to it. Furthermore, the
actual geographic boundary for the data is the outer boundaries of Blocks extracted.^ The
9. That is, actual data aggregated at the Census Block level may extend beyond the stated radii or fall short of it,
since specified distances only pertain to the location of the centers of the blocks and blocks are not drawn on
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methodology used to extract the socioeconomic data in this way is described in detail in
Appendix H.2.
The second part of the analysis on site cleanup combined the socioeconomic characteristics
around the sites at the one mile radius (generated in the first part of report) with cleanup
characteristics for NPL sites. Cleanup characteristics were obtained from parameters available
from the U.S. EPA database.10
the basis of where the entire block areas - in particular, their inner and outer boundaries - are located.
Differences between the area of a circle and the area resulting from the block aggregates were compared and
found to be relatively small except for extremely small distances from a site (under 1 mile).
10. An extensive analysis of the suitability and sufficiency of those parameters as indicators or measures of
cleanup is contained in the Appendix H.3., entitled "Initial Selection of NPL Event Type Codes" (September
9, 1993).
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PART I. LOCATION AND EQUITY ISSUES
Introduction
The geographic scope for the evaluation of social and economic characteristics of areas around
NPL sites encompasses sites in New York and New Jersey and generally not in Puerto Rico or
the Virgin Islands, except where indicated. The Census database for Puerto Rico and the
Virgin Islands is substantially different from and not comparable to the data for the continental
United States. The analysis of regulatory data in Part II, however, includes Puerto Rico and
Virgin Island sites as well.
A geographic standard of comparison usually becomes a necessity in evaluating the nature and
extent of environmental equity from socioeconomic patterns in a given area. A geographic
standard of reference can be an immediately adjacent area, some larger geographic area within
which the site is contained, or another area with similar sites or no sites. The use of several
geographic reference points is important, since the choice of any given reference is inevitably
judgmental. This study uses Region 2, the states, and substate areas such as urbanized areas
and municipalities within which the NPL sites are located as geographic reference points for
site characteristics.
Several descriptive statistical measures were used to portray overall socioeconomic
characteristics of the areas around each site that supplement frequency distributions and
multivariate analyses. First, an unweighted mean was used, where each site is weighted
equally regardless of the number of people at a given distance around each site. Second, the
median was used to portray any skewing of the data in any given direction. Third, a population
weighted mean was used, especially where medians and unweighted means were dissimilar.
The weighting was implicitly based on the number of people around a site within a given
distance.
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General Locational Findings
Rural/Urban Location
NPL sites are primarily located outside of major cities, in areas of relatively low density. As
one frame of reference, the population per square mile in New Jersey and New York averaged
1,042 and 381 persons per square mile respectively in 1990, however, the densities of the
large urban areas within those two states (identified by the Census as cities) are many times
that amount ranging to a maximum of 44,000 in New Jersey and 52,000 in New York State. * ^
As shown in Table 1 below, even though the average densities of areas surrounding the NPL
sites exceeded statewide averages, the NPL sites seemed to be in areas whose densities typify
areas outside of major urban areas. This holds for a variety of distances from the site's center.
Moreover, the distribution of NPL sites by density varies considerably. For example, over a
third of the sites had densities of 500 persons per square mile or less, and over half had
densities of 1,000 persons per square mile or less within about one mile of the site's center.
This pattern is related in part to the manner in which sites are scored for listing on the NPL,
which is discussed below. One component of the scoring system, which emphasizes
groundwater routes of exposure tends to identify sites in relatively less dense areas, whereas
another component of the scoring system emphasizes areas with high density.
11. U.S. Bureau of the Census, 1990 Census of Population and Housing. Summary Population and Housing
Characteristics. New York (1990-CPH-1-34) and New Jersey (1990-CPH-1-32) (Washington, D.C.: U.S.
GPO, August 1991), Table 16.
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Table 1. Population Density and Distance from NPL Site
Population Density
Distance
0.25
0.50
0.75
1.00
2.00
3.00
4.00
(persons per square mile)
Mean S.D.*
1808 3050
2052 3692
1764 2950
1892 3331
1962 3371
1939 3055
1931 3179
Median
440
665
829
863
1041
985
910
*S.D.: Standard Deviation
The discrepancy between the mean and the median for a given distance underscores further the
considerable effect that a few very dense areas can have on the average density. The medians,
in general, are dramatically different from the means, i.e., they are generally much lower.
This suggests the presence, among the 200 NPL sites, of a few high density, highly urban city
sites. This is confirmed by an examination of the actual distribution of the sites according to
population density.
The distribution of the mean population density for the seven distances is shown in Figure 1.
Figure 1 shows that: (1) a similar distribution of the sites across population density categories
exists regardless of the distance from the site at which the data are aggregated; (2) the majority
of sites are clustered around smaller densities, yet there are nine sites, for example, that have
densities within one mile exceeding 8,000 persons per square mile and three of these sites are
in areas exceeding 10,000 persons per square mile. These higher densities are more typical of
urban area densities. The NJ average was 1,042 and the NY average was 381.
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The pattern that emerges for population density is also true for population size, since the two
are strongly related to one another. This is reflected in the very high correlation between size
and density for populations within one mile of the NPL sites (r=.9953; n = 195; p=.00).12
12. Although a high correlation between population size and population density would appear to be a self-
fulfilling prophecy if the areas within the mile boundary of all of the sites were equal, it is not actually the
case since the areas around the sites are not equal. The inequality of site areas is an outcome of the fact that
the one mile boundary is only an approximate one used to guide the selection of Census block units around
the NPL sites. In reality, the areas do vary since the boundary around each site is an irregular one. A full set
of correlation coefficients is contained in Appendix E.
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Figure 1. Distribution of National Priority List (NPL) Sites by POPULATION DENSITY for
Alternative Distances, 1990
DISTANCE
.25
500 1500 2500 3500 6000 10000
1000 2000 3000 4000 8000 >10000
Population Density
NOTES:
(1) The numbers on the horizontal axis represent the upper bounds of the intervals over which the data are
aggregated. For example, "500" means sites with population densities of 500 persons per square mile or
less.
(2) The arrow (just before the "2000" mark on the horizontal axis) indicates the mean value for population
density for all NPL sites at the 1 mile distance.
(3) This Sgure is based on 210 NPL sites. "Missing" cases either have no available Census data (sites in PR and
the VI) or have no Census block centers within 1 mile. Missing cases usually increase with decreasing
distance from the site.
EXPL AN ATI ON: Population Density (persons per square mile). Figure 1 shows that: (1) a similar distribution
of the sites across population density categories exists regardless of the distance from the site at which the
data are aggregated; (2) the majority of sites are clustered around smaller densities, yet there are nine
sites, for example, that have densities within one mile exceeding 8,000 persons per square mile and three
of these sites exceed 10,000 persons per square mile. These higher densities are more typical of urban
area densities. The average density for NJ is 1,042 and for NY, 381 persons per square mile.
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HRS Scoring Procedures as a Factor in Site Distribution
One explanation for the tendency of NPL sites to be located outside of urban areas is the
nature of the selection process for sites on the List. The basis for listing is the Hazard Ranking
System (HRS) score. Sites are scored according to groundwater, surface water, air and now
with the revised system, soil exposure routes.1^ Those sites scoring over 28.5 are placed on
the NPL list (unless sites with lower scores are designated through ATSDR procedures).
As discussed below, the sites currently on the NPL reflect scoring procedures that have tended
to emphasize groundwater exposure routes. Furthermore, the groundwater exposure route
score generally tends to weight the use of groundwater as a potable water supply. The
combination of these two factors would tend to lead to an underrepresentation of sites in major
cities, since most major cities are dependent upon surface water for their water supplies rather
than groundwater. This dependency of large urban areas on surface water supplies is
particularly true in New Jersey and New York. *4
The dominance of sites on the NPL with high groundwater scores does not necessarily imply,
however, that such sites are in areas of low density with low minority populations, since
minority populations live in areas of low density as well as high density and some sites with
high groundwater scores are in urbanized areas that have relatively high population densities
and minority populations such as sites on Long Island and in Bergen County, NJ. For
example, 22 sites had population densities greater than 4,000 persons per square mile within
13. U.S. EPA, "Hazard Ranking System; Final Rule," Federal Register, Vol. 55, No. 241 (December 14, 1990),
Book 2, pp. 51532-51667.
14. Arecent summary of water supply sources in the State of New Jersey, for example, underscored the
emphasis upon surface water supplies.(Bruno Tedeschi, "Liquid Assets. How water stays on tap in dry spell,"
The Sunday Record, December 5, 1993. The five cities with the largest populations: Newark, Jersey City,
Paterson, Elizabeth and Trenton, for example, all draw their water from surface water supplies. Jji New
York State, the largest City of New York City, Buffalo, Rochester, Yonkers and Syracuse were listed in the
New York State Bureau of Water Supply's inventory as relying exclusively on surface water, at least with
respect to the supplies it directly produces rather than purchasing.
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one mile and groundwater route scores greater than 50. Of these, eight sites also have % Black
populations greater than 12%.
Reasons for the way sites are selected may also originate in the manner in which inactive
hazardous waste sites come to the attention of the U.S. EPA for scoring and potential listing.
In contrast to the old HRS, which was used to score most of the sites in this study, the new
scoring system gives a relatively greater weight to sites in large cities, since population factor
values are not capped."
The following patterns in the prevalence of groundwater route scores in the scoring are evident
for the sites currently on the NPL in Region 2.
(1) The groundwater route is scored more frequently than any other route, as is apparent in the
table below.
Table 2. Frequency of Use of HRS Score Components
Route Score Components Number of sites
of the Total HRS Score with Component Score
GW score only
SW score only
Air score only
GW and SW only
GW and Air only
SW and Air only
All three scores
No score (ATSDR site)*
60
2
1
103
4
3
24
3
Total 200
*Sites can be listed on the NPL if the Agency for Toxic Substances
and Disease Registry (ATSDR) recommends them, regardless of the
status of the HRS score.
15. U.S. EPA, "Hazard Ranking System; Final Rule," p. 51542.
16. The HRS used prior to the 1990 revisions is analyzed here, since practically all of the finalized sites were
scored using the old HRS and are not rescored under the new system.
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Sites that are listed by virtue of the magnitude of the groundwater route score alone clearly
dominate the list, accounting for 60 sites or 30% of the NPL sites. A total of 82% of the NPL
sites have either a groundwater route score only (60 sites) or the groundwater score in
combination with a surface water route score (an additional 103 sites).
Of the 103 sites with both a groundwater and surface water route score, the magnitude of the
groundwater route score always exceeded the surface water route score, with the exception of
only a half dozen of the sites.
(2) The mean value of the groundwater route score is substantially and significantly higher
than the mean values of the other two route scores, i.e., it accounts for the greatest proportion
of the score than any other route (see Table 3 below).17
Table 3. Mean HRS Scores for Score Components
Total HRS score
Groundwater route score
Surface water route score
Air route score
Mean
41.2
61.8
13.7
9.1
Standard
Deviation
11.1
23.5
19.1
21.6
Note: Each route score has a potential maximum value of 100.
(3) Finally, as shown in Table 4, the groundwater score was more strongly correlated with the
total score than any of the other route scores.
17. Based on a t-test of means, the difference between the means of each of the pairs of route scores and each
route score with the total score was statistically significant at the p=0.01 level.
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Table 4. Correlations Among HRS Score Components
Pearson Correlation Coefficients for HRS Total and Route Scores
Region 2 NPL Sites (n=200)
GW Score SW Score Air Score
n = 200, 2-tailed significance, p=0.00
Total HRS 0.5627 0.4561 0.3865
GW Score -0.2197 -0.3048
SW Score 0.2650
The relatively stronger contribution of the groundwater route score to the total score is related
to a number of scoring procedures and practices. First, scoring procedures start by screening
all routes first. If a given route was sufficient to obtain the 28.5 minimum score for listing on
the NPL, other routes might not be scored. Which route was chosen first for scoring is usually
determined from information about the site, for example, from the Site Investigation report.
The air score was almost never used in scoring, because an observed release to air was
necessary to evaluate the pathway. This left groundwater and surface water scores under the
HRS system that predated the 1990 revisions. Second, scoring practices under the old HRS
tended to score the groundwater route first, given the historical public concern over the
potential effect of hazardous waste sites on drinking water. Where that route was not sufficient
to yield a high enough score, other routes would then be scored one at a time.
Socioeconomic Characteristics of Areas Surrounding NPL Sites
The major source of social and economic characteristics used here to describe populations
around NPL sites is the U.S. Bureau of the Census. The number of parameters collected by the
Census varies enormously for the different population samples they use and the surveys they
conduct. The availability of parameters that describe socioeconomic characteristics of the
population varies for each of the different geographic levels of aggregation (e.g., data are
available for more parameters at the "Block Group" level than at the "Block" level).
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The Census block level, which is the smallest sized unit for which Census data are available,
was used to obtain greater accuracy when aggregating data units containing socioeconomic
characteristics of populations in close proximity to Superfund sites. The disadvantage is that
the set of parameters available at the block level is not as extensive as the set of parameters
available at larger geographic data units (e.g., "Block Group", "Census Tract"). However,
there is often a high degree of correlation among the characteristics making the use of a large
number of characteristics unnecessary and actually undesirable from a statistical point of view.
The parameters available at the block level were reasonably well-suited to the analysis, and, in
addition, the structure of the data file (the STF-1B file) made data extraction easier. The list of
socioeconomic characteristics as they appear in the Census database that was used in this report
is given in Appendix A.
Although socioeconomic data could be aggregated at almost any distance from an NPL site,
the one-mile distance was selected for data aggregation for most of the analyses. This was
deemed advisable for several reasons. First, there is usually little variation in the
characteristics with distance beyond one mile when compared with those characteristics at one
mile. Second, at .distances less than one mile, the little variation that does occur exists because
the method of extracting the data tends to underaggregate population at small distances.^
Therefore, the values calculated for the smaller distances may not portray population as
accurately given the method used to extract the data. Thus, little is gained by repeating each
analysis for the different distances.
The statistics below treat sites in NJ and NY as a single group. In a number of critical areas,
however, the characteristics of NPL sites in the two states can differ. These differences, where
they are substantial, will be highlighted.
18. The methodology was described earlier under the "Background" section and is detailed in Appendix H.
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Racial and Ethnic Characteristics
Data for the following racial and ethnic characteristics were aggregated cumulatively for the
seven distances surrounding the NPL sites: Black, Asian, Native Americans, and Hispanic.
The category, Hispanic, is independent of and therefore, overlaps with the first three
categories. Each of these racial and ethnic characteristics were expressed as percentages of the
total population in the area contained within the distance specified (or, more precisely, of the
population living in census Blocks whose centroids were located within the area specified).
The total population within any subgroup was retained as a variable as well.
Racial and ethnic data are portrayed in a number of ways, as indicated earlier in this section,
to supplement the distributions. First, the mean of the percentages is tabulated with distance,
which implicitly weights each site area equal to any other irrespective of the number of people
in each area. Second, medians rather than means were compared in order to capture in one
measure the skewness potentially produced by extreme values (there is one site, for example,
in New York City, which potentially skews certain results upwards by virtue of the greater
population value around the site). Third, percentages are implicitly weighted by the size of the
population of each area by adding up the subgroup populations and total populations and then
dividing the former by the latter to obtain a mean (average) percentage for the particular
subgroup. Finally, a check was made for the presence of sites clustering at high values at
certain points in the distributions using methods described below.
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(1) Unweighted Means
Table 5. Means (Unweighted by Population) for Selected Racial and Ethnic
Groups around NPL Sites
Distance from
Site (miles)
0.25
0.50
0.75
1.00
2.00
3.00
4.00
NJ
NY
%Black % Native American
Mean S.D. Mean S.D.
8.0
7.4
7.0
7.3
7.0
7.1
7.3
13.4
15.9
16.9
14.7
13.2
13.7
10.9
9.5
8.6
0.7
0.4
0.9
1.0
0.8
0.7
0.6
0.2
0.3
4.0
3.0
7.6
7.6
5.9
5.7
5.3
% Asian
Mean S.D.
2.0
2.0
2.0
2.3
2.2
2.3
2.3
3.9
3.5
4.2
3.1
3.0
3.6
2.4
2.3
2.2
% Hispanic
Mean S.D.
5.1
4.6
5.2
5.0
4.9
5.1
5.2
9.6
12.3
8.7
7.9
9.6
9.4
6.4
6.3
6.1
Note: These are Means that Implicitly Weight Areas Equally Regardless of the
Absolute Magnitude of Subgroup Populations
This table of unweighted means reveals several things -
Under one mile the variations are larger, i.e., there are somewhat larger standard
deviations and larger differences among distances. This is largely an artifact of the
methodology, where far fewer sites are represented the closer in one gets, since the
centers of blocks often do not fall within those small distances. " The decline in
differences in socioeconomic characteristics with increasing distance from the site also
reflects the cumulation of population from one distance to another, which tends
somewhat to reduce differences in characteristics with distance.
19. The valid number of sites ranges from 97 at 0.25 to 197 at 4 miles, out of a total of 200 sites, reflecting the
decline in data points with declining distance from the site. Appendix H.2. contains a comparison between
this method and CIS-based methods. Although a CIS system can be programmed to obtain population
estimates closer to a site, it does so at the expense of having to assume uniform population density in the
areas for it intersects in order to generate estimates for an area with a particular shape.
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- In spite of some variations, the mean values remain relatively constant with distance.
- The mean percentage of each group is below the statewide percentages, with the
exception of Native Americans. The size of the standard deviations is rather large,
however, which necessitates further analyses of the data to obtain a better grasp of the
underlying distributions.
In order to address the variation in the distribution reflected in relatively large standard
deviations, the distributions were graphed and are shown in the accompanying figures.
Regardless of racial or ethnic subgroup, the pattern of the distributions with distance reflect, as
do the means, a constancy in the shape of the distribution regardless of distance.
Figure 2 shows the distribution of sites by percentage of Blacks in the population around NPL
sites for alternative distances from the sites. It reveals that while most of the sites have lower
proportions of Blacks in the population, there is somewhat of a bimodal or trimodal
distribution. For example, at the one mile distance, although the largest peak is between 0-1 %
Black, eight sites are in the approximately 10-15% Black category and another 17 are in the
20-50% Black category. The proportion for NJ is 13.4% and for NY, 15.9%.
Figure 3 shows a distribution of sites by percentage of Hispanics in the population for
alternative distances from the sites. The pattern is similar to that for the Black population. It
reveals that while most of the sites have lower proportions of Hispanics in the population,
there is somewhat of a bimodal or trimodal distribution. For example, at the one mile distance,
although the largest peak is at about 1-2%, eight sites are in the approximately 10-15%
Hispanic category and another seven are in the 20-50% Hispanic category. The proportion for
NJ is 9.6% and for NY, 12.3%.
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Figure 2. Distribution of National Priority List (NPL) Sites by PERCENT BLACK for
Alternative Distances, 1990
CO
0)
.c
E
-
o
O
DISTANCE
.25
.50
.75
1.00
2.00
3.00
4.00
Missing 1.0 3.0 5.0 7.0 9.0 15.0 50.0
0.5 2.0 4.0 6.0 8.0 10.0 20.0 100.0
% Black
NOTES:
(1) The numbers on the horizontal axis represent the upper bounds of the intervals over which the data are
aggregated. For example, "0.5" means sites with zero to 0.5% Black populations.
(2) The arrow (just above the "7.0" mark on the horizontal axis) indicates the mean value for % Black for all
NPL sites at the 1 mile distance.
(3) This 6gure is based on 210 NPL sites. "Missing" cases either have no available Census data (sites in PR and
the VI) or have no Census block centers within 1 mile. Missing cases usually increase with decreasing
distance from the site.
EXPLANATION: Percent Black Population. Figure 2 shows the distribution of sites by percentage of Blacks in
the population around NPL sites for alternative distances from the sites. It reveals that while most of the
sites have lower proportions of Blacks in the population, there is somewhat of a bimodal or trimodal
distribution. For example, at the one mile distance, although the largest peak is between 0-1% Black,
eight sites are in the approximately 10-15% Black category and another 17 are in the 20-50% Black
category. The proportion for NJ is 13.4% and for NY, 15.9%.
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Figure 3. Distribution of National Priority List (NPL) Sites by PERCENT HISPANIC for
Alternative Distances, 1990
Missing 1.0 3.0 5.0 7.0 9.0 15.0 50.0
0.5 2.0 4.0 6.0 8.0 10.0 20.0 100.0
% Hispanic
NOTES:
(1) The numbers on the horizontal axis represent the upper bounds of the intervals over which the data are
aggregated. For example, "0.5" means sites with zero to 0.5% Hispanic populations.
(2) The arrow (just about at the "5.0" mark on the horizontal axis) indicates the mean value for % Hispanic for
all NPL sites at the 1 mile distance.
(3) This figure is based on 210 NPL sites. "Missing" cases either have no available Census data (sites in PR and
the VI) or have no Census block centers within 1 mile. Missing cases usually increase with decreasing
distance from the site.
EXPLANATION: Percent Hispanic. Figure 3 shows a distribution of sites by percentage of Hispanics in the
population for alternative distances from the sites. The pattern is similar to that for the Black population.
It reveals that while most of the sites have lower proportions of Hispanics in the population, there is
somewhat of a bimodal or trimodal distribution. For example, at the one mile distance, although the
largest peak is at about 1-2%, eight sites are in the approximately 10-15% Hispanic category and another
seven are in the 20-50% Hispanic category. The proportion for NJ is 9.6% and for NY, 12.3%.
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The small amount of clustering at the high end of the distributions indicates the need for
analyzing the clusters of sites at the high end of the distribution. Such an analysis was
conducted, and is discussed later in this report.
(2) Medians
The medians for race and ethnicity portray the existence of these extremes more clearly. In all
cases, the medians are consistently far lower than the means (thus indicating a skewed
population distribution).
Table 6. Medians for Selected Racial and Ethnic Groups around NPL Sites
Distance
0.25
0.50
0.75
1.00
2.00
3.00
4.00
NJ
NY
Median Percentages
%Black % Native American %Asian
0.9
1.5
1.6
2.0
3.6
3.8
4.3
13.4
15.9
0.0
0.0
0.1
0.1
0.2
0.2
0.2
0.2
0.3
0.0
0.8
0.9
1.2
1.6
1.7
1.7
3.9
3.5
%Hispanic
2.3
2.1
2.6
2.7
2.8
3.0
3.1
9.6
12.3
Note: These are Medians that Implicitly Weight Areas Equally Regardless of the
Absolute Magnitude of Subgroup Populations
(3) Weighted Means
Given the existence of a few sites that are at the upper ends of the distribution, it is worthwhile
trying to capture them through a weighting process that takes into account the numbers of
people in each subgroup category.
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Table 7. Population Weighted Means for Selected Racial and Ethnic
Groups around NPL Sites
Distance
0.25
0.50
0.75
1.00
2.00
3.00
4.00
NJ
NY
% Black
12.2
11.8
12.0
11.8
13.8
14.3
14.4
13.4
15.9
% Native American
0.2
0.2
0.2
0.2
0.3
0.3
0.2
0.2
0.3
% Asian
2.8
3.7
4.0
4.3
4.5
4.2
4.0
3.9
3.5
% Hispanic
8.3
9.2
9.4
9.8
10.9
11.2
11.8
9.6
12.3
Note: These are Averages that Implicitly Weight Areas According to the
Absolute Magnitude of Subgroup Populations
In most cases, the weighting by the magnitude of subgroup populations brings the means for
the minority subpopulations closer to the state proportions, but the means in most cases still do
not exceed the state proportions. At greater distances, the percentages for Native Americans
and Asians peak or level off at about 2-3 miles from the site. The percentages for Blacks
appear to be leveling off at 3-4 miles, and the percent Hispanic continues to increase slightly.
As mentioned earlier, among the smaller distances, the one mile distance is the more reliable
distance given the problems of extracting the data at smaller distances.
The findings here with respect to the proportion of Blacks near NPL sites differ from what was
found for a national set of NPL sites where population characteristics were defined at the
municipality level. 0
20. R. Zimmerman, "Social Equity and Environmental Risk," Risk Analysis: An International Journal, Vol. 13,
No. 6 (December 1993), pp. 649-666.
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The racial and ethnic composition of areas within one mile of the NPL sites varies by State in
EPA's Region 2. Sites in NJ tend to have a greater mean (unweighted) percentage of their
population residing within one mile that is Black (9.6%), Hispanic (6.3%), and Asian (2.6%)
than sites in NY do (4.4% Black, 3.3% Hispanic and 1.9% Asian). Sites in NY have a
relatively higher mean percentage of their population residing within that distance that is
Native American (1.7%) than sites in NJ (0.5% Native American).
(4) Population Subgroup Clusters and Site Location: Race and Ethnicity
Although on average the areas around NPL sites approximate the state proportions in terms of
major race and ethnicity categories, the nature of sites concentrated at the high end of
distribution of population, race, ethnicity and household value was explored further.
Since the socioeconomic characteristics of the NPL sites show a high degree of variability as
indicated by very large standard deviations around the means, attention to distributions rather
than to strict averages is critical. In particular, sites whose population characteristics were at
the high end of the distribution were examined in more detail. Identifying the extent to which
sites with relatively high minority populations exist and where they are located is an important
component of an equity study as an initial indicator of a potential equity problem and in order
to analyze whether such sites differ in any way from the total set of NPL sites.
The means (weighted or unweighted) and medians and their relationship to the state
proportions to some extent obscure some of the extreme values at both ends of the distribution.
For example, using the averages for the NPL sites as a group and the statewide proportions as
thresholds or benchmarks, at one mile from the sites, the following groups of sites emerge
above these thresholds.
- 41 sites (one fifth of the sites) have populations that are greater than 10% Black within
one mile of the site (10% is between the unweighted and weighted averages for %
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Black for the NPL sites); 30 sites have Black populations greater than 15% Oust about
equal to the NY proportion and above the NJ proportion) and 20 sites have populations
greater than 20% Black.
- 27 sites (about 14% of the sites) have populations that are greater than 4% Asian
(which is approximately equal to both the NPL site average and the statewide
proportion) within one mile of the site.
- 56 sites or 30% are above 0.25% Native American. About 5% of the sites (9 sites)
have populations that are 5% or more Native Americans within one mile of the site.
- 19 sites (one tenth of the sites) have populations that are greater than 10% Hispanic
within one mile of the site.
In contrast, a number of sites have very low proportions of minorities:
- 51 sites (about a quarter of the sites) have 0.5 % or less Blacks.
- 69 sites (over a third sites) have 0.5% or less Asians.
- 138 sites (three quarters of the sites) have 0.25% or less Native American.
- 29 sites (about 15%) have 0.5% or less Hispanics.
As discussed above, the interpretation of these extremes depends on the geographic area to
which these figures are compared, just as the interpretation of weighted and unweighted means
and medians does.
One way of looking at this, to supplement the use of the state proportion as a reference point,
is to determine how localized the racial and ethnic concentrations are around NPL sites, in
terms of the extent to which the percentages exceed the percentages in the municipality within
which the sites are located. Since the municipality data base is somewhat more difficult to
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work with than other Census units, not all of the percentages were available for each
municipality.
Table 8 below gives the distribution of sites according to the ratio of the percentage of the
population that is Black living within one mile from the site to the percentage in the
municipality in which the site is located. The first group in Table 8 gives the summary of these
ratios for all of the NPL sites. The second group gives the same distribution of ratios for a
subset of those sites whose Black populations exceed 15% within one mile of the site. The
third group gives the distribution of the ratios for a subset of sites which are located in
municipalities whose average Black population exceeds 15%. The site by site data upon which
this summary table is based is contained in Appendix C.
Table 8 indicates that:
- For the entire set of NPL sites, over one third (40%) of the sites (equivalent to 78 sites)
have %Black populations that exceed their municipality's proportion (and that
91
municipality proportion may or may not be higher than the State proportion)/1
- In order to determine how much more localized this phenomenon may be, one can look
at additional subgroups. As noted earlier, the % Black population within one mile of 30
sites is greater than the State proportions, that is, on average, 15 % or more of their
population is Black. Of these 30 sites, 19 sites or two-thirds exceed their municipality's
percentage of Blacks as well as the State's proportion. (Additionally, these 19 sites
account for about one-quarter of the 78 sites that exceed just the % Black in the
municipality they are located in.)
21. The reader should be alerted to the fact that the extent to which the municipality assigned to an NPL site is
representative of where it is, may be limited in certain areas where sites are located on the border of two or
more municipalities.
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Finally, if one focuses on just those sites whose municipality Black populations exceed
the state proportion of 15%, one finds 22 sites in this category; furthermore, six if
these sites or about one quarter of the 22 sites exceed the municipality proportion for
Blacks.
Table 8. Ratio of the Percent Blacks within 1 Mile of NPL Sites
and the Percent Blacks in Their Municipalities
(1) (2) (3)
Ratio Total NPL Sites Sites > 15% Black Municipality > 15% Black
0-0.5
0.51-1.0
1.01-1.5
1.51-2.0
2.0 +
Missing
Total
No.
65
36
25
20
33
16
195
Percent
33.3%
18.5
12.8
10.3
16.9
8.2
100.0
No.
2
7
4
4
11
2
30
Percent
6.7%
23.3
13.3
13.3
36.7
6.7
100.0
No.
9
8
3
2
1
0
22
Percent
40.9
36.4
13.6
9.1
4.6
0
100.0
Note:
Percentages may not add to 100% due to rounding.
*15% is the approximate statewide averages for NJ (13.4%) and NY (15.9%).
(5) Population Subgroup Clusters and Site Location: Population Density
High density areas lend another perspective to the extreme ends of the distribution. 55 sites, or
about one-quarter of the total number of NPL sites, have 5,000 or more people living within
one mile of the site. These are predominantly in large urban areas that have high densities: 55
sites or one quarter of the sites have densities of 2,000 persons per square mile or more -
above the mean for the NPL sites as a group. Areas of large numbers of people are also areas
of high population density (the two are highly correlated for populations within 1 mile of the
NPL sites).
Given the potential for exposure of such a large population just by virtue of numbers, it is
important to look at the characteristics of these areas.
-26-
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Table 9. Densely Populated Areas and Selected Population Characteristics
% Black
% Asian
% Native Americans
All
NPL Sites
(n = 195)
7.3
2.3
1.0
NPL Sites with
> 5000 persons
within 1 mile
(n=55)
11.2
3.4
0.3
% Hispanic
5.0
8.6
Notes: These means (which are unweighted by population size) have
relatively large standard deviations (not shown).
The sample size, n, in this table is reduced to 195, since the
sites in Puerto Rico and one in the Virgin Islands (whose
socioeconomic data have not been included) and sites whose Block
centers fall outside of the one mile radius become classified as
missing data.
What emerges is that these NPL sites with large numbers of people residing within one mile
(and also having higher densities) have somewhat higher percentages of Blacks, Hispanics and
Asians within one mile than the total set of NPL sites, but standard deviations are large.
Age and Family Status
Table 10. Characteristics of Age and Family Status of Populations
within 1 Mile of NPL sites
Unweighted
Mean S.D.
Weighted
Median Ave.
i Occupied housing units with
single householders having
1+ persons under 18
8.0 8.1
6.6
NJ
8.4
NY
% Under 18
% 65 years old and older
23.4
12.3
6.0
5.6
23.4
12.0
21.8
13.5
23.3
13.4
23.7
13.1
10.2
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The mean percentages of persons under 18 and persons 65 years old or older for populations
within 1 mile of the NPL sites, shown above, are practically identical to the state proportions.
This is also true for weighted averages and medians.
As in the case of race and ethnicity, the means and their relationship to the state proportions to
some extent obscure some of the extreme values at the upper end. For example, for the
population within one mile of the site:
- Although about two-thirds of the sites had at or below 10% of their occupied housing
units with single householders with one or more person under 18, for the other third of
the sites, the average percentage of such housing units exceeded 10% (10% is
approximately the upper end of the state proportions of such households: the NJ
proportion is 8.4% and the NY proportion is 10.2%).
- Although about two-thirds of the site had at or below 15 % of the population that was
elderly (13.1% and 13.4% are the statewide proportions for NY and NJ respectively),
for the other third of the sites, the average percentage of the population that was elderly
was greater than 15%.
Figures 4 and 5 show the distributions across percentage categories for persons under 18 and
those 65 years old and over by distance from the site.
Figure 4 shows the distributions of sites by percentage of persons under 18 years old for
alternative distances from the sites. The figure shows that with respect to the proportion of
persons under 18, the distribution patterns are similar regardless of distance. In addition, the
peak is in the upper bound of the interval that contains the unweighted means which range
from 22-24%, depending on distance. For purposes of comparison, the statewide proportions,
shown in Table 10, were 23.3% for NJ and 23.7% for NY.
-28-
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Figure 5 shows the distributions of sites by percentage of persons in the population who are 65
years old and over for alternative distances from the sites. The figure shows that with respect
to the proportion of persons 65 years old or older, the distribution patterns are similar
regardless of distance. In addition, the peak is close to the unweighted means which range
from 12-14%, depending on distance. For purposes of comparison, the statewide proportions,
shown in Table 10, were 13.4% for NJ and 13.1% for NY.
Figure 6 shows the distributions of sites by percentage of occupied housing units with single
person householders with one or more persons under 18. The figure shows that the distribution
patterns are similar regardless of distance. In addition, the peak is close to the unweighted
means which are always 7-8% depending on distance. For purposes of comparison, the
statewide proportions of occupied housing units with single householders with one or more
person under 18, shown in Table 10, is 8.4% for NJ and 10.2 % for NY.
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Figure 4. Distribution of National Priority List (NPL) Sites by PERCENTAGE OF PERSONS
UNDER 18 YEARS OLD, for Alternative Distances, 1990
120
Missing 5.0
10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 >45.0
% Under 18 Years Old
NOTES:
(1) The numbers on the horizontal axis represent the upper bounds of the intervals over which the data are
aggregated. For example, "5.0" means sites with zero to 5.0% persons under 18 years old.
(2) The arrow (just before the "25.0" mark on the horizontal axis) indicates the mean value for percentage of
persons under 18 years old for all NPL sites at the 1 mile distance.
(3) This figure is based on 210 NPL sites. "Missing" cases either have no available Census data (sites in PR and
the VI) or have no Census block centers within 1 mile. Missing cases usually increase with decreasing
distance from the site.
EXPLANATION: Percentage of the Population Under 18 Years Old. Figure 4 shows the distributions of sites
by percentage of persons under 18 years old for alternative distances from the sites. The figure shows that
with respect to the proportion of persons under 18, the distribution patterns are similar regardless of
distance. In addition, the sites peak at the upper bound of the interval that contains the unweighted means
which range from 22-24%, depending on distance. For purposes of comparison, the statewide proportions
were 23.3% for NJ and 23.7% for NY.
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Figure 5. Distribution of National Priority List (NPL) Sites by PERCENTAGE OF PERSONS
65 YEARS OLD AND OLDER for Alternative Distances, 1990
2.00
3.00
4.00
Missing
10.0
15.0
20.0
30.0
40.0
50.0
70.0 80.0
% 65 Years Old and Over
NOTES:
(1) The numbers on the horizontal axis represent the upper bounds of the intervals over which the data are
aggregated. For example, "5.0" means sites with zero to 5.0% persons 65 years old and over.
(2) The arrow (just before the "15.0" mark on the horizontal axis) indicates the mean value for the percentage of
persons in the population who are 65 years old and over for all NPL sites at the 1 mile distance.
(3) This figure is based on 210 NPL sites. "Missing" cases either have no available Census data (sites in PR and
the VI) or have no Census block centers within 1 mile. Missing cases usually increase with decreasing
distance from the site.
EXPLANATION: Percentage of Persons 65 Years Old and Over. Figure 5 shows the distributions of sites by
percentage of persons in the population who are 65 years old and over for alternative distances from the
sites. The figure shows that with respect to the proportion of persons 65 years old or older, the
distribution patterns are similar regardless of distance. In addition, the sites peak at close to the
unweighted means which range from 12-14%, depending on distance. For purposes of comparison, the
statewide proportions, shown in Table 10, were 13.4% for NJ and 13.1% for NY.
-31-
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Figure 6. Distribution of National Priority List (NPL) Sites by PERCENTAGE OF SINGLE
PERSON HOUSEHOLDERS WITH CHILDREN for Alternative Distances, 1990
Missing
.5
8.0 10.0 >10.0
% Single-Parent HHs
NOTES:
(1) The numbers on the horizontal axis represent the upper bounds of the intervals over which the data are aggregated.
For example, "0.5" means sites with zero to 0.5% of occupied housing units with single person householders with
one or more persons under 18 years old.
(2) The arrow (just before the "8.0" mark on the horizontal axis) indicates the mean value for percentage of occupied
housing units with single householders with one or more persons under 18 for all NPL sites at the 1 mile distance.
(3) This figure is based on 210 NPL sites. "Missing" cases either have no available Census data (sites in PR and the VI)
or have no Census block centers within 1 mile. Missing cases usually increase with decreasing distance from the
site.
EXPLANATION: Percentage of Occupied Housing Units with Single Person Householders With One or More Persons
Under 18. Figure 6 shows the distributions of sites by percentage of occupied housing units with single person
householders with one or more persons under 18. The figure shows that the distribution patterns are similar
regardless of distance. In addition, the peak is close to the unweighted means which are always 7-8% depending on
distance. For purposes of comparison, the statewide proportions of occupied housing units with single householders
with one or more person under 18 is 8.4% for NJ and 10.2 % for NY.
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Interrelationships Among Socioeconomic Characteristics
A number of the socioeconomic characteristics described above, are statistically correlated
with one another. Table 11 presents correlations exceeding 0.4 for the characteristics discussed
above were as follows for characteristics aggregated at 1.0 mile from the site. Though there is
no universally accepted criterion for what is a significant correlation or not, for the purpose of
the analysis 0.4 was chosen as sufficiently great to warrant some attention, though it is
considered a weak correlation. 0.5-0.7 is considered a strong correlation, and above 0.7 was
considered so high as to affect the structure of a regression analysis in the next section of the
99
report. ^
Table 11. Association Between Selected Socioeconomic Characteristics
of Areas Within 1 Mile of NPL Sites
Characteristic Correlation Coefficient
% Black and % Hispanic 0.4757 (p=0; n = 194)
% Black and % Occupied 0.5917 (p=0; n=192)
housing units with
single householders having
1+persons under 18
% Hispanic and % Occupied 0.7040 (p=0; n=192)
housing units with
single householders having
1+ persons under 18
- The percentage of Blacks within one mile of an NPL site, is weakly correlated with the
percentage of Hispanics;
- The percentage of occupied housing units with single householders having one or more
persons under 18 is strongly correlated with the percentage of Blacks and Hispanics;
22. A full table of correlation coefficients is given in (lie Appendix.
-33-
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- the percentages of the population that are Native American or Asian are not correlated
statistically to one another (since their correlation coefficients are well below 0.4) or to
the percentages of the population that are Black or Hispanic.
Housing Characteristics
Housing Tenure, Condition and Value
Housing characteristics are an important component of an equity study for several reasons.
First, housing characteristics, in particular tenure, value, and conditions related to occupancy -
to some extent reflect wealth. * A previous study of NPL sites nationwide, which defined
socioeconomic characteristics at the level of the municipality in which the NPL sites were
located, found a very high correlation of r=0.73 (p=.00) between 1985 per capita income and
house value. Thus, house value is to some extent used in this study as a surrogate for
income, however, independent of income, house value can reflect and be a measure of
disamenities (i.e., local conditionsthat would reduce the desirability of living in such a house),
such as those potentially related to an inactive hazardous waste site. There is, in fact, an
extensive literature that explores associations between property values (often in terms of house
value) and actual or perceived adverse environmental conditions. Second, housing tenure - the
extent of homeownership - is significant because homeowners are considered more actively
engaged in policy debates that are seen as affecting their economic well-being, in particular,
their property values.
For the one mile distance from the sites, the following characteristics appeared:
23. In order to obtain income variables from Census data, data would have had to be aggregated at the larger
block group level. House value and rent are reasonable surrogates for income since they correlate highly
with income.
24. R. Zimmerman, "Social Equity and Environmental Risk." Risk Analysis: An International Journal. Vol. 13,
No. 6 (December 1993), p. 659.
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Table 12. Selected Occupied Housing Characteristics of Areas
Within One Mile of NPL Sites
% Home ownership
Mean house value
Median house value
% Renter
Mean contract rent
Median contract rent
Values at 1 Mile
Mean S.D.
71.1 20.8
$146,528 $73,257
$141,706
28.9 20.8
$472 $196
$483
Statewide
NJ
64.9
$185,300
$162,300
35.1
$514
$521
NY
52.2
$158,300
$131,600
47.8
$461
$428
% Crowding
(1.01+ persons/room) 3.3 8.0 3.9 6.5
Note: Each of these percentages refers to a percentage computed
in terms of occupied housing units.
Homeownership is clearly higher around NPL sites than is typical of the total population in the
two states. Figure 7 shows the distributions of sites by the percentage of occupied housing
units that is owner-occupied rather than renter-occupied for alternative distances. Beyond 1
mile, the distribution patterns are similar. The unweighted means range from 70-72%,
depending on distance.
Both mean and median house value and mean and median rent at the 1 mile distance fall
between the state proportions for these values. This warrants disaggregating the figures by
state.
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Figure 7. Distribution of National Priority List (NPL) Sites by PERCENTAGE OF OWNER-
OCCUPIED HOUSING UNITS for Alternative Distances, 1990
DISTANCE
.25
.50
.75
1.00
2.00
3.00
4.00
Missing 10.0 20.0 30.0 40.0 50.0
% Owner Occupied
60.0
70.0
80.0
90.0 100.0
NOTES:
(1) The numbers on the horizontal axis represent the upper bounds of the intervals over which the data are
aggregated. For example, "10.0" means sites with zero to 10.0% of occupied housing units that are
owner-occupied.
(2) The arrow (just after the "70.0" mark on the horizontal axis) indicates the mean value for the percentage of
occupied housing units that is owner-occupied for all NPL sites at the 1 mile distance.
(3) This figure is based on 210 NPL sites. "Missing" cases either have no available Census data (sites in PR and
the VI) or have no Census block centers within 1 mile. Missing cases usually increase with decreasing
distance from the site.
EXPLANATION: Percentage of Owner-Occupied Housing Units. Figure 7 shows the distributions of sites by
the percentage of occupied housing units that is owner-occupied rather than renter-occupied for
alternative distances. Beyond 1 mile, the distribution patterns are similar. The unweighted means range
from 70-72%, depending on distance.
-36-
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Table 13. House Value and Rent by State for Areas
Within One Mile of NPL Sites
NPL Sites (1 Mile) Entire State
NJ NY NJ* NY*
(n=108) (n=83)
Mean house value
Standard deviation
Median house value
Mean contract rent
Standard deviation
Median contract rent
$160,504
(63,784)
$157,428
$ 509
(168)
$ 514
$128,341
(80,832)
. $98,668
$ 424
(219)
$ 340
$185,300
$162,300
$ 514
$ 521
$158,300
$131,600
$ 461
$ 428
The sources of statewide means and medians in this table and in the
preceding table are as follows:
Means were obtained from the state summary in the U.S. Census
STF-1B file. Thus, they represent aggregate means from block
level data.
Medians were obtained from the U.S. Bureau of the Census,
1990 Census of Population and Housing. Summary
Population and Housing Characteristics. New York (1990-CPH-1-34)
and New Jersey (1990-CPH-1-32) (Washington, D.C.: U.S. GPO,
August 1991), Table 9.
Both means and medians are for "specified owner-occupied housing
units" which are a portion of total occupied housing units.
The total of 191 sites (108 in NJ and 83 in NY) represents the 200
finalized sites, minus 8 in PR and 1 in the VI.
When the figures are disaggregated by state, mean and median house value and mean and
median contract rent for residents within 1 mile of the NPL sites are lower than the analogous
means or medians for the states within which the sites are located. The disparity is more
pronounced for NY sites than NJ sites, especially when medians rather than means are the
basis of comparison. In interpreting these findings, it should be kept in mind that many factors
influence house value, not only whether or not an NPL site is located nearby.
-37-
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For areas within a mile of NPL sites, house value and rent are highly correlated with one
another: r=0.6462 (p=0; n=190). That is, where house value is high, rental units are likely
to be high as well. House value and percent owner-occupancy are uncorrelated with one
another: r=.1022 (p=.161; n = 190). This implies that ownership often occurs in poorer as
well as wealthier areas. Though this result may be suprising to many, it reflects the fact that
home ownership is just as common in lower income neighborhoods as higher income
neighborhoods.
Figure 8 and 9 show the distribution of sites with respect to house value and rent.
Figure 8 shows the distributions of the sites with respect to house value (in dollars) for
alternative distances around the sites. The distributions are bimodal, regardless of distance,
beyond the .25 milepoint. The two peaks are at about $50-100,000 and about $150-200,000.
This bimodality may partially be explained by the fact that the two states and also areas around
the sites within the two states differ dramatically from one another with respect to mean and
median house value. The means for all NPL sites (in both states) range from $146,000 to
$150,000 depending on distance.
Figure 9 shows the distributions of the sites with respect to monthly contract rent (in dollars)
for alternative distances around the sites. The figure shows a similar, but less pronounced
bimodal distribution of the sites with respect to rent, regardless of distance beyond the .25
milepoint. The two peaks are at about $3-400 and about $7-800. This can partially be
explained by the fact that the two states and also the areas around the sites within the two states
differ dramatically from one another with respect to rent. The means for all NPL sites (in both
states) range from $466 to $499, depending on distance.
Crowding within occupied housing units varies dramatically on average between the two states
as a whole with NJ being considerably lower than NY. The average housing unit crowding for
-38-
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areas within 1 mile of NPL sites is somewhat lower than what is typical in either of the states,
although there is considerable variability.
Figure 10 shows the distribution of sites with respect to the percentage of the occupied housing
units that is characterized by crowded conditions (defined as 1.01 persons per room or more)
for alternative distances around the sites. Regardless of distance, the distribution patterns are
similar. In addition, the peak is close to the unweighted means which average 3 percent,
regardless of distance. There is a trail of sites that is above the mean, with slightly less than
ten sites causing a slight peaking at about 20%.
-39-
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Figure 8. Distribution of National Priority List (NPL) Sites by HOUSE VALUE (in dollars) for
Alternative Distances, !990
DISTANCE
.25
.50
.75
1.00
Missing
50,000 100,000 150,000 200,000 300,000 400,000 >400,000
Mean House Value
NOTES:
(1) The numbers on the horizontal axis represent the upper bounds of the intervals over which the data are
aggregated. For example, "50,000" means sites with house values ranging from zero to $50,000.
(2) The arrow (just before the "150,000" mark on the horizontal axis) indicates the mean value for house value
for all NPL sites at the 1 mile 'distance.
(3) This figure is based on 210 NPL sites. "Missing" cases either have no available Census data (sites in PR and
the VI) or have no Census block centers within 1 mile. Missing cases usually increase with decreasing
distance from the site.
EXPLANATION: House Value. Figure 8 shows the distributions of the sites with respect to house value (in
dollars) for alternative distances around the sites. The distributions are bimodal, regardless of distance,
beyond the .25 milepoint. The two peaks arc at about $50-100,000 and about $150-200,000. This
bimodaliry may partially be explained by the fact that the two states and also areas around the sites within
the two states differ dramatically from one another with respect to mean and median house value. The
means for all NPL sites (in both states) range from $146,000 to $150,000 depending on distance.
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Figure 9. Distribution of National Priority List (NPL) Sites by MONTHLY RENT (in dollars)
for Alternative Distances, 1990
4.00
200
300
400
500
600
800 1,000 1,200 >1,200
Mean Rent
NOTES:
(1) The numbers on the horizontal axis represent the upper bounds of the intervals over which the data are
aggregated. For example, "100" means sites with monthly rents ranging from zero to $100 per month.
(2) The arrow (just before the "500" mark on the horizontal axis) indicates the mean value for monthly rent for
all NPL sites at the 1 mile distance.
(3) This figure is based on 210 NPL sites. "Missing" cases either have no available Census data (sites in PR
and the VI) or have no Census block centers within 1 mile. Missing cases usually increase with
decreasing distance from the site.
EXPLANATION: Monthly Rent. Figure 9 shows the distributions of the sites with respect to monthly contract
rent (in dollars) for alternative distances around the sites. The figure shows a similar, but less pronounced
bimodal distribution of the sites with respect to rent, regardless of distance beyond the .25 milepoint. The
two peaks are at about $3-400 and about $7-800. This can partially be explained by the fact that the two
states and also the areas around the sites within the two states differ dramatically from one another with
respect to rent. The means for all NPL sites (in both states) range from $466 to $499, depending on
distance.
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Figure 10. Distribution of National Priority List (NPL) Sites by PERCENTAGE OF
CROWDING IN HOUSING UNITS for Alternative Distances, 1990
120
DISTANCE
.25
Missing
4.0
6.0
8.0 10.0 20.0 30.0 40.0 60.0 80.0 100.0
% Crowded
NOTES:
(1) The numbers on the horizontal axis represent the upper bounds of the intervals over which the data are
aggregated. For example, "2.0" means sites with zero to 2.0% of the occupied housing units that are crowded
(more than one person per room).
(2) The arrow (just before the "4.0" mark on the horizontal axis) indicates the mean value for the percentage of
the occupied housing units that is characterized by crowded conditions for all NPL sites at the 1 mile distance.
(3) This figure is based on 210 NPL sites. "Missing" cases either have no available Census data (sites in PR and
the VI) or have no Census block centers within 1 mile. Missing cases usually increase with decreasing distance
from the site.
EXPLANATION: Percentage of Occupied Housing Units that is Crowded (Persons per Room). Figure 10
shows the distribution of sites with respect to the percentage of the occupied housing units that is characterized
by crowded conditions (defined as 1.01 persons per room or more) for alternative distances around the sites.
Regardless of distance, the distribution patterns arc similar. In addition, the peak is close to the unweighted
means which average 3 percent, regardless of distance. There is a trail of sites that is above the mean, with
slightly less than ten sites causing a slight peaking at about 20%.
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Interrelationships between Population and Housing Characteristics
Several of the population and housing characteristics are correlated with one another, as one
would expect from similar relationships for the population at large. The correlations exceeding
0.4 for the variables examined were as follows for characteristics aggregated at 1.0 mile from
the site:
Table 14. Selected Relationships Between Housing
Characteristics and Race and Ethnicity
Characteristics Correlation Coefficient
% Crowding and % Black 0.4534 (p=0; n=191)
% Crowding and % Hispanic 0.4773 (p=0; n=191)
% Crowding and % Occupied 0.7126 (p=0; n=191)
housing units with single
householders with 1 + persons
under 18
Rent and % Asian 0.4432 (p=0; n = 191)
House Value and % Occupied -0.4038 (p=0; n=191)
housing units with single
householders with 1 + persons
under 18
% Owner and % Black -0.4542 (p=0; n=191)
% Renter and % Black 0.4001 (p=0; n=191)
% Owner and % Hispanic -0.4718 (p=0; n=191)
% Renter and % Hispanic 0.4241 (p=0; n = 191)
Note: The percentages for all housing characteristics refer to occupied
housing units, whereas the percentages for population
characteristics refer to population.
These correlations can be interpreted in the following way (using the criteria set forth earlier
for interpreting correlation coefficients):
- The percentage of Black or Hispanic populations residing within one mile of NPL sites
is weakly, but negatively correlated with the extent of homeownership and as one
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would expect, is weakly but positively correlated with the extent of rentership and the
extent of crowding within the residences;
- The percentage of occupied housing units with single householders with one or more
persons under 18 (regardless of race and ethnicity) residing within one mile of NPL
sites, is weakly and negatively correlated with house value, but strongly and positively
correlated with the extent of crowding within the residences;
- The percentage of Asians is weakly correlated with rent.
Site Distribution by Municipality and County
One dimension of equity is whether in fact any given locality is bearing more than its share of
any undesirable land use or facility. * In order to examine the extent of clustering with respect
to existing NPL sites, the distributions of the sites by municipality and county were calculated.
These distributions are shown in Tables 15 and 16.
A concentration of sites within certain parts of the State is clearly apparent from the
geographical distribution by county and municipality (the distribution by county includes sites
in Puerto Rico). Table 15 shows that over a quarter of the sites (27.1 %) or 57 sites are located
in only 23 municipalities that contain more than one NPL site. There are 567 incorporated
municipal governments in New Jersey designated by statute (320 municipalities and 247
townships) and 619 incorporated places in New York (62 cities and 557 villages). Therefore,
the 23 municipalities account for 0.2 % of the total of 1186 incorporated places.^
25. For example, New York City has recently focused on this issue in its policy regarding fair siting of unwanted
facilities. This policy is contained in the New York City Planning Commission document, "Locating City
Facilities: A Guide to the "Fair Share" Criteria" (June 1991).
26. The count of the number of municipalities in each state was obtained from the U.S. Bureau of the Census, A_
Guide to State and Local Census Geography (Washington. D.C.: Bureau of the Census, June 1993, pp.
72-73; 76-77. The term municipality as assigned to the NPL sites and counted within the state totals
encompasses incorporated places.
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Table 15
Municipalities with More than 1 NPL Site
Municipality
Bound Brook
Bridgeport
Edison Township
Franklin Township
Galloway Township
Minotola
Newark
Old Bridge Township
Pemberton Township
Plumstead Township
Rockaway Township
Sayreville
Vineland
Wall Township
Woodland Township
Farmingdale
Glen Cove
Hicksville
Niagara Falls
Oyster Bay
Town of Vestal
Vestal
Barceloneta
State
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NY
NY
NY
NY
NY
NY
NY
PR
Num Sites
2
2
3
2
2
2
2
3
2
5
3
2
2
2
2
4
2
2
5
2
2
2
2
% Black
2.29
12.18
5.57
0.90
7.36
58.46
2.31
22.82
2.86
1.49
3.23
11.48
0.57
0.77
7.80
0.78
15.58
3.26
1.80
1.80
% Hispanic
12.92
2.53
4.33
1.57
26.07
3.81
8.05
2.83
3.97
4.01
23.60
1.05
7.49
11.49
4.92
1.20
4.58
1.79
1.79
57
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Table 16
Counties with More than 1 NPL Site
County
ATLANTIC
BERGEN
BURLINGTON
CAMDEN
CUMBERLAND
ESSEX
GLOUCESTER
HUDSON
HUNTERDON
MIDDLESEX
MONMOUTH
MORRIS
OCEAN
PASSAIC
SOMERSET
SUSSEX
BROOME
CHEMUNG
DELAWARE
DUTCHESS
ERIE
GENESEE
NASSAU
NIAGARA
ONEIDA
ORANGE
OSWEGO
PUTNAM
ROCKLAND
SARATOGA
ST. LAWRENCE
SUFFOLK
State
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
Num Sites
8
9
13
4
4
6
6
2
2
13
9
11
12
2
7
2
8
2
2
3
2
2
12
6
2
3
4
2
2
3
2
12
177
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Table 16 shows that most of the sites (84.3%) are located in counties that have more than one
site. These concentrations by county appear in 32 (or 39%) out of a total of 83 counties in NJ
and NY. At the county level, although counties in older industrial areas seem to predominate,
some of the newer, high growth traditionally non-industrial areas appear to have concentrations
of several sites as well, such as Ocean County in NJ and Orange and Rockland counties in
NY. These areas may represent what might have been the outskirts of the large cities to which
wastes were transported for disposal.
Looking more closely at the 23 municipalities, one sees that 7 of the communities containing
17 of the 57 sites, clearly exceed the state proportions for the proportion of either Blacks,
Hispanics or both.
Thus, the clustering of NPL sites needs to be explored further as a potential equity issue.
Summary: Site Location
The foregoing analysis focused upon the characteristics of the location of existing NPL sites
with respect to socioeconomic characteristics of populations residing within one mile of the
site. This distance was warranted by the fact that many of the characteristics do not change
with distance from one mile onward and that below one mile the data are not as complete, not
that one mile is a particularly notable distance. This distance can underrepresent populations in
areas surrounding very large sites, such as military installations, if no people reside on such
sites. It is also confined to resident populations in proximity to the site rather than including
other populations by virtue of potential for exposure. For example, it does not include persons
who might come in contact with the site by virtue of their occupational or recreational
behavior. It also does not include populations far from the sitewho might become exposed by
virtue of migration of pollutants from it.
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On average, the populations within one mile of the sites are below or approximate the state
proportions with respect to most population characteristics, with the exception of density,
where sites are much higher in both density and numbers of people than the state in general.
With respect to race and ethnicity, it should be recalled that both NJ and NY already have very
high minority populations relative to the rest of the country, which shifts the interpretation of
the findings. Outside of the South, NY ranks the highest among other states in the proportion
of its population that is Black, and NJ ranks fourth. Additionally, NY has the highest number
of Blacks of any state in the country (even including the South), and ranks third in the number
of Hispanics.
House value and rent, whether measured in terms of means or medians are lower in areas
within one mile of the sites than the comparable state figures are for the states in which they
are located, but except for mean house value the differences in NJ are quite small.
Disaggregation of these figures by state is important given the different pattern of house values
in the two states. Overcrowding, which is another housing characteristic, is not substantially
different in areas surrounding the sites and the states in general.
The distribution of NPL sites according to their socioeconomic characteristics shows a high
degree of variability as indicated by very large standard deviations around most indicators of
such characteristics. This necessitates attention to distributions as well as to averages.
The distribution of socioeconomic characteristics of populations within one mile of NPL sites
reveals a set of sites with higher proportions of minority populations and greater population
density relative to either the states or the municipalities within which they are located.
A concentration of sites within certain parts of the State is apparent from the geographical
distribution by county and municipality. A quarter of the sites are in 23 municipalities that
have more than one NPL site. 84% of the sites are in counties with more than one NPL site.
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PART II. CLEANUP AND EQUITY ISSUES
Introduction
An understanding of the stages in the cleanup of NPL sites is an important backdrop for the
evaluation of whether socioeconomic characteristics of the sites have been associated in any
way with the cleanup process.
Two types of cleanup actions occur at NPL sites: long-term (remedial) cleanup and relatively
shorter-term removal actions. The long-term or remedial actions for the cleanup of an NPL
site generally proceed according to a prescribed series of steps which are set forth in agency
guidance documents and administrative procedures. Removal actions may occur at an NPL site
at any time over the course of the long-term remedial action.
Briefly, the stages in a remedial action generally proceed in the following order after the site
has been listed: a Remedial Investigation/Feasibility Study (RI/FS), the adoption of a cleanup
plan by means of a Record of Decision (ROD), Remedial Design (RD), Remedial Action
(RA), and ultimately deletion of the site from the NPL.
The stages in a removal action are a removal investigation (RS), the preparation of an action
memorandum (which is analogous to a ROD for remedial action), and the removal action (RV)
itself.
The number of times each step occurs at a site is an important factor in how the dates of each
of the steps is analyzed. Each step can and often does occur separately for each "operable unit"
97
at a site/' This is true for both remedial and removal actions.
27. An "operable unit" is a portion of the site that is physically separated or separable from the other portions of
the site. It is therefore possible to take action to correct the condition leading to the current or potential
release of contaminants at each operable unit independent of any action at other units at the same site.
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The extent to which the correct sequencing of steps occurs also affects the analysis. The steps
outlined above to attain remedial action and the deletion of the site from the NPL almost
always occur after a site has been proposed for the NPL, and usually occur after the site has
been finalized for the NPL. In some cases, however, the data indicate that cleanup activity has
begun prior to NPL proposal and fmalization. The steps for removal actions, in contrast, can
occur at any time relative to NPL proposal and fmalization dates. Removal actions are in
general more frequent and are of shorter duration than remedial actions.
Taking into account the above considerations, the following aspects or dimensions of the
cleanup process and socioeconomic characteristics of areas surrounding an NPL site were
used.
(1) The attainment of a particular step or event, i.e., has a particular step been completed or
not (for example, whether a ROD or a removal action has occurred or not);
(2) The number of times certain events occur or the frequency of events; in particular, for
remedial action these are combined RI/FSs, RODs, Remedial Designs and Remedial Actions;
this is often a function of the number of operable units; and
(3) The timing of the initiation and completion dates for events that have occurred, and to a
98
lesser extent the duration of an event or similar events/0
28. An extensive analysis was undertaken of the duration of each of the major types of events for a given site
and the difference in time between different events that ostensibly follow one another in a sequence.
Because of the complexity introduced by numerous operable units, and anomalies in the data resulting in
steps being out of sequence, duration was substituted by the simpler variables: start and completion dates.
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Indicators of Cleanup Activity
A handful of indicators of actions and progress applicable to remedial actions and removal
actions were selected out of dozens of indicators for which data are maintained by the U.S.
EPA.29
The parameters were selected on the basis of the following criteria: (1) The parameter
represents a major milestone in the Superfund cleanup process and is not wholly contained
within another component, and (2) relatively complete data exist in WasteLAN for the
parameter. WasteLAN is a computerized database, with standardized data entries, which
includes data on various milestones in the Superfund cleanup process. Occasionally, there were
parameters that were considered very valuable as indicators of cleanup, but no data existed for
these parameters or the data were available for too few sites. Some parameters initially
included in the list below were deleted from regression equations if they were highly correlated
with others (for example, the finalized date, since it is highly correlated with the proposed
date).30
A detailed analysis of how the selection was performed is contained in Appendix H.3. entitled
"Initial Selection of NPL Event Type Codes". The parameters selected are listed below.
For site listing and scoring, the parameters are:
Date of Discovery (C2101 DS)
Total Hazard Ranking System Score
Date of Proposed to NPL (C2101 NP)
Date of Final Listing on NPL (C2101 NF)
29. These characteristics of cleanup are outlined in agency guidance documents. See for example, U.S. EPA,
The RPM Primer. An Introductory Guide to the Role and Responsibilities of the Superfund Remedial
Project Manager (Washington, D.C.: U.S. EPA. September 1987), Exhibit 3 (p. 10) and Exhibit 4 (p. 13).
30. For this purpose, two parameters were considered "highly correlated" if the correlation coefficient exceeded
0.7. This is consistent with the practice followed throughout this report.
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For remedial actions, the parameters are the date and number of:
Community Relations Plan (C2101 CR)
Combined RI/FS (C2101 CO)
Record of Decision (C2101 RO)
Remedial Design (C2101 RD)
Remedial Action (C2101 RA)
For removal actions, the parameters are the date and number of:
Removal Investigation at NPL Sites (C2101 RS)
Removal Action (C2101 RV)
Each of the above measures takes one of two forms. The counts are expressed as whole
numbers. The timing is expressed in terms of when an event occurred computed as number of
months back from September 1993. Data on the counts and scheduling of each of the events
were obtained from WasteLAN.
Highlights of Regulatory Characteristics of NPL Sites
As indicated earlier, the number, frequency and timing of the overall regulatory characteristics
of the NPL sites in New York and New Jersey provide a baseline or frame of reference for
comparing socioeconomic characteristics of areas around the NPL sites and the regulatory
status of the sites. Before introducing the socioeconomic dimension, overall trends in the
regulatory data will be briefly described.
(1) Number
For the regulatory parameters described above, the following characteristics emerge with
respect to the number of regulatory events per site:
- Combined RI/FS. Over half (52.7%) of the sites which reported RI/FS starts,
completions, or both, had exactly one RI/FS, and 83.4% had no more than two. Five
sites had five or more RI/FSs.
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- Records of Decision (ROD). Three-quarters of the sites had exactly one ROD, and
another fifth of the sites had exactly two. The remaining five percent had between three
and 11 RODs.
The number of RI/FSs and RODs is typically a function of the number of operable units for a
given site. The correlation coefficient between number of operable units and number of RI/FSs
is 0.95 (p=0.0; n=199) and between number of operable units and the number of RODs is
0.8965 (p=0.0; n=151). The lower number of cases for RODs occurs because RODs are later
in the process and there are fewer of them.
(2) Timing
Characteristics which emerge with respect to the timing of the regulatory activity, reflect the
sequencing of steps prescribed agency guidance documents. Below are the means and medians
for the timing of successive steps in the cleanup process for remedial events. The average
timing for all of the steps in the cleanup process reflects a generally clear progression from one
step to the next as prescribed in agency procedures.
Table 17. Means and Medians of Steps in the Remediation Process for NPL Sites
Mean S.D. Median n
(in number of months back from September 1993)
Discovery (DS)
Proposed Date (NP)
Finalized Date (NF)
RI/FS Earliest Start (CORIFS1)
RI/FS Latest Start (CORIFS2)
RI/FS Earliest Completion (CORIFS3)
RI/FS Latest Completion (CORIFS4)
First ROD Signed (RO1)
Last ROD Signed (RO2)
146
109
97
88
68
61
46
54
45
36
31
28
31
33
33
29
31
28
148
120
108
90
66
60
36
48
36
209
210
205
204
204
159
159
151
151
Note: Appendix A contains the means and standard deviations and medians of
these regulatory characteristics for the sites in NJ, NY and PR.
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The only break in the sequence occurs in the RI/FS completion dates relative to the signing of
the RODs. RI/FSs may start for some operable units, while RODs are still being completed for
others.
The number of cases diminishes with successive steps because not all sites have advanced to
the end of the process.
The frequency distribution of sites across the various time categories provides further insight
into the timing of the stages.-'*
- Discovery. The discovery dates for over 40% of the sites were more than 15 years
prior to September 1993.
- Proposed Date for the NPL. Over half (53.8%) of the sites were proposed ten years or
earlier prior to September 1993.
- Finalized Date for the NPL. All of the sites which had been finalized by September
1993 had been finalized within ten years and over half were finalized nine years prior
to September 1993.
- Combined RI/FS - Starts. Slightly over half of the sites with earlier start dates and
almost three-quarters of the sites that had later RI/FS start dates had start dates that
occurred within the eight years prior to September 1993.
- Combined RI/FS - Completions. Paralleling the timing of when the site was proposed
for and finalized on the NPL, the completion of the combined RI/FSs occurred
primarily within the past few years. Of the completed combined RI/FSs, slightly over
half (53.5%) of the earliest RI/FSs and almost three-quarters (74.8%) of the latest
RI/FSs were completed within the past five years. A clear pattern emerges with respect
31. A full set of frequency distributions for the regulatory variables is contained in Appendix D.
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to the time of year that the RI/FSs are completed, regardless of whether they are the
earlier or later ones for a given site: within the past five year period, 47.8% of reported
later RI/FSs and 34.0% of reported earlier RI/FSs occurred at regular 12 month
intervals, during September when the federal fiscal year ends. The starting dates of the
combined RI/FSs do not show the same twelve month interval pattern as the completion
dates.
- Records of Decision. The date at which a ROD is signed is the milestone that was
measured. One date represents the earliest date a ROD for a site was signed. A second
date represents the latest date a ROD for a site was signed. About two thirds of the sites
had a date for the earliest ROD more than 60 months prior to September 1993. In
contrast, three quarters of the latest RODs were signed within the five years prior to
September 1993.
Throughout this report it should be kept in mind that differences exist between New Jersey,
New York and Puerto Rico with respect to the timing of these steps.-^ As a general rule, NJ
started earlier in,the cleanup process than NY, and the difference averages about 15 months for
the earlier steps of discovery and proposal and about 10-12 months for the later steps. By
comparison, Puerto Rico shows a pattern that is less similar to NJ and NY, but in general is
closer in its timing to sites in NJ with earlier start dates.
32. See Appendix B for a breakdown of the averages for each of these three areas.
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Modeling the Relationships: From Site Proposal to the Record of Decision
A basic model, in the form of a series of regression equations, was set up to evaluate the
relationship between the timing of various steps in the cleanup process under remedial actions
and the socioeconomic characteristics of the areas around the NPL sites.
Part of the analysis using this model examines the strength of the relationship of the timing of
cleanup parameters to one another in terms of when a given step occurred relative to the ones
immediately preceding it.
Another part of the analysis using this model is another series of relationships, in the form of
regression equations, to explore whether the social and economic characteristics of the area
around the site (within 1 mile) are associated with the timing of the various characteristics of
the cleanup stages to a greater or lesser extent than the cleanup stages are associated with one
another.
STEP 1 - The Basic Model of Regulatory Parameters
In step 1, several sets of regression equations were set up which reflect advancing stages in the
cleanup process.
Set 1. Dates of Discovery (DS), Proposed to NPL (NP) and Finalized for NPL (NF) and
HRS score
Usually prior to most of the remedial work, a site after being discovered is first scored under
the Hazard Ranking System. There are intermediate stages between site discovery and the
assignment of a score - the Preliminary Assessment and Site Investigation - which have not
been included in regression equations (sec Appendix H.3. for explanations). Sites above a
certain HRS score threshold are proposed on a certain date for the NPL and later finalized on
the NPL usually within about two years after that. In some exceptional cases, cleanup might
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begin while the NPL designation process is underway. In addition, removal action may occur
prior to designation as well (the analysis of removal actions is dealt with in a different section
of this report).
If the sequence of discovery, proposal and finalization hold to the stated procedures, then the
dates of each of these events should be ordered sequentially. A number of measures of
association show that this sequencing does, in fact, occur.
The mean dates for discovery, proposal and finalization given above are shown in the Table
below:
Table 18. Average Timing for Discovery, Site Proposal and Finalization
for NPL Listing
Mean S.D.
(in months back from
September 1993)
Date of Discovery (C2101 DS) 146 36~~
Date of Proposed to NPL (C2101 NP) 109 31
Date of Final Listing on NPL (C2101 NF) 97 28
Note: In this and subsequent tables (except where indicated otherwise),
months are computed back from September 1993, that is, the
greater the number of months, the earlier the step occurred.
On average, and as one would expect, from the sequence of events that have been adopted
explicitly by the Superfund program, site discovery occurred earlier than proposal, and
proposal occurred earlier than final listing as one would expect.
The correlation coefficients generally support this finding:
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Table 19. Correlations Among Timing for Discovery, Site Proposal
and Finalization for NPL Listing
Proposed Finalized
Pearson Correlation Coefficient
(significance: p=.00)
Discovery 0.4175 0.3681
Proposed 0.9513
Discovery date is more closely associated with the immediately next step, which is site
proposal date for the NPL, and proposal date is very closely associated with the finalization
date. Because of the high correlation between proposed and final dates, the proposed date has
o-i
been used subsequently for further analyses rather than the final date. J
The Hazard Ranking System (HRS) score is assigned prior to site proposal and after discovery.
Theoretically, the value of the score is typically not associated with how fast a site is proposed
or finalized, that is, once the site is scored the score is not used for setting any subsequent
priorities. This association was initially explored through correlation analysis and then
subsequently in the regression equations.
33. In a few cases, the final date occurs after a number of the cleanup steps begin, and thus using it rather than
the proposed date, makes the analysis more complex.
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Table 20. Correlations Among Hazard Ranking Scores and the Average Timing for
Discovery, Site Proposal, and Finalization for NPL Listing
Magnitude of the
Hazard Ranking System Score
Discovery
Proposed
Finalized
Pearson Correlation Coefficient
(significance: p=.00)
.3634
.4484
.4542
These correlations imply that sites with higher HRS scores, were generally discovered,
proposed and finalized for the NPL earlier than sites with lower scores.
The form of the hypothesized relationships between the proposal date, the discovery date and
the HRS score and additionally, the number of operable units at the site is illustrated by the
following generalized regression equation:
NP = DS + HRS + NUMOPUN Equation 1
where NP = the number of months ago that the site was proposed
(calculated back from September 1993)
DS = the number of months ago that the site was discovered
(calculated back from September 1993)
HRS = the total HRS score
NUMlOPUN = the number of operable units at the site
The results of the multiple regression are shown in the tables below.
Table 21. NP = DS + HRS + NUMOPUN
NP = the number of months ago that the site was proposed
DS = the number of months ago that the site was discovered
HRS = the total HRS score
NUMOPUN = the number of operable units at the site
Dependent: NP
Coefficient (B)
95% C.I.
Signif. (t-test)
Beta
R-Square: .31
Adj. R-Square: .30
Independent Variables
DS HRS
.249
,151to.348
.000
.320
.858
.544 to 1.171
.000
.344
NUMOPUN
-2.415
-4.065 to -.766
.004
-.172
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These results show that:
- Both the date of discovery and the magnitude of the HRS score - are positively
associated with the date the site was proposed for the NPL. That is, the earlier the date
proposed, the earlier the discovery and the higher the HRS score.
- The proposed date is negatively associated with the number of operable units (given the
negative sign of the coefficient), that is, the earlier the date proposed, the fewer the
number of operable units.
- The number of operable units has by far the largest influence (and it is a negative one)
on the proposed date, based on the relative sizes of the coefficients (however, it should
be kept in mind that the parameters are expressed in different units).
- These three factors - discovery date, HRS score, and number of operable units -
explained about a third of the variance of the proposed date.
Set 2. Combined RI/FS
The next major step in the remediation step after a site is finalized (and often earlier) is the
preparation of a remedial investigation (RI) followed by a feasibility study (FS). In most cases,
the two steps are combined, so that the timing of the combined RI/FS was used. In about a
dozen or so cases, however, separate RIs and FSs were indicated. In all of these cases,
however, where a site had an operable unit with only an FS or only an RI, it also had a
combined RI/FS. Given the small number of individual RIs and FSs and the fact that a
combined RI/FS also existed for a site, only the combined step was used in the analysis.
The generalized formulation between combined RI/FS, discovery and NPL proposal is as
follows:
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CORIFS = NP + COCOUNT + HRS Equation 2
Note: NP was used instead of NF in this regression formulation as well as
in subsequent ones, since the two dates were highly correlated:
r=0.95(p=0.0;n=199).
This regression equation was modified to take into account the various start dates and
completion dates for a combined RI/FS introduced by the fact that close to half of the sites
have more than one RI/FS. The HRS score and the number of combined RI/FSs that exist for
a given site were added to the equations as well.
CORIFS 1 = HRS + NP + COCOUNT Equation 2.1
CORIFS2 = HRS + NP + COCOUNT Equation 2.2
CORIFS3 = HRS + NP + COCOUNT Equation 2.3
CORIFS4 = HRS + NP + COCOUNT Equation 2.4
where CORIFS 1 = the earliest start date of a combined RI/FS for the site
CORIFS2 = the latest start date of a combined RI/FS for the site
CORIFS3 = the earliest completion date of a combined RI/FS for the site
CORIFS4 = the latest completion date of a combined RI/FS for the site
HRS = the total HRS score
COCOUNT = the number of combined RI/FSs that exist for a given site
Note: The number of combined RI/FSs, which is a function of the number
of operable units, was used instead of number of operable units,
since the two were highly correlated - 0.9167, n=205, p=.00.
DS was dropped from the equation since it precedes NP and is related
to NP; also, regression runs with DS consistently showed that it is
not significantly related to the dependent variables, CORIFS.
The results are shown in the following tables:
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Table 22. CORIFS1 = HRS + NP + COCOUNT
CORIFS1 = the earliest start date of a combined RI/FS for the site
HRS = the total HRS score
NP = the number of months ago that the site was proposed
COCOUNT = the number of combined RI/FSs that exist for a given site
Independent Variables
HRS NP COCOUNT
Dependent: CORIFS1
Coefficient (B) .410 .707 .387
95% C.I. .097to.724 .582 to .832 -1.311 to 2.085
Signif. (t-test) .011 .000 .654
Beta .147 .631 .023
R-Square: .498
Adj. R-Square: .490
Note: In the CORIFS1 equation, introducing HRS as the first independent
variable increases R-Square to 0.17, adding NP more than doubles the
R-Square to 0.498, but adding the number of RI/FSs, which is COCOUNT,
only increases R-Square insignificantly to 0.500 and the coefficient is
not statistically significant.
Table 23. CORIFS2 = HRS + NP + COCOUNT
CORIFS2 = the latest start date of a combined RI/FS for the site
HRS = the total HRS score
NP = the number of months ago that the site was proposed
COCOUNT = the number of combined RI/FSs that exist for a given site
Independent Variables
HRS NP COCOUNT
Dependent: CORIFS2
Coefficient (B) .265 .472 -4.833
95% C.I. -.148 to .678 .307 to .637 -7.072 to-2.594
Signif. (t-test) .208 .000 .000
Beta .087 .388 -.266
R-Square: .263
Adj. R-Square: .251
Note: In the CORIFS2 equation, introducing HRS as the first independent
variable increases R-Square to barely above zero to 0.05 and
furthermore, the HRS coefficient is not significant at either the p=.01
or .05 levels; adding NP increases the R-Square substantially to 0.19,
but adding the number of RI/FSs, which is COCOUNT, only increases
R-Square slightly to 0.26.
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Table 24. CORIFS3 = HRS + NP + COCOUNT
CORIFS3 = the earliest completion date of a combined RI/FS for the site
HRS = the total HRS score
NP = the number of months ago that the site was proposed
COCOUNT = the number of combined RI/FSs that exist for a given site
Independent Variables
HRS NP COCOUNT
Dependent: CORIFS3
Coefficient (B) .662 .625 1.484
95% C.I. .226 to 1.097 .435 to .815 -.719 to 3.687
Signif. (t-test) .003 .000 .185
Beta .222 .482 .087
R-Square: .39
Adj. R-Square: .38
Note: In the CORIFS3 equation, introducing HRS as the first independent
variable increases R-Square to 0.21, adding NP more than doubles the
R-Square to 0.38, but adding the number of RI/FSs, which is COCOUNT,
only increases R-Square insignificantly to 0.39 and the coefficient is
not statistically significant at p=.01.
Table 25. CORIFS4 = HRS + NP + COCOUNT
CORIFS4 = the latest completion date of a combined RI/FS for the site
HRS = the total HRS score
NP = the number of months ago that the site was proposed
COCOUNT = the number of combined RI/FSs that exist for a given site
Independent Variables
HRS NP COCOUNT
Dependent: CORIFS4
Coefficient (B) .576 .334 -3.193
95% C.I. .167to.984 .156 to .512 -5.259 to-1.127
Signif. (t-test) .006 .000 .003
Beta .227 .302 -.219
R-Square: .260
Adj. R-Square: .245
Note: In the CORIFS4 equation, introducing HRS as the first independent
variable increases R-Square to 0.12, adding NP more than doubles the
R-Square to 0.21, but adding the number of RI/FSs, which is COCOUNT,
only increases R-Square insignificantly to 0.26.
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In summary, the proposed date (NP) consistently has the strongest association with the start
and completion dates for the RI/FS compared with the HRS and the COCOUNT (and DS
also). The HRS score, however, continues to play a significant role in the timing of the RI/FS
for practically all RI/FS steps (with the exception of the latest start date for the RI/FS): the
higher the score, the earlier the RI/FS is started and completed. All three variables account for
far more of the variance (measured by the R Square) in the earliest start or earliest end date of
an RI/FS for a site rather than the later ones, which are usually introduced for other operable
units. This is in part because the timing of later RI/FSs (beyond the first one) tend to be much
more complex. Nevertheless, the contribution of NP, HRS and COCOUNT at most explain
less than 50% of the variance in the start and completion dates for the RI/FS (based on the
equations in the previous four tables).
Set 3. Record of Decision
RO = HRS + NP + CORIFS + ROCOUNT Equation 3
where all variables, except counts are expressed in units of time (months)
from September 1993.
RO = Record of Decision (ROD) (first one and last one for each site run as
separate variables in separate regression equations)
NP = Proposed to NPL
CORIFS = Combined RI/FS (first one for each site)
ROCOUNT = The number of RODs
Note: The number of RODs, ROCOUNT, was introduced, which is a function of the
number of operable units. ROCOUNT was used instead of number of operable
units, since the two were highly correlated - 0.8965, n = 151, p=.00.
Furthermore, the number of combined RI/FSs, COCOUNT, is not used
together with ROCOUNT, since these two are highly correlated - 0.8605,
n=150, p=.00.
Site discovery has been deleted, since it has largely been replaced
by an intermediate step the proposed date, NP, in order to avoid
redundancy.
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The ROD parameter, RO, was subdivided into those RODs that were signed first (RO1) and
those that were signed last (RO2). In the construction of the regression equation, R01, the
earliest ROD, was paired with CORIFS1 and CORIFS3 (earliest start and end dates for the
RI/FS) and RO2, the last ROD, was paired with CORIFS2 and CORIFS4 (latest start and end
dates for the RI/FS).
The first pair of equations illustrates the hypothesis that the earlier the RI/FS is started and
completed, the earlier the first ROD is likely to be signed. The second pair of equations
illustrates the hypothesis that the later the RI/FS is started and completed, the earlier the
second ROD is likely to be signed. The majority - or 74.8% - of the sites had only one ROD,
an additional 20.5% had two RODs, and the rest had three or more RODs.
Therefore, the altered formulations become-
RO1 = HRS + NP + CORIFS1 + ROCOUNT Equation 3.1
RO1 = HRS + NP + CORIFS3 + ROCOUNT Equation 3.2
RO2 = HRS + NP + CORIFS2 + ROCOUNT Equation 3.3
RO2 = HRS + NP + CORIFS4 + ROCOUNT Equation 3.4
where RO1 = The date of the earliest ROD
RO2 = The date of the latest ROD
ROCOUNT = the number of RODs that exist for a given site
The results are shown in the following tables:
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Table 26. RO1 = HRS + NP + CORIFS3 + ROCOUNT
RO1 = The date of the earliest ROD
HRS = the total HRS score
NP = the number of months ago that the site was proposed
CORIFS3 = the earliest completion date of a combined RI/FS for the site
ROCOUNT = the number of RODs that exist for a given site
Independent Variables
HRS NP CORIFS3 ROCOUNT
Dependent: RO1
Coefficient (B) .492 .109 .518 2.557
95% C.I. .121to.864 -.066 to 2.84 .384 to 651 -.872 to 5.985
Signif. (t-test) .010 .221 .000 .143
Beta .182 .091 .086 .567
R-Square: .564
Adj. R-Square: .551
Table 27. RO1 = HRS + NP + CORIFS1 + ROCOUNT
RO1 = The date of the earliest ROD
HRS = the total HRS score
NP = the number of months ago that the site was proposed
CORIFS1 = the earliest start date of a combined RI/FS for the site
ROCOUNT = the number of RODs that exist for a given site
Independent Variables
HRS NP CORIFS1 ROCOUNT
Dependent: RO1
Coefficient (B) .649 .135 .472 4.256
95% C.I. .237tol.062 -.083 to .353 .283 to .661 .403 to 8.108
Signif. (t-test) .002 .223 .000 .031
Beta .236 .109 .423 .139
R-Square: .457
Adj. R-Square: .441
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Table 28. RO2 = HRS + NP + CORIFS4 + ROCOUNT
RO2 = The date of the latest ROD
HRS = the total HRS score
NP = the number of months ago that the site was proposed
CORIFS4 = the latest completion date of a combined RI/FS for the site
ROCOUNT = the number of RODs that exist for a given site
Independent Variables
HRS NP CORIFS4 ROCOUNT
Dependent: RO2
Coefficient (B) .365 .061 .600 -3.513
95% C.I. .057to.673 -.075 to. 198 .482 to .718 -.567 to-.135
Signif. (t-test) .021 .375 .000 .020
Beta .155 .059 .641 -.135
R-Square: .598
Adj. R-Square: .586
Table 29. RO2 = HRS + NP + CORIFS2 + ROCOUNT
RO2 = The date of the latest ROD
HRS = the total HRS score
NP = the number of months ago that the site was proposed
CORIFS2 = the latest start date of a combined RI/FS for the site
ROCOUNT = the number of RODs that exist for a given site
Independent Variables
HRS NP CORIFS2 ROCOUNT
Dependent: RO2
Coefficient (B) .633 .203 .212 -6.277
95% C.I. .239 to 1.028 .022 to .384 .090 to .335 -10.026 to-2.528
Signif. (t-test) .002 .028 .001 .001
Beta .262 .186 .258 -.232
R-Square: .349
Adj. R-Square: .330
-67-
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The timing of the RI/FS (the step that immediately precedes the ROD) contributes more to
when a ROD is signed than does the timing of the earlier proposed date (NP). For example,
the earliest completion date of the RI/FS (CORIFS3) is associated more strongly with the
signing of the first ROD (RO1) than is the proposed date (NP) (as indicated by the higher
coefficient for CORIFS3). Similarly, the latest completion date of the RI/FS (CORIFS4) is
associated more strongly with the signing of the last ROD than is the proposed date (NP).
As in the case of the contribution of the number of RI/FSs to the timing of the RI/FS, the
count of the number of RODs contributes the least to when the first ROD was signed as one
would logically expect, and in fact, the relationship is not statistically significant even at the
0.1 significance level. The number of RODs is more significantly related to the timing of the
signing of the last ROD, but in an inverse direction: the greater the number of RODs, the
more recently the last ROD was signed. The HRS continues to be practically always significant
as a factor influencing the timing of the ROD as it was with all of the other steps: the higher
the score, the earlier the ROD was signed.
Thus, the timing of the earliest ROD is determined to a greater extent by when the RI/FS was
completed than by the step predating the RI/FS - when the site was proposed. The HRS Score
continued to be associated with the timing of RI/FS as it was with earlier steps. The HRS, the
timing of the NP, and either the latest start or latest completion date for the RI/FS (Tables 26
and 28) account for well over 50% of the timing of the signing of a ROD, whether it is the
earliest or the latest ROD.
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STEP 2 - The Basic Model of Regulatory Parameters Plus Socioeconomic Characteristics
Step 2 consists of a series of equations that gradually introduce key socioeconomic variables
into the equations above describing cleanup to determine whether adding these characteristics
change the relationship among the cleanup variables alone. Socioeconomic variables were
obtained from the Census and aggregated at the Block level for a given distance from the site.
The figures shown earlier and the t-tests performed on these variables indicate that beyond 1
mile from the site, socioeconomic variables tend not to vary substantially with distance from
the site. Furthermore, data for distances under one mile may be distorted and the sample of
sites for which data are available is considerably reduced given the procedures used to extract
the data. Thus, characteristics at a single distance at one or more miles from the site should be
a reasonable approximation for social and economic characteristics in general around the site.
Because of the intercorrelations among the demographic variables discussed earlier, a restricted
set of demographics was used to portray the socioeconomic characteristics of the area within
one mile around an NPL site. This restricted set consists of:
Population Density (POPDEN)
Percentage of Hispanics (PHISP)
Percentage of Minority Races (combined), i.e.,
the sum of the percentages of
Blacks, Asians and Native Americans (PMIN)
House Value (HOUSEVAL)
Each of the above equations was adapted to incorporate this restricted set of demographic
variables to see the extent to which the demographics improve the R-Square.
Prior to that, however, selected regulatory variables were related directly in a series of
equations to only the socioeconomic variables. These formulations and results are discussed in
Set 4 below.
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Set 4. Dependent Regulatory Variables with Independent Demographic Variables Only
Demographics were first regressed against each of the regulatory variables.
DS = POPDEN + PMIN + PHISP + HOUSEVAL
NP = POPDEN + PMIN + PHISP + HOUSEVAL
CORIFS1 = POPDEN + PMIN + PHISP + HOUSEVAL
CORIFS2 = POPDEN + PMIN + PHISP + HOUSEVAL
CORIFS3 = POPDEN + PMIN + PHISP + HOUSEVAL
CORIFS4 = POPDEN + PMIN + PHISP + HOUSEVAL
RO1 = POPDEN + PMIN + PHISP + HOUSEVAL
RO2 = POPDEN + PMIN + PHISP + HOUSEVAL
This group of demographics results in such a small R-Square that it is not worth examining the
relationships further as a linear model. One notable exception is that population density
(POPDEN) is significantly and negatively related to the earlier stages of cleanup, namely, site
discovery (DS) and proposal for the NPL (NP). That is, the lower the density, the earlier the
site was discovered and proposed for the NPL. 34 This is consistent with some of the foregoing
findings that site discovery and proposals for the NPL were targeted to low density areas due
oĞ
to the emphasis upon groundwater scores in the HRS scoring process.JJ
The R-Square values and coefficients shown in Table 30 below for POPDEN were obtained
for each of the dependent variables listed below and after POPDEN, PMIN, PHISP,
HOUSEVAL were all entered into the equation as independent variables. Unlike the equations
involving regulatory variables only, the equation is not always statistically significant at the
p=.01 level, as portrayed by the significance of the F-statistic.
34. In absolute terms population density contributes little to the variability of the dependent variables, that is, it
contributes little the R Square. Also, the coefficient is extremely small.
35. See the discussion at the beginning of the report on the HRS scoring process.
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Table 30. Regression Equation Results for Regulatory Variables (as Dependent
Variables Only) and Population Density (the only Significant
Independent Socioeconomic Variable)
Dependent R-Square Signif POPDEN
Variable F Coefficient Signif t
DS .120 .000 -3.734 .000
NP .046 .066 -2.600 .010
CORIFS1 .022 .401 * *
CORIFS2 .029 .255 * *
CORIFS3 .030 .355 * *
CORIFS4 .008 .873 * *
RO1 .024 .499 * *
RO2 .019 .630 * *
Note: An asterisk (*) signifies that the coefficient for population density is
not significant at the p=.01 level. The coefficients for the other
three Socioeconomic variables (PMIN, PHISP, and HOUSEVAL) were not
significant with any of the dependent variables shown.
Set 5. Dependent Regulatory Variables with Independent Regulatory Variables
and Independent Socioeconomic Variables
In order to explore further the relationship between Socioeconomic variables and regulatory
variables relating to site cleanup, Equation sets 1, 2, and 3 were altered by simply adding three
Socioeconomic variables PMIN, PHISP, and HOUSEVAL. The resulting equations are given
below.
CORIFS1 = HRS+NP+COCOUNT + PMIN + PHISP + HOUSEVAL
Equation 5.1
CORIFS2 = HRS+NP+COCOUNT + PMIN + PHISP + HOUSEVAL
Equation 5.2
CORIFS3 = HRS+NP+COCOUNT + PMIN + PHISP + HOUSEVAL
Equation 5.3
CORIFS4 = HRS+NP+COCOUNT + PMIN + PHISP + HOUSEVAL
Equation 5.4
RO1 = HRS+NP+CORIPS1+ROCOUNT + PMIN + PHISP + HOUSEVAL
Equation 5.5
RO1 = HRS+NP+CORIFS3+ROCOUNT + PMIN + PHISP + HOUSEVAL
Equation 5.6
RO2 = HRS+NP+CORIPS2+ROCOUNT + PMIN + PHISP + HOUSEVAL
Equation 5.7
RO2 = HRS+NP+CORH7S4+ROCOUNT + PMIN + PHISP + HOUSEVAL
Equation 5.8
-71 -
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These equations were farther altered with the addition of POPDEN, which had been a
relatively strong demographic variable in previous equations.
Table 31. Improvements in R-Square with the Introduction of
Demographic Variables
R-Square R-Square Improvement
Without Demographics With Demographics
CORIFS1
CORIFS2
CORIFS3
CORIFS4
RO1 (with CORIFS1)
RO1 (with CORIFS3)
RO2 (with CORIFS2)
RO2 (with CORIFS4)
.498
.263
.390
.260
.457
.564
.349
.598
.511
.333
.465
.272
.532
.648
.381
.608
Adding in the four socioeconomic variables improves only negligibly the strength of the
procedural relationships in determining the initiation or completion dates of the RI/FS and the
dates that the first and the last RODs are signed.-^
The general absence of a relationship between milestones in the cleanup process and racial and
ethnic populations and income as portrayed by house value, is consistent with the The National
Law Journal analysis, which did not cite the northeast as a region having such disparities,
although other regions were cited. 37
36. The extent to which these sets of regressions are significantly different from Equation sets 1, 2, and 3 can be
more rigorously tested using a Chow test or Test of Structural Change. See, for example, J. Kmenta,
Elements of Econometrics (New York: Macmillan, 1971), p. 373, Equation 10.48; W. H. Green,
Econometric Analysis (New York: MacMillan, 1993), 2nd edition, p. 216-220.
37. M. Lavelle and M. Coyle, "Unequal Protection. The Racial Divide in Environmental Law," The National
Law Journal. Special Investigation (September 21, 1992), pages S4, S6. In fact, sites in Region 2 were cited
as being cleaned up more rapidly in minority areas. This finding is more directly examined in the analyses of
sites with RODs and those without RODs below.
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Selected Population Subgroups and Cleanup
Since a number of the sites emerged as outliers in the patterns and trends discussed above,
clusters of sites at the extremes of the distribution were again evaluated with respect to cleanup
progress. The following subgroups were examined: Comparisons between each of these
subgroups and the total set of NPL sites were made using both descriptive statistics and
regression analyses. With respect to regressions, differences were noted in the structure of the
regression equations between the subgroups and the total set of NPL sites.
(1) Overall Cleanup Milestones and Populations Subgroups
High Population/Density areas. Selected cleanup milestones were evaluated for 55 sites, which
were in areas which had more than 5,000 persons residing within 1 mile of the site. Population
and density are highly correlated.
Table 32. Selected Regulatory Characteristics of NPL Sites
in High Population Areas
NPL Sites with
All > 5000 persons
NPL Sites (within 1 mile)
(Mean in months from September 1993)
Discovery Date
Proposed Date
First RI/FS Start
First ROD Date
Last ROD Date
145.9
109.3
88.0
54.3
44.7
133.2
103.9
83.1
46.7
34.9
In terms of cleanup milestones, the sites in these highly populated (which are also dense areas)
have somewhat more recent average start dates than the total set of NPL sites, but in all cases
the difference is less than 12 months.
The structure of the regression equations used to characterize the cleanup process (Equation
Sets 1, 2 and 3 above) were examined for this high population subgroup. A comparison of the
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R Squares between the equations used with the entire set of NPL sites and those obtained using
the same equations for the subset of sites with greater than 5,000 persons within one mile are
shown below.-*8 For the high population subgroup, the R Squares are substantially higher for
the equations run for the subgroup than for all NPL sites; however, these may be an artifact of
the structure of the equation: as the number of cases approaches the number of variables, the R
Square can increase. Focusing more on the significance of the coefficients, the coefficients
that are significant are those that are expected from the sequence of steps in the cleanup
process as is the case for the set of all NPL sites. That is, coefficients are usually significant
for the independent variables representing the timing of the step prior to the step represented
by the dependent variable. The one exception is that the HRS score is no longer statistically
significant as a factor influencing any of the steps as it was for the total set of NPL sites. This
implies that the regulatory model, with some exceptions, is applicable to an even greater extent
for the sites in these subgroups than for the set of all NPL sites.
Table 33. Regression equations for the Subgroup of NPL Sites with
Greater than 5,000 Persons within 1 Mile of the Site
NPL Sites with
All > 5000 persons
Equation NPL Sites (within 1 mile)
R Square Values
NP = DS* + HRS + NUMOPUN .310 .454
CORIFS1 = HRS + NP* + COCOUNT .498 .554
CORIFS4 = HRS + NP* + COCOUNT .260 .321
RO1 = HRS + NP + CORIFS1* + ROCOUNT* .457 .769
R02 = HRS + NP + CORIFS4* + ROCOUNT* .598 .950
An asterisk (*) indicates which variables in the equation had statistically
significant coefficients (B values) at the p=0.01 level.
38. As in the case of the discussion accompanying Table 31, the relationship among the equations can be more
rigorously evaluated using a Chow test.
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Minority Areas. Sites with large Hispanic populations, seem to have entered the Superfund
process later, and thus, subsequent steps began later for them than for other minority groups.
This relationship is more pronounced with the medians than with the means. Sites with large
Black populations (defined in a couple of ways as > 15% and > 20% Black), did not show
on average any difference from the total set of NPLs. This may be a result of the fact that
Hispanic populations might have mobilized later than Blacks in their efforts with respect to
pollution in general, and inactive hazardous waste sites in particular. These relationships are
summarized in Table 34 below.
The structure of the regression equations used to characterize the cleanup process (Equation
Sets 1, 2 and 3 above) were also examined for the three race and ethnic population subgroups.
A comparison of the R Squares between the equations used with the entire set of NPL sites and
those obtained using the same equations for the subset of sites with greater than 10% Hispanic,
15% Black, and 20% Black respectively within one mile are shown below in Tables 35 and
36. As in the case of high population subgroup discussed above, the R Squares are
substantially higher for the equations run for the subgroup than for all NPL sites, however,
these may be an artifact of the structure of the equation: as the number of cases approaches the
number of variables, the R Square can increase.
The major point is that the coefficients that are significant are those that are expected from the
sequence of steps in the cleanup process as is the case for the set of all NPL sites. That is,
coefficients are usually significant for the independent variables representing the timing of the
step prior to the step represented by the dependent variable. The one exception is that the HRS
score is no longer statistically significant as a factor influencing any of the steps as it was for
the total set of NPL sites. This implies that the regulatory model, with some exceptions, is
applicable to an even greater extent for the sites in these subgroups than for the set of all NPL
sites.
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Table 34. Selected Regulatory Characteristics of Sites in Areas with
High Proportions of Minorities
Selected Subsets
All
NPL Sites
Hispanic
(n = 19)
>15% >20%
Black Black
(n=30) (n=20)
A. Means
(Mean months from September 1993)
Discovery Date 146 137 147 138
Proposed Date 109 94 114 112
First RI/FS Start 88 71 89 91
First ROD Date 54 41 53 55
Last ROD Date 45 37 44 43
B. Medians
(Median months from September 1993)
Discovery Date 148 133 148 134
Proposed Date 120 107 120 114
First RI/FS Start 90 65 88 102
First ROD Date 48 28 48 48
Last ROD Date 36 27 36 36
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Table 35. Regression equations for the Subgroup of NPL Sites with
Greater than 10% Hispanic within 1 Mile of the Site
NPL Sites with
All > 10% Hispanic
Equation NPL Sites (within 1 mile)
R Square Values
NP = DS* + HRS + NUMOPUN .310 .341
CORIFS1 = HRS + NP* + COCOUNT .498 .636
CORIFS4 = HRS + NP + COCOUNT .260 .455
RO1 = HRS + NP + CORIFS1 + ROCOUNT .457 .653
RO2 = HRS + NP + CORIFS4* + ROCOUNT .598 na
Notes: "na" means that there were too few cases to produce meaningful results.
An asterisk (*) indicates which variables in the equation had statistically
significant coefficients (B values) at the p=0.01 level.
Table 36. Regression equations for the Subgroup of NPL Sites with
Greater than 15% Black and Greater than 20% Black
within 1 Mile of the Site
NPL Sites with NPL Sites with
All >15% Black >20% Black
Equation NPL Sites (within 1 mile)
R Square Values
NP = DS* + HRS + NUMOPUN .310 .504 .769
CORIFS1 = HRS + NP* + COCOUNT .498 .443 .596
CORIFS4 = HRS + NP* + COCOUNT .260 .630 .767
RO1 = HRS + NP + CORIFS1 + ROCOUNT
.457 .670 .802
RO2 = HRS + NP + CORIFS4 + ROCOUNT
.598 na na
Notes: "na" means that there were too few cases to produce meaningful results.
An asterisk (*) indicates which variables in the equation had statistically
significant coefficients (B values) at the p=0.01 level for both the
greater than 15% Black and greater than 20% Black subgroups.
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(2) Existence of RODs39
The signing of a ROD is a major milestone in the Superfund cleanup process. In previous
studies at the national scale the attainment of a ROD has found to be related to when a site was
placed on the NPL and to certain socioeconomic characteristics.4^
A comparison of sites with RODs and those without RODs was undertaken for the set of NPL
sites that were finalized as of September 1993. 58 sites or 29% of the total of 200 finalized
sites had no RODs.
As shown in the table below, The sites without RODs were discovered on average at the same
as sites with RODs. For sites without RODs, however, subsequent cleanup steps (including
proposal for the NPL) were started more recently, i.e., later than they were for sites with
RODs. The sites with and without RODs average about the same time for the last RI/FS is
completed (the closest step to the signing of a ROD). The observation that the proposed date is
considerably later for sites without RODs than sites with RODs is consistent with the fact that
RODs take a long time and the later the site is put on the list, the later one would expect the
ROD would be completed. Thus, the date the site was proposed is one explanation for the
absence of a ROD. The mean total HRS scores are practically identical for the sites without
RODs (HRS = 40.7) and sites with RODs (41.5).
39. An analysis similar to the one conducted in this section on RODs could be performed for any of the
Superfund cleanup stages identified, e.g., for the preparation of an RI/FS, however, so few sites did not have
an RI/FS by September 1993 that an RI/FS vs. no RI/FS comparison is not meaningful.
40. R. Zimmerman, "Social Equity and Environmental Risk." Risk Analysis: An International Journal, Vol. 13,
No. 6 (December 1993), pp. 660-663. The set of NPL sites included in this nationwide analysis is not entirely
comparable to the NPL set used in this report. The nationwide study had an earlier cutoff for RODs since it
was conducted earlier, did not include federal facilities nor sites in Puerto Rico, and slightly
underrepresented sites in very small communities.
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Table 37. Existence of RODs and Cleanup Characteristics of NPL Sites,
for NPL sites that have been finalized only (n=200)
Cleanup Sites with RODs Sites Without RODs
Characteristic (n=142)* (n=58)*
(Figures in months from September 1993)**
Mean S.D. Median Mean S.D. Median
Discovery Date 148 36 148 144 36 148
Proposed Date 115 25 129 96 37 107
First RI/FS Start 96 28 101 67 29 72
Latest RI/FS Start 75 33 73 51 27 50
First RI/FS
Completion 62 33 60 50 29 37
Latest RI/FS
Completion 47 29 36 49 27 37
Note:
The sample size (n) that is indicated only refers to the total number of
sites in each of the two categories. The number of valid cases will
vary for each of the characteristics due to missing data items.
**The larger the number of months the earlier in time the activity started.
The key issue is whether this population of 58 sites without RODs differs in terms of
socioeconomic characteristics from the set of sites that has advanced to the ROD stage,
especially since the two groups were on average discovered at about the same time. The table
below compares the means and medians of NPL sites with RODs with those sites without
RODs for selected socioeconomic characteristics.
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Table 38. Existence of RODs and Socioeconomic Characteristics of NPL Sites
within 1 Mile of the NPL site, for NPL sites that have been
finalized only (n=200)
Socioeconomic
Characteristic
(IMile)
Sites with RODs*
(n=142)**
Sites Without RODs
(n=58)**
Mean
S.D. Median
Mean
S.D. Median
% Asian
% Black***
% Hispanic
% Native American
% Minority****
% Aged
Population
Population Density
Mo. Rent ($s)
House Value ($s)
2.2
8.4
5.1
1.3
12.0
11.9
4478
1724
463
147810
3.2
15.2
10.6
9.1
18.0
5.9
9676
3552
- 202
80263
1.2
2.0
2.5
0.1
4.7
11.7
1961
732
476
137050
2.6
4.7
4.7
0.3
7.6
13.0
6057
2322
500
147776
4.8
9.2
5.6
1.1
10.9
5.3
7748
2912
190
52712
1.6
1.7
2.8
0.1
4.0
13.1
2286
934
504
150849
Notes:
*Sites with RODs means sites with at least one ROD.
**The sample size (n) that is indicated only refers to the total number of
sites in each of the two categories. The number of valid cases (cases
for which all data items are available) will vary from the total n,
but is the same for each of the Socioeconomic characteristics (i.e.,
if data for one variable is available, data for all variables are
almost always available). The number of valid cases for sites without
RODs is 51 (with house value being valid for 50). The number of valid
cases for sites with RODs is 134-135).
***The difference in the mean % Black and in the mean % Minority between the
ROD and NoROD groups was statistically significant, using a
difference of means test. The difference between the medians,
however, is very small.
****% Minority is the sum of the three major minority groups: % Asian, % Black
and % Native Americans.
The table above reveals a few patterns with respect to the prevalence of RODs and
Socioeconomic characteristics within 1 mile of the site.
Population Size and Population Density. Sites without RODs generally have higher populations
and population densities (both with large standard deviations though) within 1 mile of the site
than NPL sites with RODs.
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% Black Population. The finding with respect to the % Black population for sites with and
without RODs is somewhat mixed. On the one hand, the mean % Black for sites with RODs
(8.4%) is higher than the % Black for sites without RODs (4.7%), this difference is a small
one in absolute terms being only 4% (but large percentagewise), is statistically significant, and
is consistent with the National Law Journal finding that EPA Region 2 Superfund sites seem to
be cleaned up faster in minority areas. On the other hand, the median % Black in the two
groups is practically identical - 2%'for sites with RODs and 1.7% for sites with RODs. This
indicates some skewing of the data.
Looking at this picture through frequency distributions rather than averages and as the
advancement to the ROD stage for population subgroups (i.e., starting with the population
subgroup first and then analyzing it with respect to ROD attainment), adds some additional
insights into the pattern of means and medians. A relatively greater proportion of sites whose
surrounding areas have high Black populations have RODs. Only 8% of the sites without
RODs compared with 17% of the sites with RODs, were in communities with 15% or more
Black populations. Only 2% of the sites without RODs compared with 11 % of the sites with
RODs, were in communities with 20% or more Black populations. This is consistent with the
means and medians in the table above where the %Black population is somewhat larger for
sites with RODs than sites without RODs.
Other Socioeconomic Characteristics. Sites without RODs are similar to the sites with RODs in
terms of other socioeconomic characteristics, that is, the differences where they occur are very
small.
A previous study of NPL sites nationwide (where socioeconomic characteristics were
aggregated at the level of the municipality) found a difference in the %Black populations of
sites with and without RODs, but that this difference was partially explained by the fact that
sites with relatively larger % Black populations appeared on the NPL later than sites with
-81-
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lower % Black populations. In other words, whatever differences in % Black was appearing in
the database was occuring at the point of site designation rather than site cleanup.
In light of the findings above the nature of differences among NPL sites with RODs signed
before and after the Superfund Amendments and Reauthorization Act (SARA) of 1986
(effective January 1987) was explored.** In comparing RODs signed before January 1987 and
after 1987, what appears is the following:
- Within the set of sites with RODs, there is a dramatic difference in population size and
population density for RODs signed before and after SARA. In particular, sites with
RODs signed in 1/87 and after had much larger populations and population densities
than RODs signed earlier. Sites whose RODs were signed prior to 1/87 had a mean
population density of 1190 and a population size of 3,001. Sites whose RODs were
signed in 1/87 or after had a mean population density of 1990 (almost double the
earlier group) and a population size of 5,215 (almost double the earlier group).
- There is otherwise virtually no difference in the socioeconomic characteristics of sites
whose RQDs were signed before and after SARA became effective. In particular, only
a small difference appears in the % Black population in the two groups. For sites with
RODs signed before 1/87, % Black is 7.9% (S.D. 15.0); for sites whose RODs were
signed after 1/87 the % Black is 8.7% (S.D. 15.4). The results for Hispanic
populations are as follows: For sites with RODs signed before 1/87 % Hispanic is
6.6% (S.D. 16.7); for sites with RODs signed after 1/87 the % Hispanic is 4.3% (S.D.
5.4).
41. The ROD date that was used here was the date of the first ROD signed for a particular site. The total
number of sites with RODs prior to 1/87 was 46 and the number after 1/87 was 96. One might argue that
the 1/87 cutoff is somewhat arbitrary, and furthermore, that anomolous activity might have occured right
around the time the amendments were passed. The distribution of RODs signed around that time were
evaluated. Of the 142 sites with RODs only a relatively few sites had RODs signed between 9/86 and 9/87,
which would have been the period in which the most anomalies would occur.
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Remedial Design (RD) and Remedial Action (RA)
The signing of the ROD is followed by a remedial design (RD) and a remedial action (RA).
These were analyzed in a much more limited way because there were fewer of them, they are
more recent and of shorter duration. Furthermore, since the previous stages in remediation had
been unrelated using a linear model with socioeconomic characteristics, it is unlikely that
subsequent steps, such as RD and RA, that are far fewer in number would be related to those
characteristics.
Durations rather than detailed start and completion dates were examined for these two events.
Duration is defined as the number of months from the earliest start of an event to the latest
completion (where there are more than one events). Remedial designs and remedial events
each take about 2-1/2 years on average to complete.
As in the case of previous regulatory events in connection with cleanup, the scheduling of
remedial designs and remedial actions were statistically related to the scheduling of events
preceding them.
No statistically significant correlation existed between duration of each of these two steps and
any of the socioeconomic characteristics used (even going to a p=.l significance level). The
one exception is that there was a weak positive relationship between the duration of remedial
design and the number of Hispanics within 1 mile of the site: r=.1816 (p=.047; n= 120),
i.e., the more Hispanics in an area, the longer the design took. This relationship did not occur,
however, with percentage of Hispanics (as distinct from the number).
The relationship between the number of remedial designs and actions was also explored. The
number of remedial designs and actions were highly correlated with one another: r=.7979,
p=.0, n=87). With one exception, no statistically significant correlation existed between the
number of remedial designs and remedial actions and any of the socioeconomic characteristics
-83-
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used. The exception was that a small but statistically significant positive correlation between
the number of remedial designs and actions and the percentage of elderly around the site
occurred: for the percentage of persons 65 years old and older and the number of remedial
actions, r=.2320 (n=86, p=.032) and for the number of remedial designs, r=.2139 (n = 119,
p=.00). Though further analysis is needed to verify this, this result seems to suggest that
perhaps elderly people are in areas with more complex, older sites which require a larger
number of RAs and RDs.
Emergency Removal (NPL Sites Only)
Emergency removals are conducted on both NPL and non-NPL CERCLIS sites as well as
some sites that are not initially on CERCLIS when the emergency removals are conducted.
Two steps in the emergency removal process were evaluated - the preparation of a removal
investigation for an NPL site (RS) and (2) the removal action (RV).4^ Although most of the
210 sites had removal investigations (192 out of the 210 sites or 91.4%), not all of the
investigations resulted in removal actions by September-1993. Only 72 sites or 34.3% had
actual removal actions performed. Where remedial investigations and/or removal actions were
listed as being performed, all of them were completed by September 1993 (with exception of
about a dozen removal actions which were still in progress, i.e., listed as having start dates but
no end dates).
Most of the sites (91.7%) had two removal investigations. About half (54.2%) of the sites had
only one removal action and another 31.9% had two such actions. Unlike remedial actions, the
number of removal investigations and actions are not related to the number of operable units.
The duration of both removal investigations and removal actions averages about two years. As
42. The emergency removal process was evaluated for the entire set of 210 sites, i.e., finalized, proposed, and
deleted.
-84-
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one would expect, the longer the duration of a removal investigation the more there were
(r=.3968; p=.00; n=192). Similarly, the longer the duration of the removals, the more
removals there were (r=.4115; p=.00; n=72).
Practically no correlations existed between any of the socioeconomic characteristics and the
timing or number of removal investigations or actions.
Summary: Site Cleanup
The Superfund cleanup process for long-term or remedial actions has been portrayed in terms
of the timing of key steps in the process - site discovery, proposal and finalization, the
preparation of the remedial investigation and feasibility studies, the signing of the clean up
plan or Record of Decision and to a lesser extent, the remedial design and remedial action.
Other aspects of the process are included as well, namely, the magnitude of the Hazard
Ranking Score and the number of RI/FSs and RODs that have been undertaken at a site.
Results of multivariate analyses show that the timing of the steps in the cleanup process, once
a site is on the NPL are largely internally driven by prescribed timetables in the Superfund
program. Adding socioeconomic characteristics into the regression equations only negligibly
improves the strength of the relationship.
-85-
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SUMMARY AND CONCLUSION
The interest in environmental equity began with a focus primarily upon case studies, in a
manner similar to the way the cleanup of inactive hazardous waste sites started with Love
Canal. Since the mid-1980s, a number of studies have appeared which explore patterns of
socioeconomic characteristics and their relationship to the distribution of such waste sites and
cleanup efforts. This work continues along those lines, concentrating primarily on a more
indepth analysis of sites in one federal region.
Basic Findings
SITE LOCATION appears to show some patterns with respect to the social and economic
makeup of the area within one mile of the site:
1. Average house values and rents are lower than what is typical for the states within
which the sites are located.
2. The distribution of NPL sites shows that the majority of sites are below state
proportions of minority populations, although a number of sites do exist in areas that
have high minority populations relative to the states in which they are located, and for
Blacks, the municipalities in which they are located also.
3. Few other socioeconomic characteristics appear to differ on average for the area
surrounding these sites and what is typical of the state.
4. Some clustering of NPL sites appears geographically -
- A couple of dozen municipalities (accounting for well under 1 % of all
municipalities in NJ and NY) have two or more NPL sites, and the NPL sites in
these municipalities account for 28.5% of the NPL sites.
-86-
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- A third of the counties in NJ and NY have two or more NPL sites, and the NPL
sites in these counties account for 84% of the NPL sites,
CHARACTERISTICS OF THE CLEANUP PROCESS, defined in terms of when cleanup
activities occur, show no association with socioeconomic characteristics of the area within one
mile of the site:
1. The timing of the steps in the cleanup process, for sites currently on the NPL, is
largely internally driven by prescribed timetables in the Superfund program.
2. Considering socioeconomic characteristics only negligibly improves the strength of the
relationship.
Data Considerations and Qualifications
The nature of the data sources and the methodological approaches used in this analysis
necessitates a dispussion of some of the limitations of the data and their uncertainties. Some of
the assumptions made in this study in connection with the various data considerations are
discussed below.
The latitude and longitude was used to represent the location of an NPL site. Any errors in the
location of this point could lead to substantial errors in the representation of the socioeconomic
characteristics around the site, since such data is being defined at such small distances. More
importantly (especially for large sites), it is important to think through just what reference
point on the site is the desirable one for a socioeconomic analysis. If the location is assumed to
be a surrogate for potential exposure, then the center of waste concentration or an exposure
point associated with the site is perhaps a better location than the geometric center.
-87-
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Socioeconomic characteristics were derived from Census data, since that data are the most
readily available, inexpensive to use, and consistently defined across the set of NPL sites.
Issues associated with the accuracy of the collection of Census data in terms of population
counts and their characteristics is addressed extensively elsewhere, and the reader is referred to
Appendix H.3 of this report as a starting point. Population undercounting, especially of the
homeless, the consistency of categorizing race and ethnicity, and the reporting problems for
income as a reflection of economic status are just some of the issues associated with using
Census data.
An important intervening factor in how representative NPL sites are of a general
contamination condition is the nature of how NPL sites arrive on the list and more
importantly, how they are considered for the list either through CERCLIS or other means.
That is, there is a reporting dimension and a contamination dimension, and an analysis of
equity in connection with exposure and cleanup is only accurate to the extent that the sites
reported reflect the extent of contamination.
Future Research Needs
The conclusions of this report as well as limitations on the scope of work for the study of NPL
sites as a result of data limitations and uncertainties beyond the control of the Superfund
program suggest a number of directions for future equity research.
Locational data for waste sites needs to be improved, not only for accurate location but to
ensure that the location is representative of the point that is the desirable reference point (the
geometric center of the site, a border, the location of waste concentrations on the property).
Limitations of Census data are well recognized, but the extent to which these limitations affect
an equity analysis need to be ascertained. Certainly, if one minority group is undercounted or
underrepresented, this could seriously affect the results of an equity analysis. An assessment
-------
should be made of the extent to which direct population counts and assessments of population
characteristics around sites is a worthwhile endeavor over and above using Census data.
Approaching the equity issues in geographic terms, assumes that one can define impacted
communities geographically. The difficulty of defining impact areas in terms of conventional
health risk criteria was raised at the beginning of the report. A direct handle on this problem is
needed before impact studies can proceed with an equity dimension. Geographic Information
Systems and other analytical tools are valuable means for carving out areas for analysis along
with the characteristics of populations within those areas. At this moment, however, the tools
are outpacing the ability to arrive at suitable criteria for defining the geographic areas of
concern.
Monitoring the existence of equity problems and their resolution is an important complement
to an indepth analysis of equity. Criteria developed in this report are a first step in this
direction.
A better handle on the definition of equity is needed. This report assumed conventional
definitions of race and ethnicity used by the U.S. Census. The simplifications these impose
have been the subject of a number of intensive workshops and reports. The implications for
equity analysis of alternative definitions need to be assessed.
Finally, this study was confined to sites already on the NPL. The issue of equity has also
arisen in connection with inactive waste sites in CERCLIS, not yet on the NPL. An analogous
study of non-NPL sites is being planned to fill this gap.
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APPENDICES
Contents
APPENDIX A. VARIABLE LIST AND DEFINITIONS
APPENDIX B. SUMMARY STATISTICS INCLUDING MEANS, STANDARD DEVIATIONS
AND MEDIANS OF MAJOR VARIABLES
B. 1. Summary Table of Means, Standard Deviations and
Medians of Major Variables
B.2. Miscellaneous Statistics (for Central Tendency)
for Major Variables
APPENDIX C. COMPARISONS IN RACIAL COMPOSITION: 1 Mile from Sites and
Municipality Proportions
APPENDIX D. FREQUENCIES
APPENDIX E. CORRELATION COEFFICIENTS
APPENDIX F. REGRESSION EQUATIONS
APPENDIX G. CROSS-TABULATIONS
APPENDIX H. METHODOLOGY
H.I. Description of the NPL Data Set (Site Selection)
H.2. Method of Extraction for Census Data
H.3. Regulatory Variable Selection Criteria
H.4. List of Other Interim Reports and Analyses
-------
APPENDIX A. VARIABLE LIST AND DEFINITIONS
-------
VARIABLE LIST AND DEFINITIONS
Variable
Name
Variable
Code
WasteLAN
Code
Regulatory Parameters
COC Duration of RI/FS (mos.)
RAC Duration of Remedial Actions (mos.)
RDC Duration of Remedial Designs (mos.)
RSC Duration of Removal Investigations (mos.)
RVC Duration of Removal Actions (mos.)
CORIFS1 First RI/FS Start (mos.)
CORIFS2 Last RI/FS Start (mos.)
CORIFS3 First RI/FS End (mos.)
CORIFS4 Last RI/FS End (mos.)
R01 First ROD Date (mos.)
R02 Last ROD Date (mos.)
DSC Discovery Date (mos.)
NFC Finalized Date (mos.)
NPC Proposed Date (mos.)
CDCOUNT Number of Potentially
Responsible Parties
(Involved in Consent
Decrees)
COCOUNT Number of RI/FSs
RACOUNT Number of Remedial Actions
RDCOUNT Number of Remedial Designs
ROCOUNT Number of RODs
RSCOUNT Number of Removal Investigations
'RVCOUNT Number of Removals
NUMOPUN Number of Operable Units
HRSSCOR Total HRS Score
C2101 CO
C2101 RA
C2101 RD
C2101 RS
C2101 RV
C2101 CO
C2101 CO
C2101 CO
C2101 CO
C2101 RO
C2101 RO
C2101 DS
C2101 NF
C2101 NP
C1720
NOTES:
All dates and durations are calculated in terms of months from September 1993.
A-l
-------
Variable
Code
Variable
Name
Census
Code
Socioeconomic Parameters
PAGED % 65 yrs. +
PASIAN % Asian
PBLACK % Black
PCROWDED
% 1.01+ persons/rm
PHISP % Hispanic
PNATTVE % Native Americans
PMIN % Minority (Aggreg.)
POP100 Total Population
POPDEN Population Density
POWNER % Owner-occupied
PRENTER % Renter-occupied
PUNDER18 % Under 18 yrs.
P1PAR % 1 Person Hh with
children under 18
RENT Mo. Rent ($s)
HOUSEVAL
House Value ($s)
P2BX002 Number of Blacks
P2BX003 Number of Native Amer.
P2BX004 Number of Asian
P3BX001 Number of Hispanics
P4BX001 Number of Under 18 yrs.
P4BX002 Number of 65 yrs. +
P4BX0002
P2BX0004
P2BX0002
H6BX0001.2
P3BX0001
P2BX0003
P2BX0002,3,4
P1BX0001
H3BX0001
H3BX0002
P4BX0001
H8BX0002
H5BX0001
H4BX0001
P2BX0002
P2BX0003
P2.BX0004
P3BX0001
P4BX0001
P4BX0002
A-2
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COMPUTED REGULATORY VARIABLES - EVENTS
Computation of Timing/Scheduling
(1) For DS, NF, and NP: The difference (in months) from 9/93 is computed
(these variables only have one date [n=210])
(2) For RO: The data items used are the minimum (earliest) end date and the
maximum (latest) end date [n=206];
Start dates are ignored (since there are only about 8).
The difference is computed between the minimum end date and 9/93 = RO1
The difference is computed between the maximum end date and 9/93 = R02
The same calculation is made for CO = CORIFS3 and CORIFS4, using the
minimum and maximum end dates.
The same calculation is made for
RV = RV1 and RV2 (new variable names), and
RS = RSI and RS2 (new variable names)
For CO, RV and RS similar variables are computed, this time using the
minimum and maximum start dates and their difference from 9/93,
generating the new variables:
CORIFS1 and CORIFS2
RV1 and RV2
RSlandRS2
Minimum = earliest date
Maximum = latest date
NOTE: A "C" attached to the end of a date parameter indicates that it
has been computed as a difference from September 1993.
An "I" attached to the end of any parameter indicates that it
is interval data (for frequency distributions and cross-
tabulations.
A-3
-------
APPENDIX B. SUMMARY STATISTICS INCLUDING MEANS, STANDARD
DEVIATIONS AND MEDIANS OF MAJOR VARIABLES
Appendix B. 1. Summary Table of Means, Standard Deviations and Medians
of Major Variables
.Appendix B.2. Miscellaneous Statistics (for Central Tendency) for
Major Variables
-------
SUMMARY TABLE OF MEANS, STANDARD DEVIATIONS AND MEDIANS OF
MAJOR VARIABLES
Variable
Code
Variable
Description
Mean
S.D. Median
Selected Cleanup Parameters:
coc
CRC
RAC
RDC
RSC
RVC
CORIFS1
CORIFS2
CORIFS3
CORIFS4
R01
R02
DSC
NFC
NPC
CDCOUNT
COCOUNT
RACOUNT
RDCOUNT
ROCOUNT
RSCOUNT
RVCOUNT
NUMOPUN
HRSSCOR
Dur. RI/FS
Dur. CRP
Dur.-Remed.Act.
Dur.-Remed.Des.
Dur.-Remov.Inv.
Dur.-Remov.Act.
First RI/FS Start
Last RI/FS Start
First RI/FS End
Last RI/FS End
First ROD Date
Last ROD Date
Discovery Date
Finalized Date
Proposed Date
No. PRP(Consent Dec)
No. RI/FSs
No. Remedial Actions
No. Remedial Designs
No. RODs
No. Removal Investig.
No. Removals
No. Oper. Units
Total MRS Score
52.0
72.6
29.9
31.7
23.7
24.6
88.0
67.7
61.2
46.4
54.3
44.7
145.9
97.4
109.3
17.4
1.9
1.7
1.9
1.4
2.1
1.7
2.9
41.2
25.6
32.5
25.0
27.0
8.6
23.2
30.9
33.3
33.0
28.6
31.1
27.8
35.7
27.9
30.7
48.6
1.8
1.4
1.2
1.0
0.3
1.0
2.0
11.1
48
74
23
25
20
15
90
66
60
36
48
36
148
108
120
1
1
1
2
1
2
1
2
39.7
B-l
-------
Variable
Code
Variable
Description
Mean
S.D. Median
Selected Population Characteristics:
PAGED % 65 yrs. +
PASIAN % Asian
PBLACK % Black
PCROWDED
% 1.01+ persons/rm
PHISP % Hispanic
PNATIVE % Native Americans
PMIN % Minority (Aggreg.)
POP100 Total Population
POPDEN Population Density
POWNER % Owner-occupied
PRENTER % Renter-occupied
PUNDER18 % Under 18 yrs.
P1PAR % 1 Person Hh
RENT Mo. Rent ($s)
HOUSEVAL
P2BX002
P2BX003
P2BX004
P3BX001
P4BX001
P4BX002
House Value ($s)
No. Blacks
No. Native Amer.
No. Asian
No. Hispanics
No. Under 18 yrs.
No. 65 yrs. +
12.3
2.3
7.3
2.8
5.0
1.0
10.6
4896
1892
70.9
28.6
23.4
8.0
472
146527
579
12
212
481
1067
659
5.6
3.6
13.7
3.8
9.4
7.6
16.2
9036
3331
20.7
20.2
6.0
8.1
196
73257
2029
28
1102
1976
1779
1320
12.0
1.2
2.0
1.6
2.7
0.1
4.5
2128
863
75.7
24.0
23.4
6.6
483
141705
42
3
22
47
474
232
Note: Dates and durations are given in months from September 1993.
B-2
-------
Variable
Cases
Mean
Std Dev
coc
CRC
RAC
RDC
RSC
RVC
CORIFS1
CORIFS2
CORIFS3
CORIFS4
RO1C
RO2C
DSC
NFC
NPC
CDCOUNT
COCOUNT
RACOUNT
RDCOUNT
ROCOUNT
RSCOUNT
RVCOUNT
NUMOPUN
HRS SCOR
PAGED
PAS I AN
PBLACK
PCROWDED
PHISP
PMIN
PNATIVE
POP100
POPDEN
POWNER
PRENTER
PUNDER18
P1PAR
RENT
HOUSEVAL
P2BX0002
P2BX0003
P2BX0004
P3BX0001
P4BX0001
P4BX0002
204
105
92
128
192
72
204
204
159
159
151
151
209
205
210
74
205
92
128
151
192
72
210
200
194
194
194
191
194
194
194
195
195
191
191
194
192
191
191
195
195
195
195 -
195
195
52.0343
72.5524
29.9457
31.6563
23.6979
24.5972
87.9461
67.7402
61.2390
46.3774
54.2649
44.7020
145.9043
97.4098
109.3333
17.4054
1.8537
1.7283
1.8672
1.3642
2.0469
1.6944
2.8476
41.1970
12.2918
2.2550
7.3392
2.7509
5.0018
10.6002
1.0060
4895.8154
1892.0701
70.9036
28.5729
23.4205
8.0100
471.8580
146527.6517
578.5282
11.9128
212.0051
480.8410
1067.3795
659.2103
25.5797
32.5260
24.9854
27.0408
8.5769
23.1673
30.9083
33.2987
32.9888
28.5574
31.0476
27.7985
35.7155
27.8455
30.7279
48.6334
1.7790
1.4381
1.2255
.9898
.3119
1.0020
1.9480
11.1258
5.6203
3.6241
13.6838
3.7757
9.3747
16.1529
7.5750
9036.1944
3330.7445
20.7270
20.1824
6.0279
8.0961
196.3039
73256.7725
2029.2112
28.3928
1102.3962
1975.6721
1779.2630
1319.5075
B-3
-------
STATE: NJ
Number of valid observations (listwise) =
Variable
Mean
Std Dev Minimum
5.00
Valid
Maximum N Label
HOUSEVAL 160504
RENT
PBLACK
PHISP
COC
CRC
RAG
RDC
RSC
RVC
C01C
C02C
C03C
C04C
R01C
R02C
DSC
NFC
NPC
CDCOUNT
COCOUNT
RACOUNT
RDCOUNT
NUMOPUN
ROCOUNT
RSCOUNT
RVCOUNT
HRS SCOR
PAGED
PAS IAN
PCROWDED
PNATIVE
POP100
PMIN
POPDEN
POWNER
PRENTER
PUNDER18
PI PAR
P2BX0002
P2BX0003
P2BX0004
P3BX0001
P4BX0001
P4BX0002
509
9
6
56
80
26
35
23
27
63
51
95
75
58
48
150
103
115
24
1
1
1
2
1
2
1
43
11
2
2
4810
12
1838
69
29
23
774
9
172
488
1095
639
.17
.01
.56
.34
.61
.17
.29
.11
.47
.10
.77
.81
.74
.74
.76
.18
.10
.43
.74
.07
.80
.51
.81
.93
.40
.05
.88
.45
.60
.56
.96
.51
.80
.63
.78
.93
.14
.33
.08
.25
.29
.08
.61
.89
.14
63784
168
16
11
25
31
23
29
8
24
34
31
28
32
31
28
35
26
27
56
2
2
1
1
11
5
3
4
3
6770
17
2555
23
22
5
2585
19
393
1349
1516
1052
.19
.24
.46
.59
.72
.01
.34
.96
.89
.33
.10
.44
.04
.90
.65
.77
.72
.76
.31
.57
.16
.86
.97
.28
.17
.35
.06
.47
.44
.25
.53
.62
.11
.72
.89
.32
.49
.98
.10
.00
.07
.94
.46
.14
.82
11660.000
2
2
1
12
3
27
2
5
3
37
24
4
1
1
1
1
1
1
1
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
403009.95
969.29000
90.02516
95.69892
135.00000
140.00000
117.00000
131.00000
41.00000
88.00000
162.00000
162.00000
164.00000
137.00000
133.00000
111.00000
321.00000
120.00000
143.00000
224.00000
21.00000
4.00000
4.00000
24.00000
11.00000
4.00000
6.00000
75.60000
30.29772
17.38086
40.00000
38.06228
30958.000
90.73115
11360.330
97.43590
97.04433
42.47788
1.00000
17940.000
110.00000
3382.0000
11030.000
7311.0000
5049.0000
108
108
110
110
110
48
55
70
104
42
84
84*
110
110
84
84
114
113
114
28
111
55
70
114
84
104
42
108
110
110
108
110
111
110
111
108
108
110
109
111
111
111
111
111
111
B-4
-------
STATE: NY
Number of valid observations (listwise)
Variable
Mean
Std Dev Minimum
HOUSEVAL 128341.34
RENT
PBLACK
PHISP
COC
CRC
RAC
RDC
RSC
RVC
C01C
CO2C
CO3C
CO4C
R01C
RO2C
DSC
NFC
NPC
CDCOUNT
COCOUNT
RACOUNT
RDCOUNT
NUMOPUN
ROCOUNT
RSCOUNT
RVCOUNT
HRS SCOR
PAGED
PAS IAN
PCROWDED
PNATIVE
POP100
PMIN
POPDEN
POWNER
PRENTER
PUNDER18
PI PAR
P2BX0002
P2BX0003
P2BX0004
P3BX0001
P4BX0001
P4BX0002
423.52
4.43
3.25
46.05
64.47
35.76
28.04
23.76
21.78
56.72
40.57
78.89
58.57
49.08
39.98
141.12
88.07
100.92
-14.88
'1.93
2.12
2.00
2.79
1.34
2.05
1.41
38.73
13.20
1.85
2.48
1.66
5008.15
7.94
1962.49
72.17
27.83
23.53
.08
319.89
15.38
264.76
470.57
1029.70
685.74
80832.22 32281.818
219.48
8.04
4.73
23.88
33.00
27.33
23.49
8.62
22.01
31.98
24.42
31.24
31.23
30.27
26.57
36.82
27.32
32.49
45.65
1.23
2.06
1.55
1.53
.73
.27
.84
10.56
5.76
4.05
2.48
10.74
11405.45
13.49
4154.97
16.82
16.82
6.12
.04
805.97
37.16
1621.70
2590.31
2085.80
1612.23
.00000
.00000
.00000
4.00000
.00000
6.00000
.00000
2.00000
.00000
6.00000
6.00000
4.00000
4.00000
6.00000
6.00000
53.00000
37.00000
4.00000
.00000
1.00000
1.00000
1.00000
1.00000
1.00000
1.00000
1.00000
.00000
.00000
.00000
.00000'
.00000
29.00000
.00000
17.61000
1.30168
4.35714
.00000
.02688
.00000
.00000
.00000
.00000
.00000
.00000
390913.27
922.19000
40.81633
25.68807
104.00000
120.00000
122.00000
118.00000
39.00000
74.00000
137.00000
109.00000
152.00000
152.00000
137.00000
111.00000
264.00000
120.00000
143.00000
250.00000
8.00000
10.00000
9.00000
9.00000
5.00000
3.00000
5.00000
70.80000
36.71400
31.88406
12.07594
98.00000
98010.000
98.00000
35687.600
95.64286
98.69832
40.62500
.30303
4324.0000
217.00000
14883.000
23619.000
17379.000
13611.000
4.00
Valid
Maximum N Label
83
83
84
84
84
49
34
52
80
27
68-
68
84
84
61
61
86
83
86
41
84
34
52
86
61
80
27
83
84
84
83
84
84
84
84
83
83
84
83
84
84
84
84
84
84
B-5
-------
STATE: PR
Number of valid observations (listwise)
.00
Valid
Variable
HOUSEVAL
RENT
PBLACK
PHISP
COC
CRC
RAG
RDC
RSC
RVC
C01C
C02C
C03C
C04C
R01C
R02C
DSC
NFC
NPC
CDCOUNT
COCOUNT
RACOUNT
RDCOUNT
NUMOPUN
ROCOUNT
RSCOUNT
RVCOUNT
HRS SCOR
PAGED
PAS IAN
PCROWDED
PNATIVE
POP100
PM1N
POPDEN
POWNER
PRENTER
PUNDER18
PI PAR
P2BX0002
P2BX0003
P2BX0004
P3BX0001
P4BX0001
P4BX0002
Mean
Variable
Variable
Variable
Variable
55.67
84.29
31.00
22.67
26.00
6.50
74.71
37.57
84.89
61.00
44.00
44.00
138.56
107.89
118.67
.80
1.89
1.33
1.33
2.44
1.00
2.00
1.00
36.98
Variable
Variable
Variable
Variable
Variable
Variable
Variable
Variable
Variable
Variable
Variable
Variable
Variable
Variable
Variable
Variable
Variable
is
is
is
is
is
is
is
is
is
is
is
is
is
is
is
is
is
is
is
is
is
Std Dev
missing
missing
missing
missing
29.34
22.23
20.66
12.50
.00
7.78
24.92
16.16
33.19
34.07
22.34
22.34
13.18
23.56
21.32
1.79
.78
.58
.52
.53
.00
.00
.00
5.04
missing
missing
missing
missing
missing
missing
missing
missing
missing
missing
missing
missing
missing
missing
missing
missing
missing
Minimum
for
for
for
for
15.
61.
12.
9.
26.
1.
36.
24.
18.
11.
24.
24.
108.
47.
63.
m
1.
1.
1.
2.
1.
2.
1.
31.
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
every
every
every
every
00000
00000
00000
00000
00000
00000
00000
00000
00000
00000
00000
00000
00000
00000
00000
00000
00000
00000
00000
00000
00000
00000
00000
10000
every
every
every
every
every
every
every
every
every
every
every
every
every
every
every
every
every
Maximum
case.
case.
case.
case.
96.00000
116.00000
53.00000
35.00000
26.00000
12.00000
107.00000
60.00000
120.00000
116.00000
72.00000
72.00000
148.00000
120.00000
129.00000
4.00000
3.00000
2.00000
2.00000
3.00000
1.00000
2.00000
1.00000
43.10000
case.
case.
case.
case.
case.
case.
case.
case.
case.
case.
case.
case.
case.
case.
case.
case.
case.
N
9
7
3
6
8
2
7-
7
9
9
6
6
9
9
9
5
9
3
6
9
6
8
2
9
Label
B-6
-------
COG
Mean
Mode
Kurtosis
S E Skew
Maximum
52.034
48.000
-.381
.170
135.000
Std err
Std dev
S E Kurt
Range
1.791
25.580
.339
133.000
Median
Variance
Skewness
Minimum
48.000
654.319
.281
2.000
Percentile Value
25.00 33.000
Valid cases 204
Percentile Value
50.00 48.000
Missing cases 6
Percentile
75.00
Value
71.000
CRC
Mean
Mode
Kurtosis
S E Skew
Maximum
72.552
91.000
-.238
.236
140.000
Std err
Std dev
S E Kurt
Range
3.174
32.526
.467
140.000
Median
Variance
Skewness
Minimum
74.000
1057.942
-.431
.000
Percentile Value
25.00 51.500
Valid cases 105
Percentile Value
50.00 74.000
Missing cases 105
Percentile Value
75.00 93.000
RAG
Mean
Mode
Kurtosis
S E Skew
Maximum
29.946
12.000
2.391
.251
122.000
Std err
Std dev
S E Kurt
Range
2.605
24.985
.498
122.000
Median
Variance
Skewness
Minimum
23.000
624.272
1.453
.000
Percentile Value
25.00 12.000
Valid cases 92
Percentile Value
50.00 23.000
Missing cases 118
Percentile
75.00
Value
43.000
B-7
-------
RDC
'Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
RSC
Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
RVC
. Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
31.656
27.000
2.623
.214
131.000
Value
12.000
128
23.698
17.000
-.968
.175
41.000
Value
17.000
192
24.597
6.000
-.061
.283
88. 000-
Value
6.000
72
Std err
Std dev
S E Kurt
Range
Percentile
50.00
Missing casi
Std err
Std dev
S E Kurt
Range
Percentile
50.00
Missing cas
Std err
Std dev
S E Kurt
Range .
Percentile
50.00
Missing ca;
2.390
27.041
.425
131.000
Value
25.500
ss 82
.619
8.577
.349
41.000
Value
20.000
es 18
2.730
23.167
.559
88.000
Value
15.000
ses 138 .
Median
Variance
Skewness
Minimum
Percentile
75.00
Median
Variance
Skewness
Minimum
Percentile
75.00
Median
Variance
Skewness
Minimum
Percentile
75.00
25.500
731.204
1.485
.000
Value
47.750
20.000
73.553
-.010
.000
Value
33.000
15.000
536.723
.986
.000
Value
36.500
B-8
-------
CORIFS1
Mean
Mode
Kurtosis
S E Skew
Maximum
87.946
108,000
-.412
.170
164.000
Std err
Std dev
S E Kurt
Range
2.164
30.908
.339
160.000
Median
Variance
Skewness
Minimum
90.000
955.322
-.245
4.000
Percentile Value
25.00 66.000
Valid cases 204
Percentile Value
50.00 90.000
Missing cases 6
Percentile Value
75.00 113.750
CORIFS2
Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
CORIFS3
Mean
Mode
Kurtosis
S E Skew
Maximum
67.740
60.000
-.740
.170
152.000
Value
41.000
204
61.239
36.000
-.554
.192
162.000
Std err
Std dev
S E Kurt
Range
Percentile
50.00
Missing cas
Std err
Std dev
S E Kurt
Range
2.331
33.299
.339
150.000
Value
66.000
es 6
2.616
32.989
.383
156.000
Median
Variance
Skewness
Minimum
Percentile
75.00
Median
Variance
Skewness
Minimum
66.000
1108.804
.094
2.000
Value
95.250
60.000
1088.259
.404
6.000
Percentile Value
25.00 36.000
Valid cases 159
Percentile Value
50.00 60.000
Missing cases 51
Percentile Value
75.00 86.000
B-9
-------
CORIFS4
Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
R01C
Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
RO2C
Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
46.377
36.000
1.203
.192
162.000
Value
27.000
159
54.265
36.000
-.588
.197
137.000
Value
30.000
151
44.702
36.000
-.388
.197
111.000
Value
24.000
151
Std err
Std dev
S E Kurt
Range
Percentile
50.00
Missing casi
Std err
Std dev
S E Kurt
Range
Percentile
50.00
Missing cas
Std err
Std dev
S E Kurt
Range
Percentile
50.00
Missing cas
2.265
28.557
.383
159.000
Value
36.000
as 51
2.527
31.048
.392
132.000
Value
48.000
es 59
2.262
27.799
.392
108.000
Value
36.000
>es 59
Median
Variance
Skewness
Minimum
Percentile
75.00
Median
Variance
Skewness
Minimum
Percentile
75.00
Median
Variance
Skewness
Minimum
Percentile
75.00
36.000
815.528
1.054
3.000
Value
62.000
48.000
963.956
.492
5.000
Value
74.000
36.000
772.757
.683
3.000
Value
63.000
B-10
-------
DSC
Mean
Mode
Kurtosis
S E Skew
Maximum
145.904
133.000
3.468
.168
321.000
Std err
Std dev
S E Kurt
Range
2.470
35,115
.335
284.000
Median
Variance
Skewness
Minimum
148.000
1275.597
.632
37.000
Percentile -Value
25.00 122.000
Valid cases 209
Percentile Value
50.00 148.000
Missing cases 1
Percentile Value
75.00 165.000
NFC
Mean
Mode
Kurtosis
S E Skew
Maximum
97.410
120.000
-.495
.170
120.000
Std err
Std dev
S E Kurt
Range
1.945
27.845
.338
96.000
Median
Variance
Skewness
Minimum
108.000
775.370
-.941
24.000
Percentile Value
25.00 87.000
Valid cases 205
Percentile Value
50.00 108.000
Missing cases 5
Percentile Value
75.00 120.000
NPC
Mean
Mode
Kurtosis
S E Skew
Maximum
109.333
129.000
1.332
.163
143.000
Std err
Std dev
S E Kurt
Range
2.120
30.728
.334
139.000
Median
Variance
Skewness
Minimum
120.000
.944.204
-1.377
4.000
Percentile Value
25.00 107.000
Valid cases - 210
Percentile Value
50.00 120.000
Missing cases 0
Percentile Value
75.00 129.000
B-ll
-------
CDCOUNT
Mean
Mode
Kurtosis
S E Skew
Maximum /
Percentile
25.00
Valid cases
COCOUNT
Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
NUMOPUN
Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
17.405
.000
12.258
.279
'50.000
Value
.000
74
1.854
1.000
67.388
.170
21.000
Value
1.000
205
2.848
2.000
68.620
.168
24.000'
Value
2.000
210
Std err
Std dev
S E Kurt
Range 21
Percentile
50.00
Missing cases
Std err
Std dev
S E Kurt
Range
Percentile
50.00
Missing cases
Std err
Std dev
S .E Kurt
Range
Percentile
50.00
Missing case:
5.654
98.633
.552
50.000
Value
1.000
136
.124
1.779
.338
20.000
Value
1.000
5
.134
1.948
.334
23.000
Value
2.000
3 0
Median
Variance 2'.
Skewness
Minimum
Percentile
75.00
Median
Variance
Skewness
Minimum
Percentile
75.00
Median
Variance
Skewness
Minimum
Percentile
75.00
1.000
365.203
3.506
.000
Value
4.000
1.000
3.165
6.929
1.000
Value
2.000
2.000
3. 795
.7.066
1.000
Value
3.000
B-12
-------
RACOUNT
Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
RDCOUNT
Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
ROCOUNT
Mean
Mode
Kurtosis
S E""STcew
Maximum
Percentile
25.00
Valid cases
1.728
1.000
17.820
.251
10.000
Value
1.000
92
1.867
1.000
10.120
.214
9.000
Value
1.000
128
1.364
1.000
60.823
.197
11.000'
Value
1.000
151
Std err
Std dev
S E Kurt
Range
Percentile
50.00
Missing cases
Std err
Std dev
S E Kurt
Range
Percentile
50.00
Missing cases
Std err
Std dev
S E Kurt
Range
Percentile
50.00
Missing case:
.150
1.438
.498
9.000
Value
1.000
118
.108
1.226
.425
8.000
Value
2.000
82
.081
.990
.392
10.000
Value
1.000
i 59
Median
Variance
Skewness
Minimum
Percentile
75.00
Median
Variance
Skewness
Minimum
Percentile
75.00
Median
Variance
Skewness
Minimum
Percentile
75.00
1.000
2.068
3.755
1.000
Value
2.000
2.000
1.502
2.552
1.000
Value
2.000
1.000
.980
6.737
1.000
Value
2.000
B-13
-------
RSCOUNT
Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
RVCOUNT
Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
HRSJ3COR
Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
2.047
2.000
13.190
.175
4.000
Value
2.000
192
1.694
1.000
5.176
.283
6.000
Value
1.000
72
41.197
33.600
2.449
.172
75.600"
Value
33.725
Std err
Std dev
S E Kurt
Range
Percentile
50.00
Missing cases
Std err
Std dev
S E Kurt
Range
Percentile
50.00
Missing cases
Std err
Std dev
S E Kurt
Range
Percentile
50.00
.023
.312
.349
3.000
Value
2.000
18
.118
1.002
.559
5.000
Value
1.000
138
.787
11.126
.342
75.600
Value
39.700
Median
Variance
Skewness
Minimum
Percentile
75.00
Median
Variance
Skewness
Minimum
Percentile
75.00
Median
Variance
Skewness
Minimum
Percentile
75.00
2.000
.097
2.157
1.000
Value
2.000
1.000
1.004
2.036
1.000
Value
2.000
39.700
123.784
.066
.000
Value
48.250
Missing cases 10
B-14
-------
PAGED
Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
PAS I AN
Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
PBLACK
Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
12.292
.000
2.113
.175
36.714
Value
9.210
194
2.255
.000
26.149
.175
31.884
Value
.198
194
7.339
.000
13.517
.175
90.025
Value
.452
194
Std err
Std dev
S E Kurt
Range
Percentile
50.00
Missing cases
Std eri
Std dev
S E Kurt
Range
Percentile
50.00
Missing cases
Std err
Std dev
S E Kurt
Range
Percentile
50.00
Missing case:
.404
5.620
.347
36.714
Value
12.032
16
.260
3.624
.347
31.884
Value
1.199
i 16
.982
13.684
.347
90.025
Value
1.952
5 16
Median
Variance
Skewness
Minimum
Percentile
75.00
Median
Variance
Skewness
Minimum
Percentile
75.00
Median
.Variance
Skewness
Minimum
Percentile
75.00
12.032
31.588
.582
.000
Value
15.537
1.199
13.134
4.274
.000
Value
2.784
1.952
187.247
3.343
.000
Value
6.908
B-15
-------
PCROWDED
Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
PHISP
Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
PNATIVE
Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
2.751
.000
50.430
.176
40.000
Value
.911
191
5.002
.000
49.965
.175
95.699
Value
1.076
194
1.006
.000
143.715
.175.
98.000
Value
.000
194
Std err
Std dev
S E Kurt
Range
Percentile
50.00
Missing cases
Std err
Std dev
S E Kurt
Range
Percentile
50.00
Missing cases
Std err
Std dev
S E Kurt
Range
Percentile
50.00
Missing case;
.273
3.776
.350
40.000
Value
1.630
19
.673
9.375
.347
95.699
Value
2.688
3 16
.544
7.575
.347
98.000
Value
.099
s 16
Median
Variance
Skewness
Minimum
Percentile
75.00
Median
Variance
Skewness
Minimum
Percentile
5.00
Median
Variance
Skewness
Minimum"
Percentile
75.00
1.630
14.256
5.786
.000
Value
3.379
2.688
87.885
6.144
.000
Value
5.296
.099
57.380
11.619
.000
Value
.299
B-16
-------
POP100
Mean
Mode
Kurtosis
S E Skew
Maximum
4895.815
181.000
58.368
.174
98010.000
Std err 647.095
Std dev 9036.194
S E Kurt .346
Range 98010.000
Median 2128.000
Variance 81652809.5
Skewness 6.286
Minimum .000
* Multiple modes exist. The smallest value is shown.
Percentile
25.00
Valid cases
PMIN
Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
POPDEN
Value
463.000
195
10.600
.000
10.363
.175
98.000
Value
1.764
194
Mean 1892.070
Mode .000.
Kurtosis 54.765
S E Skew .174
Maximum 35687.600
Percentile
50.00
Value
2128.000
Percentile
75.00
Value
5974.000
Missing cases 15
Std err
Std dev
S E Kurt
Range
Percentile
50.00
1.160
16.153
.347
98.000
Value
4.541
Median
Variance
Skewness
Minimum
Percentile
75.00
4.541
260.91.6
2.972
.000
Value
12.940
Missing cases 16
Std err
Std dev
S E Kurt
Range
238.520
3330.745
.346
35687.600
Median 863.200
Variance 11093859.1
Skewness 6.026
Minimum .000
* Multiple modes exist. The smallest value is shown.
Percentile Value
25.00 194.650
Valid cases 195
Percentile Value
50.00 863.200
Missing cases 15
Percentile Value
75.00 2446.160
B-17
-------
POWNER
Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
PRENTER
Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
PUNDER18
Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
70.904
90.909
1.364
.176
97.436
Value
60.726
191
28.573
9.091
1.175
.176
98.698
Value
13.282
191
23.420
.000
3.145
.175
42.478'
Value
20.312
194
Std err
Std dev
S E Kurt
Range
Percentile
50.00
Missing case
Std err
Std dev
S E Kurt
Range
Percentile
50.00
Missing cas<
Std err
Std dev
S E Kurt
Range
Percentile
50.00
Missing cas
1.500
20.727
.350
97.436
Value
75.726
:s 19
1.460
20.182
.350
98.698
Value
24.014
2S 19
.433
6.028
.347
42.478
Value
23.404
es 16
Median
Variance
Skewness
Minimum
Percentile
75.00
Median
Variance
Skewness
Minimum
Percentile
75.00
Median
Variance
Skewness
Minimum
Percentile
75.00
75.726
429.610
-1.233
.000
Value
86.364
24.014
407.330
1.168
.00,0
Value
38.262
23.404
36.336
-.425
.000
Value
26.478
B-18
-------
PI PAR
Mean
Mode
Kurtosis
S E Skew
Maximum
* Multiple m
Percentile
25.00
Valid cases
RENT
Mean
Mode
Kurtosis
S E Skew
Maximum
Percentile
25.00
Valid cases
.080
.067
87.967
.175
1.000
odes exist.
Value
.046
192
471.858
,000
-.393
.176
969.290
Value.
331.130
191
Std err
Std dev
S E Kurt
Range
The smallest
Percentile
50.00
Missing case
Std err
Std dev
S E Kurt
Range
Percentile
50.00
Missing case
.006
.081
.349
1.000
value is
Value
.066
s 18
14.204
196.304
.350
969.290
Value
482.620
is 19
Median
Variance
Skewness
Minimum
shown.
Percentile
75.00
Median
Variance
Skewness
Minimum
Percentile
75.00
.066
.007
8.106
.000
Value
.091
482.620
38535.234
.136
.000
Value
612.960
HOUSEVAL
Mean 146527.652
Mode 11660.000.
Kurtosis .451
S E Skew .176
Maximum 403009.948
Std err 5300.674
Std dev 73256.772
S E Kurt .350
Range 391349.948
Median 141705.925
Variance 5366554712
Skewness .716
Minimum 11660.000
* Multiple modes exist. The smallest value is shown.
Percentile Value
25.00 85808.397
Valid cases 191
Percentile Value
50.00 141705.925
Missing cases 19
Percentile Value
75.00 191349.194
B-19
-------
P2BX0002
Mean
Mode
Kurtosis
S E Skew
Maximum
578.528
.000
41.289
.174
17940.000
Std err 145.315
Std dev 2029.211
S E Kurt .346
Range 17940.000
Median 42.000
Variance 4117698.18
Skewness 6.086
Minimum .000
Percentile Value
25.00 6.000
Valid cases 195
Percentile Value
50.00 42.000
Missing cases 15
Percentile Value
75.00 265.000
P2BX0003
Mean
Mode
Kurtosis
S E Skew
Maximum
11.913
.000
27.190
.174
217.000
Std err
Std dev
S E Kurt
Range
2.033
28.393
.346
217.000
Median
Variance
Skewness
Minimum
3.000
806.152
4.812
.000
Percentile Value
25.00 .000
Valid cases 195
Percentile Value
50.00 3.000
Missing cases 15
Percentile
75.00
Value
10.000
P2BX0004
Mean
Mode
Kurtosis
s E Skew
Maximum
212.005
.000
164.099
.174
14883.000'
Std err 78.944
Std dev 1102.396
S E Kurt ' .346
Range 14883.000
Median 22.000
Variance 1215277.34
Skewness 12.410
Minimum .000
Percentile Value
25.00 1.000
Valid cases 195
Percentile Value
50.00 22.000
Missing cases 15
Percentile Value
75.00 160.000
B-20
-------
P3BX0001
Mean
'Mode
Kurtosis
S E Skew
Maximum
480.841
.000
101.781
.174
23619.000
Std err 141.481
Std dev 1975.672
S E Kurt .346
Range 23619.000
Median 47.000
Variance 3903280.14
Skewness 9.357
Minimum .000
Percentile Value
25.00 8.000
Valid cases 195
Percentile Value
50.00 47.000
Missing cases 15
Percentile Value
75.00 244.000
P4BX0001
Mean 1067.379
Mode .000
Kurtosis 37.072
S E Skew .174
Maximum 17379.000
Std err 127.416
Std dev 1779.263
S E Kurt .346
Range 17379.000
Median 474.000
Variance 3165776.90
Skewness 4.893
Minimum .000
* Multiple modes exist. The smallest value is shown.
Percentile
25.00
Value
97
Valid cases
P4BX0002
Mean
Mode
Kurtosis
S E Skew
Maximum
659
48
13611
.000
195
.210
.000.
.829
.174
.000
Percentile
50.00
Value
474
Missing cases
Std err
Std dev
S E Kurt
Range
94
1319
13611
.000
15
.492
.508
.346
.000
Percentile Value
75.00
Median
Variance
Skewness
Minimum
1336.
.232.
1741100
5.
000
000
.14
805
000
Percentile Value
25.00 40.000
Valid cases 195
Percentile Value
50.00 232.000
Missing cases 15
Percentile Value
75.00 734.000
B-21
-------
APPENDIX C. COMPARISONS IN RACIAL COMPOSITION: 1 Mile from Sites and
Municipality Proportions
-------
NRL Site °/o Black (1 mile) vs Municipality % BlacK
20-Nov-93
1-mile Muni
State Site Name Municipality County %Black %Black Ratio
NJ
AMERICAN CYANAMID CO
AO POLYMER
ASBESTOS DUMP
BEACHWOOD/BERKELEY WELLS
BOG CREEK FARM
BRICK TWP LF
BRIDGEPORT RENTAL & OIL SER
BROOK INDUSTRIAL PARK
BURNT FLY BOG
CALDWELL TRUCKING CO.
CHEMICAL CONTROL CORPORATI
CHEMICAL INSECTICIDE CORP
CHEMICAL LEAMAN TANK LINES I
CHEMSOL, INC.
CIBA-GEIGY CORP
CINNAMINSON GROUND WATER
COMBE FILL NORTH LF
COMBE FILL SOUTH LF
COOPER ROAD SITE
COSDEN CHEMICAL COATINGS C
CPS/MADISON INDUSTRIES
CURCIO SCRAP METAL
D'IMPERIO PROPERTY
DAYCO CORP/L E CARPENTER
DELILAH RD
BOUND BROOK
SPARTA TOWNSHIP
MILLINGTON
BERKLEY TOWNSHIP
HOWELL TOWNSHIP
BRICK TOWNSHIP
BRIDGEPORT
BOUND BROOK
MARLBORO TOWNSHIP
FAIRFIELD
ELIZABETH
EDISON TOWNSHIP
BRIDGEPORT
PISCATAWAY
TOMS RtVER
CINNAMtNSON TOWNSHIP
MOUNT OLIVE TWP
CHESTER TOWNSHIP
VOORHEES TOWNSHIP
BEVERLY V
OLD BRIDGE TOWNSHIP
SADDLE BROOK TWP
HAMILTON TOWNSHIP
WHARTON BOROUGH
EGG HARBOR TOWNSHIP
SOMERSET
SUSSEX
MORRIS
OCEAN
MONMOUTH
OCEAN
GLOUCESTER
SOMERSET
MONMOUTH
ESSEX
UNION
MIDDLESEX
GLOUCESTER
MIDDLESEX
OCEAN
BURLINGTON
MORRIS
MORRIS
CAMDEN
BURLINGTON
MIDDLESEX ,
BERGEN
ATLANTIC
MORRIS
ATLANTIC
0.56391
0
0.38911
0.45587
0.7722
1.50748
19.8895
2.82732
7.8125
0.47909
20.99282
5.00741
0
16.05927
1.10468
42.68451
2.16853
0.96286
7.05467
17.86931
18.43972
2.14059
24.4567
3.4651
42.18341
2.2873
0.3827
1 .0397
3.0651
0.6303
12.1819
2.2873
3.6212
0.2101
19.8478
5.565
12.1819
17.6177
2.6582
5.2938
2.8334
1.0406
6.4661
24.588
1 .7877
1.0605
14.7702
2.6087
9.3383
0.247
0
0.438
0.252
2.392
1.633
1.236
2.157
2.280
1.058
0.9
0
0.912
0.416
8.063
0.765
0.925
1.091
0.727
10.31
2.018
1.656
1.328
4.517
-------
State Site Name
Municipality
County
%Black KBlack Ratio
DENZER & SCHAFER X-RAY CO.
DEREWAL CHEMICAL CO.
DIAMOND ALKALI CO.
DOVER MUNICIPAL WELL 4
ELLIS PROPERTY
EVOR PHILLIPS LEASING
EWAN PROPERTY
FAA TECHNICAL CENTER
FAIR LAWN WELL FIELDS
FLORENCE LAND RECONTOURIN
FORT DIX LANDFILL
FRIED INDUSTRIES
FRIEDMAN PROPERTY
GARDEN STATE CLEANERS
GEMS LANDFILL
GLEN RIDGE RADIUM SITE
GLOBAL LANDFILL
GOOSE FARM
HELEN KRAMER LF
HERCULES INC
HIGGINS DSPL SERVICE INC
HIGGINSFARM
HOPKINS FARM
IMPERIAL OIL CO. INC./CHAMPIO
INDUSTRIAL LATEX
JACKSON TWP LF
JIS LANDFILL
KAUFFMAN & MINTEER INC
BAYVJLLE
KINGWOOD TOWNSHIP
NEWARK
DOVER TOWNSHIP
EVESHAM TOWNSHIP
OLD BRIDGE TOWNSHIP
SHAMONG TOWNSHIP
FAIR LAWN
FLORENCE TOWNSHIP
PEMBERTON TOWNSHIP
EAST BRUNSWICK TWP
UPPER FREEHOLD TWP
MINOTOLA
GLOUCESTER TOWNSHIP
GLEN RIDGE
OLD BRIDGE TOWNSHIP
PLUMSTEAD TOWNSHIP
MANTUA TOWNSHIP
GIBBSTOWN
KINGSTON
FRANKLIN TOWNSHIP
PLUMSTEAD TOWNSHIP
MORGAN VILLE
WALLINGTON BOROUGH
JACKSON TOWNSHIP
JAMESBURG/S. BRNSWCK
JOBSTOWN
OCEAN
HUNTERDON
ESSEX
MORRIS
BURLINGTON
MIDDLESEX
BURLINGTON
BERGEN
BURLINGTON
BURLINGTON
MIDDLESEX
MONMOUTH
ATLANTIC
CAMDEN
ESSEX
MIDDLESEX
OCEAN
GLOUCESTER
GLOUCESTER
SOMERSET
SOMERSET
OCEAN
MONMOUTH
BERGEN
OCEAN
MIDDLESEX
BURLINGTON
1.30961
0.35907
15.01713
6.84755
0
7.44681
0.1056
0
9.00939
0.98039
49.85251
0.18569
4.33245
4.87731
10.14235
48.67065
9.26829
6.82624
2.35921
0.13072
8.19672
12.32877
3.73514
4.95461
2.24801
0.78809
0.40486
1.1583
1.0397
0.6617
58.4567
6.0999
2.8435
3.3537
1.8213
0.5925
7.0232
22.8224
2.2251
1.6173
6.1416
3.1939
1.7877
2.8643
1.3004
0.692
5.9217
1.3501
2.8643
11.4039
2.7059
3.1354
8.6513
1.26
0.543
0.257
1.123
0
2.220
0.058
15.21
0.14
2.184
0.083
2.679
1.651
15.24
5.184
2.383
1.814
0.189
1.384
9.132
1.304
0.434
0.831
0.251
0.047
-------
State Site Name
Municipality
County
V.fc\ack VğB\ack Ratio
KIN-BUG LF
KING OF PRUSSIA
KRYSOWATYFARM
LANDFILL & DEVELOPMENT CO
LANG PROPERTY
LIPARI LANDFILL
LODI MUNICIPAL WELLS
LONE PINE LF
M&T'DELISALF
MANNHEIM AVE DUMP
MAYWOOD CHEMICAL CO.
METALTEC/AEROSYSTEMS
MONITOR DEVICES/INTERC1RCUI
MONROE TOWNSHIP LANDFILL
MONTCLAIR/WEST ORANGE RADI
MONTGOMERY TOWNSHIP HOUS
MYERS PROP
NASCOLITE CORP
NAVAL AIR ENGINEERING CENTE
NAVAL WEAPONS STATION EARL
NL INDUSTRIES INC.
PEPE FIELD
PICATINNY ARSENAL
PIJAK FARM
PJP LANDFILL
POHATCONG VALLEY GROUNDW
POMONA OAKS WELL CONTAMIN
PRICE LF #1
EDISON TOWNSHIP
WINSLOW TOWNSHIP
HILLSBOROUGH
MOUNT HOLLY
PEMBERTON TOWNSHIP
PITMAN
LODI
FREEHOLD TOWNSHIP
ASBURY PARK
GALLOWAY TOWNSHIP
MAYWOOD/ROCHELLE PK
FRANKLIN BOROUGH
WALL TOWNSHIP
MONROE TOWNSHIP
MONTCLAIR/W ORANGE
MONTGOMERY TOWNSHIP
FRANKLIN TOWNSHIP
MILLVILLE
LAKEHURST
COLTS NECK
PEDRICKTOWN
BOONTON
ROCKAWAY TOWNSHIP
PLUMSTEAD TOWNSHIP
JERSEY CITY
WARREN COUNTY
GALLOWAY TOWNSHIP
PLEASANTVILLE
MIDDLESEX
CAMDEN
SOMERSET
BURLINGTON
BURLINGTON
GLOUCESTER
BERGEN
MONMOUTH
MONMOUTH
ATLANTIC
BERGEN
SUSSEX
MONMOUTH
MIDDLESEX
ESSEX
SOMERSET
HUNTERDON
CUMBERLAND
OCEAN
MONMOUTH
SALEM
MORRIS
MORRIS
OCEAN
HUDSON
WARREN COUNTY
ATLANTIC
ATLANTIC
9.39199
0
1.21547
15.15294
0
1.75342
3.20104
0
28.30986
0
4.31367
0.43924
2.15983
2.1058
43.27114
1.22058
1.27551
4.28353
Ğ
0
19.96644
3.61232
0
2.43902
23.1453
1.66736
5.3878
49.17492
5.565
22.9501
3.256
18.7236
22.8224
0.4805
2.8897
4.7025
59.3904
7.3596
1.6996
0.5224
0.573
3.1454
31.7183
3.995
0.456
8.4449
9.6166
3.6219
3.2362
1.6554
2.8643
29.695
7.3596
55.0696
1.688
0
0.373
0.809
0
3.649
1.108
0
0.477
0
2.538
0.841
3.769
0.669
1.364
0.306
2.797
0.507
1.116
0
0.852
0.779
0.732
0.893
-------
State Site Name
Municipality
County
VoBlack %Black Ratio
RADIATION TECHNOLOGY INC
REICH FARMS
RENORA INC
R/NGWOOD MINES /LF
ROCKAWAY BORO WELLFIELD
ROCKAWAY TWP WELLS
ROCKY HILL MUNICIPAL WELL
ROEBLING STEEL CO
SAYREV1LLE LF
SCIENTIFIC CHEMICAL PROCESSI
SHARKEY LF
SHIELD ALLOY CORP
SOUTH BRUNSWICK LF
SOUTH JERSEY CLOTHING CO
SPENCE FARM
SWOPE OIL & CHEM CO
SYNCON RESINS
TABERNACLE DRUM DUMP
UNIVERSAL OIL PROD INC
UPPER DEERFIELD TOWNSHIP SL
US RADIUM
VENTRON/VELSfCOL
VINELAND CHEMICAL CO INC
VINELAND STATE SCHOOL
W R GRACE/WAYNE INTERIM ST
WALDICK AEROSPACE DEVICES I
WHITE CHEMICAL CORP.
WILLIAMS PROPERTY
ROCKAWAY TOWNSHIP
PLEASANT PLAINS
EDISON TOWNSHIP
R/NGWOOD BOROUGH
ROCKAWAY TOWNSHIP
ROCKAWAY
ROCKY HILL BOROUGH
FLORENCE
SAYREVILLE
CARLSTADT
PARSIPPANYfTROY HLS
NEWFIELD BOROUGH
SOUTH BRUNSWICK
MINOTOLA
PLUMSTEAD TOWNSHIP
PENNSAUKEN
SOUTH KEARNY
TABERNACLE TOWNSHIP
EAST RUTHERFORD
UPPER DEERFIELD TWP
ORANGE
WOOD RIDGE BOROUGH
VINELAND
VINELAND
WAYNE TOWNSHIP
WALL TOWNSHIP
NEWARK
SWAINTON
MORRIS
OCEAN
MIDDLESEX
PASSAIC
MORRIS
MORRIS
SOMERSET
BURLINGTON
MIDDLESEX
BERGEN
MORRIS
GLOUCESTER
MIDDLESEX
ATLANTIC
OCEAN
CAMDEN
HUDSON
BURLINGTON
BERGEN
UMBERLAND
ESSEX
BERGEN
CUMBERLAND
UMBERLAND
PASSAIC
WDNMOUTH
ESSEX
CAPE MAY
0.21598
0.50378
5.70773
35.29412
0.83373
0.66398
2.39386
4.08103
5.70207
2.11033
7.41309
1.93766
6.18729
4.80881
1.24224
20.24277
85.66308
0.23419
3.46015
1.24688
57.94948
1.3269
16.78161
8.63743
0.54978
0.05729
90.02516
11.58433
1.6554
0.1164
5.565
1.7983
1.1533
1.6554
1.443
7.9052
3.2299
1.0889
3.5954
1.2563
6.1841
2.8643
14.69
1.4538
2.7715
10.8128
70.3258
0.7461
11.4768
11.4768
1.0611
0.573
58.4567
0.130
4.328
1.026
19.63
0.723
0:401
1.659
0.516
1.765
1.938
2.062
1.542
1.001
0.434
1.378
0.161
1.248
0.115
0.824
1.778
1.462
0.753
0.518
0.1
1.540
-------
State Site Name Municipality County %Black %Black Ratio
WILSON FARM
WITCO CHEMICAL CORP
PLUMSTEAD TOWNSHIP
OAKLAND
OCEAN/MONMOUTH
BERGEN
0
0.69819
2.8643
1.0836
0
0.644
-------
Stats
Site Name
Municipality
County
1-mlte Muni
%Black %Black Ratio
NY
ACTION ANODIZING & PLATING
AMERICAN THERMOSTAT CO.
ANCHOR CHEMICALS
APPLIED ENVIRONMENTAL SERVJ
BATAVIA LF
EEC TRUCKING
BIOCLINICAL LABORATORIES INC
BREWSTER WELLFIELD
BROOKHAVEN NATIONAL LABOR
BYRON BARREL AND DRUM
C & J DISPOSAL SITE
CARROL & DUBIES
CIRCUITRON CORPORATION
CLAREMONT POLYCHEMICAL
CLOTHIER DISPOSAL
COLESVILLE MUNICIPAL LF
CONKLIN DUMPS
CORTESE LF
ENDICOTT VILLAGE WELL FIELD
FACET ENTERPRISES
FMC-DUBLIN RD
FOREST GLEN SUBDIVISION
FULTON TERMINALS
GE - MOREAU SITE
GENERAL MOTORS/CENTRAL FO
GENZALE PLATING COMPANY
GOLDISC RECORDINGS INC
COPIAGUE
SOUTH CAIRO
HICKSVJLLE
GLENWOOD LANDING
BATAVIA
TOWN OF VESTAL
BOHEMIA
PUTNAM COUNTY
UPTON
BYRON
HAMILTON
PORT JERVIS
EAST FARMINGDALE
OLD BETHPAGE
TOWN OF GRANBY
TOWN OF COLESVILLE
CONKLIN
VIL OF NARROWSBURG
VILLAGE OF ENDICOTT
ELMIRA
TOWN OF SHELBY
NIAGARA FALLS
FULTON
SOUTH GLEN FALLS
MASSENA
FRANKLIN SQUARE
HOLBROOK
SUFFOLK
GREENE
NASSAU
NASSAU
GENESEE
BROOME
SUFFOLK
PUTNAM
SUFFOLK
GENESEE
MADISON
ORANGE
SUFFOLK
NASSAU
OSWEGO
BROOME
BROOME
SULLIVAN
BROOME
CHEMUNG
ORLEANS
NIAGARA
OSWEGO
SARATOGA
ST. LAWRENCE
NASSAU
SUFFOLK
j 27.07577
0.51546
18.99146
0.9018
4.2654
1.69452
0.92421
2.73051
0
40.81633
0
1.06525
24.91506
16.18321
0
0
0.95511
4.41989
0.43362
1.12322
2.73438
1.96706
0
0.31436
0
0.21129
1.71238
4.6752
0.3322
0.7841
0.1174
0.9083
1.7955
0.4814
1.1087
3.4828
2.2406
15.5876
1.2299
0.4563
0.322
0.7981
1.8254
0.9946
6.6255
15.5789
0.5569
3.6554
0.2731
0.1737
1.3453
5.79Y
1.552
24.22
7.681
4.696
0.944
1.92
36.81
0
0.475
1.598
13.16
0
0
1.197
0.238
1.129
0.413
0.126
0
0.086
0
1.216
1.273
-------
State Site Name
Municipality
County
%B(ack %Black Ratio
GRIFFISS AIR FORCE BASE
HAVILAND COMPLEX
HERTEL LANDFILL
HOOKER- 102ND STREET
HOOKER - HYDE PARK
HOOKER CHEM /RUCO POLYMER
HOOKER CHEM S-AREA
HUDSON RIVER PCBS
ISLIP SLF
JOHNSTOWN CITY LF
JONES CHEMICAL INC
JONES SANITATION
KATONAH MUNICIPAL WELL
KENMARK TEXTILE CORP
KENTUCKY AVE WELLFIELD
LI TUNGSTON CORP
LIBERTY IND FINISHING
LOVE CANAL
LUDLOW SAND & GRAVEL
MARATHON BATTERY CO.
MATTIACE PETROCHEMICALS CO
MERCURY REFINING, INC.
NEPERA CHEMICAL CO INC
NIAGARA CTY REFUSE
NIAGARA MOHAWK/OPERATION
NORTH SEA MUNICIPAL LF
OLD BETHPAGE LF
CLEAN WELL FIELD
ROME
TOWN OF HYDE PARK
PLATTEKILL
NIAGARA FALLS
NIAGARA FALLS
HICKSVILLE
NIAGARA FALLS
HUDSON RIVER
ISLIP
TOWN OF JOHNSTOWN
CALEDONIA
HYDE PARK
TOWN OF BEDFORD
FARMINGDALE
HORSEHEADS
GLEN COVE
FARMINGDALE
NIAGARA FALLS
CLAYVILLE
COLD SPRINGS
GLEN COVE
COLONIE
MAYBROOK
WHEATFIELD
SARATOGA SPRINGS
NORTH SEA
OYSTER BAY
OLEAN
ONEIDA
DUTCHESS
ULSTER
NIAGARA
NIAGARA
NASSAU
NIAGARA
RENSSELAER, WASHING
SUFFOLK
FULTON
LIVINGSTON
DUTCHESS
WESTCHESTER
SUFFOLK
CHEMUNG
NASSAU
NASSAU
NIAGARA
ONEIDA
PUTNAM
NASSAU
ALBANY
ORANGE
NIAGARA
SARATOGA
SUFFOLK
SIASSAU
CATTARAUGUS
6.70611
1.82371
1.53404
0.59465
13.35227
0.15371
3.90662
0.74983
3.19939
0
4.00191
3.02013
4.75035
6.85971
0.92109
12.82013
0.47366
1.44404
0.68493
0.11503
13.01688
5.66906
3.75335
0.04374
2.03998
1.48462
0.19973
2.0816
7.9504
4.3335
4.4764
15.5789
15.5789
0.7841
15.5789
3.2393
0.7791
3.7577
4.3335
0.7112
0.7729
1.0879
7.7974
0.7729
15.5789
0.6479
1.2227
7.7974
3.0927
5.7816
0.3056
3.2759
0.9881
4.8153
2.6909
0.843
0.421
0.343
0.038
0.857
0.196
0.251
0.988
0
1.065
0.697
6.679
8.875
0.847
1.644
0.613
0.093
1.057
0.094
1.669
1.833
0.649
0.143
0.623
1.502
0.041
0.774
-------
State Site Name
Municipality
County
%Black VoBlack Ratio
PASLEY SOLVENTS & CHEMICAL I
PLATTSBURGH AIR FORCE BASE
POLLUTION ABATEMENT SERVIC
PORT WASHINGTON LANDFILL
PREFERRED PLATING CORP
RADIUM CHEMICAL
RAMAPO LF
RICHARDSON HILL SITE
ROBINTECH INC/NATIONAL PIPE
ROCKET FUEL SITE
ROSEN SITE
ROWE INDUSTRIES GROUNDWAT
SARNEY FARM
SEALAND RESTORATION
SENECA ARMY DEPOT
SIDNEY LF
SINCLAIR REFINERY
SMS INSTRUMENTS INC
SOLVENT SAVERS
SUFFERN VILLAGE WELL FIELD
SYOSSET LF
TRI-CITY BARREL
TRONIC PLATING COMPANY, INC
VESTAL WATER SUPPLY 1-1
VESTAL WATER SUPPLY 4-2
VOLNEY MUNICIPAL LF
WARWICK LANDFILL
WIDE BEACH DEVELOPMENT
HEMPSTEAD
PLATTSBURGH
OSWEGO
PORT WASHINGTON
FARMINGDALE
NEW YORK CITY
RAMAPO
SIDNEY CENTER
TOWN OF VESTAL
MALTA
CORTLAND
NOYACK/SAG HARBOR
AMENIA
LISBON
ROMULUS
SIDNEY
WELLSVILLE
DEER PARK
LINCKLAEN
VILLAGE OF SUFFERN
OYSTER BAY
PORT CRANE
FARMINGDALE
VESTAL
VESTAL
TOWN OF VOLNEY
WARWICK
BRANT
NASSAU
CLINTON
OSWEGO
NASSAU
SUFFOLK
QUEENS
ROCKLAND
DELAWARE
BROOME
SARATOGA
CORTLAND
SUFFOLK
DUTCHESS
ST. LAWRENCE
SENECA
DELAWARE
ALLEGANY
SUFFOLK
CHENANGO
ROCKLAND
NASSAU
BROOME
SUFFOLK
BROQME
BROOME
OSWEGO
ORANGE
ERIE
37.02071
11.92446
0.26293
0.67231
26.04416
2.92011
0
0
1.61533
3.69762
1.40121
0.67873
0.60851
0
3.125
0
0.37951
14.89017
0
8.72472
0.28035
0.1321
0
1.40894
1.23816
0.34965
5.22088
0
58.8195
12.0737
2.2549
2.918
0.7729
28.7128
13.8354
0.9322
1.7955
1.1188
1.3989
9.5595
4.8508
0.0534
7.9779
0.96
0.3434
7.6248
4.2696
1.7116
0.7729
1.7955
1.7955
0.229
3.7934
2.5956
0.629
0.988
0.117
0.230
33.7
0.102
0
0
0.9
3.305
1.002
0.071
0.125
0
0.392
0
1.105
1.953
2.043
0.164
0
0.785
0.69
1.527
1.376
0
-------
State Site Name
Municipality
County
%Black %Black Ratio
YORK OIL CO.
MOIRA
FRANKLIN
0
0.2235
0
-------
APPENDIX D. FREQUENCIES
-------
NOTE:
The interval categories indicated to the left of each frequency distribution
usually are assigned labels. Where they are not, the single numbers indicated
under the "Value" column should be read or interpreted as the range from the
previous number up to that number.
-------
DSI
Value Label
Value Frequency Percent
Valid Cum
Percent Percent
Valid cases
209
100.00000
110.00000
120.00000
130.00000
140.00000
150.00000
160.00000
170.00000
180.00000
190.00000
200.00000
Total 210 100.0
Missing cases 1
15
13
18
15
28
23
11
54
17
2
13
1
7.1
6.2
8.6
7.1
13.3
11.0
5.2
25.7
8.1
1.0
6.2
.5
7.2
6.2
8.6
7.2
13.4
11.0
5.3
25.8
8.1
1.0
6.2
Musing
7.2
13.4
22.0
29.2
42.6
53.6
58.9
84.7
92.8
93.8
100.0
100.0
NFI
Value Label
Valid Cum
Value Frequency Percent Percent Percent
40.00000
60.00000
80.00000
100.00000
120.00000
8
29
11
33
124
5
3.8
13.8
5.2
15.7
59.0
2.4
3.9
14.1
5.4
16.1
60.5
Missing
3.9
18.0
23.4
39.5
100.0
Valid cases
205
Total
Missing cases
210
100.0
100.0
D-l
-------
NPI
Valid Cum
Value Label Value Frequency Percent Percent Percent
20.00000 4 1.9 1.9 1.9
40.00000 2 1.0 1.0 2.9
60.00000 10 4.8 4.8 7.6
80.00000 24 11.4 11.4 19.0
100.00000 8 3.8 3.8 22.9
120.00000 65 31.0 31.0 53.8
140.00000 78 37.1 37.1 91.0
160.00000 19 9.0 9.0 100.0
Total 210 100.0 100.0
Hi-Res Chart # 9:Histogram of npi
Valid cases 210 Missing cases 0
D-2
-------
CORIFS1I
Value Label
Valid cases 204
Valid Cum
Value Frequency Percent Percent Percent
20.00000
40.00000
60.00000
80.00000
100.00000
120.00000
140.00000
160.00000
Total
Missing cases
4
10
28
43
35
60
18
6
6
1.9
4.8
13.3
20.5
16.7
28.6
8.6
2.9
2.9
2.0
4.9
13.7
21.1
17.2
29.4
8.8
2.9
Missing
2.0
6.9
20.6
41.7
58.8
88.2
97.1
100.0
210
100.0
100.0
CORIFS2I
Value Label
Valid cases 204
Valid Cum
Value Frequency Percent Percent Percent
20.00000
40.00000
60.00000
80.00000
100.00000
120.00000
140.00000
160.00000
Total
Missing cases
19
31
39
44
27
33
10
1
6
9.0
14.8
18.6
21.0
12.9
15.7
4.8
.5
2.9
9.3
15.2
19.1
21.6
13.2
16.2
4.9
.5
Missing
9.3
24.5
43.6
65.2
78.4
94.6
99.5
100.0
210
100.0
100.0
D-3
-------
CORIFS3I
Value Label
Value Frequency Percent
Valid Cum
Percent Percent
16
41
28
24
26
20
3
1
51
7.6
19.5
13.3
11.4
12.4
9.5
1.4
.5
24.3
10.1
25.8
17.6
15.1
16.4
12.6
1.9
.6
Missing
10.1
35.8
53.5
68.6
84.9
97.5
99.4
100.0
Valid cases
159
20.00000
40.00000
60.00000
80.00000
100.00000
120.00000
140.00000
160.00000
Total 210 100.-0
Missing cases 51
100.0
CORIFS4I
Value Label
Valid cases
159
Valid Cum
Value Frequency Percent Percent Percent
20.00000
40.00000
60.00000
80.00000
100.00000
120.00000
140.00000
160.00000
Total
Missing cases
26
64
29
16
15
7
1
1
51
12.4
30.5
13.8
7.6
7.1
3.3
.5
.5
24.3
16.4
40.3
18.2
10.1
9.4
4.4
.6
.6
Missing
16.4
56.6
74.8
84.9
94.3
98.7
99.4
100.0
210
100.0
51
100.0
D-4
-------
R01I
Value Label
Valid Cum
Value Frequency Percent Percent Percent
20.00000
40.00000
60.00000
80.00000
100.00000
120.00000
140.00000
Total
19
46
29
21
22
12
2
59
9.0
21.9
13.8
10.0
10.5
5.7
1.0
28.1
12.6
30.5
19.2
13.9
14.6
7.9
1.3
Missing
12.6
43.0
62.3
76.2
90.7
98.7
100.0
210
100.0
100.0
Hi-Res Chart # 5:Histogram of roli
Valid cases 151 Missing cases 59
D-5
-------
R02I
Value Label
Valid Cum
Value Frequency Percent Percent Percent
20.00000
40.00000
60.00000
80.00000
100.00000
120.00000
Total
Hi-Res Chart # 6:Histogram of ro2i
Valid cases 151 Missing cases
28
58
25
19
13
8
59
13.3
27.6
11.9
9.0
6.2
3.8
28.1
18.5
38.4
16.6
12.6
8.6
5.3
Missing
18.5
57.0
73.5
86.1
94.7
100.0
210
100.0
100.0
59
D-6
-------
CDCOUNTI
Value Label
Valid Cum
Value Frequency Percent Percent Percent
,00000
,00000
.00000
.00000
.00000
5.00000
Total
25
18
8
4
3
16
136
11.9
8.6
3.8
1.9
1.4
7.6
64.8
33.8
24.3
10.8
5.4
4.1
21.6
Missing
33.8
58.1
68.9
74.3
78.4
100.0
210
100.0
100.0
Hi-Res Chart
Valid cases
10:Histogram of cdcounti
74 Missing cases 136
D-7
-------
COCOUNTI
Value Label
Valid Cum
Value Frequency Percent Percent Percent
1.00000
2.00000
3.00000
4.00000
5.00000
Total
108
63
22
6
6
5
51.4
30.0
10.5
2.9
2.9
2.4
52.7
30.7
10.7
2.9
2.9
Missing
52.7
83.4
94.1
97.1
100.0
210
100.0
100.0
Hi-Res Chart # 11:Histogram of cocounti
Valid cases 205 Missing cases 5
RACOUNTI
Value Label
Valid Cum
Value Frequency Percent Percent Percent
1.00000
2.00000
3.00000
4.00000
5.00000
Total
57
20
9
4
2
118
27.1
9.5
4.3
1.9
1.0
56.2
62.0
21.7
9.8
4.3
2.2
Missing
62.0
83.7
93.5
97.8
100.0
210
100.0
100.0
Hi-Res Chart # 12:Histogram of racounti
Valid cases 92 Missing cases 118
D-8
-------
RDCOUNTI
Value Label
Valid Cum
Value Frequency Percent Percent Percent
1.00000
2.00000
3.00000
4.00000
5.00000
Total
63
39
15
8
3
82
30.0
18.6
7.1
3.8
1.4
39.0
49.2
30.5
11.7
6.3
2.3
Missing
49.2
79.7
91.4
97.7
100.0
210
100.0
100.0
Hi-Res Chart # 13:Histogram of rdcounti
Valid cases 128 Missing cases 82
ROCOUNTI
Value Label
Valid Cum
Value Frequency Percent Percent Percent
1.00000
2.00000
3.00000
5.00000
Total
113
31
5
2
59
53.8
14.8
2.4
1.0
28.1
74.8
20.5
3.3
1.3
Missing'
74.8
95.4
98.7
100 1'b
210
100.0
100.0
Hi-Res Chart f 14:Histogram of rocounti
Valid cases 151 Missing cases 59
D-9
-------
RSCOUNTI
Value Label
Valid Cum
Value Frequency Percent Percent Percent
1.00000
2.00000
3.00000
4.00000
Total
4
176
11
1
18
1.9
83.8
5.2
.5
8.6
2.1
91.7
5.7
.5
Missing
2.1
93.8
99.5
100.0
210
100.0
100.0
Hi-Res Chart # 15:Histogram of rscounti
Valid cases 192 Missing cases 18
RVCOUNTI
Value Label
Valid Cum
Value Frequency Percent Percent Percent
1.00000
2.00000
3.00000
4.00000
5.00000
Total
39
23
6
2
2
138
18.6
11.0
2.9
1.0
1.0
65.7
54.2
31.9
8.3
2.8
2.8
Missing
54.2
86.1
94.4
97.2
100.0
210
100.0
100.0
Hi-Res Chart # 16:Histogram of rvcounti
Valid cases 72 Missing cases 138
D-10
-------
NUMOPUNI
Valid Cum
Value Label Value Frequency Percent Percent Percent
1.00000 3 1.4 1.4 1.4
2.00000 108 51.4 51.4 52.9
3.00000 69 32.9 32.9 85.7
4.00000 19 9.0 9.0 94.8
5.00000 11 5.2 5.2 100.0
Total 210' 100.0 100.0
Hi-Res Chart # 17:Histogram of numopuni
Valid cases 210 Missing cases 0
D-ll
-------
HRSSCI
Value Label
Valid Cum
Value Frequency Percent Percent Percent
20.00000
30.00000
40.00000
50.00000
60.00000
70.00000
80.00000
Total
3
11
90
54
34
4
4
10
1.4
5.2
42.9
25.7
16.2
1.9
1.9
4.8
1.5
5.5
45.0
27.0
17.0
2.0
2.0
Missing
1.5
7.0
52.0
79.0
96.0
98.0
100.0
210
100.0
100.0
Hi-Res Chart # 18:Histogram of hrssci
Valid cases 200 Missing cases 10
D-12
-------
PAGEDI
Value Label
Valid cases 194
Valid Cum
Value Frequency Percent Percent Percent
5.00000
10.00000
15.00000
20.00000
30.00000
40.00000
Total 210 100.0 100.0
Missing cases 16
19
40
78
46
9
2
16
9.0
19.0
37.1
21.9
4.3
1.0
7.6
9.8
20.6
40.2
23.7
4.6
1.0
Missing
9.8
30.4
70.6
94.3
99.0
100.0
PASIANI
Value Label
Valid cases 194
Valid Cum
Value Frequency Percent Percent Percent
.50000
1.00000
2.00000
3.00000
4.00000
5.00000
10.00000
11.00000
Total 210 100.0 100.0
Missing cases 16
69
22
31
26
19
8
12
7
16
32.9
10.5
14.8
12.4
9.0
3.8
5.7
3.3
7.6
35.6
11.3
16.0
13.4
9.8
4.1
6.2
3.6
Missing
35.6
46.9
62.9
76.3
86.1
90.2
96.4
100.0
D-13
-------
PBLACKI
Value Label
Value Frequency Percent
.50000
1.00000
2.00000
Valid Cum
Percent Percent
00000
.00000
,00000
.00000
.00000
51
21
26
15
11
11
6
5
4
3
3
8
10
17
19
24.3
10.0
12.4
7.1
5.2
5.2
2.9
2.4
1.9
1.4
1.4
3.8
4.8
8.1
9.0
26.7
11.0
13.6
7.9
5.8
5.8
3.1
2.6
2.1
1.6
1.6
4.2
5.2
8.9
Missing
26.7
37.7
51.3
59.2
64.9
70.7
73.8
76.4
78.5
80.1
81.7
85.9
91.1
100.0
Valid cases 191
8.00000
9.00000
10.00000
15.00000
20.00000
50.00000
Total 210 100.0
Missing cases 19
100.0
PCROWDI
Value Label
Valid cases 191
Valid Cum
Value Frequency Percent Percent Percent
2.00000
4.00000
6.00000
8.00000
10.00000
20.00000
40.00000
110
46
18
7
5
4
1
19
52.4
21.9
8.6
3.3
2.4
1.9
.5
9.0
57.6
24.1
9.4
3.7
2.6
2.1
.5
Missing
57.6
.81.7
91.1
94.8
97.4
99.5
100.0
Total 210
Missing cases 19
100.0
100.0
D-14
-------
PHISPI
Value Label
Valid cases 192
Value Frequency Percent
.50000
1.00000
2.00000
3.00000
4.00000
5.00000
6.00000
7.00000
8.00000
9.00000
10.00000
15.00000
20.00000
50.00000
Total 210 100.0
Missing cases 18
Valid Cum
Percent Percent
29
13
35
30
23
14
10
5
9
4
3
8
2
7
18
13.8
6.2
16.7
14.3
11.0
6.7
4.8
2.4
4.3
1.9
1.4
3.8
1.0
3.3
8.6
15.1
6.8
18.2
15.6
12.0
7.3
5.2
2'; 6
4.7
2.1
1.6
4.2
1.0
3.6
Missing
15.1
21.9
40.1
55.7
67.7
75.0
80.2
82.8
87.5
89.6
91.1
95.3
96.4
100.0
100.0
D-15
-------
PMINI
Value Label
Value Frequency Percent
Valid Cum
Percent Percent
1,
2,
3.
4,
5.
6.
7,
Valid cases 194
.50000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
8.00000
9.00000
10.00000
15.00000
20.00000
50.00000
Total 210 100.0
Missing cases 16
25
11
17
19
20
13
11
5
6
7
3
14
14
29
16
11.9
5.2
8.1
9.0
9.5
6.2
5.2
2.4
2.9
3.3
1.4
6.7
6.7
13.8
7.6
12.9
5.7
8.8
9.8
10.3
6.7
5.7
2.6
3.1
3.6
1.5
7.2
7.2
14.9
Missing
12.9
18.6
27.3
37.1
47.4
54.1
59.8
62.4
65.5
69.1
70.6
77.8
85.1
100.0
100.0
PNATIVEI
Value Label
Valid cases 194
Valid Cum
Value Frequency Percent Percent Percent
.25000
.50000
.75000
1.00000
2.00000
4.00000
5.00000
138
34
9
4
4
1
-4
16
65.7
16.2
4.3
1.9
1.9
.5
1.9
7.6
71.1
17.5
4.6
2.1
2.1
.5
2.1
Missing
71.1
88.7
'93.3
95.4
97.4
97.9
100.0
Total 210
Missing cases 16
100.0
100.0
D-16
-------
POP100I
Value Label
Value Frequency Percent
Valid Cum
Percent Percent
Valid cases
195
50.00000
100.00000
200.00000
300.00000
400.00000
500.00000
1000.00000
2000.00000
3000.00000
4000.00000
5000.00000
6000.00000
Total 210 100.0
Missing cases 15
9
6
11
12
7
5
17
27
27
11
8
55
15
4.3
2.9
5.2
5.7
3.3
2.4
8.1
12.9
12.9
5.2
3.8
26.2
7.1
4.6
3.1
5.6
6.2
3.6
2.6
8.7
13.8
13.8
5.6
4.1
,28.2
Missing
4.6
7.7
13.3
19.5
23.1
25.6
34.4
48.2
62.1
67.7
71.8
100.0
100.0
POPDENI
Valufc Label Value Frequency Percent
200.00000
400.00000
600.00000
800.00000
1000.00000
1500.00000
2000.00000
3000.00000
4000.00000
Total 210 100.0
Valid cases 195 Missing cases 15
Valid Cum
Percent Percent
52
16
15
13
12
23
9
17
38
15
24.8
7.6
7.1
6.2
5.7
11.0
4.3
8.1
18.1
7.1
26.7
8.2
7.7
6.7
6.2
11.8
4.6
8.7
19.5
Missing
26.7
34.9
42.6
49.2
'55.4
67.2
71.8
80.5
100.0
100.0
D-17
-------
POWNERI
Value Label
Valid cases 159
Valid Cum
Value Frequency Percent Percent Percent
10.00000
20.00000
30.00000
40.00000
50.00000
60.00000
70.00000
80.00000
90.00000
Total 210 100.0 1QO.O
Missing cases 51
3
3
4
7
12
17
28
40
45
51
1.4
1.4
1.9
3.3
5.7
8.1
13.3
19.6
21.4
24.3
1.9
1.9
2.5
4.4
7.5
10.7
17.6
25.2
28.3
Missing
1.9
3.8
6.3
10.7
18.2
28.9
46.5
71.7
100.0
PRENTERI
Value Label
Valid cases 189
Value Frequency Percent
10.00000
20.00000
30.00000
40.00000
50.00000
60.00000
70.00000
80.00000
90.00000
Total
210
Valid Cum
Percent Percent
34
46
38
28
17
12
7
4
3
21
16.2
21.9
18.1
13.3
8.1
5.7
3.3
1.9
1.4
10.0
18.0
24.3
20.1
14.8
9.0
6.3
3.7
2.1
1.6
Missing
18.0
42.3
62.4
77.2
86.2
92.6
96.3
98.4
100.0
Missing cases
21
100.0
100.0
D-18
-------
PUND18I
Value Label
Valid cases
194
Valid Cum
Value Frequency Percent Percent Percent
10.00000
20.00000
30.00000
40.00000
50.00000
5
37
130
20
2
16
2.4
17.6
61.9
9.5
1.0
7.6
2.6
19.1
67.0
10.3
1.0
Missing
2.6
21.6
88.7
99.0
100.0
Total 210
Missing cases 16
100.0
100.0
HSEVALI
Value Label
Valid Cum
Value Frequency Percent Percent Percent
50000.00000
100000.00000
150000.00000
200000.00000
300000.00000
400000.00000
500000.00000
13
50
39
45
37.
6
1
19
6.2
23.8
18.6
21.4
17.6
2.9
.5
9.0
6.8
26.2
20.4
23.6
19.4
3.1
.5
Missing
6.8
33.0
53.4
77.0
96.3
99.5
100.0
Valid cases
191
Total 210 100.0 100.0
Missing cases 19
D-19
-------
RENT I
Value Label
Valid Cum
Value Frequency Percent Percent Percent
100.00000
200.00000
300.00000
400.00000
500.00000
600.00000
800.00000
1000.00000
2
14
23
38
25
37
40
12
19
1.0
6.7
11.0
18.1
11.9
17.6
19.0
5.7
9.0
1.0
7.3
12.0
19.9
13.1
19.4
20.9
6.3
Missing
1.0
8.4
20.4
40.3
53.4
72.8
93.7
100.0
Valid cases
191
Total 210 100.0 100.0
Missing cases 19
D-20
-------
APPENDIX E. CORRELATION COEFFICIENTS
-------
- - Correlation Coefficients - -
COC
CRC
RAG
RDC
RSC
RVC
1.0000
( 204)
P=
.2447
( 104)
P= .012
.0320
( 90)
P= .765
.2003
( 127)
P= .024
-.0201
( 188)
P= .784
.1208
( 71)
P- .316
.3779
( 204)
P- .000
.0212
( 204)
P= .764
.0984
( 158)
P= .219
-.5061
( 158)
P= .000
.0233'
( 149)
P= .778
.2447
( 104)
P- .012
1.0000
( 105)
.0442
( 49)
P= .763
.2197
( 73)
P= .062
-.0125
( 104)
P= .900
-.0728
( 49)
P= .619
.5014
( 104)
P= .000
.1985
( 104)
P= .043
.3804
( 87)
P= .000
.2017
( 87)
P= .061
.2295
( 81)
P= .039
.0320
( 90)
P= .765
.0442
( 49)
P= .763
1.0000
( 92)
P= .
.2095
( 87)
P= .051
-.1306
( 86)
P= .231
-.0644
( 36)
P= .709
.2965
( 90)
P= .005
.0348
( 90)
P= .745
.4278
( 87)
p= .-ooo
.2589
( 87)
P= .015
.5005
( 89)
P= .000
.2003
( 127)
P= .024
.2197
( 73)
P= .062
.2095
( 87)
P= .051
1.0000
( 128)
P= .
-.1057
( 122)
P= .247
-.1082
( 50)
P= .454
.3974
( 127)
P= .000
.1975
( 127)
P= .026
.4338
( 123)
P= .000
.1621
( 123)
P= .073
.5810
( 126)
P= .000
-.0201
( 188)
P= .784
-.0125
( 104)
P= .900
-.1306
( 86)
P= .231
-.1057
( 122)
*= .247
1.0000
( 192)
P-
-.0717
( 70)
P= .555
.0071
( 188)
P= .923
.0572
( 188)
P= .436
.0548
( 152)
P= .502
.0572
( 152)
P= .484
.0464
( 143)
P= .582
.1208
( 71)
P= .316
-.0728
( 49)
P= .619
-.0644
( 36)
P= .709
-.1082
( 50)
P= .454
-.0717
( 70)
P= .555
1.0000
( 72)
P= .
-.1346
( 71)
P= .263
-.014-7
( 71)
P= .903
-.2254
( 56)
P= .095
-.2243
( 56)
P= .097
-.3051
( 57)
P= .021
COC
CRC
RAG
RDC
RSC
RVC
CORIFS1
CORIFS2
CORIFS3
CORIFS4
R01C
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
E-l
-------
- - Correlation Coefficients - -
R02C
DSC
NFC
NPC
CDCOUNT
COCOUNT
RACOUNT
RDCOUNT
ROCOUNT
RSCOUNT
RVCOUNT
COG
CRC
RAC
RDC
RSC
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
RVC
_
(
p=
(
p=
(
p=
(
p=
(
p=
3297
149)
.000
2674
203)
.000
2727
201)
.000
3391
204)
.000
2488
74)
.033
.1239
(
P=
204)
.077
.1425
(
P=
(
P=
(
P=
(
P=
{
P=
90)
.180
.2350
127)
.008
.2155
149)
.008
.0576
188)
.432
.1098
71)
.362
.1036
( 81)
P= .357
.2414
( 104)
P= .014
.4359
( 104)
P= .000
.4292
( 105)
P= .000
.2034
( 54)
P= .140
.2867
( 104)
P= .003
.1436
( 49)
P= .325
.1083
( 73)
P= .362
.1844
( 81)
P= .099
.0555
( 104)
P= .576
.1381
( 49)
P= .344
.2779
( 89)
P= .008
.0388
( 92)
P= .713
.2313
( 92)
P= .027
.2483
( 92)
P= .017
-.0123
( 38)
P= .942
-.0054
( 91)
P= .959
.3222
( 92)
P= .002
.2889
( 87)
P= .007
.1189
( 89)
P= .267
-.0565
( 86)
P= .605
-.1221
( 36)
P= .478
.2772
( 126)
P= .002
.1562
( 128)
P= .078
. .2808
( 128)
P= .001
.3381
( 128)
P= .000
.3303
( 59)
P= .011
.0110
( 128)
P= .902
.4727
( 87)
P= .000
.5256
( 128)
P= .000
.1814
( 126)
P= .042
-.0164
( 122)
P= .858
-.0328
( 50)
P= .821
.0791
( 143)
P= .347
.1118
( 192)
P= .123
.1153
( 191)
P= .112
.1617
( 192)
P= .025
-".1237
( 73)
P= .297
-.0623
( 188)
P= .395
-.1937
( 86)
P= .074
-.0776
( 122)
P= .395-
-.0966
( 143)
P= .251
.3968
( 192)
P= .000
-.2687
( 70)
' P= .025
-.2848
{ 57)
P= .032
-.1952
( 71)
P= .103
-.1713
( 70)
P= .156
-.1907
( 72)
P= .103
-.1298
( 30)
P= .494
-.0342
( 71)
P= .777
-11739
( 36)
P= .310
-.1448
( 50)
P= .316'
-.0464
( 57)
P= .732
.0776
( 70)
P= .523
.4115
( 72)
P= .000
E-2
-------
coc
- Correlation Coefficients
CRC RAC RDC
RSC
RVC
.0669
( 204)
P= .342
.1452
{ 196)
P= .042
.1535
( 190)
P= .034
-.1072
( 190)
P= .141
.0000
( 190)
P=1.000
-.1025
( 188)
P= .162
-.0932
( 190)
P= .201
-.0144
( 190)
P= .843
.0225
( 190)
P= .758
-.0480
( 191)
P= .510
-.0311
( 191)
P= .669
.3352
( 105)
P= .000
.2682
( 102)
P= .006
'-.1671
( 97)
P= .102
-.0366
( 97)
P= .722
.0202
( 97)
P= .844
.0482
( 97)
P= .639
.1473
( 97)
P= .150
.0233
( 97)
P= .821
.0224
( 97)
P= .828
-.0422
( 97)
P= .682
-.0354
. ( 97)
P- .730
.0351
( 92)
P= .740
.1541
( 91)
P= .145
.1294
( 86)
P= .235
-.1410
( 86)
P= .195
.0207
( 86)
P= .850
-.0483
( 84)
P= .663
.1010
( 86)
P= .355
-.0100
( 86)
P= .927
-.0210
( 86)
P= .848
.0753
( 87)
P= .488
.0887
( 87)
P= .414
.0210
( 128)
P= .814
.4184
( 127)
P= .000
.0075
( 119)
P= .936
.0286
( 119)
P= .757
-.0455
( 119)
P= .623
.0792
( 117)
P= .396
.0402
( 119)
P= .664
-.0621
( 119)
P= .503
'-.0514
( 119)
P= .578
.0110
{ 120)
P= .905
.0590
( 120)
P= .522
-.1335
( 192)
P= .065
.2258
( 188)
P= .002
.0160
( 181)
P= .830
-.0037
( 181)
P= .961
-".1183
( 181)
P= .113
.0588
( 180)
P= .433
-.0214
( 181)
P= .775
-.0639
( 181)
' P= .393-:
.0766
( 181)
P= .306
-.0813
( 181)
P= .277
-.0820
( 181)
P= .273
-.0100
( 72)
P= .934
-.1312
( 70)
P= .279
.1495
( 69)
P= .220
-.0977
( 69)
P= .425
.0317
( 69)
P= .796
.0246
( 69)
P= .841
-^0608
( 69)
P= .620
.0151
( 69)
P= .902
.0291
( 69)
P= .812
.1223
( 69)
P= .317
.1271
( 69)
P= .298
NUMOPUN
HRS SCOR
PAGED
PAS I AN
PBLACK
PCROWDED
PHISP
PMIN
PNATIVE
POP100
POPDEN
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
E-3
-------
- - Correlation Coefficients - -
POWNER
PRENTER
PUNDER18
PI PAR
RENT
HOUSEVAL
P2BX0002
P2BX0003
P2BX0004
P3BX0001
P4BX0001
COC
CRC
RAC
.RDC
RSC
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
RVC
-.0149
( 188)
P= .840
.0170
( 188)
P= .817
.0279
( 190)
P= .702
-.0264
( 189)
P= .718
-.0147
( 188)
P= .841
-.0892
( 188)
P= .224
..1103
( 191)
P= .129
-.0852
( 191)
P= .241
-.1101
{ 191)
P= .130
-.1011
( 191)
P= .164
-.0296
( 191)
P= .685
-.0035
( 96)
P= .973
-.0107
( 97)
P= .917
.1375
( 97)
P= .179
.0713
( 97)
P= .488
-.1168
( 96)
P= .257
-.1592
( 97)
P= .119
.0928
( 97)
P= .366
.0089
( 97)
P= .931
-.0905
( 97)
P- .378
.0023
( 97)
P- .983
-.0110
( 97)
Pğ .915
.0214
( 84)
P= .847
-.0395
( 84)
P= .721
-.0583
( 86)
P= .594
.0836
( 85)
P= .447
-.0397
( 84)
P= .720
-.0946
( 84)
P= .392
.0776
( 87)
P= .475
.1315
( 87)
P= .225
.0199
( 87)
P= .855
.0928
( 87)
P= .393
.0992
( 87)
P= .361
-.0494
( 117)
P= .597
.0709
( 117)
P= .447
-.0453
( 119)
P= .625
-.0241
( 118)
P= .795
.0169
( 117)
P= .856
-.0143
( 117)
P= .879
.1363
( 120)
P= .138
.1085
( 120)
P= .238
-.0963
( 120)
P= .296
.1816
( 120)
P= .047
.0554
( 120)
P= .548
-.0103
( 180)
P= .891
.0048
( 180)
P= .949
.0254
( 181)
P= .734
.0109
( 181)
P= .884
-.0055
( 180)
P= .942
-.1044
( 180)
P= .163
-.1662
( 181)
P= .025
-.0677
( 181).
P= .365
r.0388
( 181)
P= .604
-.0684
( 181)
P= .360
-.0984
( 181)
P= .188
-.0710
( 68)
P= .565
.0691
( 69)
P= .573
.0502
( 69)
P= .682
.0997
( 69)
P= .415
-.1254
( 68)
P= .308
-.2540
( 69)
P= .035
.1272
( 69)
P= .298
.0948
( 69)-'
P= .438
.0172
( 69)
. P= .888
.0363
( 69)
P= .767
.1301
( 69)
P= .287
E-4
-------
- - Correlation Coefficients - -
COC CRC RAG RDC RSC RVC
P4BX0002 -.0293 -.0476 .0973 -.0134 -.0634 .1473
< 191) < 97) ( 87) { 120) { 181) ( 69)
P= .687 P= .644 P= .370 P= .885 P= .397 P= .227
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
E-5
-------
CORIFS1
- - Correlation Coefficients -
CORIFS2 CORIFS3 CORIFS4
R01C
RO2C
.3779
( 204)
P= .000
.5014
( 104)
P= .000
.2965
( 90)
P- .005
.3974
( 127)
P= .000
.0071
( 188)
P= .923
-.1346
( 71)
P= .263
1.0000
( 204)
P= .
.5714
( 204)
P= .000
.8035
( 158)
P= .000
.5926
( 158)
P= .000
.6064
( 149)
P= .000
.0212
( 204)
P= .764
.1985
( 104)
P= .043
.0348
( 90)
P= .745
.1975
( 127)
P= .026
.0572
( 188)
P= .436
-.0147
( 71)
P= .903
.5714
( 204)
P= .000
1.0000
( 204)
P= .
.4357
( 158)
P= .000
.5700
( 158)
P= .000
.3585
( 149)
P= .000
.0984
( 158)
P= .219
.3804
( 87)
P= .000
.4278
( 87)
P= .000
.4338
( 123)
P= .000
.0548
( 152)
P= .502
-.2254
( 56)
P= .095
.8035
( 158)
P= .000
.4357
( 158)
P= .000
1.0000
( 159)
P= .
.6675
( 159)
P= .000
.7258
( 145)
P= .000
-.5061
( 158)
P= .000
.2017
( 87)
P= .061
.2589
( 87)
P= .015
.1621
( 123)
P= .073
.0572
( 152)
P= .484
-.2243
( 56)
P= .097
.5926
( 158)
P= .000
.5700
( 158)
P= .000
.6675
( 159)
P= .000
1.0000
( 159)
P= .
.5845
( 145)
P= .000
.0233
( 149)
P= .778
.2295
( 81)
P= .039
.5005
( 89)
P= .000
.5810
< 126)
P- .000
.0464
( 143)
P= .582
-.3051
( 57)
P= .021
.6064
( 149)
P= .000
.3585
( 149)-.
P= .'000
.7258
( 145)
P= .000
.5845
( 145)
P= .000
1.0000
( 151)
-.3297
( 149)
P= .000
.1036
( 81)
P= .357
.2779
( 89)
P= .008
.2772
( 126)
P= .002
.0791
( 143)
P= .347
-.2848
( 57)
P= .032
.4408
( 149)
P= .000
.4485
( 149 )Ğ
P= .000
.5146
( 145)
P= .000
.7644
( 145)
P= .000
.7723
( 151)
P= .000
coc
CRC
RAC
RDC
RSC
RVC
CORIFS1
CORIFS2
CORIFS3
CORIFS4
R01C
(Coefficient / (Cases) / 2-tailed Significance)
Ğ . " is printed if a coefficient cannot be computed
E-6
-------
- - Correlation Coefficients - -
RO2C
DSC
NFC
NPC
CDCOUNT
COCOUNT
RACOUNT
RDCOUNT
ROCOUNT
RSCOUNT
RVCOUNT
CORIFS1
CORIFS2
CORIFS3
CORIFS4
RO1C
(Coefficient / (Cases) / 2-tailed Significance)
Ğ . " is printed if a coefficient cannot be computed
RO2C
.4408
( 149)
P= .000
.3920
( 203)
P= .000
.6769
( 201)
P= .000
.7122
( 204)
P= .000
.2350
( 74)
P= .044
.0145
( 204)
P= .837
.1340
( 90)
P= .208
.1853
( 127)
P= .037
.0139
{ 149)
P= .867
-.1007
( 188)
P= .169
-.0103
P= .932
.4485
( 149)
P= .000
.2128
( 203)
P= .002
.4184
( 201)
P= .000
.4681
( 204)
P= .000
.1161
( 74)
P= .324
-.2701
( 204)
P= .000
.0623
( 90)
P= .559
.0100
( 127)
P= .911
-.1149
( 149)
P= .163
-.0976
( 188)
P= .183
.0256
( 71)
P= .832
.5146
( 145)
P= .000
.2986
( 159)
P= .000
.5542
( 159)
P= .000
.5800
( 159)
P= .000
.1307
( 62)
P= .311
.0572
( 159)
P= .474
.2789
( 87)
P= .009
.2765
( 123)
P= .002
.1128
( 145)
P= .177
-.0590
( 152)
P= .470
-.1494
( 56)
P= .272
.7644
( 145)
P= .000
.1567
( 159)
P= .049
.4375
( 159)
P= .000
.4360
( 159)
P= .000
-.0896
( 62)
P= .489
-.2347
( 159)
P= .003
-.0089
( 87)
P= .934
-.0447
( 123)
P= .624
-.1975
( 145)
P= .017
-.0482
( 152)
P= .555
-.1302
( 56)
P= .339
.7723
( 151)
P= .000
.2944
( 151)
P= .000
.4709
{ 151)
P= .000
.5058
( 151)
P= .000
.1423
( 60)
P= .278
-.0028
( 150)
P= .973
.3798
( 89)
P= .000
.4270
( 126)
P= .000
.1606
( 151)
P= .049
.0404
( 143)
P= .632
-.2479
( 57)
P= .063
1.0000
( 151)
P= .
.2022
( 151)
P= .013
.4051
( 151)
P= .000
.4136
( 151)
P= .000
-.0299
( 60)
P= .820
-.2375
( 150)
P= .003
.0216
( 89)
P= .841
.0023
( 126)
P= .979
-.2340
( 151)
P= .004
.0812
( 143)
P= .335
-.2031
( 57)
P= .130
E-7
-------
CORIFS1
- Correlation Coefficients
CORIFS2 CORIFS3 CORIFS4
R01C
R02C
p=
p=
p=
p=
p=
p=
p=
p=
p=
p=
p=
0509
204)
.470
4155
196)
.000
,0181
190)
.805
,1236
190)
.089
.0351
190)
.631
.1101
188)
.133
.0103
190)
.887
.0193
190)
.791
.0405
190)
.579
.1279
191)
.078
.1277
191)
.078
-.2032
( 204)
P= .004
.2311
( 196)
P= .001
-.0500
( 190)
P= .493
-.1411
( 190)
P= .052
.0276
( 190)
P= .705
-.0504
( 188)
P= .492
.0571
{ 190)
P= .434
.0254
( 190)
P= .728
.0730
( 190)
P= .317
-.1578
( 191)
P= .029
-.1600
( 191)
P= .027
-.0052
( 159)
P= .948
.4624
{ 153)
P= .000
.0280
( 149)
P= .735
-.0306
( 149)
P= .711
-.0184
( . 149)
P= .824
-.0337
( 147)
P= .685
-.0206
( 149)
P= .803
.0350
( 149)
P= .671
.1008
( 149)
P= .221
-.1225
( 150)
P= .135
-.1134
( 150)
P= .167
-.1967
( 159)
P= .013
.3529
( 153)
P= .000
-.1471
( 149)
P= .073
.0103
( 149)
P= .901
.0410
( 149)
P= .619
-.0049
( 147)
P= .953
.0772
( 149)
P= .349
.0295
( 149)
P= .721
-.0096
( 149)
P= .908
-.0731
( 150)
P= .374
-.0812
( ISO)
P= .323
.0018
( 151)
P= .983
.5090
( 145)
P= .000
.0419
( 142)
P= .620
-.0807
( 142)
P= .339
-.0323
( 142)
P= .702
-.0262
( 140)
P= .758
.0295
( 142)
P= .728
-.0606
( 142)
P= .473
-.0381
( 142)
P= .653
-.1094
( 143)
P= .193
-.1009
( 143)
P= .230
-.2371
( 151)
P= .003
.4049
( 145)
P= .000
-.0699
< 142)
P= .408
-.0605
( 142)
P= .475
.0100
( 142)
P= .906
.0072
( 140)
P= .933
.0603
( 142)
P= .476
-.0239
( 142)
P= .778
-.0434
( 142)
P= .608
-.1065
{ 143)
P= .205
-.1105
( 143)
P= .189
NUMOPUN
HRS SCOR
PAGED
PAS I AN
PBLACK
PCROWDED
PHISP
PMIN
PNATIVE
POP100
POPDEN
(Coefficient / (Cafe'es) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
E-8
-------
CORIFS1
- Correlation Coefficients -
CORIFS2 CORIFS3 CORIFS4
R01C
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
R02C
POWNER
PRENTER
PUNDER18
P1PAR
REKT
HOUSEVAL
P2BX0002
P2BX0003
P2BX0004
P3BX0001
P4BX0001
.0991
( 188)
P= .176
-.1310
( 188)
P= .073
.0276
( 190)
P= .706
.0548
( 189)
P= .454
-.0003
( 188)
P= .997
-.0331
{ 188)
P= .652
.0141
( 191)
P= .846
-.0628
( 191)
P= .388
-.1229
( 191)
P= .090
-.1001
( 191)
P= .168
-.1147
( 191)
P= .114
.1151
( 188)
P= .116
-.1617
( 188)
P= .027
-.0447
( 190)
P= .540
.1138
( 189)
P= .119
-.0522
( 188)
P= .476
-.0250
( 188)
P= .733
-.1099
( 191)
P= .130
-.0324 .
{ 191)
P= .656
-.0829
{ 19D
P= .254
-.0668
( 191)
P= .358
-.1627
( 191)
P= .024
.0831
( 147)
P= .317
-.0961
( 147)
P= .247
-.0589
( 149)
P= .475
.0591
{ 148)
P= .476
-.0682
( 147)
P= .412
-.1309
( 147)
P= .114
-.0640
( 150)
P= .436
-.0192
( 150)
P= .816
-.0821
( 150)
P= .318
-.0586
( 150)
P= .476
-.1200
( 150)
P= .143
.0947
( 147)
P= .254
-.1273
( 147)
P= .124
-.0635
( 149)
P= .442
.0856
(-.146)
P= .301
.0194
( 147)
P= .815
-.0128
( 147)
P= .877
-.0551
( ISO)
P= .503
-.0133
( ISO)
P= .872
-.0358
( 150)
P= .664
-.0184
( ISO)
P= .823
-.0729
( 150)
P= .375
.0535
( 140)
P= .530
-'."(ill 7
( 140)
P= .400
-.1009
( 142)
P= .232
.0570
( 141)
P= .502
-.0415
( 140)
P= .626
-.0956
( 140)
P= .261
-.0838
( 143)
P- .320
-.0364
( 143)-.
P= .666
-.0733
( 143)
P= .384
-.0184
( 143)
P= .827
-.1006
( 143)
P= .232
.0619
( 140)
?= .467
-.0950
( 140)
P= .264
-.0731
( 142)
P= .387
.0976
( 141)
P= .249
-.0391
( 140)
P= .646
-.0693
( 140)
P= .416
-.0867
( 143)
P= .303
-.0358
( 143 )/
P= .671
-.0401
I 143)
.P= .635
-.0196
( 143)
P- .817
-.1080
( 143)
P= .199
E-9
-------
- - Correlation Coefficients - -
CORIFS1 CORIFS2 CORIFS3 CORIFS4 RO1C RO2C
P4BX0002 -.1308 -.1657 -.1247 -.0911 -.1158 -.1196
( 191} ( 191) ( 150) ( 150) ( 143) ( 143)
P= .071 P= .022 P= .128 P= .268 P= .169 P= .155
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if .a coefficient cannot be computed
E-10
-------
This page is intentionally left blank
E-ll
-------
- - Correlation Coefficients - -
R02C
DSC
NFC
NPC
CDCOUNT
COCOUNT
RACOUNT
RDCOUNT
ROCOUNT
RSCOUNT
RVCOUNT
DSC
NFC
NPC
CDCOUNT
COCOUNT
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
RACOUNT
.2022
( 151)
P= .013
1.0000
( 209)
P= .
.3681
( 205)
P= .000
.4175
( 209)
P= .000
.3159
( 74)
P= .006
.1258
( 204)
P= .073
.2680
( 92)
P= .010
.1620
{ 128)
P= .068
.0945
( 151)
P= .248
.0354
( 192)
P- .626
-.1175'
( 71)
P= .329
.4051
( 151)
P= .000
.3681
( 205)
P= .000
1.0000
( 205)
P= .
.9513
( 205)
P= .000
.1234
( 73)
P= .298
-.0803
( 202)
P= .256
.1827
( 92)
P= .081
.1159
( 128)
P= .192
-.0390
( 151)
P= .634
-.0155
( 191)
P= .831
-.2612
( 70)
P= .029
.4136
( 151)
P= .000
.4175
( 209)
P= .000
.9513
( 205)
P= .000
1.0000
' ( 210)
P= .
.1990
( 74)
P= .089
-.0274
( 205)
P= .697
.2400
( 92)
P= .021
.1621
( 128)
P= .068
-.0043
( 151)
P= .958
.0691
( 192)
P= .341
-.2867
( 72)
P= .015
-.0299
( 60)
P- .820
.3159
( 74)
P- ;006
.1234
( 73)
P= .298
.1990
( 74)
P= .089
1.0000
( 74)
P= .
.0018
( 74)
P= .988
-.0407
( 38)
P= .808
.0416
( 59)
P= .755
.0876
( 60)
P= .506
-.0763
( 73)
P= .521
.0586
( 30)
P- .758
-.2375
( 150)
P- .003
.1258
( 204)
P= .073
-.0803
( 202)
P= .256
-.0274
( 205)
T= .697
.0018
( 74)
P= .988
1.0000
( 205)
P= .
.2745
( 91)
P= .008
.2995
( 128)
P= .001
.8605
( 150)
P= .000
-.0110
( 188)
P= .881
-.0496
( 71)
P= .681
.0216
( 89)
P= .841
.2680
( 92)
P= .010
.1827
( 92)
P= .081
.2400
( 92)
P= .021
-.0407
( 38)
P= .808
.2745
( 91)
P= .008
1.0000
( 92)
P= .
.79t9
( 87)
P= .000
.4578
( 89)
P= .000
-.1038
( 86)
P= .342
-.3078
( 36)
P= .068
E-12
-------
- - correlation Coefficients - -
NUMOPUN
HRS SCOR
PAGED
PAS IAN
PBLACK
PCROWDED
PHISP
PMIN
PNATIVE
POP100
POPDEN
DSC
NFC
NPC
CDCOUNT
COCOUNT
(Coefficient / (Cases) / 2-tailed Significance)
ğ . " is printed if a coefficient cannot be computed
RACOUNT
.1001
( 209)
P= .149
.3634
( 200)
P= .000
-.0056
{ 194)
P= .939
-.1562
( 194)
P= .030
-.1246
( 194)
P= .084
-.0083
( 191)
P= .910
-.0778
( 194)
P=. .281
-.1191
( 194)
P= .098
.0458
( 194)
P= .526
-.2862
( 195)
P= .000
-.2774'
( 195)
P= .000
-.1476
( 205)
P- .035
.4542
( 199)
P= .000
.0304
( 193)
P= .675
-.0721
( 193)
P= .319
-.0325
( 193)
P= .654
-.1068
( 190)
P= .142
.0035
( 193)
P= .962
-.0260'
( 193)
P= .719
.0377
( 193)
P= .603
-.1608
( 194)
P= .025
-.1601
( 194)
. P= .026
-.0629
( 210)
P= .364
.4484
( 200)
P= .000
.0518
( 194)
P= .473
-.1106
( 194)
P= .125
-.0494
( 194)
P= .494
-.1396
( 191)
P= .054
-.0121
( 194)
P= .867
-.0538
( 194)
P= .456
.0273
( 194)
P= .706
-.1979
( 195)
P= .006
. -.1931
( 195)
P= .007
-.0191
( 74)
P- .871
.2241
( 73)
P- .057
-.1126
( 68)
P- .361
.3144
( 68)
P- .009
.1231
( 68)
P= .317
.2424
( 68)
P= .046
.3179
( 68)
P= .008
.1850
( 68)
P= .131
-.0437
( 68)
P= .724
.0905
( 68)
P= .463
.1514
( 68)
P= .218
.9167
( 205)
P= .000
.0744
( 197)
P= .299
.0237
( 191)
P= .745
-.0318
( 191)
T>= .662
-.0363
( 191)
P= .618
.0042
( 188)
P= .954
-.0883
( 191)
P= .224
-.0398
( 191)
P= .585
-.0049
( 191)
. P= .946
-.0495
( 192)
P= .495
-.0386
( 192)
P= .595
.3350
( 92)
P= .001
.1486
( 91)
P= .160
.2320
( 86)
P= .032
-.1827
( 86)
P= .092
-.1372
( 86)
P= .208
-.0523
( 84)
P= .637
-.0547
( 86)
P= .617
-.1683
.( 86)
P= .121
-.0450
( 86)
P= .681
-.0609
( 87)
P= .575
-.0494
( 87)
P= .649
E-13
-------
- - Correlation Coefficients - -
DSC
NFC
NPC
CDCOUNT
COCOUNT
(Coefficient / leases) / 2-tailed Significance)
' . " is printed if a coefficient cannot be computed
RACOUNT
POWNER
PRENTER
PUNDER18
PI PAR
RENT
HOUSEVAL
P2BX0002
P2BX0003
P2BX0004
P3BX0001
P4BX0001
.0552
( 191)
P= .448
-.0504
{ 191)
P= .489
.1921
{ 194)
P= .007
-.0411
( 192)
P= .572
-.1237
( 191)
P= .088
-.2124
( 191)
P= .003
-.2131
( 195)
P= .003
-.2434
( 195)
P= .001
-.1825
( 195)
P= .011
-.1788
( 195)
P= .012
-.2821
( 195)
P= .000
.1637
( 190)
P= .024
-.1895
( 190)
P= .009
.0890
( 193)
P= .218
-.0292
( 191)
P= .688
-.0056
{ 190)
P= .939
.0245
( 190)
P= .737
-.0581
( 194)
P= .421
-.1141
( 194)
P= .113
-.1243
( 194)
P= .084
-.1141
( 194)
P= .113
-.1438
{ 194)
P= .045
.1704
( 191)
P= .018
-.1952
( 191)
P= .007
.0791
( 194)
P= .273
-.0453
( 192)
P= .533
.0049
( 191)
P= .947
-.0129
( 191)
P= .859
-.1093
( 195)
P= .128
-.1545
{ 195)
P= .031
-.1570
( 195)
P= .028
-.1438
( 195)
P= .045
-.1837
( 195)
P= .010
-.1896
( 68)
P- .122
.1896
( 68)
P- .122
-.0147
( 68)
P- .905
.0706
( 68)
P= .567
.1353 .
( 68)
P= .271
.1518
( 68)
P= .217
.2200
( 68)
P= .071
.0712
( 68)
P= .564
.0528
< 68)
P= .669
.3385
( 68)
P= .005
.1570
( 68)
P= .201
-.0908
( 188)
P= .215
.1104
( 188)
P= .131
.0741
( 191)
P= .308
-.0218
( 189)
"P= .766
- -.0633
( 188)
P= .388
-.1754
( 188)
P= .016
.0171
( 192)
P= .814
-.0466
( 192)
P= .521
-.0458
( 192)
P= .528
-.0485
( 192)
P= .504
-.0446
( 192)
P= .539
.0282
( 84)
P= .799
-.0011
( 84)
P= .992
-.0536
( 86)
P= .624
-.0573
( 85)
P= .602
-.1743
( 84)
P= .113
-.1246
( 84)
P= .259
-.0338
( 87)
P= .756
-.0119
( 87)
P= .913
-.0729
{ 87)
P= .502
-.0279
( 87)
P= .798
-.0500
( 87)
P= .646
E-14
-------
- - Correlation Coefficients - -
DSC NFC NPC CDCOUNT COCOUNT RACOUNT
P4BX0002 -.2830 -.1565 -.1862 .0030 -.0462 -.0466
( 195) ( 194) { 195) ( 68) ( 192) ( 8T)
P= .000 P= .029 P= .009 P= .981 P= .524 P= .668
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
E-15
-------
- - Correlation Coefficients - -
COG
CRC
RAG
RDC
RSC
RVC
CORIFS1
CORIFS2
CORIFS3
CORIFS4
RO1C
RDCOUNT
ROCOUNT
RSCOUNT
RVCOUNT
NUtiOPUN
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
HRS SCOR
.2350
( 127)
P= .008
.1083
( 73)
P= .362
.2889
( 87)
P= .007
.5256
{ 128)
P= ".000
-.0776
( 122)
P= .395
-.1448
( 50)
P= .316
.1853
( -127)
P= .037
.0100
( 127)
P= .911
.2765
( 123)
P= .002
-.0447
( 123)
P= .624
.4270
( 126)
P= .000
.2155
( 149)
P= .008
.1844
( 81)
P= .099
.1189
( 89)
P= .267
.1814
( 126)
P= .042
-.0966
( 143)
P= .251
-.0464
( 57)
P= .732
.0139
( 149)
P= .867
-.1149
( 149)
P= .163
.1128
( 145)
P= .177
-.1975
( 145)
P= .017
.1606
( 151)
P= .049
.0576
( 188)
P= .432
.0555
( 104)
P= .576
-.0565
( 86)
P= .605
-.0164
( 122)
P= .858
.3968
( 192)
P= .000
.0776
( 70)
P= .523
-.1007
( 188).
P= .169
-.0976
( 188)
P= .183
-.0590
( 152)
P= .470
-.0482
( 152)
P= .555
.0404
I 143)
P= .632
.1098
( 71)
P= .362
.1381
( 49)
P= .344
-.1221
( 36)
P= .478
-.0328
( 50)
P= .821
-.2687
( 70)
P= .025
.4115
( 72)
P= .000
-.0103
( 71)
P= .932
.0256
( 71)
P= .832
-.1494
( 56)
P= .272
-.1302
( 56)
P= .339
-.2479
( 57)
P= .063
.0669
( 204)
P= .342
.3352
( 105)
P= .000
.0351
( 92)
P= .740
.0210
( 128)
P= .814
-.1335
( 192)
P= .065
-.0100
( 72)
P= .934
-.0509
( 204)
P= .470
-.20312
( 204)
P= .004
-.0052
( 159)
P= .948
-.1967
( 159)
P= .013
.0018
( 151)
P= .983
.1452
( 196)
P= .042
.2682
( 102)
P= .006
.1541
( 91)
P= .145
.4184
{ 127)
P= .000
.2258
( 188)
P= .002
-.1312
( 70)
P= .279
.4155
( 196)
P= .000
.2311
( 196)
P= .001
.4624
' ( 153)
P= .000
.3529
( 153)
P= .000
.5090
( 145)
P= .000
E-16
-------
- - Correlation Coefficients - -
R02C
DSC
NFC
NPC
CDCOUNT
COCOUNT
RACOUNT
RDCOUNT
ROCOUNT
RSCOUNT
RVCOUNT
RDCOUNT
ROCOUNT
RSCOUNT
RVCOUNT
NUMOPUN
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient.cannot be computed
HRS SCOR
.0023
( 126)
P= .979
.1620
( 128)
P= .068
.1159
( 128)
P= .192
.1621
( 128)
P= .068
.0416
( 59)
P= .755
.2995
( 128)
P= .001
.7979
{ 87)
P= .000
1.0000
( 128)
P= .
.4953
( 126)
P= .000
-.0300
( 122)
P= .743
-.2157
( 50)
P= .133
-.2340
( 151)
P= .004
.0945
( 151)
P= .248
-.0330
( 151)
P= .634
-.0043
( 151)
P= .958
.0876
{ 60)
P= .506
.8605
{ 150)
P= .000
.4578
( 89)
P= .000
.4953
( 126)
P= .000
1.0000
( 151)
P= .
-.0484
( 143)
P= .566
-.0312
( 57)
P= .818
.0812
( 143)
P= .335
.0354
( 192)
P= .626
-.0155
( 191)
P= .831
.0691
( 192)
P= .341
-.0763
( 73)
P= .521
-.0110
( 188)
P= .881
-.1038
( 86)
P= .342
-.0300
( 122)
P= .743
-.0484
( 143)
P= .566
1.0000
( 192)
P= .
-.0593
( 70)
P= .626
-.2031
( 57)
P- .130
-.1175
( 71)
P= .329
-.2612
( 70)
P=- .029
-.2867
( 72)
P= .015
.0586
( 30)
P=> .758
-.0496
( 71)
P= .681
-.3078
( 36)
P= .068
-.2157
( 50)
P= .133
-.0312
( 57)
P= .818
-.0593
( 70)
P= .626
1.0000
( 72)
P= .
-.2371
( 151)
P= .003
.1001
{ 209)
P= .149
-.1476
C 205)
P= .035
-.0629
J 210)
P= .364
-.0191
( 74)
P= .871
.9167
{ 205)
P= .000
.3350
( 92)
P= .001
.3794
( 128)
P= .000
.8965
( 151)
P= .000
.0098
( 192)
P= .893
.1311
( 72)
P= .272
.4049
( 145)
P= .000
.3634
( 200)
P= .000
.4542
( 199)
P= .000
.4484
( 200)
P= .000
.2241
( 73)
P= .057
.0744
( 197)
P= .299
.1486
( 91)
P= .160
i
.2643
( 127)
P= .003
.1259
( 145)
P= .131
.1239
{ 188)
P= .090
-.0930
( 70)
P= .444
E-17
-------
- - Correlation Coefficients - -
RDCOUKT
ROCOUNT
RSCOUNT
RVCOUNT
NUMOPUN
HRS SCOR
.3794
( 128)
P= .000
.2643
( 127)
P= .003
.2139
( 119)
P= .020
-.0942
( 119)
P= .308
-.0639
( 119)
P= .490
.0529
( 117)
P= .571
-.0556
( 119)
P= .548
-.0619
( 119)
P= .504
.0124
( 119)
P= .894
.0268
( 120)
P=_ .772
.0664'
( 120)
P= .471
.8965
( 151)
P= .000
.1259
( 145)
P= .131
.1321
( 142)
P= .117
-.0499
( 142)
P= .556
-.0345
( 142)
P= .684
-.0337
( 140)
P- .693
-.0560
( 142)
P= .508
-.0037
( 142)
P= .965
.0684
( 142)
P= .418
-.0287
( 143)
P= .733
-.0234
( 143)
P= .781
.0098
( 192)
P= .893.
.1239
( 188)
P= .090
.0022
( 181)
P= .977
-.0737
( 181)
P= .324
-.1554
( 181)
P= .037
.1225
( 180)
P= .101
-.0501
( 181)
P= .503
-.1120
( 181)
P= .133
.0670
( 181)
P= .370
-.0735
( 181)
P= .325
-.0760
( 181) .
P= .309
.1311
( 72)
P= .272
-.0930
( 70)
P= .444
-.0803
( 69)
P= .512
-.0552
( 69)
P= .652
.1986
( 69)
P= .102
.0379
( 69)
P= .757
-.0147
( 69)
P= .905
.1778
( 69)
P= .144
.0507
( 69)
P= .679
.0728
( 69)
P= .552
.0750
( 69)
P= .540
1.0000
( 210)
P= .
.0673
( 200)
P= .343
-;0813
( 194)
P= .260
.0953
( 194)
P= .186
.0346
( 194)
P= .632
.0419
( 191)
P= .565
-.0112
( 194)
P= .877
.05S6
( 194)
P= .442
.0105
( 194)
P= .885
-.0378
( 195)
P= .600
-.0287
( 195)
P= .691
.0673
{ 200)
P= .343
1.0000
( 200)
P= .
-.0482
( 188)
P= .511
-.0594
( 188)
P= .418
-.0325
( 188)
P= .657
-.0934
( 185)
P= .206
-.0565
{ 188)
P= .441
-.029*7
( 188)
P= .685
.0225
( 188)
P= .760
-.2298
( 189)
P= .001
-.2251
( 189)
P= .002
NUMOPUN
HRS SCOR
PAGED
PAS IAN
PBLACK
PCROWDED
PHISP
PMIN
PNATIVE
POP100
POPDEN
(Coefficient / (Cases) / 2-tailed Significance)
ğ . " is printed if a coefficient cannot be computed
E-18
-------
- - Correlation Coefficients - -
POWNER
PRENTER
PUNDER18
P1PAR
RENT
HOUSEVAL
P2BX0002
P2BX0003
P2BX0004
P3BX0001
P4BX0001
RDCOUNT
ROCOUNT
RSCOUNT
RVCOUNT
NUMOPUN
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
HRS SCOR
-.0366
( 117)
P= .695
P=
P=
P=
P=
P=
P=
P=
0580
117)
.535
0379
119)
.682
0354
118)
.703
1256
117)
.177
1590
117)
.087
0823
120)
.372
1693
120)
.065
-.1019
( 120)
P= .268
.0281
( 120)
P= .761
.0434
( 120)
P= .638
-.0614
( 140)
P= .471
.0813
( 140)
P= .340
-.0856
( 142)
P= .311
-.0429
( 141)
P= .614
-.0455
( 140)
P= .594
-.0380
( 140)
P= .656
.0238
( 143)
P= .778
.0029
( 143)
P- .972
-.0433
{ 143)
P- .607
-.0234
( 143)
P= .782
-.0226
( 143)
P- .789
-.0124
( 180)
P= .869
-.0092
( 180)
P= .902
.0063
( 181)
P= .933
.0076
( 181)
P= .919
-.0720
( 180)
P= .337
-.0653
( 180)
P= .384
-.1573
( 181)
P= .034
-.0151
( 181)
PĞ .840
-.0197
( 181)
P= .792
-.0375
( . 181)
P= .616
-.1071
( 181)
P- .151
-.0715
( 68)
P= .562
.0421
( 69)
P= .731
.0371
( 69)
P= .762
.0876
( 69)
P= .474
.0128
( 68)
P= .918
-.1256
( 69)
P= .304
.2200
( 69)
P= .069
.0569
( 69)
P= .643
-.0610
( 69)
P= .618
-.0266
( 69)
P= .828
.1006
( 69)
P= .411
-.1651
( 191)
P= .022
.1644
( 191)
P= .023
-.0451
( 194)
P= .533
' .0608
( 192)
f- .402
-.0453
( 191)
P= .534
-.1468
( 191)
P= .043
.0170
( 195)
P= .814
-.0274
( 195)
P= .704
-.0420
( 195)
P= .560
-.0343
( 195)
P= .634
-.0326
( 195)
P= .651
.0733
( 185)
P= .322
-.0825
( 185)
P= .264
-.0254
( 188)
P= .729
-.0336
( 186)
P= .649
.0583
( 185)
P= .431
-.0329
( 185)
P= .657
-.1451
( 189)
P= .046
-.198'7
( 189)
P= .006
-.2687
( 189)
P= .000
-.2644
( 189)
P= .000
-.2208
( 189)
P= .002
E-19
-------
- - Correlation Coefficients - -
RDCOUNT ROCOUNT RSCOUNT RVCOUNT NUMOPUN HRS_SCOR
P4BX0002 .0500 -.0231 .0360 .0683 -.0321 -.2082
( 120) ( 143) ( 181) (69) ( 195) ( 189)
P= .588 P= .784 P= .630 P= .577 P= .656 P= .004
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
E-20
-------
- - correlation Coefficients - -
COC
CRC
RAC
RDC
RSC
RVC
CORIFS1
CORIFS2
CORIFS3
CORIFS4
R01C
PAGED
PAS I AN
PBLACK
PCROWDED PHISP
PMIN
.1535
( 190)
P= .034
-.1671
( 97)
P= .102
P-
P=
P=
P=
P=
P=
P=
P=
1294
86)
.235
0075
119)
.936
0160
181)
.830
1495
69)
.220
0181
190)
.805
0500
190)
.493
0280
149)
.735
,1471
149)
.073
.0419'
( 142)
P= .620
-.1072
( 190)
P= .141
-.0366
( 97)
P= .722
-.1410
( 86)
P= .195
.0286
( 119)
P= .757
-.0037
( 181)
P= .961
-.0977
( 69)
P= .425
-.1236
( 190)
P= .089
-.1411
( 190)
P= .052
-.0306
( 149)
P= .711
.0103
( -149)
P= .901
-.0807
( 142)
P= .339
.0000
( 190)
P=1.000
.0202
( 97)
P= .844
.0207
( 86)
P= .850
-.0455
( 119)
P= .623
-.1183
( 181)
P= .113
.0317
( 69)
P= .796
.0351
( 190)
P= .631
.0276
( 190)
P= .705
-.0184
( 149)
P= .824
.0410
( 149)
P= .619
-.0323
( 142)
P= .702
-.1025
( 188)
P- .162
.0482
( 97)
Pğ .639
-.0483
( 84)
P- .663
.0792
( 117)
P- .396
.0588
( 180)
P= .433
.0246
( 69)
P= .841
-.1101
( 188)
P= .133
-.0504
( 188)
P= .492
-.0337
( 147)
P= .685
-.0049
( 147)
P= .953
-.0262
( 140)
P= .758
-.0932
( 190)
P= .201
.1473
( 97)
P= .150
(
P=
(
ĞP=
(
P=
(
P=
(
P=
(
P=
(
P=
1010
86)
.355
0402
119)
.664
0214
181)
.775
0608
69)
.620
0103
190)
.887
0571
190)
.434
,0206
149)
.803
.0772
( 149)
P= .349
(
P=
.0295
142)
.728
-.0144
( 190)
P= .843
.0233
( 97)
P= .821
-.0100
( 86)
P= .927
-.0621
( 119)
P= .503
-.0639
( 181)
P= .393
.0151
( 69)
P= .902
.0193
( 190)
P= .791
.0254
( 190)
P= .728
.0350
( 149)
P= .671
.0295
( 149)
P= .721
-.0606
( 142)
P= .473
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if ..a coefficient cannot be computed
E-21
-------
- - Correlation Coefficients - -
R02C
DSC
NFC
NPC
CDCOUNT
COCOUNT
RACOUNT
RDCOUNT
ROCOUNT
RSCOUNT
RVCOUNT
PAGED
PASIAN
PBLACK
PCROWDED PHlSP
PMIN
-.0699
( 142)
P= .408
-.0056
( 194)
P= .939
P=
P=
P=
P=
P-
P=
P-
P=
P=
0304
193)-
.675
0518
194)
.473
1126
68)
.361
0237
191)
.745
2320
86)
.032
.2139
119)
.020
.1321
142)
.117
.0022
181)
.977.
.0803
69)
.512
-.0605
( 142)
P= .475
-.1562
( 194)
P= .030
-.0721
( 193)
P= .319
-.1106
( 194)
P= .125
.3144
( 68)
P= .009
-.0318
( 191)
P= .662
-.1827
( 86)
P= .092
-.0942
{ 119)
P= .308
-.0499
( 142)
P= .556
-.0737
( 181)
P= .324
-.0552
( 69)
P= .652
.0100
( 142)
P= .906
-.1246
( 194)
P= .084
-.0325
( 193)
P= .654
-.0494
( 194)
P= .494
.1231
( 68)
P= .317
-.0363
( 191)
P= .618
-.1372
( 86)
P= .208
-.0639
( 119)
P- .490
-.0345
( 142)
P= .684
-.1554
( 181)
P= .037
.1986
( 69)
P= .102
.0072
( 140)
P= .933
-.0083
( 191)
P= .910
-.1068
( 190)
P= .142
-.1396
( 191)
P= .054
.2424
( 68)
P= .046
.0042
( 188)
P= .954
-.0523
( 84)
P= .637
.0529
( 117)
P= .571
-.0337
( 140)
P= .693
.1225
( 180)
P= .101
.0379
( 69)
P= .757
.0603
( 142)
P- .476
-.0778
( 194)
P= .281
.0035
( 193)
P= .962
-.0121
( 194)
*P= .867
.3179
( 68)
P= .008
-.0883
( 191)
P= .224
-.0547
( 86)
P= .617
-.0556
( 119)
P- .548
-.0560
( 142)
P= .508
-.0501
( 181)
P= .503
-.0147
( 69)
P= .905
-.0239
( 142)
P= .778
-.1191
( 194)
P= .098
-.0260
( 193)
P= .719
-.0538
( 194)
P= .456
.1850
( 68)
P= .131
-.0398
( 191)
P= .585
-.1683
( 86)
P= .121
-.oeig
( 119)
P= .504
-.0037
( 142)
P= .965
-.1120
( 181)
P= .133
.1778
( 69)
P= .144
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
E-22
-------
PAGED
- Correlation Coefficients -
PASIAN PBLACK PCROWDED
PHISP
PMIN
-.0813
( -194)
P- .260
-.0482
( 188)
P= .511
1.0000
( 194)
P- .
-.2330
( 194)
P= .001
-.1782
( 194)
P= .013
-.1457
( 191)
P= .044
-.1840
( 194)
P= .010
-.2196
( 194)
P- .002
-.0351
( 194)
P= .627
.1140
( 194)
P= .113
.1203
( 194)
P= .095
.0953
( 194)
P= .186
-.0594
( 188)
P- .418
-.2330
( 194)
P= .001
1.0000
( 194)
P= .
.0311
( 194)
P= .667
.0991
( 191)
P= .173
.0873
( 194)
P= .226
.2192
( 194)
P= .002
-.0670
( 194)
P= .353
.3129
( 194)
P= .000
.3113
( 194)
P= .000
.0346
( 194)
P= .632
-.0325
( 188)
P= .657
-.1782
( 194)
P- .013
.0311
( 194)
P= .667
1.0000
( 194)
P= .
.4534
( 191)
P= .000
.4757
( 194)
P= .000
.8626
( 194)
P= .000
.0181
( 194)
P= .802
.1788
( 194)
P= .013
.1787
( 194)
P= .013
.0419
( 191)
P- .565
-.0934
( 185)
P=. .206
-.1457
( 191)
P- .044
.0991
( 191)
P= .173
.4534
( 191)
P= .000
1.0000
( 191)
P= .
.4773
( 191)
P= .000
.5817
( 191)
P= .000
.3781
( 191)
P= .000
.2711
( 191)
P= .000
.2703
( 191)
P= .000
-.0112
( 194)
P= .877
-.0565
( 188)
P= .441
-.1840
( 194)
P= .010
.0873
( 194)
*P= .226
.4757
( 194)
P= .000
.4773
( 191)
P= .000
1.0000
( 194)
P= .
.4065
( 194)
P= .000
-.0470
( 194)
P= .516
.2809
( 194)
P= .000
.2815
( 194)
P= .000
.0556
( 194)
P= .442
-.0297
( 188)
P= .685
-.2196
( 194)
P- .002
.2192
( 194)
P= .002
.8626
( 194)
P= .000
.5817
( 191)
P= .000
.4005
( 194)
P= .000
1.0060
( 194)
P= .
.4693
( 194)
P= .000
.1959
( 194)
P= .006
.1946
( 194)
P= .007
NUMOPUN
HRS SCOR
PAGED
PAS I AN
PBLACK
PCROWDED
PHISP
PMIN
PNATIVE
POP100
POPDEN
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
E-23
-------
- - Correlation Coefficients - -
PAGED
PAS I AN
PBLACK
PCROWDED PHISP
PMIN
POWNER
PRENTER
PUNDER18
PI PAR
RENT
HOUSEVAL
P2BX0002
P2BX0003
P2BX0004
P3BX0001
P4BX0001
p-
p=
""*
p=
p=
p=
p=
p=
p=
p=
p=
p=
0956
191)
.183
0384
191)
.598
2473
194)
.001
2037
192)
.005
1472
191)
.042
.1465
191)
.043
.0064
194)
.929
.0041
194)
.955
.0191
194)
.791
.0170
194)
.814
.0820
194)
.256
-.2389
( 191)
P- .001
.2642
( 191)
P= .000
-.2224
( 194)
P= .002
-.1210
( 192)
P= .095
.4432
( 191)
P= .000
.3845
( 191)
P= .000
.0758
( 194)
P= .294
.1529
( 194)
P- .033
.3894
( 194)
P= .000
.2869
( 194)
P= .000
.3093
( 194)
P= .000
-.4542
( 191)
P- .000
.4001
( 191)
P= .000
-.0064
(194)
P= .929
.5917
( 192)
P= .000
.0555
( 191)
P= .446
-.0530
( 191)
P= .466
.6472
{ 194)
P= .000
.3034
( 194)
P= .000
.0202
( 194)
P= .780
.1143
( 194)
P= .113
.2445
( 194)
P= .001
-.2542
( ISO)
P- .000
.2580
< 191)
P= .000
.1768
( 191)
P= .014
.7126
{ 191)
P= .000
-.0671
( ISO)
P= .357
-.1448
( 191)
P= .046
.3049
( 191)
P= .000
.5050
< 191)
P- .000
.2296
( 151)
P= .001
.3751
{ 151)
P= .000
.3160
( 151)
P= .000
-.4718
( 191)
P- .000
.4241
( 191)
P= .000
-.1360
( 194)
P= .059
.7040
( 192)
1>= .000
.1364
( 191)
P= .060
.0750
( 191)
P= .302
.1961
{ 194)
P= .006
.2ll4
( 194)
P= .003
.2011
( 194)
P= .005
.4335
( 194)
P= .000
.3283
( 194)
P= .000
-.3981
( 191)
P= .000
.3473
( 191)
P= .000
.0134
( 194)
P= .853
.6053
( 192)
P= .000
.0643
( 191)
P= .373
-.0263
( 191)
P= .718
.5537
( 194)
P= .000
.43$!
( 194)
P= .000
.0948
' ( 194)
P= .189
.1495
( 194)
P= .037
.2485
. ( 194)
P= .000
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
E-24
-------
- - correlation Coefficients - -
PAGED PASIAN . PBLACK PCROWDED PHISP PMIN
P4BX0002 .2118 .2608 .1310 .2172 .2283 .1451
( 194) ( 194} { 194) ( 191) ( 194) ( 194)
P= .003 P- .000 P= .069 P= .003 P= .001 P= .044
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
E-25
-------
- - Correlation Coefficients - -
COG
CRC
RAG
RDC
RSC
RVC
CORIFS1
CORIFS2
CORIFS3
CORIFS4
R01C
PKATIVE
POP100
POPDEN
POWNER
PRENTER
{Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
PUNDER18
p=
p=
p=
p=
p=
p=
I
p=
p=
p=
p=
p=
,0225
190)
.758
,0224
97)
.828
.0210
86)
.848
.0514
119)
.578
.0766
181)
.306
.0291
69)
.812
.0403.
190)
.579
.0730
190)
.317
.1003
149)
.221
.0096
149)
.903
.0381
142)
.653
-.0480
( 191)
P= .510
-.0422
( 97)
P= .682
.0753
( 87)
P= .488
.0110
( 120)
P= .90S
-.0813
( 181)
P= .277
.1223
( 69)
P= .317
-.1279
I 191)
P= ,078
-.1578
( 191)
P= .029
-.1225
( 150)
P= .135
-.0731
{ 150)
P= .374
-.1094
( 143)
P= .193
-.0311
< 191)
P= .669
-.0354
( 97)
P= .730
.0887
( 87)
P= .414
.0590
( 120)
P= .522
-.0820
( 181)
P= .273
.1271
( 69)
P= .298
-.1277
( 191)
P= .078
-.1600
( 191)
P= .027
-.1134
( ISO)
P= .167
-.0812
< 150)
P= .323
-.1009
( . 143)
P= .230
-.0149
( 138)
P= .840
-.0035
( 96)
P= .973
.0214
( 84)
P= .847
-.0494
( 117)
P= .597
-.0103
( 180)
P= .891
-.0710
( 68)
P= .565
.0991
( 188)
P= .176
.1151
( 138)
P= .116
.0831
( 147)
P= .317
.0947
( 147)
P= .254
.0535
( 140)
P= .530
.0170
( 188)
P- .817
-.0107
( 97)
P- .917
-.0395
< 84)
P- .721
' .0709
( 117)
P- .447
.0048
( 180)
P= .949
.0691
( 69)
P= .573
-.1310
( 188)
P= .073
-.1617
( 188)
P= .027
-.0961
( 147)
P= .247
-.1273
{ 147)
P= .124
-.0717
( 140)
P= .400
.0279
( 190)
P= .702
.1375
( 97)
P= .179
-.0583
( 86)
P= .594
-.0453
< 119)
P= .625
.JD254
P= .734
.0502
( 69)
P= .682
.0276
( 190)
P= .706
-.0447
{ 190)
P= .540
-.0589
( 149)
P= .475
-.0635
{ 149)
P= .442
. -.1009
< 142)
P= .232
E-26
-------
- - Correlation Coefficients - -
R02C
DSC
NFC
NPC
CDCOUNT
COCOUNT
RACOUNT
RDCOUNT
ROCOUNT
RSCOUNT
RVCOUNT
PNATIVE
POP100
POPDEN
POWNER
PRENTER
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
PUNDER18
-.0434
( 142)
P= .608
.0458
( 194)
P= .526
P=
P=
P=
P=
P=
P=
P=
P=
P=
0377
193)
.603
0273
194)
.706
0437
68)
.724
0049
191)
.946
,0450
86)
.681
.0124
119)
.894
.0684
142)
.418
.0670
181)
.370
.0507
69)
.679
-.1065
( 143)
P- .205
-.2862
{ 195)
P= .000
-.1608
( 194)
P= .025
-.1979
( 195)
P= .006
.0905
( 63)
P- .463
-.0495
{ 1S2)
P=- .495
-.0609
( 87)
P= .575
.0268
( 120)
P= .772
-.0237
( 143)
P= .733
-.0735
( 181)
P= .325
.0723
( c = )
P= .552
-.1105
( 143)
P= .189
-.2774
( 195)
P= .000
-.1601
( 194)
P= .026
-.1931
( 195)
P= .007
.1514
( 68)
P= .218
-.0386
( 192)
P= .595
-.0494
( 87)
P= .649
.0664
( 120)
P= .471
-.0234
( 143)
P= .781
-.0760
( 181)
P= .309
.0750
( 69)
P= .540
.0619
( 140)
P= .467
.0552
( 191)
P= .448
.1637
( -190)
P= .024
.1704
( 191)
P= .018
-.1896
( 68)
P= .122
-.0908
( 188)
P= .215
.0282
( 84)
P= .799
-.0366
( 117)
P= .695
-.0614
( 140)
P= .471
-.0124
( 180)
P= .869
-.0715
( 68)
P= .562
-.0950
( 140)
P= .264
-.0504
( 191)
P= .489
-.1895
( 190)
P= .009
-.1952
( 191)
^>= .007
.1896
{ 68)
P= .122
.1104
( 188)
P= .131
-.0011
( 84)
P= .992
.0580
( 117)
P= .535
.0813
( 140)
P= .340
-.0092
( 180)
P= .902
.0421
( 69)
P= .731
-.0731
( 142)
P= .387
.1921
( 194)
P= .007
.0890
( 193)
P= .218
.0791
( 194)
P= .273
-.0147
( 68)
P= .905
.0741
( 191)
P= .308
-.0536
( 86)
P= .624
-.0319
( 119)
P= .682
-.0856
( 142)
P= .311
.0063
( 181)
P= .933
.0371
( 69)
P= .762
E-27
-------
PNATIVE
- Correlation Coefficients
POP100 POPDEN POWNER
PRENTER
PUNDER18
NtJMOPUN
HRS SCOR
PAGED
PAS IAN
PBLACK
PCROWDED
PHISP
PMIN
PNATIVE
POP100
POPDEN
p=
p=
p=
p=
p=
p=
p=
0105
194)
.885
0225
188)
.760
0351
194)
.627
0670
194)
.353
0181
194)
.802
3781
191)
.000
,0470
194)
.516
.4693
( 194)
P= .000
1.0000
( 194)
P= .
P=
P=
.0550
194)
.446
.0568
194)
.431
-.0378
( 195)
P= .600
-.2298
( 189)
P= .001
.1140
( 194)
P= .113
.3129
( 194)
P= .000
.1788
( 194)
P= .013
.2711
( 191)
P= .000
.2809
( 194)
P= .000
.1959
( 194)
P= .006
-.0550
( 194)
P= .446
1.0000
( 195)
P= .
.9953
{ 195)
P= .000
-.0287
( 195)
P= .691
-.2251
( 189)
P= .002
.1203
( 194)
P= .095
.3113
( 194)
P= .000
.1787
( 194)
P= .013
.2703
( 191)
P= .000
.2815
( 194)
P= .000
.1946
( 194)
P= .007
-.0568
( 194)
P= .431
.9953
( 195)
P= .000
1.0000
( 195)
P= .
-.1651
( 191)
P= .022
.0733
{ 185)
P= .322
.0956
( 191)
P= .188
-.2389
( 191)
P= .001
-.4542
( 191)
P= .000
-.2542
( 190)
P= .000
-.4718
{ 191)
P= .000
-.3981
{ 191)
P= .000
.0675
( 191)
P= .354
-.3420
( 191)
P= .000.
-.3508
( 191)
P= .000
.1644
( 191)
P= .023
-.0825
( 185)
P= .264
-.0384
( 191)
P= .598
' .2642
( 191)
1?= .000
.4001
{ 191)
P= .000
.2580
( 191)
P= .000
.4241
( 191)
P= .000
.3473
( 191)
P= .000
-;0658
( 191)
P= .366
.3649
( 191)
P= .000
.3745
( 191)
P= .000
-.0451
( 194)
P= .533
-.0254
( 188)
P= .729
-.2473
( 194)
P= .001
-.2224
( 194)
P= .002
-.0064
( 194)
P= .929
.1768
( 191)
P= .014
-.1360
( 194)
P= .059
.01 3*4
( 194)
P= .853
.1466
( 194)
P= .041
-.1467
( 194)
P= .041
-.1500
( 194)
P= .037
(Coefficient / (Cases) / 2-taiied Significance)
" . " is printed if a coefficient cannot be computed
E-28
-------
- - Correlation Coefficients - -
PNATIVE
POP100
POPDEN
POWNER
PRENTER
PUNDER18
t
(
p=
_
(
p=
(
p=
(
p=
_
(
p=
(
p=
_
(
p=
0675
191)
.354
0658
191)
.366
1466
194)
.041
2630
192)
.000
1335
191)
.066
1135
191)
.118
,0248
194)
.732
.3150
(
P=
_
(
P=
_
(
P=
_
(
P=3
194)
.000
.0206
194)
.775
.0248
194)
.731
.0599
194)
.407
-.3420
( 191)
P= .000
.3649
{ 191)
P= .000
-.1467
( 194)
P= .041
.0131
( 192)
P= .856
.2100
( 191)
P= .004
.1788
( 191)
P= .013
.4696
( 195)
P= .000
.6577
( 195)
P= .000
.8356
( 195)
P= .000
.8528
( 195)
P= .000
.9823
( 195)
P= .000
-.3508
( 191)
P= .000
.3745
( 191)
P= .000
-.1500
( 194)
P= .037
.0168
( 192)
P= .817
.2109
( 191)
P= .003.
.1707
( 191)
P= .018
.4701
( 195)
P= .000
.6506
( 195)
P= .000
.8265
( 195)
P= .000
.8478
( 195)
P= .000
.9808
( 195)
P= .000
1.0000
( 191)
P= .
-1.0000
( 190)
P= .000
.1020
( 191)
P= .160
-.3606
( 191)
P= .000
-.0283
( 191)
P= .698
.1022
( 190)
P= .161
-.3399
( 191)
P= .000
-.2926
( 191)
P= .000
-.2255
( 191)
P= .002
-.3185
( 191)
P= .000
-.3654
{ 191)
P= .000
-1.0000
( 190)
P= .000
1.0000
( 191)
P= .
-.0508
( 191)
P= .485
.2928
( 191)
"p= .000
.0099
( 190)
P= .892
-.0946
( 191)
P= .193
.3544
( 191)
P= .000
.3li6
( 191)
P= .000
.2366
( 191)
P= .001
.3310
( 191)
P= .000
.3911
( 191)
P= .000
.1020
( 191)
P= .160
-.0508
( 191)
P= .485
1.0000
( 194)
P= .
-.0383
( 192)
P= .598
-.2188
( 191)
P= .002
-.2422
( 191)
P= .001
.0154
( 194)
P= .831
-.0315
( 194)
P= .663
-.0881
' ( 194)
P= .222
-.0418
( 194)
P= .563
-.0840
( 194)
P= .244
POWNER
PRENTER
PUNDER18
P1PAR
RENT
HOUSEVAL
P2BX0002
P2BX0003
P2BX0004
P3BX0001
P4BX0001
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
E-29
-------
- - Correlation Coefficients - -
PNATIVE POP100 POPDEN POWNER PREMIER PUNDER18
P4BX0002 -.0521 .9799 .9747 -.3087 .3302 -.1910
( 194) ( 195) ( 195) ( 191) { 191) { 194)
P= .471 P= .000. P= .000 P= .000 P= .000 P= .008
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
E-30
-------
PI PAR
Correlation Coefficients
RENT HOUSEVAL P2BX0002
P2BX0003 P2BX0004
COG
CRC
RAC
RDC
RSC
RVC
CORIFS1
CORIFS2
CORIFS3
CORIFS4
R01C
-.0264
( 189)
P= .718
.0713
( 97)
P= .488
.0836
( 85)
P= .447
-.0241
( 118)
P= .795
.0109
( 181)
P= .884
.0997
( 69)
P= .415
.0548
( 189)
P= .454
.1138
( 189)
P= .119
.0591
( 148)
P= .476
.0856
( 148)
P= .301
.0570
( 141)
P= .502
-.0147
( 188)
P= .841
-.1168
( 96)
P= .257
-.0397
( 84)
P= .720
.0169
( in)
P= .856
-.0055
( 180)
P= .942
-.1254
( 68)
P= .308
-.0003
( 188)
P= .997
-.0522
( 188)
P= .476
-.0682
( 147)
P= .412
.0194
( 147)
P= .815
-.0415
( 140)
P= .626
-.0892
( 188)
P= .224
-.1592
( 97)
P= .119
-.0946
( 84)
P= .392
-.0143
( 117)
P= .879
-.1044
( 180)
P= .163
-.2540
( 69)
P= .035
-.0331
( 188)
P= .652
-.0250
( 188)
P= .733
-.1309
( 147)
P= .114
-.0128
( 147)
P= .877
-.0956
( 140)
P= .261
.1103
( 191)
P= .129
.0928
( 97)
P= .366
.0776
( 87)
P= .475
.1363
( 120)
P= .138
-.1662
( 181)
P= .025
.1272
( 69)
P= .298
.0141
( 191)
P= .846
-.1099
( 191)
P= .130
-.0640
( 150)
P= .436
-.0551
( 150)
P= .503
-.0838
( 143)
P= .320
-.0852
{ 191)
-P= .241
.0089
( 97)
P= .931
.1315
( 87)
P= .225
.1085
( 120)
*P= .238
-.0677
( 181)
P= .365
.0948
( 69)
P= .438
-.0628
( 191)
P= .388
-.0324
( 191)
P= .656
-.0192
( 150)
P= .816
-.0133
( 150)
. P= .872
-.0364
( 143)
P= .666
-.1101
( 191)
P= .130
-.0905
( 97)
P= .378
.0199
( 87)
P= .855
-.0963
( 120)
P= .296
-.0388
( 181)
P= .604
.0172
( 69)
P= .888
-.1229
( 191)
P= .090
-.0829
( 191)
P= .254
-.0821
( 150)
P= .318
-.0358
( 150)
P= .664
-.0733
( 143)
P= .384
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
E-31
-------
Pi PAR
- Correlation Coefficients -
RENT HOUSEVAL P2BX0002
P2B'X0003 P2BX0004
RO2C
DSC
NFC
NPC
CDCOUNT
COCOUNT
RACOUNT
RDCOUNT
ROCOUNT
RSCOUNT
RVCOUNT
p=
p=
p=
p=
p=
p=
p=
p=
p=
p=
p=
0976
141)
.249
0411
192)
.572
0292
191)
.688
,0453
192)
.533
.0706
68)
.567
.0218
189)
.766
.0573
85)
.602
.0354
118)
.703
.0429
141)
.614
.0076
181)
.919
.0876
69)
.474
-.0391
( 140)
P=ğ .646
-.1237
( 191)
P= .088
-.0056
( 190)
P= .939
.0049
< 191)
P= .947
.1353
( 68)
P= .271
-.0633
( 188)
P= .388
-.1743
{ 84)
P= .113
-..1256
( 117)
P= .177
-.0455
( 140)
P= .594
-.0720
( 180)
P= .337
.0128
( 68)
P= .918
-.0693
{ 140)
P= .416
-.2124
( 191)
P= .003
.0245
( 190)
P= .737
-.0129
( 191)
P= .859
.1518
( 68)
P= .217
-.1754
< 188)
P= .016
-.1246
( 84)
P= .259
-.1590
( 117)
P= .087
-.0380
( 140)
P= .656
-.0653
( 180)
P= .384
-.1256
( 69)
P= .304
-.0867
( 143)
P= .303
-.2131
( 195)
P= .003
-.0581
( 194)
P= .421
-.1093
( 195)
P= .128
.2200
( 68)
P= .071
.0171
< 192)
P= .814
-.0338
( 87)
P= .756
.0823
( 120)
P= .372
.0233
1 143)
P= .778
-.1573
! 181)
P= .034
.2200
( 69)
P= .069
-.0358
{ 143)
P= .671
-.2434
{ 195)
P= .001
-.1141
( 194)
P= .113
-.1545
J 195)
P= .031
.0712
( 68)
P= .564
-.0466
( 192)
P= .521
-.0119
( 87)
P= .913
.1693
( 120)
P= .065
.0029
( 143)
P= .972
-.0151
< 181)
P= .840
.0569
( 69)
P= .643
-.0401
( 143)
P= .635
-.1825
( 195)
P= .011
-.1243
( 194)
P= .084
-.1570
( 195)
P= .028
.0528
( 68)
P= .669
-.0458
( 192)
P= .528
-.0729
{ 87)
P= .502
-.1019
( 120)
P= .268
-.0433
( 143)
P= .607
-.0197
( 181)
P= .792
-.0610
( 69)
P= .618
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
E-32
-------
- - Correlation Coefficients - -
PI PAR
RENT
HOUSEVAL P2BX0002 P2BX0003 P2BX0004
.0608
( 192)
P= .402
-.0336.
( 186)
P= .649
-.2037
( 192)
P= .005
-.1210
( 192)
P= .095
.5917
( 192)
P= .000
.7126
( 191)
P= .000
.7040
( 192)
P= .000
.6053
( 192)
P= .000
.2630
( 192)
P= .000
.0131
( 192)
P= .856
.0168
( 192)
P= .817
-.0453
( 191)
P=ğ .534
.0583
( 185)
P=> .431
-.1472
( 191)
P" .042
.4432
( 191)
P= .000
.0555
( 191)
P= .446
-.0671
( 190)
P= .357
.1364
( 191)
P= .060
.0648
{ 191)
P= .373
-.1335
( 191)
P= .066
.2100
( 191)
P= .004
.2109
( 191)
P= .003
-.1468
( 191)
P= .043
-.0329
( 185)
P= .657
-.1465
( 191)
P= .043
.3845
( 191)
P= .000
-.0530
( 191)
P= .466
-.1448
( 191)
P= .046
.0750
( 191)
P= .302
-.0263
( 191)
P= .718
-.1135
( 191)
P= .118
.1788
( 191)
P= .013
.1707
( 191)
P= .018
.0170
( 195)
P= .314
-.1451
( . 189)
P= .046
.0064
( 194)
P= .929
.0758
( 194)
P= .294
.6472
( 194)
P= .000
.2049
( 191)
P= .000
.1961
( 194)
P= .006
.E537
( 194)
P= .000
-.0248
( 194)
P= .732
.4696
( 195)
P= .000
.4701
( 195)
P= .000
-.0274
( 195)
P=- .704
-.1987
( 189)
P= .006
.0041
( 194)
P= .955
.1529
J 194)
P= .033
.3034
( 194)
P= .000
.5050
( 191)
P= .000
.2114
( 194)
P= .003
.4391
( 194)
P= .000
.3150
( 194)
P= .000
.6577
( 195)
P= .000
.6506
( 195)
P= .000
-.0420
( 195)
P= .560
-.2687
{ 189)
P= .000
.0191
( 194)
P= .791
.3894
( 194)
P= .000
.0202
( 194)
P= .780
.2296
( 191)
P= .001
.2011
( 194)
P= .005
.0948
( 194)
P= .189
-.0206
{ 194)
P= .775
.8356
( 195)
P= .000
.8265
( 195)
P= .000
NUMOPUN
HRS SCOR
PAGED
PAS I AN
PBLACK
PCROWDED
PHISP
PMIN
PNATIVE
POP100
POPDEN
(Coefficient / (Cases) / 2-tailed Significance)
11 . " is printed if a coefficient cannot be computed
E-33
-------
P1PAR
- Correlation Coefficients -
RENT HOUSEVAL P2BX0002
P2BX0003 P2BX0004
POWNER
PRENTER
PUNDER18
P1PAR
RENT
HOUSEVA1
P2BX0002
P2BX0003
P2BX0004
P3BX0001
P4BX0001
-.3606
( 191)
P= .000
.2928
( 191)
P= .000
-.0383
( 192)
P= .598
1.0000
( 192)
P= .
-.1078
( 191)
P= .138
-.4038
( 191)
P= .000
.1891
( 192)
P= .009
.1847
( 192)
P= .010
-.0106
( 192)
P- .884
.0886
( 192)
P= .222
.0454
( 192)
P= .532
-.0283
{ 191)
P- .698
.0099
( 190)
P= .892
-.2188
( 191)
P= .002
-.1078
( 191)
P= .138
1.0000
( 191)
P= .
.6462
( 190)
P= .000
.0429
( 191)
P- .556
.0529
( 191)
P= .467
.0955
( 191)
P= .189
.0644
( 191)
P= .376
.2216
( 191)
P= .002
.1022
( 190)
P= .161
-.0946
( 191)
P= .193
-.2422
( 191)
P= .001
-.4038
( 191)
P= .000
.6462
( 190)
P= .000
1.0000
( 191)
P= .
.0357
( 191)
P= .624
-.0030
( 191)
P= .967
.1166
( 191)
P= .108
.0779
( 191)
P= .284
.1727
( 191)
P- .017
-.3399
( 191)
P- .000
.3544
( 191)
P= .000
.0154
( 194)
P= .831
.1891
( 192)
P= .009
.0429
( 191)
P= .556
.0357
( 191)
P= .624
1.0000
( 195)
P= .
.4851
( 195)
P= .000
.1721
( 195)
P= .016
.2998
( 195)
P= .000
.5575
( 195)
P= .000
-.2926
( 191)
P= .000
.3116
( 191)
P= .000
-.0315
( 194)
P= .663
' .1847
L 192)
P= .010
.0529
( 191)
P= .467
-.0030
( 191)
P= .967
.4851
( 195)
P= .000
1.0000
( 195)
P= .
.5682
( 195)
P= .000
.6115
( . 195)
P= .000
.6633
( 195)
P= .000
-.2255
( 191)
P= .002
.2366
( 191)
P= .001
-.0881
( 194)
P= .222
-.0106
( 192)
P= .884
.0955
( 191)
P= .189
.1166
( 191)
P= .108
.1721
( 195)
P= .016
.5682
( 195)
P= .000
1.0000
( 195)
P= .
.8855
{ 195)
P= .000
.7720
{ 195)
P= .000
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
E-34
-------
- - Correlation Coefficients - -
P1PAR RENT HOUSEVAL P2BX0002 P2BX0003 P2BX0004
P4BX0002 -.0118 .1807 .1743 .4238 .6148 .7923
( 192) ( 191) ( 191) ( 195) ( 195) ( 195)
P- .871 P= .012 P= '.016 P- .000 P= .000 P= .000
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
E-35
-------
COG
CRC
RAC
RDC
RSC
RVC
CORIFS1
CORIFS2
CORIFS3
CORIFS4
RO1C
P3BX0001
-.1011
( 191)
P= .164
.0023
( 97)
P= .983
.0928
( 87)
P= .393
.1816
( 120)
P= .047
-.0684
( 181)
P= .360
.0363
( 69)
P= '.767
-.1001
( 191)
P= .168
-.0668
{ 191)
P= .358
-.0586
( 150)
P- .476
-.0184
( ISO)
P= .823
-.0184
( 143)
P= .827
Correlation Coefficients
P4BX0001 P4BX0002
-.0296
{ 191)
P= .685
-.0110
( 97)
P= .915
.0992
( 87)
P= .361
.0554
( 120)
P= .548
-.0984
( 181)
P= .188
.1301
( 69)
P= .287
-.1147
{ 191)
P- .114
-.1627
( 191)
P= .024
-.1200
( 150)
P= .143
-.0729
( 150)
P= .375
-.1006
{ 143)
P= .232
-.0293
( 191)
P= .687
-.0476
( 97)
P- .644
.0973
{ 87)
P= .370
-.0134
( 120)
P= .885
-.0634
( 181)
P= .397
.1473
( 69)
P= .227
-.1308
( 191)
P= .071
-.1657
( 191)
P= .022
-.1247
( 150)
P= .128
-.0911
( 150)
P= .268
-.1158
( 143)
P= .169
(Coefficient / (Cases) / 2-tailed Significance)
11 . " is printed if a coefficient cannot be computed
E-36
-------
R02C
DSC
NFC
NPC
CDCOUNT
COCOUNT
RACOUNT
RDCOUNT
ROCOUNT
RSCOUNT
RVCOUNT
P3BX0001
-.0196
( 143)
P= .817
-.1788
( 195)
P= .012
-.1141
( 194)
P= .113
-.1438
( 195)
P= .045
.3385
( 68)
P= .005
-.0485
( 192)
P= .504
-.0279
( 87)
P= .798
.0281
( 120)
P- .761
-.0234
( 143)
P= .782
-.0375
( 181)
P= .616
-.0266
( 69)
P= .828
Correlation Coefficients
P4BX0001 P4BX0002
-.1080
( 143)
P= .199
-.2821
( 195}
P= .000
-.1438
( 194)
P= .045
-.1837
( 195)
P= .010
.1570
( 68)
P= .201
-.0446
( 192)
P= .539
-.0500
( 87)
P= .646
.0434
( 120)
P- .638
-.0226
( 143)
P= .789
-.1071
( 181)
P= .151
.1006
( 69)
P- .411
-.1196
( 143)
P= .155
-.2830
( 195)
P= .000
-.1565
( 194)
P= .029
-.1862
( 195)
P= .009
.0030
( 68)
P= .981
-.0462
( 192)
P= .524
-.0466
( 87)
P= .668
.0500
( 120)
P= .588
-.0231
( 143)
P= .784
-.0360
( 181)
P= .630
.0683
( 69)
P= .577
(Coefficient / (Cases) / 2-tailed Significance)
" . "is printed if a coefficient cannot be computed
E-37
-------
- - Correlation Coefficients - -
P3BX0001 P4BX0001 P4BX0002
NUMOPUN
HRS SCOR
PAGED
PAS IAN
PBLACK
PCROWDED
PHISP
PMIN
PNATIVE
POP100
POPDEN
-.0343
( 195)
P= .634
-.2644
( 189)
P= .000
.0170
( 194)
P= .814
.2869
( 194)
P= .000
.1143
( 194)
P= .113
.3751
( 191)
P= .000
.4335
( 194)
P= .000
.1495
( 194)
P= .037
-.0248
( 194)
P= .731
.8528
( 195)
P= .000
.8478
( 195)
P= .000
-.0326
( 195)
P= .651
-.2208
( 189)
P=Ğ .002
.0820
( 194)
P= .256
.3093
( 194)
P= .000
.2445
( 194)
P= .001
.3160
( 191)
P= .000
.3283
( 194)
P- .000
.2485
{ 194)
P= .000
-.0599
( 194)
P= .407
.9823
( 195)
P= .000
.9808
( 195)
P= .000
-.0321
( 195)
P= .656
-.2082
( 189)
P= .004
.2118
{ 194)
P= .003
.2608
C 194)
P= .000
.1310
( 194)
P= .069
.2172
( 191)
P= .003
.2283
< 194)
P= .001
.1451
( 194)
P= .044
-.0521
( 194)
P= .471
.9799
< 195)
P= .000
.9747
( 195)
P= .000
(Coefficient / {Cases) / 2-tailed Significance)
" . " is printed -if a coefficient cannot be computed
E-38
-------
POWNER
PRENTER
PUNDER18
PI PAR
RENT
HOUSEVAL
P2BX0002
P2BX0003
P2BX0004
P3BX0001
P4BX0001
P3BX0001
-.3185
( 191)
P= .000
.3310
( 191)
P= .000
-.0418
( 194)
P= .563
.0886
( 192)
P= .222
.0644
( 191)
P- .376
.0779
( 191)
P= .284
.2998
( 195)
P" .000
.6115
( 195)
P= .000
.8855
( 195)
P= .000
1.0000
( 195)
P= .
.8448
( 195)
P- .000
Correlation Coefficients
P4BX0001 P4BX0002
-.3654
( 191)
P- .000
.3911
( 191)
P- .000
-.0840
( 194)
P= .244
.0454
( 192)
P= .532
.2216
{ 191)
P= .002
.1727
( 191)
P= ..017
.5575
( 195)
P- .000
.6633
( 195)
P- .000
.7720
( 195)
P= .000
.8448
( 195)
P= .000
1.0000
( 195)
-.3087
( 191)
P= .000
.3302
{ 191)
P= .000
-.1910
( 194)
P= .008
-.0118
( 192)
P= .871
.1807
{ 191)
P= .012
.1743
( 191)
P= .016
.4238
( 195)
P= .000
.6148
( 195)
P= .000
.7923
( 195)
P= .000
.7899
( 195)
P= .000
.9443
( 195)
P= .000
{Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
E-39
-------
- - Correlation Coefficients
P3BX0001 P4BX0001 P4BX0002
P4BX0002 .7899 .9443 1.0000
( 195) ( 195) { 195)
P- .000 P- .000 P= .
(Coefficient / (Cases) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
E-40
-------
APPENDIX F. REGRESSION EQUATIONS
NP = DS + HRS + NUMOPUN
NP = DS
NP =
CQRIFS1
CORIFS2
CORIFS3
CORIFS4
CORIFS1
CORIFS2
CORIFS3
CORIFS4
CORIFS1
CORIFS2
CORIFS3
CORIFS4
RO1C
R01C
RO2C
RO2C
RO1C
R01C
R02C
R02C
+ HRS + PMIN + HOUSEVAL + PHISP + POPDENS
HRS + PMIN + HOUSEVAL + PHISP + POPDENS
= HRS + NP + COCOUNT
HRS + NP + COCOUNT
HRS + NP + COCOUNT
HRS + NP + COCOUNT
PMIN + HOUSEVAL + PHISP + POPDENS-
PMIN + HOUSEVAL + PHISP + POPDENS
PMIN + HOUSEVAL + PHISP + POPDENS
PMIN + HOUSEVAL + PHISP + POPDENS
HRS + NP + COCOUNT + PMIN + HOUSEVAL + PHISP + POPDENS
HRS + NP + COCOUNT + PMIN + HOUSEVAL + PHISP + POPDENS
HRS + NP + COCOUNT + PMIN + HOUSEVAL + PHISP + POPDENS
HRS + NP + COCOUNT + PMIN + HOUSEVAL + PHISP + POPDENS
HRS + NP + CORIFS1 + ROCOUNT
HRS + NP + CORIFS3 + ROCOUNT
HRS + NP + CORIFS2 + ROCOUNT
HRS + NP + CORIFS4 + ROCOUNT
HRS + NP 4- CORIFS1 + ROCOUNT + PMIN
HRS + NP + CORIFS3 + ROCOUNT + PMIN
HRS + NP + CORIFS2 + ROCOUNT + PMIN
HRS + NP + CORIFS4 + ROCOUNT + PMIN
+ HOUSEVAL + PHISP + POPDENS
+ HOUSEVAL + PHISP + POPDENS
+ HOUSEVAL + PHISP + POPDENS
+ HOUSEVAL + PHISP + POPDENS
ROCOUNT = HRS + CORIFS4 + RO2 + PMIN + HOUSEVAL +.PHISP + POPDENS
COCOUNT - HRS + NP + CORIFS4 + PMIN + HOUSEVAL + PHISP + POPDENS
Notes:
(1) For the sake of expediency the variable name for the Total HRS Score,
HRSSCOR (which appears in the following printouts) is abbreviated
in the text and in this listing of the equations as HRS.
(2) A "C" next to a variable name indicates that it is the date as computed
as the number of months from September 1993, rather than the raw date.
-------
* * * *
MULTIPLE REGRESSION
* * * *
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable..
Block Number 1. Method: Enter DSC
NPC
HRS SCOR NUMOPUN
Variable(s) Entered on Step Number
1.. NUMOPUN
2.. HRS_SCOR
3.. DSC
Multiple R .55788
R Square .31123
Adjusted R Square .30068
Standard Error 23.22631
Analysis of Variance
Regression
Residual
F = 29,
Variable
DSC
HRS SCOR
NUMOPUN
(Constant)
Variable
DSC
HRS SCOR
NUMOPUN
(Constant)
DF Sum of Squares Mean Square
3 47776.71838 15925.57279
196 105734.47662 539.46162
.52123 Signif F = .0000
B SE B 95% Confdnce Intrvl B Beta
.249404 .049719
.857706 .158934
-2.415394 .836283
46.096845 8.136040
T Sig T
5.016 .0000
5.397 .0000
-2.888 .0043
5.666 .0000
.151351 .347458 .320239
.544266 1.171145 .343579
-4.064663 -.766126 -.172173
30.051425 62.142265
End Block Number 1 All requested variables entered.
F-l
-------
**** MULTIPLE REGRESSION .****
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. NPC
Block Number 1. Method: Enter
DSC HRS_SCOR PMIN HOUSEVAL PHISP POPDEN
Variable(s) Entered on Step Number
1.. '
2..
3..
4. .
5..
6..
POPDEN
HOUSEVAL
HRS SCOR
PMIN
PHISP
DSC
Multiple R .55759
R Square .31091
Adjusted R Square .28768
Standard Error 23.36244
Analysis of Variance
DF Sura of Squares Mean Square
Regression 6 43834.64690 7305.77448
Residual 178 97153.00716 545.80341
F = 13.38536 Signif F - .0000
F-2
-------
+ + ** MULTIPLE REGRESSION * * * - *
Equation Number 1 Dependent Variable.. NPC
Variable
- Variables in the Equation
B SE B 95% Confdnce Intrvl B
Beta
DSC
HRS SCOR
PMIN '
HOUSEVAL
PHISP
POPDEN
(Constant)
2.
-3.
.222736
.875180
-.034811
21164E-05
-.091958
33908E-04
40.311328
2
5
.053665
.164586
.117784
.4492E-05
.284900
.9967E-04
10.039390
.116834
.550389
-.267243
-2.62160E-05
-.654174
-.001517
20.499788
7
8
.328638
1.199970
.197621
.04487E-05
.470258
.49475E-04
60.122868
.292882
.361281
-.019306
.058362
-.022721
-.041031
in
Variable
DSC
HRS_SCOR
PMIN
HOUSEVAL
PHISP
POPDEN
(Constant)
End Block Number
T Sig T
4.150
5.317
-.296
.903
-.323
-.557
4.015
.0001
.0000
.7679
.3677
.7472
.5784
.0001
All requested variables entered.
F-3
-------
* * * *
MULTIPLE REGRESSION ****
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. NPC
Block Number 1. Method: Enter
HRS SCOR PMIN HOUSEVAL PHISP POPDEN
Variable(s) Entered on Step Number
1.. POPDEN
2.. HOUSEVAL
3. . HRS_SCOR
4.. PMIN
5.. PHISP
Multiple R .49419
R Square .24422
Adjusted R Square .22311
Standard Error 24.39837
Analysis of Variance
DF Sum of Squares Mean Square
Regression 5 34432.48474 6886.49695
Residual 179 106555.16931 595.28028
F. jfğ 11.56850 Signif F = .0000
F-4
-------
**** MULTIPLE REGRESSION
Equation Number 1 Dependent Variable.. NPC
+ * * *
Variable
HRS SCOR
PMIN
HOUSEVAL
PHISP
POPDEN
(Constant)
Variable
HRS SCOR
PMIN
HOUSEVAL
PHISP
POPDEN
(Constant)
VdZ.XctUJ.eii J.I1
B SE B
1.108825 .161517
-.064952 .122773
3.14497E-06 2.5129E-05
.027916 .296000
-8.50148E-04 6.1264E-04
66.833896 8.086313
T Sig T
6.865 .0000
-.529 .5974
.125 .9005
.094 .9250
-1.388 .1670
8.265 .0000
uiie tcjuduj-uii
95% Confdnce
.790103
-.307220
-4.64418E-05 5.
-.556183
-.002059 3.
50.877131
Intrvl B
1.427548
.177315
27317E-05
.612015
58786E-04
82.790662
Beta
.457732
-.036023
.008299
.006898
-.104467
End Block Number
All requested variables entered.
F-5
-------
**** MULTIPLE REGRESSION *Ğ**
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. CORIFS1
Block Number 1. Method: Enter HRS SCOR NPC
COCOUNT
Variable(s) Entered on Step Number
1.. COCOUNT
2. . NPC
3.. HRS SCOR
Multiple R
R Square
Adjusted R Square
Standard Error
.70563
.49791
.49006
21.61687
Analysis of Variance
Regression
Residual
F - 63
Variable
HRS SCOR
NPC
COCOUNT
(Constant)
Variable
HRS SCOR
NPC
COCOUNT
(Constant)
DF Sum of Squares Mean Square
3 88972.51875 29657.50625
192 89719.48125 467.28896
.46717 Signif F = .0000
B SE B 95% Confdnce Intrvl B
.410329 .158792 .097129 .723529
.706911 .063451 .581761 .832061
.387098 .860917 -1.310973 2.085168
-7.945410 7.626151 -22.987204 7.096384
T Sig T
2.584 .0105
11.141 .0000
.450 .6535
-1.042 .2988
Beta
.146647
.631164
.023144
End Block Number 1 All requested variables entered.
F-6
-------
* * * *
MULTIPLE REGRESSION
* * * *
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. CORIFS2
Block Number 1. Method: Enter HRS SCOR NPC
COCOUNT
Variable(s) Entered on Step Number
1.. COCOUNT
2.. NPC
3 . . HRS SCOR
Multiple R
R Square
Adjusted R Square
Standard Error
.51268
.26284
.25132
28.50323
Analysis of Variance
Regression
Residual
F = 22
Variable
HRS SCOR
NPC
COCOUNT
(Constant)
Variable
HRS SCOR
NPC
COCOUNT
(Constant)
DF Sura of Sqi
3 55617.;
192 155987.
.81918 Signif F = .
B SB B
.264532 .209377
.472391 .083664
-4.832987 1.135175
13.081297 10.055573
T Sig T
1.263 .2080
5.646 .0000
-4.257 .0000
1.301 .1949
uares Mean Square
27093 18539.09031
40254 812.43439
0000
95% Confdnce Intrvl B Beta
-.148442 .677507 .086878
.307372 .637409 .387586
-7.072002 -2.593971 -,265533
-6.752280 32.914873
End Block Number 1 All requested variables entered.
F-7
-------
MULTIPLE REGRESSION
* * * *
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. CORIFS3
Block Number 1. Method: Enter HRS SCOR NPC
COCOUNT
Variable(s) Entered on Step Number
1.. COCOUNT
2.. HRS_SCOR
3.. NPC
Multiple R
R Square
Adjusted R Square
Standard Error
.62316
.38833
.37601
26.26200
Analysis of Variance
Regression
Residual
F - 31
Variable
HRS SCOR
NPC
COCOUNT
(Constant)
Variable
HRS SCOR
NPC
COCOUNT
(Constant)
DF Sum of Squares Mean Square
3 65241.17113 21747.05704
149 102764.17528 689.69245
.53153 Signif F = .0000
B SE B 95% Confdnce Intrvl B
.661553 .220411 .226019 1.097088
.625136 .096010 .435419 .814853
1.483687 1.114841 -.719253 3.686628
-40.750026 10.736171 -61.964842 -19.535209
T Sig T
3.001 .0032
6.511 .0000
1.331 .1853
-3.796 .0002
Beta
.222148
.481941
.086636
End Block Number 1 All requested variables entered.
F-8
-------
**** MULTIPLE REGRESSION
* * * +
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. CORIFS4
Block Number 1. Method: Enter HRS SCOR NPC
COCOUNT
Variable(s) Entered on Step Number
1.. COCOUNT
2. . HRS_SCOR
3.. NPC
Multiple R
R Square
Adjusted R Square
Standard Error
.50964
.25973
.24483
24.62952
Analysis of Variance
Regression
Residual
F = n
Variable
HRS SCOR
NPC
COCOUNT
(Constant)
Variable
HRS SCOR
NPC
COCOUNT
(Constant)
DF Sum of Squares
3 31712.89347
149 90385.34182
.42621 Signif F =
B SE B
.575732 .206710
.334132 .090042
-3.193349 1.045541
-10.001976 10.068796
T Sig T
2.785 .0060
3.711 .0003
-3.054 .0027
-.993 .3221
.0000
the Eq
95
-5.
-29.
Mean
10570
606
uation
% Confdnce
Square
.96449
.61303
Intrvl B
167271 .984193
156207 .512056
259352 -1.127346
898050 9.894099
Beta
.226780
.302165
-.218729
End Block Number 1 All requested variables entered.
F-9
-------
**** MULTI P'L E REGRESSION '****
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. CORIFS1
Block Number 1. Method: Enter PMIN PHISP
HOUSEVAL POPDEN
Variable(s) Entered on Step Number
1. . POPDEN
2 . . HOUSEVAL
3.. PMIN
4.. PHISP
Multiple R
R Square
Adjusted R Square
Standard Error
.14733
.02171
.00032
29.70808
Analysis of Variance
DF
Regression 4
Residual 183
F = 1.01505
Sum of Squares
3583.39311
161510.28242
Signif F = .4009
Mean Square
895.84828
882.56985
Variable
PMIN
PHISP
HOUSEVAL
POPDEN
(Constant)
- Variables in the Equation
B SE B 95% Confdnce Intrvl B
.081948
-.200418
-3.05412E-06
-.001106
91.801896
.159725 -.233192
.359873 -.910451
3.0005E-05 -6.22545E-05
7.2860E-04 -.002544
5.149902 81.641078
.397088
.509615
5.61462E-05
3.31095E-04
101.962713
Beta
.039059
-.045785
-.007565
-.125251
Variable
PMIN
PHISP
HOUSEVAL
POPDEN
(Constant)
in
T Sig T
.513
-.557
-.102
-1.519
17.826
.6085
.5783
.9190
.1306
.0000
F-10
-------
ğ * * *
MULTIPLE REGRESSION
* * * *
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. CORIFS2
Block Number 1. Method: Enter PMIN PHISP
HOUSEVAL POPDEN
Variable(s) Entered on Step Number
1.. POPDEN
2 . . HOUSEVAL
3.. PMIN
4.. PHISP
Multiple R
R Square
Adjusted R Square
Standard Error
.16893,
.02854
.00730
32.28232
Analysis of Variance
DF
Regression 4
Residual 183
F = 1.34401
Sum of Squares
5602.64586
190713.09882
Signif F = .2553
Mean Square
1400.66147
1042.14808
Variable
PMIN
PHISP
HOUSEVAL
POPDEN
(Constant)
- Variables in the Equation
B SE B 95% Confdnce Intrvl B
.060550
.050285
2.22996E-06
-.'001708
71.356575
.173566 -.281887 .403007
.391056 -.721274 .821843
3.2605E-05 -6.21002E-05 6.65601E-05
7.9174E-04 -.003270 -1.45603E-04
5.596147 60.315310 82.397840
Beta
.026470
.010534
'.005066
-.177277
Variable
in
T Sig T
PMIN
PHISP
HOUSEVAL
POPDEN
(Constant)
.349
.129
.068
-2.157
12.751
.7276
.8978
.9455
.0323
.0000
F-ll
-------
**** MULTIPLE REGRESSION ****
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. CORIFS3
Block Number 1. Method: Enter PMIN PHISP
HOUSEVAL POPDEN
Variable(s) Entered on Step Number
1.. POPDEN
2. . HOUSEVAL
3.. PMIN
4.. PHISP
Multiple R
R Square
Adjusted R Square
Standard Error
.17400
.03028
.00296
33.50966
Analysis of Variance
DF
Regression 4
Residual 142
F = 1.10840
Sum of Squares
4978.48343
159451.40773
Signif F = .3550
Mean Square
1244.62086
1122.89724
Variable
Variables in the Equation
SE B
95% Confdnce Intrvl B
Beta
PMIN
PHISP
HOUSEVAL
POPDEN
(Constant)
-5
-7
.079173
-.287227
.00764E-05
.62647E-04
70.448120
3
8
.197295
.426359
.6345E-05
.9096E-04
6.493780
-.310842
-1.130059
-1.21923E-04
-.002524
57.611145
2
9
.469188
.555605
.17704E-05
.98618E-04
83.285095
.033951
- .061451
- . 115498
-.078718
Variable
PMIN
PHISP
HOUSEVAL
POPDEN
(Constant)
in
T Sig T
.401
-.674
-1.378
-.856
10.849
.6888
.5016
.1704
.3935
.0000
F-12
-------
+ + * *
MULTIPLE REGRESSION
* * * *
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. CORIFS4
Block Number 1. Method: Enter PMIN PHISP
HOUSEVAL POPDEN
Variable(s) Entered on Step Number
1.. POPDEN.
2. . HOUSEVAL
3.. PMIN
4.. PHISP
Multiple R
R Square
Adjusted R Square
Standard Error
.09255
.00857
-.01936
29.34298
Analysis of Variance
DF
4
142
Regression
Residual
.30673
Sum of Squares
1056.38966
122263.46068
Signif F = .8731
Mean Square
264.09741
861.01029
Variable
- Variables in the Equation
B SE B 95% Confdnce Intrvl B
PMIN -.010503
PHISP .178399
HOUSEVAL 5.82545E-07
POPDEN -8.44949E-04
(Constant) 47.621644
.172763 -.352023
.373345 -.559632
3.1826E-05 -6.23306E-05
7.8018E-04 -.002387
5.686325 36.380853
.331016
.916431
6.34957E-05
6.97316E-04
58.862434
Beta
-.005201
.044073
J001551
-.100705
Variable
in
T Sig T
PMIN
PHISP
HOUSEVAL
POPDEN
(Constant)
-.061
.478
.018
-1.083
8.375
.9516
.6335
.9854
.2806
.0000
F-13
-------
+ *** MULTIPLE REGRESSION ****
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. CORIFS1
Block Number 1. Method: Enter
HRS_SCOR NPC COCOUNT PMIN PHISP HOUSEVAL POPDEN
Variable(s) Entered on Step Number
1..
2..
3..
4..
5..
6..
7..
POPDEN
COCOUNT
NPC
PMIN
HOUSEVAL
PHISP
HRS SCOR
Multiple R .71456
R Square .51060
Adjusted R Square .49091
Standard Error 21.33235
Analysis of Variance
DF Sum of Squares Mean Square
Regression 7 82610.95786 11801.56541
Residual 174 79182.03664 455.06918
F = 25.93356 Signif F = .0000
F-14
-------
**** MULTIPLE REGRESSION
Equation Number 1 Dependent Variable.. CORIFS1
* * * *
Variable
HRS SCOR
NPC~
COCOUNT
PMIN
PHISP
HOUSEVAL
POPDEN
(Constant)
Variables in the Equation
B SE B 95% Confdnce Intrvl B
.453909 .163297 .131611 .776207
.677830 .065822 .547917 .807742
3.135654 1.335028 .500721 5.770586
-.004793 .117223 -.236155 .226570
-.135582 .260239 -.649213 .378049
2.77682E-06 2.2340E-05 -4.13159E-05 4.68695E-05
2.89026E-04 5.4134E-04 -7.79421E-04 .001357
-11.249775 9.058202 -29.127870 6.628320
Beta
.168155
.616113
.127158
.002284
.031207
.006818
.032979
Variable
HRS_SCOR
NPC
COCOUNT
PMIN
PHISP
HOUSEVAL
POPDEN
(Constant)
in
T Sig T
2.780
10.298
2.349
-.041
-.521
.124
.534
-1.242
.0060
.0000
.0200
.9674
.6030
.9012
.5.9.41
.2159
End Block Number 1 All requested variables entered.
F-15
-------
**** MULTIPLE REGRESSION ****
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable. CORIFS2
Block Number 1. Method: Enter
HRS_SCOR NPC COCOUNT PMIN PHISP HOUSEVAL POPDEN
Variable(s) Entered on Step Number
1..
2..
3..
4..
5..
6..
7..
POPDEN
COCOUNT
NPC
PMIN
HOUSEVAL
PHISP
HRS SCOR
Multiple R .57691
R Square .33282
Adjusted R Square .30598
Standard Error 26.96374
Analysis of Variance
DF Sum of Squares Mean Square
Regression 7 63107.87121 9015.41017
Residual 174 126505.49143 727.04305
F = 12.40010 Signif F = .0000
F-16
-------
**** MULTIPLE REGRESSION ****
Equation Number 1 Dependent Variable.. CORIFS2
Variable
- Variables in the Equation
B SE B 95% Confdnce Intrvl B
HRS SCOR
NPC
COCOUNT
PMIN
PHISP
HOUSEVAL
POPDEN
(Constant)
.185561
.448953
-10.198125
-.006721
-.069658
-3.45492E-05
-6.09396E-04
35.617792
.206405
.083198
1.687453
.148168
.328937
2.8238E-05
6.8425E-04
11.449416
-.221818
.284746
-13.528636
-.299160
-.718879
-9.02816E-05
-.001960
13.020178
.592940
.613160
-6.867613
.285717
.579563
2.11832E-05
7.41103E-04
58. 215407
Beta
.063500
.376952
.382015
.002959
.014810
.078357
.064230
Variable
HRS_SCOR
NPC
COCOUNT
PMIN
PHISP
HOUSEVAL
POPDEN
(Constant)
in
T Sig T
.899
5.396
-6.044
-.045
-.212
-1.224
-.891
3.111
.3699
.0000
.0000
.9639
.8325
.2228
.3744
.0022
End Block Number 1 All requested variables entered,
F-17
-------
**** MULTIPLE REGRESSION ****
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. CQRIFS3
Block Number 1. Method: Enter
HRS SCOR NPC COCOUNT PMIN PHISP HOUSEVAL POPDEN
Variable(s) Entered on Step Number
1. . POPDEN
2.. COCOUNT
3.. PMIN
4. . HOUSEVAL
5.. NPC
6.. PHISP
7.. HRS SCOR
Multiple R .68175
R Square .46478
Adjusted R Square .43661
Standard Error 25.41180
Analysis of Variance
DF Sum of Squares Mean Square
Regression 7 74582.22178 10654.60311
Residual 133 85886.04772 645.75976
F = 16.49933 Signif F = .0000
F-18
-------
* * * *
MULTIPLE REGRESSION
Equation Number 1 Dependent Variable.. CORIFS3
* * * *
Variable
- Variables in the Equation
B SE B 95% Confdnce Intrvl B
1.127582
.871840
9.477547
.312862
.452851
.02513E-05
.002625
-24.563797
HRS SCOR
NPC
COCOUNT
PMIN
PHISP
HOUSEVAL
POPDEN
(Constant)
.677576
.675077
5.835232
.006880
-.191708
-4.65126E-05
.001215
-49.954192
.227510
.099477
1.841449
.154696
.325870
2.8698E-05
7.1293E-04
12.836649
.227570
.478315
2.192917
-.299102
-.836267
-1.03277E-04
-1.95211E-04
-75.344588
Beta
.229875
.516501
.208554
.002949
-.041392
-.106321
.126629
in
Variable
HRS_SCOR
NPC
COCOUNT
PMIN
PHISP
HOUSEVAL
POPDEN
(Constant)
End Block Number
T Sig T
2.978
6.786
3.169
.044
-.588
-1.621
1.704
-3.892
.0034
.0000
.0019
.9646
.5573
.1074
.09.07
.0002
All requested variables entered.
F-19
-------
**** MULTIPLE REGRESSION
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. CORIFS4
Block Number 1. Method: Enter
HRS_SCOR NPC COCOUNT PMIN PHISP HOUSEVAL POPDEN
Variable(s) Entered on Step Number
* # * *
1..
2..
3..
4..
5..
6..
7..
POPDEN
COCOUNT
PMIN
HOUSEVAL
NPC
PHISP
HRS SCOR
Multiple R .52167
R Square .27214
Adjusted R Square .23383
Standard Error 25.27208
Analysis of Variance
DF Sum of Squares Mean Square
Regression 7 31759.69009 4537.09858
Residual 133 84944.19643 638.67817
F - 7.10389 Signif F = .0000
F-20
-------
**** MULTIPLE REGRESSION ****
Equation Number 1 Dependent Variable.. CORIFS4
Variable
- Variables in the Equation
B SE B 95% Confdnce Intrvl B
HRS_SCOR
NPC~
COCOUNT
PMIN
PHISP
HOUSEVAL
POPDEN
(Constant)
.594884
.367902
-4.696936
-.116452
.137055
-2.73998E-05
6.63254E-04
-8.336948
.226259
.098930
1.831324
.153845
.324079
2.8540E-05
7.0901E-04
12.766070
.147352
.172221
-8.319225
-.420751
-.503960
-8.38516E-05
-7.39139E-04
-33.587740
1.042416
.563583
-1.074648
.187847
.778070
2.90520E-05
.002066
16.913844
Beta
.236656
.330067
.196846
.058526
.034699
-.073443
.081061
Variable
HRS_SCOR
NPC
COCOUNT
PMIN
PHISP
HOUSEVAL
POPDEN
(Constant)
T Sig T
2.629
3.719
-2.565
-.757
.423
-.960
.935
-.653
.0096
.0003
.0114
.4504
.6730
.3388
.3512
.5148
End Block Number 1 All requested variables entered.
F-21
-------
+ * * *
MULTIPLE REGRESSION .**-**
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. RO1C
Block Number 1. Method: Enter HRS SCOR NPC
CORIFS1 ROCOUNT
Variable(s) Entered on Step Number
1. . ROCOUNT
2.. CORIFS1
3. . HRS_SCOR
4. . NPC
Multiple R .67609
R Square .45709
Adjusted R Square .44135
Standard Error 23.25293
Analysis of Variance
Regression
Residual
F = 29
Variable
HRS SCOR
NPC
CORIFS1
ROCOUNT
(Constant)
Variable
HRS SCOR
NPC
CORIFS1
ROCOUNT
(Constant)
DF Sum of Squares Mean Square
4 62821.84381 15705.46095
138 74616.43591 540.69881
.04660 Signif F = .0000
B SE B 95% Confdnce Intrvl B
.649370. .208675 .236755 1.061984
.134901 .110146 -.082892 .352694
.471865 .095633 .282769 .660961
. 4.255779 1.948431 .403140 8.108418
-39.992130 9.926504 -59.619842 -20.364418
i n
T Sig T
3.112 .0023
1.225 .2228
4.934 .0000
2.184 .0306
-4.029 .0001
Beta
.236239
.-108860
.422677
.138635
F-22
-------
* * * *
MULTIPLE REGRESSION
* * * *
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. RO1C
Block Number 1. Method: Enter HRS_SCOR NPC
Variable(s) Entered on Step Number
1.. CORIFS3
2. . ROCOUNT
3.. HRS_SCOR
4.. NPC
ROCOUNT CORIFS3
Multiple R
R Square
Adjusted R Square
Standard Error
.75127
.56440
.55140
20.53038
Analysis of Variance
Regression
Residual
F - 43
Variable
HRS SCOR
NPC
ROCOUNT
CORIFS3
(Constant)
Variable
HRS SCOR
NPC
ROCOUNT
CORIFS3
(Constant)
DF Sum of Squares Mean Square
4 73180.99853 18295.24963
134 56480.52665 421.49647
.40546 Signif F - .0000
B SE B 95% Confdnce Intrvl B
.492598 .187680 .121399 .863797
.108835 .088594 -.066389 .284058
2.556561 1.733262 -.871530 5.984651
.517499 .067425 .384144 .650853
-14.748357 9.134235 -32.814281 3.317568
-I -
T Sig T
2.625 .0097
1.228 .2214
1.475 .1426
7.675 .0000
-1.615 .1087
Beta
.181719
1091192
.085564
.566716
F-23
-------
**** MULTIPLE REGRESSION ****
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable. . RO2C
Block Number 1. Method: Enter HRS SCOR NPC
ROCOUNT CORIFS2
Variable(s) Entered on Step Number
1. . CORIFS2
2. . ROCOUNT
3.. HRS_SCOR
4.. NPC
Multiple R
R Square
Adjusted R Square
Standard Error
.59060
.34880
.32993
22.42769
Analysis of Variance
Regression
Residual
F = 18
Variable
HRS SCOR
NPC
ROCOUNT
CORIFS2
(Constant)
Variable
HRS SCOR
NPC
ROCOUNT
CORIFS2
(Constant)
DF Sura of Sq
4 37180.
138 69414.
.47939 Signif F = .
B SE B
.633233 .199550
.203036 .091438
-6.276845 1.895893
.212492 .061894
-13.039591 9.615553
T Sig T
3.173 .0019
2.220 .0280
-3.311 .0012
3.433 .0008
-1.356 .1773
[uares Mean Square
64316 9295.16079
19600 503.00142
0000
95% Confdnce Intrvl B Beta
.238662 1.027804 .261583
.022234 .383837 :186043
-10.025600 -2.528090 -.232178
.090109 .334875 .258105
-32.052458 5.973276
F-24
-------
* * * *
MULTIPLE REGRESSION
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. RO2C
Block Number 1. Method: Enter HRS SCOR NPC
ROCOUNT CORIFS4
Variable(s) Entered on Step Number
1.. CORIFS4
2. . ROCOUNT
3. . HRS_SCOR
4.. NPC
Multiple R
R Square
Adjusted R Square
Standard Error
.77347
.59826
.58627
17.17590
Analysis of Variance
Regression
Residual
F = 49
Variable
HRS SCOR
NPC
ROCOUNT
CORIFS4
(Constant)
Variable
HRS SCOR
NPC
ROCOUNT
CORIFS4
(Constant)
DF Sua of Squares Mean Square
4 58869.71252 14717.42813
134 39531.52489 295.01138
.88766 Signif F = .0000
B SE B 95% Confdnce Intrvl B
.365115 .155832 .056906 .673324
.061312 .068928 -.075017 .197640
-3.513224 1.489498 -6.459191 -.567258
.600104 .059833 .481766 .718443
-.910940 7.303440 -15.355871 13.533992
in "~~. ~~ *~
T Sig T
2.343 .0206
.889 .3753
-2.359 .0198
10.030 .0000
-.125 .9009
Beta
.154612
.-058971
-.134972
.640469
F-25
-------
**** MULTIPLE REGRESSION ****
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. RO1C
Block Number 1. Method: Enter
HRS_SCOR NPC PMIN PHISP HOUSEVAL POPDEN ROCOUNT CORIFS1
Variable(s) Entered on Step Number
1.. CORIFS1
2.. PMIN
3. . HOUSEVAL
4.. ROCOUNT
5.. PHISP
6. . POPDEN
1.. HRS_SCOR
8.. NPC
Multiple R .72970
R Square .53247
Adjusted R Square .50230
Standard Error 22.35210
Analysis of Variance
DF Sum of Squares Mean Square
Regression 8 70556.74775 8819.59347
Residual 124 61952.45526 499.61657
F = . , 17.65272 Signif F = .0000
F-26
-------
**** MULTIPLE REGRESSION '***
Equation Number 1 Dependent Variable.. RO1C
Variable
- Variables in the Equation
B ' SE B 95% Confdnce Intrvl B
Beta
HRS SCOR
NPC
PMIN
PHISP
HOUSEVAL
POPDEN
ROCOUNT
CORIFS1
(Constant)
.734680
.202889
-.205410
.267069
-3.39229E-05
9.33241E-04
12.810012
.374592
-48.460901
.213685
.108894
.133327
.300008
2.4956E-05
6.4746E-04
3.222263
.098222
11.472647
.311737
-.012642
-.469302
-.326731
-8.33176E-05
-3.48263E-04
6.432251
.180183
-71.168483
1.157624
.418421
.058482
.860868
1.54718E-05
.002215
19.187773
.5^9000
-25.753320
.268582
.165406
-.099495
.061565
-.085209
.105108
.253212
.333662
Variable
HRS_SCOR
NPC
PMIN
PHISP
HOUSEVAL
POPDEN
ROCOUNT
.CORIFS1
(Constant)
in
T Sig T
3.438
1.863
1.541
.890
1.359
1.441
3.975
3.814
4.224
.0008
.0648
.1260
.3751
.1765
.1520
.0001
.0002
.0000
End Block Number 1 All requested variables entered.
F-27
-------
* * * *
****. MULTIPLE REGRESSION
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. RO1C
Block Number 1. Method: Enter
HRS_SCOR NPC PMIN PHISP HOUSEVAL POPDEN ROCOUNT CORIFS3
Variable(s) Entered on Step Number
1.. CORIFS3
2.. PMIN
3.. HOUSEVAL
4.. PHISP
5. . ROCOUNT
6.. POPDEN
7.. HRS_SCOR
8.. NPC
Multiple R .80507
R Square .64814
Adjusted R Square .62449
Standard Error 19.20103
Analysis of Variance
DF Sum of Squares Mean Square
Regression 8 80816.74158 10102.09270
Residual 119 43872.87561 368.67963
F = 27.40073 Signif F = .0000
F-28
-------
**** MULTIPLE REGRESSION '**
Equation Number 1 Dependent Variable.. RO1C
Variable
- Variables in the Equation
B SE B 95% Confdnce Intrvl B
HRS SCOR
NPC
PMIN
PHISP
HOUSEVAL
POPDEN
ROCOUNT
CORIFS3.
(Constant)
.525214
.143323
-.217481
.311955
-1.31731E-05
5.99008E-04
8.829376
.497446
-24.751289
.188778
.089791
.118364
.260179
2.2205E-05
5.6865E-04
2.863049
.070042
10.449437
.151414
-.034472
-.451853
-.203225
-5.71417E-05
-5.26971E-04
3.160253
.358756
-45.442219
.899013
.321117
.016892
.827136
3.07955E-05
.001725
14.498500
.636136
-4.060360
Beta
,194943
,118583
,104661
.073537
.033438
.069360
.178881
.543217
Variable
in
T Sig T
HRS SCOR
NPC
PMIN
PHISP
HOUSEVAL
POPDEN
ROCOUNT
CORIFS3
(Constant)_
2.782
1.596
-1.837
1.199
-.593
1.053
3.084
7.102
-2.369
.0063
.1131
.0686
.2329
.5541
.2943
.0025
.0000
.0195
End Block Number 1 All requested variables entered.
F-29
-------
* * * *
MULTIPLE REGRESSION
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. RO2C
Block Number 1. Method: Enter
HRS_SCOR NPC PMIN PHISP HOUSEVAL POPDEN ROCOUNT CORIFS2
Variable(s) Entered on Step Number
1..
2..
3..
4..
5..
6..
7..
8. .
CORIFS2
PMIN
HOUSEVAL
ROCOUNT
PHISP
HRS SCOR
POPDEN
NPC
Multiple R .61744
R Square .38123
Adjusted R Square .34131
Standard Error 22.42658
Analysis of Variance
DF Sum of Squares Mean Square
Regression 8 38424.24324 4803.03040
Residual 124 62366.01240 502.95171
F = 9.54968 Signif F = .0000
F-30
-------
**** MULTIPLE REGRESSION .***
Equation Number 1 Dependent Variable.. RO2C
Variable
- Variables in the Equation
B SE B 95% Confdnce Intrvl B
HRS SCOR
NPC
PMIN
PHISP
HOUSEVAL
POPDEN
ROCOUNT
CORIFS2
(Constant)
.702205
.225068
-.132794
.066939
-3.29060E-05
9.50008E-04
-10.675413
.217633
-9.161467
.209639
.095645
.133274
.300437
2.5040E-05
6.4857E-04
3.185060
.065501
11.611437
.287270
.035759
-.396581
-.527709
-8.24678E-05
-3.33692E-04
-16.979539
.087989
-32.143753
1.117140
.414377
.130992
.661587
1.66558E-05
.002234
-4.371287
.347278
13.820819
Beta
.294345
.210388
.073752
.017693
.094772
.122682
.241954
.25S316
T Sig T
Variable
HRS_SCOR
NPC
PMIN
PHISP
HOUSEVAL
POPDEN
ROCOUNT
CORIFS2
(Constant)
End Block Number 1 All requested variables entered.
3.350
2.353
-.996
.223
1.314
1.465
3.352
3.323
-.789
.0011
.0202
.3210
.8241
.1912
.1455
.0011
.0012
.4316
F-31
-------
* * * *
**** MULTIPLE REGRESSION
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. RO2C
Block Number 1. Method: Enter
HRS_SCOR NPC PMIN PHISP HOUSEVAL POPDEN ROCOUNT CORIFS4
Variable(s) Entered on Step Number
1.. CORIFS4
2.. PMIN
3. . HOUSEVAL
4.. ROCOUNT
5.. PHISP
6.. HRS_SCOR
7 . . POPDEN
8. . NPC
Multiple R .77957
R Square .60773
Adjusted R Square .58136
Standard Error 17.46919
Analysis of Variance
DF Sum of Squares Mean Square
Regression 8 56262.71262 7032.83908
Residual 119 36315.52957 305.17252
F = 23.04545 Signif F = .0000
F-32
-------
**** MULTIPLE REGRESSION ****
Equation Number 1 Dependent Variable.. R02C
Variable
- Variables in the Equation
B SE B 95% Confdnce Intrvl B
.752583
.240612
.119854
.465049
.27261E-05
.001536
-1.756326
.655475
20.860157
HRS SCOR
NPC
PMIN
PHISP
HOUSEVAL
POPDEN
ROCOUNT
CORIFS4
(Constant)
.419077
.088689
-.093349
-.001749
-2.66987E-05
5.21226E-04
-6.815174
.570659
3.008864
.168429
.076725
.107673
.235745
1.9911E-05
5.1256E-04
2.554845
.063035
9.015350
.085572
-.063234
-.306553
-.468548
-6.61235E-05
-4.93698E-04
-11.874022
.445843
-14.842429
Beta
.180521
.085161
-.052136
-4.785E-04
-.078650
.070043
-.160240
.613023
in
T Sig T
Variable
HRS_SCOR
NPC
PMIN
PHISP
HOUSEVAL
POPDEN
ROCOUNT
CORIFS4
(Constant)
End Block Number 1 All requested variables entered.
2.488
1.156
-.867
-.007
1.341
1.017
2.668
9.053
.334
.0142
.2500
.3877
.9941
.1825
.3113
.0087
.0000
.7392
F-33
-------
* * * *
MULTIPLE REGRESSION
* * * *
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable..
COCOUNT
Block Number 1.
HRS SCOR NPC
Method:
PMIN
Enter
PHISP
HOUSEVAL POPDEN CORIFS4
Variable(s) Entered on Step Number
1..
2..
3..
4..
5..
6..
7..
CORIFS4
PMIN
HOUSEVAL
PHISP
HRS SCOR
POPDEN
NPC
Multiple R .33871
R Square .11473
Adjusted R .Square .06813
Standard Error 1.16807
Analysis of Variance
DF
Regression 7
Residual 133
F = 2.46231
Sum of Squares
23.51663
181.46210
Signif F = .0209
Mean Square
3.35952
1.36438
F-34
-------
**** MULTIPLE REGRESSION ****
Equation Number 1 Dependent Variable.. COCOUNT
Variable
- Variables in the Equation
B SE B 95% Confdnce Intrvl B
.043490
.005325
.006658
.015316
-3.95269E-07
9.28829E-05
-.002296
3.521937
HRS SCOR
NPC
PMIN
PHISP
HOUSEVAL
POPDEN
CORIFS4
(Constant)
-2
2
.022632
-.004151
-.007380
-.014231
.96366E-06
.80299E-05
-.010034
2.429794
1
3
.010545
.004791
.007097
.014938
.2985E-06
.2788E-05
.003912
.552156
-5.
-3.
.001775
-.013627
-.021417
-.043778
53205E-06
68231E-05
-.017772
1.337651
Beta
.214833
.088864
.088495
.085970
.189548
.081741
.239417
in
Variable
HRS_SCOR
NPC
PMIN
PHISP
HOUSEVAL
POPDEN
CORIFS4
(Constant)
End Block Number
T Sig T
2.146
-.866
1.040
-.953
2.282
.855
2.565
4.401
.0337
.3878
.3003
.3425
.0241
.3942
.0114
.0000
All requested variables entered.
F-35
-------
**** MULTIPLE REGRESSION ***
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. R'OCOUNT
Block Number 1. Method: Enter
HRS SCOR PMIN PHISP HOUSEVAL POPDEN CORIFS4 RO2C
Variable(s) Entered on Step Number
1..
2..
3..
4..
5..
6..
7..
R02C
PHISP
HOUSEVAL
PMIN
HRS SCOR
POPDEN
CORIFS4
Multiple R .34583
R Square .11960
Adjusted R Square .06824
Standard Error .61277
Analysis of Variance
DF Sum of squares Mean Square
Regression 7 6.12086 .87441
Residual 120 45.05882 .37549
F = 2.32871 Signif F = .0291
F-36
-------
**** MULTIPLE REGRESSION ****
Equation Number 1 Dependent Variable.. ROCOUNT
Variable
- Variables in the Equation
B SB B 95% Confdnce Intrvl B
HRS SCOR
PMIN
PHISP
HOUSEVAL
POPDEN
CORIFS4
R02C
(Constant)
4
-6
1
-2
.015498
.69187E-05
-.005353
.42655E-07
.73889E-05
.62771E-04
-.007913
1.148780
7
1
.005512
.003782
.008254
.0064E-07
.7680E-05
.002826
.003115
.245967
.004585
-.007442
-.021696
-2.02988E-06
-1.76167E-05
-.005858
-.014081
.661783
.026411
.007536
.010990
7.44570E-07
5.23946E-05
.005332
-.001746
1.635778
Beta
.283934
.001114
-.062287
-.080518
.099384
-.012006
-.336561
Variable
HRS_SCOR
PMIN
PHISP
HOUSEVAL
POPDEN
CORIFS4
RO2C
(Constant)
in
T Sig T
2.812
.012
-.649
-.917
.984
-.093
-2.540
4.670
.0058
.9901
.5179
.3609
.3273
.9261
.0123
.0000
End Block Number
All requested variables entered.
F-37
-------
APPENDIX G. CROSS-TABULATIONS (Selected)
-------
NPI by
I'BBLACKI
\)
NPI
&P.LACKI Page 1
Count '
Row Pet
Col Pet
Tot Pet
). 00000
3.00000
0.00000
0.00000
0.00000
0.00000
11.00000
y
.50000
5
50.0
9.8
2.6
2
8.7
3.9
1.0
2
28.6
. 3.9
1.0
18
30.0
35.3
9.4
19
26.8
37.3
9.9
5
26.3
.9.8
2.6
1.00000
1
4.3
4.8
.5
3
42.9
14.3
1.6
5
8.3
23.8
2.6
12
16.9
57.1
6.3
2.00000
1
10.0
3.8
.5
4
17.4
15.4
2.1
9
15.0
34.6
4.7
8
11.3
30.8
4.2
4
21.1
15.4
2.1
3.00000
1
10.0
6.7
.5
4
17.4
26.7
2.1
2
3.3
13.3
1.0
6
8.5
40.0
3.1
2
10.5
13.3
1.0
4.00000
1
10.0
9.1
.5
2
8.7
18.2
1.0
1
14.3
9.1
.5
4
6.7
36.4
2.1
3
4.2
27.3
1.6
Column 51 21 26 15 11
(Continued) Total 26\7 11.0 13.6 7.9 5.8
Row
Total
1
.5
10
5.2
23
12.0
7
3.7
60
31.4
71
37.2
19
9.9
191
100.0
G-l
-------
NPI by BPIACKI
BPLACKI
Page 3 of.3
NPI
Count
Row Pet
Col Pet
Tot Pet
1.00000
). 00000
D. 00000
0.00000
0.00000
0.00000
1.00000
Row
0.00000
1
4.3
33.3
.5
1
1.4
33.3
.5
1
5.3
33.3
.5
5.00000
1
100.0
12.5
.5
1
10.0
12.5
.5
2
8.7
25.0
1.0
1
1.7
12.5
.5
3
4.2
37.5
1.6
0.00000
1
4.3
10.0
.5
5
8.3
50.0
2.6
3
4.2
30.0
1.6
1
5.3
10.0
.5
50.00000
2
8.7
11.8
1.0
8
13.3
47.1
4.2
4
5.6
23.5
2.1
3
15.8
17.6
1.6
Total
1
.5
10
5.2
23
12.0
7
3.7
60
31.4
71
37.2
IS
9.<
Column
Total
3
1.6
8
4.2
10
5.2
17
8.9
191
100.0
G-2
-------
Chi-Square
Value
DF
Significance
Pearson 81.24763
Likelihood Ratio 72.27544
Mantel-Haenszel test for .05149
linear association
Minimum Expected Frequency - .016
Cells with Expected Frequency < 5 -
78
78
1
87 OF 98 ( .88.8%)
.37839
.66141
.82050
Statistic
Approximate
Value ASE1 Val/ASEO Significance
Phi
Cramer's V
Contingency Coefficient
.65221
.26626
.54629
.37839 *1
.37839 *1
.37839 *1
Lambda :
symmetric
with NPI dependent
with BPLACKI dependent
Goodman & Kruskal Tau :
with NPI dependent
with BPLACKI dependent
.04615
.06667
.02857
.05481
.03614
.02987
.05805
.02439
.01598
.00817
1.50891
1.11299
1.15875
.90007 *2
.18035 *2
*1 Pearson chi-square probability
*2 Based on chi-square approximation
Number of Missing Observations: 19
G-3
-------
NPI by PHISPI
PHISPI
NPI
Page 1 of 3
Row
Total
Count
Row Pet
Col Pet
Tot Pet
1.00000
). 00000
0.00000
0.00000
0.00000
0.00000
11.00000
.50000
7
70.0
24.1
3.6
2
8.7
6.9
1.0
1
14.3
3.4
.5
9
15.0
31.0
4.7
8
11.1
27.6
4.2
2
11.1
6.9
1.0
1.00000
1
4.3
7.7
.5
1
14.3
7.7
.5
4
6.7
30.8
2.1
3
4.2
23.1
1.6
4
22.2
30.8
2.1
2.00000
5
21.7
14.3
2.6
2
28.6
5.7
1.0
10
16.7
28.6
5.2
15
20.8
42.9
7.8
3
16.7
8.6
1.6
3.00000
2
8.7
6.7
1.0
1
14.3
3.3
.5
11
18.3
36.7
5.7'
16
22.2
53.3
8.3
4.00000
1
10.0
4.3
.5
2
8.7
8.7
1.0
1
14.3
4.3
.5
4
6.7
17.4
2.1
11
15.3
47.8
5.7
4
22.2
17.4
2.1
Column
(Continued) Total
29
15.1
13
6.8
35
18.2
30
15.6
23
12.0
2
1.0
10
5.2
23
12.0
7
3.6
60
31.3
72
37.5
18
9.4
192
100.0
G-4
-------
NPI by PHISPI
PHISPI
NPI
Count
Row Pet
Col Pet
Tot Pet
). 00000
3.00000
D. 00000
0.00000
0.00000
0.00000
1.00000
5.00000
1
4.3
7.1
.5
1
14.3
7.1
.5
5
8.3
35.7
2.6
5
6.9
35.7
2.6
2
11.1
14.3
1.0
6.00000
1
10.0
10.0
.5
2
8.7
20.0
1.0
5
8.3
50.0
2.6
2
2.8
20.0
1.0
7.00000
2
3.3
40.0
1.0
3
4.2
60.0
1.6
8.00000
1
4.3
11.1
.5
2
3.3
22.2
1.0
3
4.2
33.3
1.6
3
16.7
33.3
1.6
9.00000
1
4.3
25.0
.5
2
3.3
50.0
1.0
i
1.4
25.0
.5
Page 2 of 3
Row
Total
Column
(Continued) Total
14
7.3
10
5.2
5
2.6
9
4.7
4
2.1
2
1.0
10
5.2
23
12.0
7
3.6
60
31.3
72
37.5
18
9.4
192
100.0
G-5
-------
NPI by PHISPI
PHISPI
NPI
Count
Row Pet
Col Pet
Tot Pet
1.00000
). 00000
3.00000
0.00000
0.00000
0.00000
1.00000
10.00000
1
50.0
33.3
.5
2
2.8
66.7
1.0
15.00000
4
17.4
50.0
2.1
3
5.0
37.5
1.6
1
1.4
12.5
.5
20.00000
1
50.0
50.0
.5
1
1.7
50.0
.5
50.00000
1
10.0
14.3
.5
2
8.7
28.6
1.0
2
3.3
28.6
1.0
2
2.8
28.6
1.0
Page 3 of 3
Column
Total
3
1.6
6
4.2
2
1.0
7
3.6
Row
Total
2
1.0
10
5.2
23
12.0
7
3.6
60
31.3
72
37.5
18
9.4
192
100.0
G-6
-------
Chi-Square
Value
DF
Significance
Pearson 159.51950
Likelihood Ratio 94.19032
Mantel-Haenszel test for 4.40102
linear association
Minimum Expected Frequency - .021
Cells with Expected Frequency < 5 -
89 OF
78
78
1
98 { 90.8%)
.00000
.10229
.03592
Statistic
Value
ASE1
Approximate
Val/ASEO Significance
Phi
Cramer's V
Contingency Coefficient
.91150
.37212
.67365
.00000 *1
.00000 *1
.00000 *1
Lambda :
symmetric
with NPI dependent
with PHISPI dependent
Goodman & Kruskal Tau :
with NPI dependent
with PHISPI dependent
.07581
.08333
.07006
.07918
.05067
.03785
.05863
.05028
.01908
.01105
1.92747
1.36744
1.35023
.15338 *2
.00049 *2
*1 Pearson chi-square probability
*2 Based on chi-square approximation
Number of Missing Observations: 18
G-7
-------
NPI by POP100I
POP100I
NPI
Page 1 of 3
Row
Total
Count
Row Pet
Col Pet
Tot Pet
1.00000
5.00000
D. 00000
0.00000
0.00000
0.00000
1.00000
0.00000
1
10.0
11.1
.5
2
25.0
22.2
1.0
3
5.0
33.3
1.5
3
4.1
33.3
1.5
00.0000
4
40.0
66.7
2.1
1
4.3
16.7
.5
1
1.7
16.7
.5
00.0000
1
4.3
9.1
.5
3
5.0
27.3
1.5
5
6.8
45.5
2.6
2
10.5
18.2
1.0
00.0000
1
4.3
8.3
.5
3
5.0
25.0
1.5
6
8.2
50.0
3.1
2
10.5
16.7
1.0
00.0000
1
4.3
14.3
.5
3 '
5.0
42.9
1.5
2
2.7
28.6
1.0
1
5.3
14.3
.5
Column
(Continued) Total
9
4.6
6
3.1
11
5.6
12
6.2
7
3.6
2
1.0
10
5.1
23
11.8
8
4.1
60
30.8
73
37.4
19
9.7
195
100.0
G-8
-------
NPI by POP100I
POP100I
NPI
Page 2 of 3
Row
Total
Count
Row Pet
Col Pet
Tot Pet
1.00000
). 00000
3.00000
0.00000
0.00000
0.00000
1.00000
00.0000
5
8.3
100.0
2.6
000.000
1
10.0
5.9
.5
1
4.3
5.9
.5
1
12.5
5.9
.5
6
10.0
35.3
3.1
8
11.0
47.1
4.1
000.000
3
13.0
11.1
1.5
3
37.5
11.1
1.5
7
11.7
25.9
3.6
11
15.1
40.7
5.6
3
15.8
11.1
1.5
3000.000
1
10.0
3.7
.5
2
8.7
7.4
1.0
1
12.5
3.7
.5
7
11.7
25.9
3.6
11
15.1
40.7
5.6
5
26.3
18.5
2.6
000.000
3
13.0
27.3
1.5
2
3.3
18.2
1.0
4
5.5
36.4
2.1
2
10.5
18.2
1.0
Column 5 17 27 27
(Continued) Total 2.6 8.7 13.8 13.8
11
5.6
2
1.0
10
5.1
23
11.8
8
4.1
60
30.8
73
37.4
19
9.7
195
100.0
G-9
-------
NPI by POP100I
POP100I
NPI
Count
Row Pet
Col Pet
Tot Pet
40.00000
60.00000
80.00000
100.00000
120.00000
140.00000
141.00000
5000.000
3
5.0
37.5
1.5
5
6.8
62.5
2.6
6000.000
2
100.0
3.6
1.0
3
30.0
5.5
1.5
10
43.5
18.2
5.1
1
12.5
1.8
.5
17
28.3
30.9
8.7
18
24.7
32.7
9.2
4
21.1
7.3
2.1
Column 8 55
Total 4.1 28.2
Page 3 of 3
Row
Total
2
1.0
10
5.1
23
11.8
4.1
60
30.8
73
37.4
19
9.7
195
100.0
G-10
-------
Chi-Sguare
Value
DF
Significance
Pearson 102.51170
Likelihood Ratio 79.61206
Mantel-Haenszel test for .67551
linear association
Minimum Expected Frequency - .051
Cells with Expected Frequency < 5 -
74 OF
66
66
1
84 ( 88.1%)
.00267
.12027
.41114
Statistic
Value
ASE1
Val/ASEO
Approximate
Significance
Phi
Cramer's V
Contingency Coefficient
.72505
.29600
.58700
.00267 *1
.00267 *1
.00267 *1
Lambda :
symmetric
with NPI dependent
with POP100I dependent
Goodman & Kruskal Tau :
with NPI dependent
with POP100I dependent
.05344
.08197
.02857
.07479
.03817
.02556
.03512
.03148
.01499
.01035
2.04222
2.26530
.89627
.04233 *2
.09510 *2
*1 Pearson chi-square probability
*2 Based on chi-square approximation
Number of Missing Observations: 15
G-ll
-------
NPI by POPDENI
POPDENI
NPI
P.age 1 of 2
Row
Total
Count
Row Pet
Col Pet
Tot Pet
.00000
). 00000
D. 00000
0.00000
0.00000
0.00000
1.00000
200.0000
5
50.0
9.6
2.6
5
21.7
9.6
2.6
2
25.0
3.8
1.0
18
30.0
34.6
9.2
n
23.3
32.7
8.7
5
26.3
9.6
2.6
00.0000
1
10.0
6.3
.5
1
4.3
6.3
.5
1
12.5
6.3
.5
5
8.3
31.3
2.6
8
11.0
50.0
4.1
600.0000
4
50.0
26.7
2.1
3
5.0
20.0
1.5
5
6.8
33.3
2.6
3
15.8
20.0
1.5
800.0000
2
8.7
15.4
1.0
5
8.3
38.5
2.6
5
6.8
38.5
2.6
1
5.3
7.7
.5
1000.000
1
10.0
8.3
.5
1
4.3
8.3
.5
3
5.0
25.0
1.5
7
9.6
58.3
3.6
Column 52 16 15 13
(Continued) Total '26.7 8.2 7.7 6.7
12
6.2
2
1.0
10
5.1
23
11.8
8
4.1
60
30.8
73
37.4
19
9.7
195
100.0
G-12
-------
NPI by POPDENI
POPDENI
NPI
Page 2 of.2
Count
Row Pet
Col Pet
Tot Pet
.00000
). 00000
3.00000
0.00000
0.00000
0.00000
1.00000
Row
1500.000
1
10.0
4.3
.5
4
17.4
17.4
2.1
6
10.0
26.1
3.1
9
12.3
39.1
4.6
3
15.8
13.0
1.5
000.000
. 2
3.3
22.2
1.0
3
4.1
33.3
1.5
4
21.1
44.4
2.1
3000.000
2
8.7
11.8
1.0
7 .
11.7
41.2
3.6
6
8.2
35.3
3.1
2
10.5
11.8
1.0
4000.000
2
100.0
5.3
1.0
2
20.0
5.3
1.0
8
34.8
21.1
4.1
1
12.5
2.6
.5
' 11
18.3
28.9
5.6
13
17.8
34.2
6.7
1
5.3
2.6
.5
Total
2
1.0
10
5.1
23
11.8
8
4.1
60
30.8
73
37.4
IS
9.'
Column
Total
23
11.8
9
4.6
17
8.7
38
19.5
195
100.0
G-13
-------
Chi-Square
Value
DF
Significance
Pearson
Likelihood Ratio
Ma'ntel-Haenszel test for
linear association
65.19964
58.43903
1.83891
Minimum Expected Frequency - .092
Cells with Expected Frequency < 5 -
51 OF
48
48
1
63 { 81.0%)
.04975
.14374
.17508
Statistic
Value
ASE1
Val/ASEO
Approximate
Significance
Phi
Cramer's V
Contingency Coefficient
.57824
.23606
.50058
.04975 *1
.04975 *1
.04975 *1
Lambda :
symmetric
with NPI dependent
with POPDENI dependent
Goodman fi Kruskal Tau :
with NPI dependent
with POPDENI dependent
.03774
.02459
.04895
.03822
.04049
.03424
.06527
.03125
.01454
.01108
1.08156
.37224
1.53675
.61751 *2
.07369 *2
*1 Pearson chi-square probability
*2 Based on chi-square approximation
Number of Missing Observations: 15-
G-14
-------
NPI by POWNERI
POWNERI
NPI
Count
Row Pet
Col Pet
Tot Pet
.00000
). 00000
). 00000
D. 00000
D. 00000
D. 00000
1.00000
10.00000
1
12.5
33.3
.6
1
2.0
33.3
.6
1
1.8
33.3
.6
20.00000
1
4.8
33.3
.6
2
3.5
66.7
1.3
30.00000
1
50.0
25.0
.6
1
12.5
25.0
.6
1
2.0
25.0
.6
1
1.8
25.0
.6
40.00000
1
4.8
14.3
.6
3
5.3
42.9
1.9
3
20.0
42.9
1.9
50.00000
2
9.5
16.7
1.3
5
10.0
41.7
3.1
4
7.0
33.3
2.5
1
6.7
8.3
.6
Column 3 3 4 7 12
) Total 1.9 1.9 2.5 4.4 7.5
Page 1 of 2
Row
Total
2
1.3
B
5.0
21
13.2
6
3.8
50
31.4
57
35.8
15
9.4
159
100.0
G-15
-------
NPI by POWNERI
POWNERI
NPI
Count
Row Pet
Col Pet
Tot Pet
.00000
KOODOO
D. 00000
0.00000 .
0.00000
0.00000
1.00000
60.00000
1
50.0
5.9
.6
5
23.8
29.4
3.1
5
10.0
29.4
3.1
5
8.8
29.4
3.1
1
6.7
5.9
.6
70.00000
1
12.5
3.6
.6
6
28.6
21.4
3.8
7
14.0
25.0
4.4
11
19.3
39.3
6.9
3
20.0
10.7
1.9
80.00000
3
37.5
7.5
1.9
4
19.0
10.0
2.5
3
50.0
7.5
1.9
17
34.0
42.5
10.7
10
17.5
25.0
6.3
3
20.0
7.5
1.9
90.00000
2
25.0
4.4
1.3
2
9.5
4.4
1.3
3
50.0
6.7
1.9
14
28.0
31.1
8.8
20
35.1
44.4
12.6
4
26.7
8.9
2.5
Page 2 of'2
Row
Total
Column
Total
17
.0.7
28
17.6
40
25.2
45
28.3
2
1.3
8
5.0
21
13.2
6
3.8
50
31.4
57
35.8
15
9.4
159
100.0
G-16
-------
Chi-Square
Value
DF
Significance
Pearson 66.8781L.
Likelihood Ratio 53.83034
Mantel-Haenszel test for 2.09408
linear association
Minimum Expected Frequency - .038
Cells with Expected Frequency < 5 -
53 OF
48
48
1
63 ( 84.1%)
.03705
.26109
.14787
Statistic
Value
ASE1
Val/ASEO
Approximate
Significance
Phi
Cramer1s V
Contingency Coefficient
.64855
.26477
.54413
.03705 *1
.03705 *1
.03705 *1
Lambda :
symmetric
with NPI dependent
with POWNERI dependent
Goodman & Kruskal Tau :
with NPI dependent
with POWNERI dependent
.07870
.07843
.07895
.05285
.04503
.05224
.06655
.06012
.01667
.01213
1.46214
1.13595
1.26659
.39016 *2
.17708 *2
*1 Pearson chi-square probability
*2 Based on chi-square approximation
Number of Missing Observations: 51
G-17
-------
NPI by PRENTERI
PRENTERI
NPI
Page 1 of 2
Row
Total
Count
Row Pet
Col Pet
Tot Pet
). 00000
3.00000
D. 00000
0.00000
0.00000
0.00000
1.00000
10.00000
2
8.7
5.9
1.1
1
14.3
2.9
.5
10
16.9
29.4
5.3
17
23.6
50.0
9.0
4
21.1
11.8
2.1
0.00000
2
28.6
4.3
1.1
2
8.7
4.3
1.1
3
42.9
6.5
1.6
16
27.1
34.8
8.5
19
26.4
41.3
10.1
4
21.1
8.7
2.1
0.00000
3
42.9
7.9
1.6
4
17.4
10.5
2.1
3
42.9
7.9
1.6
15
25.4
39.5
7.9
10
13.9
26.3
5.3
3
15.8
7.9
1.6
0.00000
1
14.3
3.6
.5
6
26.1
21.4
3.2
7
11.9
25.0
3.7
11
15.3
39.3
5.8
3
15.8
10.7
1.6
50.00000
1
50.0
5.9
.5
5
21.7
29.4
2.6
5
8.5
29.4
2.6
5
6.9
29.4
2.6
1
5.3
5.9
.5
Column 34 46 38 28 17
(Continued) Total . 18.0 24.3 20.1 14.8 9.0
2
1.1
7
3.7
23
12.2
7
3.7
59
31.2
72
38.1
19
10.1
189
100.0
G-18
-------
NPI by PRENTERI
PRENTERI
NPI
Count
Row Pet
Col Pet
Tot Pet
.00000
). 00000
D. 00000
0.00000
0.00000
0.00000
1.00000
60.00000
2
8.7
16.7
1.1
5
8.5
41.7
2.6
4
5.6
33.3
2.1
1
5.3
8.3
.5
70.00000
1
4.3
14.3
.5
3
4.2
42.9
1.6
3
15.8
42.9
1.6
80.00000
1
50.0
25.0
.5
1
14.3
25.0
.5
1
1.7
25.0
.5
1
1.4
25.0
.5
90.00000
1
4.3
33.3
.5
2
2.8
66.7
1.1
Page 2 of.2
Row
Total
2
1.1
Column
Total
12
6.3
7
3.7
4
2.1
3
1.6
7
3.7
23
12.2
7
3.7
59
31.2
72
38.1
19
10.1
189
100.0
G-19
-------
Chi-Square
Value
DF
Significance
Pearson 71.51748
Likelihood Ratio 55.55907
Mantel-Haenszel test for 4.94334
linear association
Minimum Expected Frequency - .032
Cells with Expected Frequency < 5 -
52 OF
48
48
1
63 ( 82.5%)
.01546
.21139
.02619
Statistic
Value
ASE1
Val/ASEO
Approximate
Significance
Phi
Cramer's V
Contingency Coefficient
.61514
.25113
.52395
.01546 *1
.01546 *1
.01546 *1
Lambda :
symmetric
with NPI dependent
with PRENTERI dependent
Goodman £ Kruskal Tau :
with NPI dependent
with PRENTERI dependent
.04615
.05128
.04196
.04522
.03590
.03080
.05646
.03210
.01442
.00915
1.46344
.88649
1.28478
.35617 *2
.25595 *2
*1 Pearson chi-square probability
*2 Based on chi-square approximation
Number of Missing Observations: 21
G-20
-------
NPI by RENTI
RENT I
NPI
Page 1 of 2
Count
Row Pet
Col Pet
Tot Pet
1.00000
5.00000
D. 00000
0.00000
0.00000
0.00000
1.00000
Row
100.0000
1
14.3
50.0
.5
1
1.4
50.0
.5
00.0000
1
12.5
7.1
.5
1
4.3
7.1
.5
8
13.3
57.1
4.2
4
5.6
28.6
2.1
300.0000
2
25.0
8.7
1.0
1
4.3
4.3
.5
6
10.0
26.1
3.1
8
11.1
34.8
4.2
6
31.6
26.1
3.1
400.0000
1
50.0
2.6
.5
2
25.0
5.3
1.0
6
26.1
15.8
3.1
1
14.3
2.6
.5
10
16.7
26.3
5.2
13
18.1
34.2
6.8
5
26.3
13.2
2.6
500.0000
1
12.5
4.0
.5
4
17.4
16.0
2.1
10
16.7
40.0
5.2
9
12.5
36.0
4.7
1
5.3
4.0
.5
Total
2
1.0
8
4.2
23
12.0
7
3.7
60
31.4
72
37.7
IS
9.£
Column
(Continued) Total
2
1.0
14
7.3
23
12.0
38
19.9
25
13.1
191
100.0
G-21
-------
NPI by RENTI
RENT I
NPI
Page 2 of 2
Row
Total
Count
Row Pet
Col Pet
Tot Pet
1.00000
). 00000
3.00000
0.00000
0.00000
0.00000
1.00000
600.0000
2
25.0
5.4
1.0
5
21.7
13.5
2.6
2
28.6
5.4
1.0
8
13.3
21.6
4.2
16
22.2
43.2
8.4
4
21.1
10.8
2.1
800.0000
1
50.0
2.5
.5
4
17.4
10.0
2.1
3
42.9
7.5
1.6
13
21.7
32.5
6.8
17
23.6
42.5
8.9
2
10.5
5.0
1.0
1000.000
2
8.7
16.7
1.0
5
8.3
41.7
2.6
4
5.6
33.3
2.1
1
5.3
8.3
.5
Column
Total
37
19.4
40
20.9
12
6.3
2
1.0
8
4.2
23
12.0
7
3.7
60
31.4
72
37.7
19
9.9
191
100.0
G-22
-------
Chi-Square
Value
DF
Significance
Pearson
Likelihood Ratio
Mantel-Haenszel test for
linear association
43.45863
40.90562
.09556
Minimum Expected Frequency - .021
Cells with Expected Frequency < 5 -
45 OF
42
42
1
56 ( 80.4%)
.40906
.51893
.75722
Statistic
Approximate
Value ASE1 Val/ASEO Significance
Phi
Cramer's V
Contingency Coefficient
,47700
.19474
.43053
.40906 *1
.40906 *1
.40906 *1
Lambda :
symmetric
with NPI dependent
with RENTI dependent
Goodman & Kruskal Tau :
with NPI dependent
with RENTI dependent
.05185
.05042
.05298
.03245
.02691
.02863
.05307
.03023
.01362
.00809
1.76420
.92790
1.71874
.69019 *2
.73908 *2
*1 Pearson chi-square probability
*2 Based on chi-square approximation
Number of Missing Observations: 19
G-23
-------
NPI by HSEVALI
HSEVALI
NPI
Page 1 of 2
Row .
Total
Count
Row Pet
Col Pet
Tot Pet
.00000
). 00000
3.00000
0.00000
0.00000
0.00000
0.00000
50000.00
2
25.0
15.4
1.0
1
14.3
7.7
.5
2
3.3
15.4
1.0
5
6.9
38.5
2.6
3
15.8
23.1
1.6
100000.0
4
50.0
8.0
2.1
6
26.1
12.0
3.1
1
14.3
2.0
.5
15
25.0
30.0
7.9
16
22.2
32.0
8.4
8
42.1
16.0
4.2
150000.0
1
50.0
2.6
.5
6
26.1
15.4
3.1
2
28.6
5.1
1.0
12
20.0
30.8
6.3
14
19.4
35.9
7.3
4
21.1
10.3
2.1
200000.0
1
12.5
2.2
.5
5
21.7
11.1
2.6
1
14.3
2.2
.5
18
30.0
40.0
9.4
17
23.6
37.8
8.9
3
15.8
6.7
1.6
300000.0
1
50.0
2.7
.5
1
12.5
2.7
.5
5
21.7
13.5
2.6
2 '
28.6
5.4
1.0
11
.18.3
29.7
5.8
16
22.2
43.2
8.4
1
5.3
2.7
.5
Column
(Continued) Total
13
6.8
50
26.2
39
20.4
45
23.6
37
19.4
2
1.0
8
4.2
23
12.0
7
3.7
60
3U4
72
37.7
19
9.9
191
100.0
G-24
-------
NPI by HSEVALI
HSEVALI
NPI
Count
Row Pet
Col Pet
Tot Pet
40.00000
60.00000
80.00000
100.00000
120.00000
140.00000
160.00000
Column
Total
400000.0
2
3.3
33.3
1.0
4
5.6
66.7
2.1
500000.0
1
4.3
100.0
.5
6 1
3.1 .5
Page 2 of 2
Row
Total
2
1.0
8
4.2
23
12.0
7
3.7
60
31.4
72
37.7
19
9.9
191
100.0
G-25
-------
Chi-Square Value DF Significance
Pearson 34.58965 36 .53566
Likelihood Ratio 35.07058 36 .51262
Mantel-Haenszel test for .30176 1 .58278
linear association
Minimum Expected Frequency - .010
Cells with Expected Frequency < 5 - 39 OF 49 ( 79.6%)
Number of Missing Observations: 19
G-26
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APPENDIX H. METHODOLOGY
Contents
H.I. Description of the NPL Data Set (Site Selection)
H.2. Method of Extraction for Census Data
H.3. Regulatory Variable Selection Criteria
"Initial Selection of NPL Event Type Codes for the Superfunfl Environmental Equity Study
Analysis and Report," September 9, 1993. 36 pp.
H.4. List of Other Interim Reports and Analyses
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Appendix H.I.
DESCRIPTION OF NPL DATA SET.
Superfund Environmental Equity Study
November 19, 1993
The NPL list currently encompasses proposed and finalized sites and historically, contains
those that have been deleted. *
The analyses of the relationship between socioeconomic and regulatory characteristics of the
sites, are for all practical purposes conducted for NPL sites that have been finalized for the
NPL and were on that list as of 9/30/93. Although four proposed sites and six deleted sites
were retained in the data set, incomplete data on them usually eliminated them from most of
the analyses.
Although the regulatory portion of the analysis includes sites in Puerto Rico and the Virgin
Islands, the set excludes social and economic data for such sites because of the lack of
comparability of the Census data with NJ and NY.
Descriptions of deleted and proposed sites are given below.
Deleted Sites
Six sites were reported as deleted from the NPL in Region 2 primarily because no further
response was needed. These six were:
Beachwood/Berkley Wells
EEC Trucking
Cooper Road
Friedman Property/Upper Freehold Site
Krysowaty Farm
M&T Delisa Landfill
Several others had been deleted on the basis of not being suitable for the NPL for a variety of
reasons. These sites listed below along with reasons for deletion, were not included in the data
set.
Horstmann's Dump. This site was deleted because its HRS score was below 28.5 after
comments.
Jamie Fine. This site was incorporated into the Brook Industrial Site.
Matlack. This site was referred to the RCRA program.
1. The corresponding Wastelan codes under event code C0305 describing NPL status that were used as the basis
for this classification are: "F" for sites finalized for the NPL; "P" for sites proposed; and "D" for sites that have
been deleted.
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Proposed Sites
As of 9/30/93, four sites were proposed for the NPL, but not yet finalized. These four sites
are:
Horseshoe Road, NJ
Onondaga Lake, NY
Pfohl Brothers, NY
Tu Tu Well Fields, VI
Socioeconomic data for two of the four sites were problematic for the following reasons:
Tu Tu Well Fields, VI. No socioeconomic data from the Census exists for this site in
geographic units comparable to those available for other sites.
Onondaga Lake, NY. This site is a lake, and the delineation of socioeconomic data is not
meaningful.
Site Finalized for the NPL.
200 sites were finalized for the NPL as of 9/30/93, not including the proposed or delisted
sites. Two of the sites, White Chemical and Radium Chemical were ATSDR Health Advisory
sites and have no HRS scores. Forest Glen has a score far below the 28.5 cutoff, but that score
was not used for designation - an ATSDR advisory was.
-2-
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Appendix H.2.
METHODOLOGY FOR THE EXTRACTION OF POPULATION AND SELECTED
POPULATION CHARACTERISTICS AROUND SUPERFUND SITES,
from 1990 Census Data
November 19, 1993
Introduction
Several issues are addressed in this methodological appendix: the manner in which Census
socioeconomic data are defined and extracted around NPL sites, the implications of the
geographic level of data aggregation selected on the coverage of socioeconomic characteristics,
and a few selected issues pertaining to Census data accuracy.
Description of the Extraction Methodology
The first step in the environmental equity study was to develop a method of obtaining social
and economic data in and around the sites of interest, in this case Superfund sites. The data
generated from the application of this method to individual NPL sites in New York and New
Jersey are contained in separate data books.
The methodology below describes extractions of selected data from the U.S. Census of 1990 at
the Census block level. The Census block is the smallest geographic unit for aggregating
Census data. The Census STF-1B files on CD ROM were used as the primary data source for
Census block level data.
The extraction procedure can be summarized as follows (a more detailed presentation is
attached):
1. Longitudes and latitudes provided by the U.S. EPA Office of Policy and Management
(OPM) were used as the basis for locating the NPL sites.- The locational coordinates
had been verified using EPIC. The OPM data were supplemented by Region 2 data
where necessary. These longitude/latitude points are referred to in the discussion that
follows as the center of the sites.
2. A cross-reference file was first set up for Census blocks within a five mile radius of the
site longitudes and latitudes. This file contained the CERCLIS No. for the NPL site,
the Census identification or record number for each block within 5 miles from the NPL
site, and the actual distance between the NPL site center and the center of the block.
3. Reports were generated for various population characteristics (cumulative) for 0.25,
0.5, 0.75, 1, 2, 3 and 4 mile points that represent the distance between the centers of
the Census blocks and the centers of the NPL sites. By starting at the site's center,
people living within the site boundaries are included. Obviously, numbers would differ
if other distance criteria were used, such as starting from the boundary of the site rather
than its center.
4. Actual data aggregated at the Census block level may extend beyond the stated radii or
fall short of it, since specified distances only pertain to the location of the centers of the
blocks and blocks are not drawn on the basis of where the entire block areas are
located.
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5. For sites located near the boundary between New Jersey and New York, data was
accumulated from both states according to the distance criteria. However, no
population data outside of New York or New Jersey has been included for sites near
Pennsylvania and Connecticut borders, but this only becomes an issue beyond one mile.
No New York sites are within 5 miles of Vermont or Massachusetts.
The manner in which the latitudes and longitudes were converted into distances from the site
centers is described below.
In order to convert the longitudes and latitudes into distances from the center of the site
expressed in miles, a formula was developed based on the Pythagorean Theorem to restrict the
distance around each site to four miles. This allows all of the Census tracts/blocks to be
identified whose centroids fall within a four mile radius from the site, including any distance
within four miles. This formula was required to convert the latitude/longitude for each Census
tract/block centroid into miles in order to link census tracts/blocks to particular distances from
the Superfund sites. For New York and New Jersey the formula is:
Distance (in miles) =
68.9 x Square root of [0.75*(longitudetract/biock - longitude^e)^
+ (latitudetract/block - latitude^)2]
The correction factor of 0.75 applies to NY and NJ only and is changed for
Puerto Rico (see discussion below).
- This formula represents an approximation of a four mile radius. Distortions are
introduced by the convergence of longitude lines. The difference in longitudes (between
the NPL site and the block centroid) was multiplied by a correction factor of 0.75 to
reduce the error in the approximation. The correction factor accounts for the variation
in the magnitude in miles of a longitudinal degree as one moves from the equator
(equivalent to about 69 miles) up to the North Pole (equivalent to about 0 miles, where
the longitude lines converge at a single point). This allows the degrees specified in the
longitude and latitude to be equivalent, that is, to equal the same number of miles.
- The total distance in degrees obtained from the formula was then multiplied by 68.9 in
order to convert degrees into miles. This correction process can be made more precise
if necessary; but it is adequate for the purposes of Census block selection.
- The specification of four miles is a maximum radius which allows aggregations of
Census block information for any distance within four miles to be drawn. The four
miles is arbitrary and can be changed to any distance.
Data for successive rings is cumulative, that is, the data from smaller rings is contained within
each larger radius. Discrete rings can and were obtained as well, but that kind of data is only
useful if particularly locations near a site have to be located.
A key point should be kept in mind in interpreting the aggregation of data by Census blocks.
Since it is only the Census Block centroids which have been assigned to distances around the
Superfund sites, data aggregated at discrete distances around the sites are actually irregular
rather than neat concentric circles. The boundaries actually conform to the boundaries of the
Census tracts rather than to even circles. This does not usually present any problems. One
anomaly, however, can occur at very small distances around a Superfund site. For example, if
-2-
-------
no centroid of a Census block falls within a one mile radius around the site, no data will be
extracted even though people may live within the one mile.
Comparability with CIS-Based Methods*
The extraction procedure described above, often referred to as the Census Centroid (CC)
method, was compared rigorously against the results available for 31 sites in Region II
generated with an ARCInfo CIS procedure by the Office of Program Management (0PM),
which will be henceforth referred to as the OPM Uniform Density Method (OPM-UD). Both
procedures used the same site longitudes and latitudes and used Block level Census data. The
GIS procedure cut across blocks to produce circular areas with smooth boundaries. Differences
between the GIS method and the method used here were primarily due to the GIS subdividing
of block units at the outer boundary. One method is not more accurate than the other, they just
circumscribe different geographic areas. For this initial equity report, differences between the
two methods were not considered great enough (nor is GIS data more accurate) to warrant the
extra time and expense of using the GIS-based data for a statistical analysis, though GIS is
indispensable for mapping the information.
Also for the purposes of conducting the comparison between the two methods, the method
used for this study was adjusted temporarily to use the boundary of each Superfund site as the
starting point from which to extract population data rather than the center of the site.
The only major source of difference that remained was the outer boundary of the area that is
pulled:
The OPM-UD method uses a Geographic Information System (GIS) to extract population
within a defined distance or area. If the boundary of the defined area of interest intersects a
Census data unit (e.g., a Census defined block), the method assumes uniform population
density within the data unit and assigns the population in the intersected data unit that is
proportional to the size of the area intersected.
The CC method, in contrast, pulls data units in their entirety within the desired distance from
the point or area by pulling the centroids of each data unit that lies within the desired distance
from the point or area regardless of where the boundaries of those data units are located. No
assumption of homogeneity of population distribution is made, since the data units are not
subdivided.
The OPM-UD method produces an estimated population and estimated characteristics within a
precisely known distance from the site. The CC method produces a precisely known
population and its characteristics within an approximately known distance from the site. Both
techniques are able to determine with certainty the area for which populations are extracted.
One is not more accurate than the other. They simply use somewhat different areas - the extent
of the difference increases with larger Census data units.
1. This is drawn from: Memorandum from Dennis Santella, Pre-Remedial and Technical Support Section,
Program Support Branch, Region 2 to Tom Sheckells, Director, Office of Program Management, U.S. EPA
Headquarters, "Region 2 Approach to Environmental Equity Study of NPL. Attachment: "Comparison of the
Census Centroid Method (CC) vs. the OPM Census Tiger File Polygon/Uniform Density Method (OPM-
UD) for Extracting Populations Around NPL Sites (Based on OPM data received May 10, 1993)." (June 14,
1993).
-3-
-------
Thus, the OPM-UD method has the advantage of knowing with certainty the distance from the
site for which a population of interest is drawn, but at the expense of only approximating that
population. The CC method, in contrast, has the advantage of knowing the population with
certainty at the expense of hot knowing precisely the location of the area within which that
population is located.
Briefly, the major findings of the comparison are as follows:
- The CC method is less costly and time consuming, and can therefore be run on the
entire set of NPL sites rather than a sample with negligible difference in cost and time.
The CC method is easily verifiable.
- If one were to vary the starting point of the measurement, that is, start from the center
of the site (the method adopted in this study) rather than the border substantial
differences in the absolute value of populations and subpopulations between the two
methods do occur. The larger the site obviously, the larger the difference.
- The major equity indicators used in the test, which are percentage black and percentage
hispanic, have practically the same values when computed by both methods.
- The assumptions of homogeneity that the OPM-UD method makes when the designated
area boundary intersects a data point makes that method no more accurate than the CC
method - it just produces different results. The differences between the two methods
become greater the more sparsely populated the area is and the larger the Census data
unit is.
The CC method was used for this report for the following reasons:
- The cost and time involved in using the OPM-UD method (because of the CIS
interface) makes the application to the universe of NPL sites in Region 2 impractical.
The OPM-UD method is being applied for a sample of NPL sites nationwide. The CC
method can be applied .rapidly to all of the sites. The time and cost of the CC method is
not significantly a function of the number of sites analyzed.
- When the data base is completed for all NPL sites using the CIS-based OPM-UD
method, the structure of the equity study can be adapted to the new data base. The
difference between the CC and OPM-UD methods (i.e., the closeness of the
approximation between the circumference of a smooth circle of a given radius and the
boundaries of the census units within it) is improved the finer the level at which the
census data is defined. In particular, block is better than block group and block group
is better than tract. The differences are greatest in sparsely populated areas.
-4-
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Notes on the Coverage of Census Population and Housing Data
Coverage of Socioeconomic Data
Socioeconomic data used in this report are drawn from Census block data. This data, while
providing the finest geographic resolution of data, is limited in the numbers of parameters that
are provided. Block level statistics from the STF-1B file are restricted to the following
parameters and subcategories:
Persons
Race: White, Black, American Indian (and Eskimo or Aleut), Asian or Pacific Islander
Persons of Hispanic Origin
Age (under 18; 65 years and over)
Housing Units and Units in Structure (1 unit detached or attached; 10 or more units)
Mean Number of Rooms (for housing units)
Tenure of Occupied Housing Units (owner, renter)
Mean Value of Owner-Occupied Housing
Mean Contract Rent (renter-occupied housing)
Housing Units With 1.01 or More Persons Per Room (occupied housing units only)
Persons in Occupied Housing Units
Housing Unit Occupants (one-person; family householder, no spouse present with
1 or more persons under 18 years)
Block group data contained in STF-3A considerably expands the number of parameters and
their subcategories, however, at the expense of broadening the geographic unit of aggregation
to the block group level. Income data is available at the block group level, for exa/nple, but
not at the block level. Although this is theoretically the most serious limitation of using block
level data, its impact is somewhat reduced by the fact that the variable, house value, is highly
correlated with income, and house value is available at the Census block level.
Accuracy of Census Data
The accuracy of the Census data in general has been discussed in numerous publications of the
U.S. Bureau of the Census, and accompanies most of the published data. The issue of the
extent to which Census data undercounts various subpopulations, has been addressed in
numerous journal articles and government publications. The literature suggests the following:
- Distribution of Undercounts by Race. Nationwide, the total undercounting in the 1990
Census is estimated to be about 2%. It is estimated to be greater for blacks than whites
by about 5%.
- Undercounting of the Homeless. The Stewart B. McKinney Homeless Assistance
Amendments Act of 1990 requires the Census to make an assessment of methods and
procedures for counting the homeless. For targeted locations (certain shelters and
designated streets and other locations) during the nighttime, the numbers counted
nationwide were: 228.621 (178,828 in shelters and 49.793 on streets). These numbers
were not available in time to be included in the Census. These estimates, the Bureau
notes, are subject to many limitations, such as double-counting errors, omissions due to
the fact the homeless might be hidden at night, restricted access, etc.
-5-
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Census Extraction Details
For each set of census files except for the STF1B (block level) data, the data is segmented
vertically. That is, the data for a single geographical unit is split over a number of
different database files. The first segment contains identifying information and a small
amount of census data. The other segments contain the additional data including most
detailed demographic data.
The first step in the extraction process is to identify, for each site, those census records at
the desired level of aggregation whose centroids are within 5 miles of the site. The
distance from the site and the census record number are saved. The primary tool in this
process is Foxpro for Windows, because of its speed and its good SQL data language
implementation. Some of the aggregation and reporting is done using Microsoft Access
because of its ease of use.
The following is a sample SQL statement used to identify the records at the lowest level
of aggregation for the Census files in question, for a radius of up to 5 miles. This sample
finds the record numbers for all blocks (summary level 100) within 5 miles c ite, and
saves the site cerclis number, the block number, and the distance.
SELECT Npl693.cerclis_nu, StfIbxnj.logrecnu,;
68.9*SQRT((Npl693.cons_lat - VAL(StfIbxnj.intptlat)/1000000)**2
+0.75*(Npl693.cons_long+VAL(StfIbxnj.intptlng)/1000000)**2);
FROM Npl693, StfIbxnj;
WHERE 68.9*SQRT((Npl693.cons_lat-VAL(StfIbxnj.intptlat)/1000000)**2
+0.75*(Npl693.cons_long+VAL(StfIbxnj.intptlng)/1000000)**2)<5 =
.T.;
AND StfIbxnj.sumlev = "100";
INTO TABLE njblcks.dbf
The next statement analyzes one of the STF3A files for New York, and creates a cross-
reference identifying all block group records whose centroid is within 5 miles of the site.
The record ID and the distance from the site are saved on the cross-reference file.
SELECT Npl693.cerclis_nu, Stf300ny.logrecnu,;
68.9*SQRT((Npl693.cons_lat-VAL(Stf300ny.intptlat)/1000000)**2
+0.75*(Npl693.cons_long+VAL(Stf300ny.intptlng)/1000000)**2);
FROM Npl693, Stf300ny;
WHERE Stf300ny.sumlev = "090";
AND 68.9*SQRT((Npl693.cons_lat-VAL(Stf300ny.intptlat)/1000000)**2
+0.75*(Npl693.cons_long+
VAL(StfSOOny.intptlng)/1000000)**2)<5 = .T.;
INTO TABLE s300ny03.dbf
68.9 is approximately the number of miles in one great circle degree. The factor 0.75 in
the above expression represents an approximate correction for the fact that at the latitude
of NY and NJ the longitudinal degrees are smaller than the latitudinal degrees. If
-------
necessary it can be made more exact. "090" is the finest summary level on the STF3A,
and represents the block group level.
At least one such cross-reference file is created for each state; if the data in the state is
broken into more than one basic segment (as with the STF3A) one such file is created for
each segment. Cross-reference files were created for all New York and New Jersey NPL
sites for the STF3A and STF1B files. The STF1B files cross-reference files take about
10 times as much disk space as the STF3A files.
Once the cross-reference is created, the records can easily be expanded with any of the
demographic data in any of the census segments. Once all the data is identified, a
summary record with all the data for each site summarized can be created and saved in
dBase format for input into statistical analysis routines.
The following is a typical SQL statement extracting data from the selected census blocks
at one distance from a site (.25 miles in the example below).
SELECT Npl693.cerclis_nu, Npl693.site_name, SUM(StfIbxny.arealand) ,;
SUM(Stflbxny.poplOO), SUM(StfIbxny.p2bx0002), SUM(StfIbxny.p2bx0003), ;
SUM(Stflbxny.p2bx0004), SUM(StfIbxny.p3bx0001), SUM(StfIbxny.p4bx0001),;
SUM(Stflbxny.p4bx0002), SUM(StfIbxny.hlbxOOOl), SUM(Stflbxny.hlbx0002),;
SUM(Stflbxny.hlbx0003), SUM(StfIbxny.h2bx0001*stfIbxny.hlbxOOOl), ;.
SUM(Stflbxny.h3bx0001), SUM(StfIbxny.h3bx0002) ,;
SUM(Stflbxny.h4bx0001*stflbxny.h3bx0001),;
SUM(Stflbxny.h5bx0001*stflbxny.h3bx0002), SUM(StfIbxny.hGbxOOOl),;
SUM(Stflbxny.h6bx0002), SUM(StfIbxny.h7bx0001), SUM(StfIbxny.hSbxOOOl),;
SUM{StfIbxny.h8bx0002), ".25";
FROM Npl693, Nyblcks, Stflbxny;
WHERE Nyblcks.cerclis_nu = Npl693.cerclis_nu;
AND StfIbxny.logrecnu = Nyblcks.logrecnu;
AND Nyblcks.exp_3 < .25;
GROUP BY Npl693.cerclis_nu;
INTO TABLE nyr25a.dbf
For the STF3 A data many statements like the above statement are needed due to the large
number of files over which the data is spread.
This report is based on the above methodology using block-level data from the STF IB
CD-ROM.
-------
Appendix H.3.
INITIAL SELECTION OF NPL EVENT TYPE CODES
Superfund Environmental Equity Study
November 19, 19931
INTRODUCTION
The Superfund Environmental Equity study will explore the linkage between Superfund site
regulatory characteristics and socioeconomic characteristics surrounding the sites. A review
was undertaken of regulatory characteristics of NPL sites as a prerequisite to the selection of
such characteristics for the equity study.
The scope of the review described here is restricted to "Event Type" parameters contained in
Wastelan (as Code 2101). The data given for each Event Type-is the date that the event
occurred - usually in terms of a start date and a completion date. Event listings and
descriptions used as the basis for the selection were obtained from the Data Element
Dictionary, and supplemented by those in the U.S. EPA "Enforcement Project Management
Handbook" (July 1989) and discussions with Superfund managers. (Regulatory characteristics
other than the event types discussed here will be included in the equity study that are under
other codes in Wastelan, such as the number of PRPs, or not in Wastelan, such as the Hazard
Ranking Score.)
Table 1 summarizes those Event Types which were selected and those which were not and the
reasons for non-selection. Table 2 is a more complete list of the Event Types that were
selected. Criteria used to select Wastelan Event Type parameters for inclusion in the
environmental equity study were:
(1) The parameter represents a major milestone in the Superfund cleanup process and is not
wholly contained within another component.
Examples of such milestones are site discovery, proposal and finalization for the NPL, the
RI/FS, the Record of Decision, and removal and remedial actions. These milestones may not
always occur in the same order for every site.
(2) Relatively complete data exists in Wastelan for the parameter.
The completeness of coverage for each of the Wastelan Event Type codes is shown in Tables 3
and 4. Table 3 gives the number of events that are given per event type for the 201 sites in the
data set and Table 4 gives the number of sites for which a given event type is indicated.
Appendix Table A-l gives a simultaneous tabulation or cross reference between events and
sites for each event code. Appendix A-l gives the number of times each code appears by
CERCLIS number. The data in these tables only indicate the number of times a code is listed
for sites, and not whether data (i.e., start or end dates) are given for the events. The
information in all of these tables is not disaggregated by operable unit.
1. This is drawn primarily from a memorandum entitled, "Initial Selection of NPL Event Type Codes for the
Superfund Environmental Equity Study - Analysis and Report," September 9, 1993. 36 pp.
-------
The assignment of an event code is only part of the picture of data completeness. In a number
of cases, codes were assigned to a given site, but no data was entered, i.e., no dates for the
event were given. Table 5 gives the existence of both start and end dates for each of the event
codes. In a number of cases, no data at all appeared for a given site even though the code was
listed. In such cases, the parameter was not selected.
INCLUDED REGULATORY/ENFORCEMENT PARAMETERS
Event Type Codes from Wastelan
Table 2 lists those Event Type parameters that were initially selected for the inclusion in the
Superfund environmental equity study. Reasons for their inclusion are given below, along with
proposals for some further consolidations.
C2101 CO Combined RI/FS. The RI/FS is a major initial stepjn site remediation after a site is
scored, unless removal actions are undertaken prior to the RI/FS. The two studies - the RI and
the FS, though conceptually distinct, are often conducted together. In about a dozen or so
cases, however, separate RIs and FSs were indicated. The pattern of individual RIs and FSs is
explored below as a means of determining whether or not the combined RI/FS (CO) alone is
sufficient to serve as an indicator for the RI/FS process.
C2101 RI Remedial Investigation (RI). This code is reserved for sites which only have a
remedial investigation and C2101 FS Feasibility Study (FS) is reserved for sites which only
have a feasibility study. Explanations as to why these studies would occur separately rather
than in combination (listed under the CO code) are that (1) they might be relatively older sites,
(2) the distribution of funds did not allow the two to be conducted together, which in part may
be a function of who the lead organization was, (3) actions were such that only one or other of
the studies might have been needed, or (4) the combined RI/FS applies to the entire site,
whereas an isolated RI or FS represents a more focused study for a particular problem or part
of the site. Table 6 show the following:
- In all of the cases where a site had an operable unit with only an FS or only an RI, it
also had a combined RI/FS (a CO code).
- Six of the twelve RI events (covering 11 sites), did not continue on with FSs. (A
seventh site with an RI also had an FS code but no date was entered for it, indicating
that it may not have begun yet or may never begin.) Five of the six sites had RIs prior
to 1986, the date of SARA.
- These six sites represent a mix of lead organizations: two are EPA-fund financed sites
(F), and four are PRP lead sites, but two are under PRP responses under the state (S)
and two are responsible party sites only (RP). This implies that the type of lead agency
is not a significant factor in the existence of separate RIs or FSs.
- In practically all of the cases where there is both an RI and an FS, they were generally
conducted early and the RI was almost immediately followed by an FS.
- The more typical case is represented by the twelve sites with only FSs, which represent
a mix of dates and a mix of event lead organizations. It is likely that in these cases, the
RI contained in the combined RI/FS was sufficient and a more focused FS was
conducted on a particular problem or part of the site.
Given the small number of individual RIs and FSs it is recommended that only the CO be
used to represent the RI/FS milestone.
-2-
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C2101 CR Community Relations Plan. The preparation of a community relations plan is a
requirement of the RI/FS process and continues through remediation. The CR dates are,
therefore, defined relative to the RI/FS. The start date is defined in the Dictionary as the start
date of the RI/FS and the end date is the completion date for the remedial action (RA).
Although it might be considered redundant with the RI/FS (CO, RI and FS codes) and
Remediation (RA), the dates are different enough that is worth including. Should a high
correspondence between the CR dates and others occur, it would be removed as being
redundant with the other actions.
The following parameters are critical ones, for reasons that need little explanation.
C2101 DS Site Discovery
C2101 NF Final Listing on NPL
C2101 NP Proposed to NPL
C2101 RA Remedial Action
C2101 RD Remedial Design
C2101 RO Record of Decision
C2101 RS Removal Investigation at NPL Sites
C2101 RV Removal Action (the subevent, AM, which is the date of the Action
Memorandum and is analogous to the ROD for remedial action, will
probably also be included.
Parameters Used for outlier analysis only:
C2101 TG Technical Assistance Grant (TAG). TAGs are only reported for 16 sites, which is
generally too small a number for the statistical analysis. This is an important site characteristic,
since it represents community funding for site cleanup review/oversight, etc. Therefore,
characteristics of these sites will be analyzed separately.
Table 7 gives the raw data for each of the selected Event Type codes for each of the NPL
sites (listed by CERCLIS number). A few sites have not yet been listed in this table, such as
those that recently entered the NPL.
-3-
-------
ELIMINATED REGULATORY/ENFORCEMENT PARAMETERS
Event Type Codes (from Wastelan)
C2101 AR The preparation of an administrative record, which reflects the status of removal
and remedial activities. About half (117) of the sites had this code identified with only 17
not reporting any data. Since the other regulatory parameters selected reflect components of
the AR, it was not selected since other event codes capture this.
C2101 AS Maps and surveys. Although valuable as a source of site information, this data does
not provide direct regulatory information. In addition, the code is identified for only 70 of
the sites.
C2101 DA Design Assistance from a consultant procured. Too few sites with codes (20).
C2101 ED Endangerment Assessment. After 1989, the terminology changed, and such
assessments were substituted by risk assessments (no Wastelan code given for risk
assessments).
C2101 FP Forward Planning - activities possibly needed for site restoration. Too few sites
with codes (13).
C2101 GS Completion of RD/RA. Too few sites with codes (only 6).
C2101 HA Health Assessment. Since this is not an EPA action, it is not a required data entry
in Wastelan. Entries are currently unavailable for this, though it is probably available
through ATSDR, the agency conducting the health assessments.
C2101 HR Hazard Ranking/NPL Listing. The date that the hazard ranking score is assigned is
a major milestone in the NPL site designation and remediation process. Practically no data
items, however, have been entered in Wastelan after 1984 (except for one site, entered in
1988). Only 102 sites, roughly half, have such entries.
C2101IM Initial Remedial Measure. This is potentially an excellent indicator, but there are
too few entries available (only 15 events for 12 sites).
C2101IR Immediate Removal. Excellent indicator, but too few entries (only 31 events for 22
sites).
C2101 LR Long-term Response. PRP payments. Too few events (49)
C2101 MA Management Assistance - federal obligation of funds to cover state administrative
costs. Not a useful parameter.
C2101 ND NPL Deletion. Excellent indicator, but too few events for statistical analysis (only
16 events). This might be a good parameter for outlier analysis.
C2101 NR Removed from Proposed NPL (without being finalized).
C2101 OF Remedy Functioning. Too few sites with codes (1).
C2101 OH Future Events. Description of parameter is to diverse, and there are also too few
sites with codes (29).
-4-
-------
C2101 OM Post-Remediation Activities. Description of parameter is to diverse, and there are
relatively few sites with codes (64).
C2101 OS State Oversight. Too few sites with codes (2).
C2101 PA Preliminary Assessment (PA). Data on preliminary assessments is considered
unreliable in the sense that it reflects the dates that assessment starts and completions were
recorded, i.e., entered into the data base, rather than when they actually occurred. This is
based on reviews of the data conducted by the Region II Pre-Remedial and Technical
Support Section, PSB, ERRD.
C2101 PD Too few sites with codes (3).
C2101 SI Screening Site Inspection (SI). Similar to the Preliminary Assessment (PA) data,
data on site inspections is considered unreliable in the sensejhat it reflects the dates that
inspection starts and completions were recorded rather than'when they actually occurred.
This is based on reviews of the data conducted by the Region II Pre-Remedial and
Technical Support Section, PSB, ERRD.
C2101 TA Technical Assistance. Not a major milestone, and relatively few sites with codes
(31).
C2101 TS Treatability Study. Too few events and sites (34 events, 28 sites, 10 have no dates.
C2101 WP RI/FS workplan approval. A potentially useful parameter, but the date the RI/FS
begins is probably relatively close to this.
C3101 AM - Subevent, "Action Memorandum" (AM) to Removal Actions (RV)
The date of an "Action Memorandum" (AM) is potentially an important milestone for removal
actions (although according to the Wastelan Data Element Dictionary the AM can be used for
remedial actions as well). It is considered analogous to a Record of Decision for remedial
actions. As such, it could be included along with other events in the environmental equity
analysis.
The AM completion dates (there are no start dates in Wastelan) were compared against the
start and completion dates for the immediate removals and/or removal actions for which the
AMs were prepared. The enclosed table shows these comparisons.
The AM completion dates are not included in the equity study for the following reasons:
- The enclosed table shows that the difference between the AM completion dates and the
IR or RV start dates are either very small or in some cases negative. Only 12 sites have
the difference between the AM completion and IR or RV start date exceeding two
months (60 days) ("Days 1" column on the table).
- There are relatively few sites with AM dates to begin with (for only 59 of the 200 sites)
and the AMs for 15 sites apply only to immediate removals (IRs). IR was already
eliminated as a variable because only 30 sites had date entries.
- Only 25 sites have differences between AM starts and RV or IR completions that
exceed 2 years ("Days 2" column on the table).
-5-
-------
(The enclosed table assumes that the event code given for RVs and IRs in Wastelan correspond
to the event codes assigned to AMs.)
In lieu of using either the date of the Action Memoranda or the difference in time between
Action Memoranda and Removal Actions, I recommend using either the dates of or the
difference in time between a Removal Investigation (RS) and the Removal Action. The RS
typically preceeds the Action Memorandum..
Missing, but desirable codes:
Date of Risk Assessment - This is not available in Wastelan as a separate item. It is combined
with the RI/FS since it is conducted as a part of the RI/FS. It supersedes the use of the
Endangerment Assessment code as of about 1989.
-6-
-------
Table 1. Summary of Choice of Wastelan Event Type Codes
Superfund Environmental Equity Study
Code Used
Code Not Used - Reason for Exclusion
All
Event Type Code
and Descnption
AR Plans
AS Maps/Surveys
CO Combined RI/FS X
CR Community Rel. Plan X
DA Design Assistance
DS Site Discovery X
ED Endangerment Assess.
FP Forward Planning
FS Feasibility Study X
GS RD/RA Complete
HA ATSDR Health Assess.
HR Hazard Rank
IM Init. Remedial
IR Immed. Removal
LR Long-term Resp.
ND NPL Deletion
NF Final NPL Listing X
NP Proposed for NPL X
NR Removed from
Proposed NPL
OF Remedy Functioning
OH Future Events
OM Post-Rem. Activities
OS State Oversight
PA Prelim. Assess.
PD Deletion Package
RA Remedial Action X
RD Remedial Design X
RI Remedial Investig. X
RM RA Plan
RO Record of Decision X
RS Removal Investig. X
RV Removal Action X
SI Site Inspection
TA Tech. Asst.-RI/FS
TG Tech. Asst. Grant
TS Treatability Study
WP RI/FS Workplan
Outlier
Only
Useful Code, Not Used Because:
Too few Redundant Data Not
entries with others reliable
X
(X)*
X
X
X
X
X
X
X
X
X
X
X
X
X
Not
Useful
N/A
X
X
X
Note: For codes used, the earliest start date and the latest end date per site
is entered into the data base for a given site, regardless of which
operable unit the date pertains to. DS, NP, and NP only have one date.
(X)* indicates codes that may be combined with or replaced by others.
-------
Table 2. Detailed Listing of Selected Event T)pe Codes from \Vastetan
C2101 CO Combined Rl/FS
C2101 Rl Remedial Investigation
C2101 FS Feasibility Study
C2101 CR Community Relations Plan
C2I01 DS Discovery
C2101 NF Final Listing on NPL
C2101 NP Proposed to NPL
C2101 RA Remedial Action
C2101 RD Remedial Design
C2101 RO Record of Decision
C2101 RS Removal Investigation at NPL Sites
C2101 RV Removal Action
Outlier analysis only:
C2101 TG Technical Assistance Grant
-------
Table 3
Event Occurrences Number of Events
28-Aug-93
Event Code Number of Event Records
AR 172
AS _ _ 72_
CO _ . _ 397
CR _ 138_
CT _ L
DA _ 23_
DS _ 200
ED _ 63_
FP _ 1£
FS _ 18_
GS _ 6_
HR _ 102
IM 1
LR _ 49^
MA _ 149_
ND _ 16^
NF _ 200
NP _ 201
OF 1
_ 44^
OM _ _ 70_
OS _ 2_
PA _ 206
PD _ : _ 3_
RA _ 414
RD _ 407
RI _ 12_
RM _ 1£
RO _ 359
RS _ 378
RV _ _ 121_
SI _ 273
TA _ 39_
TG _ 16^
TS _ 34_
WP 94
-------
Table 4
Event Occurrences Number of Sites
28-Aug-93
Event Code Number of Sites
AR 117
AS _ 70_
CO _ 200
CR _ 115^
CT _ 1_
DA _ 20_
DS _ 200
ED _ 54_
FP _ 13_
FS _ 11
GS _ 1
HR _ 102
IM _ 12_
IR _ _ 22_
LR _ 45^
MA _ _ 120
ND _ _ : _ 16^
NF _ _ _ 199
NP _ : _ 201
OF _ L
OH _ 29^
QM _ 64_
OS _ _ 2_
PA _ _ . _ 199_
1PD 3
RA _ _ ^
RD _ 186
RI _ 11
RM _ 18
RO _ 199
RS _ 188
RV _ 7J_
SI _ 197
TA _ _ 31
TG _ _ 16^
TS _ 2^
WP 84
-------
Existence of Data for Event Dates
05-Sep-93
Table 5
Both Start
Event Code None Only Start Dates Only End Dates and End Dates
*R
*S
DO
-R
CT
)A
38
ED
3
:s
GS
Ğ
M
3
\
.R
VIA
go
C
MP
OF
171 146
1 3
28 148
24J 99
1 0
4J 8
ol o
29 19
4 0
2
1
C
C
C
45
31
14
C
C
1
OH 8
OM
OS
PA
PD
RA
RD
Rl
RM
63
1
0
:
j
$ 2:
r 1231
9
1
221
12
0
11
0
8
7
15
3
0
9
30
0
1
0
0
0
,0
5
1
1
18
0
70
132
10
1
6
377
104
254
25
0
I 4
I 69
S 1404
-------
pS and RI Details T
fc-Sep-93
Event Code Ccrclis Number
B
- NJ22 10020275
NJD041743220
- NJD053292652
NJD06 1843249
NJD063 160667
NJD980528889
NJD980532816
NJD980654099
NJD980654099
- NJD980654172
NJD980755623
NJD980761365
NYD000511659
NYD000606947
NYD09 1972554
NYD980507693
NYD980528657
NYD98 1486947
IY
a
NJ22 10020275
NJD053292652
. NJD980505754
NJD980529739
NJD980654099
NJD980654107
NJD980654172
. NYD000511360
. NYD073675514
NYD980528657
NYD980528657
. NYD980650667
Operable Unit
01
01
01
02
01
02
01
03
02
02
01
02
01
09
02
02
01
01
01
01
02
01
01
01
02
01
01
01
01
01
Start Date
11/15/86
9/13/83
1/20/83
9/29/90
9/18/90
9/28/90
9/3/82
9/28/84
9/28/84
1/30/91
7/30/89
9/30/81
5/15/91
12/17/90
12/29/82
2/4/91
8/12/85
9/30/81
11/26/91
9/28/83
9/28/84
3/21/84
9/1/89
6/26/86
4/8/83
11/15/83
9/30/81
5/15/86
End Date
7/17/91
3/30/84
12/31/84
9/27/91
9/11/91
9/20/91
9/30/84
9/30/92
9/27/91
9/29/89
4/6/82
5/15/91
3/31/92
9/24/85
2/4/91
11/15/86
5/1/82
3/16/84
9/27/90
11/15/84
6/26/86
5/22/86
8/15/85
12/1/82
9/1/86
Event Lead Org
FF
F
F
F
SN
S
S
S
F
EP
F
F
S
RP
RP
S
PS
FF
F
PS
F
S
PS
F
F
RP
RP
F
RP
Table 6
-------
Event Counts and Dates
05-Sep-93
Number
rsrrlls Operable
CercHs Un}ts
Number
NJ01 70022172
NJ1891837980
NJ221002027S
NJ321 0020704
NJ71 70023744
NJ9690510020
JNJD000565531
JNJD000607481
NJD001 502517
NJD002005106
NJD002141190
NJD0021 68748
NJD0021 73276
NJ0002349058
NJD002362705
NJD002365930
NJD002385664
NJD002493054
NJ0002517472
NJD01 171 7584
NJD030253355
NJD041 743220
NJD041 828906
NJ0045653354
NJD046644407
NJ 0047321 443
NJ0047684451
NJD048044325
[NJD048798953
NJD049860836
NJ00531 02232
NJD053280160
NJD053292652
NJD054981337
NJD061 843249
NJD063157J50
NJ006316O667
NJ0064263817
22
1
;
3
\
\
C
j
'
c
',
2
',
i
't
*
J
i
2
3
3
3
3
3
3
3
CO RA
4
1
.
i
;
2
1
1
c
1
1
^
3
1
1
1
2
1
2
2
2
5
2
^
,
4
1
4
i
1
2
1
rf
1
1
4
2
1
2
4
2
2
2
2
2
(Counts j
RO RS
2
1
3
i
1
i
,
't
;
1
1
*
4
1
J
1
1
1
2
1
1
2
2
1
1
2
2
2
2
2
2
2
1
2
^
j
2
^
2
^
2
4
4
2
2
2
2
2
2
2
2
2
2
2
2
RV
2
2
1
;
1
2
1
1
1
4
1
) Dates |
CO Start CO End CR Start CREnd OS Start DS End FS Start
9/27/9(
7/21/9C
6/19/9
10/1/9
9/2S/8
6/1/8
4/1 4/8Ğ
9/28/8
3/30/84
5/28/8
1/15/8
9/26/8
11/1/8
712ft
3/30/84
9/5/84
5/21/86
4/11/89
8/2/84
4/29/88
9/27/85
9/27/83
6/2B/85
8/25/89
6/26/87
9/29/84
7/24/86
10/3/86
3/30/64
6/11/81
5/17/90
9/20/88
9/29/88
4/12/85
4/25/86
9/27/83
9/18/90
12/20/82
3/16/9-
9/30/9"
9/30/9
9/23/8
4/24/8
1/15/9
9/13/8
3/1/9
9/28/89
9/27/90
6/28/91
6/28/91
9/27/91
9/30/92
9/28/90
9/28/89
9/30/92
9/26/91
3/29/91
6/28/89
9/11/91
9/29/86
4/14/8£
10/1 5/r
9/1/84
1/1/8
3/30/8
5/6/8
4/11/89
1/14/86
9/30/88
9/15/88
2/13/86
1/24/90
11/15/85
9/23/83
9/20/88
8/12/85
4/25/86
,
1/15/86
10/18/85
11/1/8C
8/1/8.
12/1/7
2/1 /BC
6/1/8
9/1/77
7/9/8-
5/1/7
1/1/8C
5/1/8
9/1/77
1/1/80
10/1/79
9/1/80
2/17/83
11/1/79
12/1/66
4/10/84
4/1/80
4/1/84
10/1/78
10/1/75
12/1/83
4/10/84
8/1/82
6/1/81
4/10/84
11/1/79
8/1/82
9/1/74
6/1/81
5/24/84
10/1/79
1/1/84
6/1/81
12/1/74
3/1/85
4/10/84
11/is/se
9/13/83
1/20/83
9/29/90
9/18/90
FSEnd
7/17/9
3/307B4]
12/31/84
9/27/91
9/11/91
-------
Event Counts and Dates
Cerclis
Number
NJD070281175
NJO070415005
NJD070565403
NJD073732257
NJD078251675
NJD09496661 1
NJD097400998
NJD9804B4653
NJD9B0504997
NJD960505176
NJD9B0505283
NJD980505341
NJD980505366
NJD980505382
NJD980505416
NJD980505424
NJD980505648
NJD980505671
NJD960505754
NJD980505762
NJD980505879
NJD980505887
NJD9805288B9
NJD980528996
NJD980529002
NJD980529035
NJD980529143
NJD980529192
NJD980S29408
NJD980529416
NJD980529598
NJD980529713
NJD980529739
NJD980529762
NJO980529879
NJD980529887
NJ0980529945
NJD980530t09
Operable
Units
;
;
4
4
4
2
2
;
4
4
4
A
. 4
4
A
%
4
3
2
Ğ
3
T
J
2
2
2
2
2
2
2
2
2
2
3
2
2
2
2
CO
2
;
;
1
1
1
2
*
1
j
)
1
1
4
\
4
4
1
4
4
2
1
1
1
1
1
2
1
1
1
1
2
1
1
1
1
RA
;
1
\
i
2
I
1
1
1
4
2
;
J
1
1
4
1
;
;
A
1
1
1
1
3
1
2
1
1
1
2
1
2
2
|Count
RO
Ğ
',
;
;
1
1
2
;
1
1
1
1
1
3
4
1
1
4.
1
2
4
2
1
1
1
1
I
1
1
1
1
1
2
1
1
1
1
s
RS
2
2
Ğ
1
2
2
4
4
Jt
2
4
4
4
4
2
4
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
RV
2
2
j
',
c
4
1
2
1
1
2
1
1
1
CO Start
1/6/8
5/29/8
9/30/8
6/19/B4
4/12/89
12/28^83
8W87
3129185
9/1/81
12/1/86
8/21/88
4/17/85
8/18/83
9/26/84
1/1/81
7/5/82
4/12/88
1/1/80
1000/35
12/30/83
6/13/85
6/1 3#5
7/9/84
2/1 5/84
. 3/30/84
9/26/84
3/28/84
5/11/83
6/1/86
9/30/82
9/26/84
9/28/84
6/26/87
9/21/87
9/15/86
9/19/84
9/3/82
CO End
9/29/8
S/29/8
9/14/90
9/269
9/29/86
9/29/89
9/29/88
9/28/90
9/27/85
9/29/86
7/11/88
9/28/90
3/31/80
8/28/90
9/29/86
5/1 6/90
5/16/90
9/28/90
9/30/87
9/28/90
9/30/92
6/27/86
9/27/85
3/12/92
3/27/85
9/29/89
9/30/88
9/29/88
9/30/89
9/30/87
9/27/85
CR Stai
. 4/1/87
9/30/85
2/13/86
3/30/89
8/4/87
7/30/87
4/17/85
8/18/83
6/17/66
1/14/86
3/28/86
2/15/84
1/1/83
9/28/84
7/8/88
9/21/87
11/1/85
| Dates
t CR End
3/15/84
4/15/86
3/27/85
I
DS Start
DSEm
1/1/7
5/1/8
6/1/8
5/1/8
5/1/86
11/1/79
11/1/79
4/10/84
8/1/79
12/1/80
10/1/79
1/1/77
11/1/79
2/1/80
4/1/79
6/1/78
6/1/79
6/1/79
5/1/81
1/1 /80
4/1/79
4/1/79
12/1/79
11/1/79
5/1/81
5/1/81
2/28/80
12/1/79
4/1/80
1/1/81
9/1/79
3/1/80
12/1/79
5/1/81
6/1/74
5/1/81
1/1/81
2/1/80
f FS Start
9/28/90
FSEnd
9/20/91
-------
Event Counts and Dates
O5-Sep-93\
Number
Cerclis °P"able
Number U"itS
NJD980530596
NJD980530679
NJD980532808
NJD980532816
|NJD980532824
NJD980532840
NJD980654099
NJD980654107
NJD980654115
NJD980654131
NJD980654149
NJD980654156
NJD980654164
NJD980654172
NJD980654180
NJD980654198
NJD980654214
NJD980654222
NJD980755623
NJD980761357
NJD980761365
NJD9807613.73
NJD980761399
NJD980766828
NJD980769301
NJD980769350
NJD980785638
NJD980785646
NJD980785653
NJD981 178411
NJD981 179047
NJD981 490261
NY021 3820830
NY4571 924451
NY457 1924774
NY7890008975
NYD000337295
NYD000511360
2
'.
2
2
2
t
i
3
/
^
3
j
4
2
2
3
T
2
3
2
2
3
2
2
3
4
4
3
2
3
6
9
9
7
2
3
CO RA
1
1
1| 1
1
1
4
4
11
1
1
2
4
2
4
1
2
2
1
1
4
Counts
RO RS
1
1
1
1
1
1
3
RV
2|
2
!
<
2
2
: 2
3| 1| 2
2
2
3
1
2
2
1
1
1| 1
2
1
2
2
1| 1
1
1
2
3
1|
1
1
1
2
3
3
2
2
2
5
8
9
6
2
2
2
1
8
3
3
2
2
2
'
'
3| 2| 2
1
2
2
1
1
1
2
2
2
2
2
2| 3
2
2
1
1
2|
2
1
11 2
2
1
2
2
2
1
A
1| . 2|
1
1
1
2
3
3| 3
2
1
2
5
8
7
8
1
2
2
1
2
5
8
8
6
1
2
2
2
2
2
2
2
2
2
2
2
2
7
3
| Dates
CO Start CO End CR Start CR End
11/21/8:
J 9/29/86
4/5/82| 9/30/82
9/3/82
9/3/82
2/3/87
2/3/87
. 9/28/84
3/21/84
1/9/84
3/1/87
! 9/30/84
ğ 9/30/84
9/27/90
7/25/86
9/30/91
9/30/92
4/5/82|
.
4/1/84
9/28/83J 9/27/91 1 1/10/84|
9/26/84
9/26/84
6/30/88
6/30/88
9/28/84|
5/9/88
9/26/85
12/16/86
10/1/86
9/27/90
9/28/90
2/13/86]
8/29/88
2/13/86
I
9/30/92
4/9/85| 6/30/88
3/29/85
6/28/85
9/30/87
9/20/88
6/19/87
12/24/86
3/29/85
12/13/84
9/29/88
9/29/89
9/30/91
9/26/91
9/28/90
9/28/90
6/1/90
12/13/84| 6/1/90
9/20/88
9/30/88
7/17/89
3/19/90
3/29/90
4/23/91
11/19/91
4/17/85
6/19/86
9/30/92
9/30/92
9/30/92
9/28/90
9/27/90
2/13/86
2/13/86
9/20/88
2/13/86
6/17/86
DS Start OS End FS Start FS End
11/29/89|
7/25/90
6/17/86|
-7/17/89
5/31/86
7/1 /7£
)
6/1 /79|
2/1 /8C
2/1 /8C
6/1/81
2/1 /8C
4/1/81
9/3/82
i
9/30/84
I
-
9/28/84
8/1/821
8/1/82
8/1/82
8/1/82
8/1/82
8/1/82
8/1/82
8/1/82
4/1/80
11/1/80
8/1/82
8/31/90
9/30/92
I
1/30/91
4/10/83)
10/1/84
4/10/84
4/10/84
4/3/81
4/10/84
7/30/89
4/10/84|
2/1/85
12/1/83
12/1/83
11/1/85
5/1/85
6/27/86|
11/1/73
1/1/80
5/1/78
2/24/87
4/10/80
11/1/79
9/27/91
9/29/89
.
-------
Event Counts and Dates
05-Sep-93
Cerclts
Number
NYD000511451
NYD000511493
NYD000511576
NYD000511659
NYD000511733
NYD0005118S7
NYD000512459
NYD0005142S7
NYD000606947
NYD000813428
NYD000831644
NYD001485226
NYDOOt 533165
NYD001 567872
NYD002044584
NYD00205011Q
NYDCO2059517
NY0002066330
NYD002232957
NYD002920312
NYD01 0959757
NYD01 096801 4
NYD01 3468939
NYD0481 48175
NYD072366453
NYD075784165
NYD091 972554
NYD980421176
NYD980506679
NYD980506B10
NYD980506927
NYD980507735
NYD980509376
3perat>(c
Units
2
2
2
4
3
2
4
2
<
2
2
2
3
3
4
3
3
3
C
1
3
2
2
2
3
2
3
2
2
2
2
3
2
3
CO
^
3
1
2
2
4
1
1
1
2
1
2
2
2
2
3
3
4
i
3
1
1
1
2
2
1
3
2
1
1
1
2
1
2
3
RA
3
2
1
4
1
10
1
1
3
)
4
t
4
1
10
2
1
1
1
2
1
2
1
2
1
1
2
1
1
2
JGount
RO
1
1
1
2
2
1
1
5
4
1
1
1
2
i
2
3
4
2
1
1
1
2
1
2
1
1
1
1
2
1
1
2
I
RS
2
2
j
2
2
^
^
^
t
2
2
2
f.
f.
A
2
2
2
4
2
2
2
2
2
2
2
2
2
2
2
3
2
2
2
2
RV
2
2
2
1
1
2
1
2
1
1
1
CO Start
3/22/88
4/1V88
12/2B/8-
3/1 2/82
9/27/83
2/9/88
9/29/St
9/23/87
9/30/8'
3/29/9'
1/19/81
6/2/89
4/1 8/87
10/18/89
3/31/88
3/31/88
7/10/87
7/1 0/87
6/29/85
9/23/87
8/5/83
4/19/85
8/24/85
8/18/83
7/17/8=
5/22/86
10/21/87
3/30/84
7/10/87
4/19/88
9/27/83
11/15/87
10/3/88
9/19/89
3/30/84
7/22/87
3/30/90
12/28/84
CO End
3/31/92
12/28/a
9/28/9I
5/21/92
6/27/9
3/30/8'
9/26/ft
11/26/85
6/14/89
6/21/90
9/28/90
3/29/91
6/7/8B
6/29/9C
3/31/92
9/28/9C
9/29/89
2/8/90
11/12/89
3/19/85
61X192
8/27/90
12/17/90
9/26/9Q
6/27/91
9/26/90
9/30/92
8/9/84
5/1 4/92
9/28/90
CR Star
4/11/88
5/31/86
9/27/83
6/26/87
It/19/84
9/25/91
11/26/85
4/9/9(
4/1 0/9C
7/1 0/87
2/13/B6
1 2/13/B6
9/23/87
9/30/86
6/1 8/91
5/6/88
4/1/86
.- 1/31/92
3/30/84
8/1 5/B7
6/26/84
3/30/84
7/22/87
12/28/84
| Dates
t CR End
5/22/86
12/17/90
'
I
DS Start
DSEnc
3/1/80
5/1 /7S
1/1/80
10/1/76
9/1/80
4/1/79
4/10/80
12/1/7S
12/1/79
4/1/80
3/1/79
9/1/84
4/1/83
7/26/88
7/1/84
9/1/84
9/1/84
5/1/82
6/1/83
11/1/79
9/1/71
4/1/80
8/1/82
6/29/87
2/1/80
4/1/80
10/1/79
3/1/79
10/1/78
12/1/79
5/1/79
5/18/82
4/1/60
I FS Start
9/30/81
5/15/91
12/17/90
FSEnd
4/6/82
3/31/92
-------
Event Counts and Dates
05-Sep-93{
Cerclis
Number
NYD980528335
NYD98052S475
NYD9805286S7
NYD960531727
NYD980534556
NYD980535124
\NYD9305351B5
NYD980535181
NYD980535215
NYD980535652
NYD980S93099
NYD980650667
NYD980651087
NYD980652259
NYD980652267
NYD980652275
NYD9806S4206
NYD960564361
NYD980762520
NYD980763767
NYD980763841
NYD9B0768683
NYD9BO763891
NYD980768717
NYD980768774
NYD980780670
NYD980780746
NYD980780779
NYD980780795
NY0980780878
NYD930785661
NYD981 184229
NYD931 486947
NYD981 486954
NYD981 560923
NYD9815619S4
NYO986B8266Q
Operable
Units
\
\
3
\
\
\
2
;
3
4
A
t
A
3
1
c
A
£
'.
2
2
'A
A
4
A
2
4
2
2
2
2
3
2
2
3
2
2
2
CO
2
2
j
1
1
1
2
3
1
J
^
^
1
4
1
1
I
Ğ
t
1
1
^
>
1
4
1
f
1
1
2
1
1
2
1
1
1
RA
\
1
Ğ
A
1
1
4
2
c
1
1
c
3
2
2
3
1
1
3
4
1
1
A
1
T
1
1
2
2
1
1
2
1
1
1
(Count!
RO
1
1
2
1
1
1
1
2
2
1
1
3
1
1
2
1
1
2
2
2
1
1
1
3
1
3
1
t
1
1
2
1
1
2
1
1
1
c
RS
2
d
^
Ğ
4
4
A
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
RV
1
1
1
1
1
c
2
1
1
1
1
2
L_ 2
1
CO Start
9/28/83
4/11/85
6/25/91
7/15/84
3/26/91
11/10/89
6/29/85
2/26/86
7/15/83
10/13/87
12/28/84
9/1/86
8/10/83
1/13/84
1/18/84
5/21/86
9X27/89
9/26/85
1/16/84
3/30/84
3/31 /B8
4/13/87
12/20/85
7/10/87
7/10/87
3/29/85
4£8/89
6(28/85
3/29/85
3/29/85
9/27/88
8/15/87
9/30/88
7/31/69
9/30/88
1/4/90
8/26/92
CO End
7/13/87
9/28/90
3/16/88
9/27/90
9/2800
9/30/91
6/24/91
9/29/89
9/28/90
9/21/90
9/30/85
9/29/88
9/30/89
9/29/89
9/27/90
9/25/84
9/30/92
3/29/91
8/27/90
9/28/92
9/29/89
9/30/92
9/27/91
9/25/87
9/25/87
9/30/87
3/29/91
2/4/91
9/30/92
12/29/89
3/29/91
CRStar
11/1/83
9/28/90
12/31/85
11/10/89
2/13/86
1201/85
10/13/87
5/31 /B6
1/10/64
12/31/85
4/1 8/85
9/27/B9
12/8/B5
7/7/fl9
3/31/88
4/13/87
7/22/91
7/30/87
9/19/88
2/13/86
4/30/90
7/31/89
1/4/90
| Dates
t CR End
1 1/15/83
3/31/86
10/18/85
4WS6,
I
DS Start
OS Em
11/1/7
1/1/75
6/1/82
10/1/79
4/i/ec
4/1 rec
4/1/8C
5/1/80
5/1/82
6/1/81
5/1/82
5/1/82
3/1/79
8/1/82
8/1/82
8/1/82
3/1/82
8/1/83
4/1/83
10/1/82
7/1/83
8/1/B3
7/1/83
7/1/83
7/31/83
7/1/82
6/1/84
9/1/84
9/1/84
9/1/84
8/1/84
3/25/86
9/6/85
5/1/83
9/19/86
2/10/86
5/29/84
f FS Start
12/29/82
FSEm!
9/24/85
-------
Event Counts and Dates
Number
Cerclis °P!"ble
Number Unlts
NYD991 292004
PR41 70027383
PRD090282757
PRD090370537
PRD980301154
PRD980509129
PRD980512362
PR0980640965
PRD980763775
PRD980763783
\flD982272569
2
3
2
2
2
2
3
2
3
2
2
CO RA
4
4
1
1
1
2
3
2
3
2
1
1
2
2
1
2
1
2
1
2
1
1
JGounts
RO RS
11 2
2
2
1
2
2
11 2
1
2
2
2
11 2
2
1
2
2
11
RV
1
1
3
| Dates |
CO Start CO End CR Start CR End OS Start
7/10/87
3/19/92
1/16/84
3/31/88
6/26/87
8/30/88
9/28/83
6/20/85
4/1/84
9/30/85
2/19/92
4/24/92
9/30/88
9/30/88
9/28/90
9/24/91
9/30/91
10/23/90
9/30/91
1/16/84
4/15/88
6/26/87
8/30/88
8/15/87
4/1/84
6/20/86
12/31/91
DS End FS Start FS End
9/1/84
9/15/84
6/1/81
5/1/81
7/1/82
5/1/81
5/1/81
7/1/81
7/1/82
7/1/82
-------
Event Counts and Dates
05-Sep-93
Dates
Cerclis
Number
NF Start NFEnd NP Start NPEnd RA Start RAEnd RO Start RDEnd Rl Start RIEnd RO Start ROEnd RS Start RSEnd RV Start RVEnd
NJ01 700221 72
NJ1 891 837980
NJ221 0020275
NJ321 0020704
NJ71 70023744
NJ9690510020
NJD000565531
NJD000607481
NJD001502517
NJD002005106
NJD002141190
NJD0021 68748
NJD0021 73276
NJD002349058
NJD002362705
NJD002365930
NJD002385664
NJD002493054
NJD002517472
NJD01 171 7584
NJD030253355
NJD041 743220
NJD041 828906
NJD045653854
NJD046644407
NJD047321443
NJD047684451
NJD048044325
NJD048798953
NJO049860836
NJD053102232
NJD053280160
NJD053292652
NJD054981337
NJD061 843249
NJD063157150
NJD0631 60667
NJD064263817
8/30/9C
9/21/84
7/22/87
2/21/90
7/22/87
8/30/90
7/22/87
9/8/83
9/8/83
9/8/83
9/8/83
7/22/87
9/8/83
9/8/83
9/21/84
9/21/84
9/21/84
3/31/89
9/8/83
7/22/87
- 9/8/83
9/8/83
6/10/86
10/4/89
9/8/83
9/21/84
9/21/84
9/21/84
9/8/83
9/8/83
8/30/90
3/31/89
9/8/83
6/10/86
9/8/83
9/8/83
3/31/89
9/8/83
1
10/15/8^
9/8/83
10/15/84
7/14/8S
9/18/85
7/14/89
1/22/87
10/23/81
12/30/82
12/30/82
12/30/82
4/10/85
12/30/82
12/30/82
9/8/83
9/8/83
9/8/83
6/24/88
12/30/82
1/22/87
12/30/82
7/23/82
10/15/84
6/24/88
12/30/82
9/8/83
9/8/83
9/8/83
12/30/82
10/23/81
6/24/88
6/24/88
10/23/81
10/15/84
12/30/82
12/30/82
6/24/88
7/23/82
8/6/92
12/15/90
2/4/9
10/8/91
7/2/84
9/1 4/89
1/1 5/91
5/3/88
9/7/88
4/21/89
5/8/86
9/30/91
10/1/92
7/29/88
5/23/89
9/24/92
4/15/92
2/15/92
2/20/90
6/30/91
6/18/91
5/30/91
4/15/88
11/6/92
6/21/91
6/4/91
10/3/90
2/4/91
10/31/89
9/26/83
6/1/89
7/28/88
9/30/89
3/26/87
12/24/91
4/20/92
8/15/86
1/3/91
4/22/87
7/15/82
3/30/92
12/24/84
1/13/88
5/21/92
5/16/86
6/26/87
8/6/92
12/15/90
3/16/92
8/19/92
12/30/92
1/16/91
4/2/92
9/7/88
9/29/92
9/23/83
3/15/88
11/15/91
11/6/92
8/10/90
2/28/89
.8/12/85
9/30/81
11/15/8e
5/1/82
9/25/89
6/15/88
7/17/91
9/28/89
3/16/92
9/30/92
9/30/92
9/23/87
4/24/89
6/28/91
9/28/89
9/27/90
6/28/91
6/28/91
9/27/91
9/28/92
9/28/90
9/28/89
9/28/92
9/26/91
12/31/84
3/29/91
9/27/91
6/28/89
9/1 1/91
9/29/86
2/2/90
2/2/90
3/30/90
3/27/90
3/12/90
3/21/90
3/19/90
3/29/90
3/14/90
3/14/90
3/14/90
2/2/90
3/19/90
3/27/90
3/19/90
3/28/90
3/12/90
3/22/90
3/22/90
3/29/90
3/21/90
2/2/90
3/30/90
3/13/90
2/2/90
3/14/90
3/29/90
3/22/90
2/2/90
3/30/90
3/13/90
3/27/90
9/23/91
B/13/91
8/12/91
12/2/92
6/4/92
12/3/92
8/14/91
10/24/91
12/1/92
11/23/92
5/28/92
9/24/91
10/24/91
12/3/92
12/21/92
8/12/91
9/23/91
8/1 4/91
2/9/93
8/27/91
9/1/92
2/9/93
2/9/93
8/13/91
8/19/91
2/5/93
1/31/91
2/9/93
1/29/91
2/9/93
2/9/93
10/28/91
2/15/85
5/28/86
11/2/8
5/6/92
8/16/90
10/4/89
3/6/87
8/6/90
2/21/80
10/14/92
2/19/90
4/22/86
1/9/89
3/15/90
1/31/91
6/9/8S
6/18/92
10/30/91
2/27/92
3/28/87
10/1/9C
10/23/82
11/17/92
10/17/90
8/2/86
6/26/92
9/24/90
-------
Event Counts and Dates
05-Sep-93
Dates
Cerclis
NJD980505176
NJO980505341
NJD980505382
NJD980529002
JNJD980529085
NJD980529416
NJD980529713
NJO980529739
NJD980S29762
NJD980529879
NJD9805 29887
NJD980530109
NF Start
NFEnd
9/8/83
9/1/83
9/8/83
9/21/84
9/8/83
9/8/83
9/8/83
9/8/83
9/8/83
9/21/84
9/8/83
9/8/83
NP Start
NPEnd
12/30/82
12/30/82
12/30/82
9/8/83
12/30/82
10/23/81
12/30/82
12/30/82
12/30/82
9/8/83
12/30/82
10/23/81
RA Start
8/15/88
5/8/86
10/19/87
11/22/88
RAEnd
11/29/88
5/30/87
6/5/89
RD Start
8/23/88
12/10/90
6/26/87
9/30/69
5/2/91
3/20/87
9/29/82
8/1/90
4/9/85
4/5/90
9/30/88
RDEnd
8/16/91
8/1 /9C
6/10/92
12/31/91
6/18/86
8/1/88
6/15/88
6/15/66
6/30/8C
8/15/9C
8/15/90
4/22/91
9/20/91
Rl Start
9/28/83
RIEnd
3/16/84
RO Start
_=
ROEnd
9/27/85
7/11/88
9/30/92
6/27/86
9/29/89
9/29/88
9/30/89
9/29/87
RSStar
3/27/90
3/16/90
3/29/90
3/29/90
2/2/90
2/2/90
3/21/90
3/22/90
3/27/90
3/27/90
3/14/90
4/5/90
t RSEnd
12/3/91
12/10/92
12/23/92
12/4/92
10/21/91
9/20/91
10/28/91
8/12/91
9/29/92
4/13/92
2/9/93
2/5/93
RV Start
3/1 3/89
6/26/87
6/1/82
1/25/88
10/22/90
RVEnd
4/19/90
6/5/89
9/21/87
10/31/88
7/30/91
-------
Event Counts and Dates
05-Sep-93
Dates
Cerclis
Number
NYDOOOS11360
NF Start
NFEnd
9/8/83
9/8/83
9/8/83
NP Start
NPEnd
10/23/81
10/23/81
9/8/K
9/8/8:
12/30/82
12/30/82
12/30/8^
12/30/82
12/30/82
12/30/82
12/30/82
12/30/82
12/30/82
5/9/91
9/8/82
9/8/83
9/8/83
9/8/83
6/24/88
10/15/84
10/15/84
10/15/84
10/15/84
10/15/84
6/24/88
6/24/88
6/24/88
10/15/84
12/30/82
RA Start
9/30/K
9/30/8;
9/16/85
9/16/85
7/23/91
9/15/87
9/13/8J
9/27/91
9/21/92
9/26/90
9/15/89
9/15/89
9/29/90
9/30/92
RAEnd
9/30/81
9/28/9C
1/4/8S
2/28/92
1/5/9C
8/19/91
RD Start
3/31/87
9/30/87
4/26/85
4/26/85
9/30/91
6/24/86
10/20/8E
3/24/8E
3/24/8S
6/14791
5/1 2/92
9/30/86
10/13/89
9/30/8S
3/30/92
. 7/9/91
12/9/87
12/9/87
2/9/93
9/30/92
4/3/91
RDEnd
4/25/8E
9/30/87
4/21 /8£
4/21 /8£
8/30/87
9/30/92
9/21/92
9/30/89
9/30/89
9/30/92
Rl Start
9/28/84
3/21/84
9/1/89
RIEnd
9/27/9C
RO Start
ROEnd
9/29/86
RS Star
3/21/90
3127/90
t RSErxl
10/24/91
8/31/92
10/24/91
RV Start
RVEnd
-------
Event Counts and Dates
05-Sep-93
Dates
Cerclis
Number NF Start NFEnd NP Start NPEnd RA Start RAEnd RD Start RDEnd Rl Start Rl End RO Start ROEnd
NYDOOOS11451
NYD000511493
NY0000511576
NYD000511659
NYD000511733
NYD000511857
NYD000512459
NYD000514257
NYD000606947
NYD000813428
MYD000831644
NYD001 485226
NYD001533165
NYD001 667872
NYD002044584
NYD002050110
NYD002059517
NYD002066330
NYD002232957
NYD002920312
NYD01 0959757
NYD01 096801 4
NYD01 3468939
NYD048143175
NYD072366453
NYD073675514
NYD075784165
NYD091 972554
NYD980421176
NYD980505679
NYD980506810
NYD980506901
NYD980506927
NYD980507677
NYD980507693
NYD980507735
NY0980509285
NY0980509376
6/10/86
9/8/83
6/10/86
9/8/83
9/8/83
6/10/86
3/31/89
9/8/83
9/8/83
2/21/90
9/8/83
6/10/86
6/10/86
11/21/89
6/10/86
7/22/87
6/10/86
9/8/83
6/10/86
6/10/86
9/8/63
2/21/90
9/8/83
9/8/83
3/31/89
9/8/83
6/10/86
9/21/84
9/8/83
3/31/89
9/8/83
3/31/89
6/10/86
3/31/89
9/8/83
7/22/87
10/4/89
6/10/86
10/15/84
12/30/82
10/15/84
10/23/81
7/23/82
10/15/84
6/24/88
10/23/81
10/23/81
6/24/88
12/30/82
10/15/84
10/15/84,
8/16/89
10/15/84
6/10/BS
10/15/84
1200/82
10/15/84
10/15/84
10/23/81
6/24/88
12/30/82
12/30/82
6/24/8?
10/23/81
10/15/84
9/8/83
12/30/82
9/18/85
12/30/82
1/22/87
10/15/84
6/24/88
10/23/81
6/10/86
5/5/89
10/15/84
6/25/91
3/12/82
3/25/91
7/12/82
8/15/67
5/17/91
6/29/90
9/25/89
6/28/91
4/28/92
9/26/88
7/1/85
3/1 5/92
1/21/93
11/23/88
3/31/92
9/28/92
8/19/91
12/31/92
9/30/92
1/2/86
4/16/92
3/28/89
6/11/81
9/29/Bf
9/30/9'
9/30/81
8/15/86
9/30/89
9/28/90
9/25/91
4/14/88
7/16/91
6/26/87
3/31/8S
4/21/85
7/1/92
7/2/91
4/20/92
10/22/91
12/18/90
9/28/90
6/25/9
5/23/86
9/25/90
7/18/91
8/15/86
8/18/92
9/22/92
4/28/92
3/30/92
6/21/85
3/15/92
4/8/83
5/22/86
3/31/92
12/28/88
6/6/84
2/9/88
6/27/91
5/15/91
11/26/85
9/29/89
6/21/90
9/28/90
3/29/91
6/29/90
3/31/92
9/28/90
9/29/59
9/30/88
6/30/92
9/4/92
3/31/92
9/28/90
6/27/91
. 9/26/90
9/30/92
7/31/87
RS Start RS End
3/9/90
4/16/90
4/4/90
2/14/90
7/18/90
4/18/90
3/23/90
5/31/90
5/30/90
4/18/90
5/30/90
3/22/90
3/21/90
2/2/90
3/21/90
3/23/90
3/21/90
4/1 6/90
4/11/90
3/22/90
4/18/90
3/9/90
5/3/90
4/17/90
3/22/90
4/17/90
3/21/90
7/1 9/90
2/21/90
4/1 6/90
5/30/90
8/1/90
5/30/90
4/4/90
4/30/9
1/31/92
8/12/9
6/12/9
8/31/92
2/3/93
2/20/91
12/22/92
8/12/91
9/5/91
6/22/92
12/2/92
12/4/92
8/8/91
5/2/91
8/12/91
12/21/92
8/8/91
4/27/92
2/3/93
4/22/91
8/27/91
2/3/93
2/3/93
8/26/91
8/31/92
8/12/91
12/3/92
8/12/91
8/29/91
6/23/92
9/1/92
12/7/92
8/26/91
RV Start RVEnd
5/8/66
4/23/87
7/27/92
2/5/88
7/26/88
9/22/88
9/3/85
5/3/39
9/1/85
9/26/89
7C2/88
10/21/87
9/4/92
6/15/88
1/17/89
5/7/92
5/10/89
9/30/85
-------
Event Counts and Dates
05-Sep-93l
Cerclis
Number
Dates
NF Start NFEnd NP Start NPEnd RA Start RAEnd RD Start RDEnd Rl Start RIEnd RO Start ROEnd RS Start RSEnd RV Start RVEnd
NYD960528335
NYD980528475
NYD980528657
NYD980531727
NYD980534556
NYD980535124
NYD980535165
NYD980535181
NYD980535215 '
NYD980535652
NY0980593099
NYD980650667
NYD980651087
NYD9S0652259
NYD980652267
NYD980652275
NYD980654206
NYD980664361
NYD980762520
NYD980763767
NYD980763841
NYD980768683
NYD980768691
NYD980768717
NYD980768774
NYD980780670
NYD980780746
NYD980780779
NYD980780795
NYD980780878
NYD98078S661
NYD981 184229
NYD981 486947
NYD981 486954
NYD981 560923
NYD981561954
NYD982272734
NYD986882660
9/8/83
6/10/86
9/8/83
9/8/83
7/22/87
7/1/87
6/10/86
8/30/90
9/8/83
6/10/86
9/8/83
9/8/83
9/8/83
9/8/83
9/8/83
9/8/83
9/8/83
2/21/90
6/10/86
9/8/83
9/21/84
3/31/89
6/10/86
6/10/86
6/10/86
6/10/86
6/10/86
6/10/86
6/10/86
6/10/86
6/10/86
3/31/89
301/89
7/22/87
11/21/89
301/89
301/89
1200/82
10/15/84
10/23/81
10/23/81
1/22/87
6/1/86
10/15/84
10/26/89
7/23/82
10/15/84
1200/82
7/23/82
12OO/82
12/30/82
12/30/82
12OO/82
12OO/82
6/24/88
10/15/84
1200/82
9/8/83
6/10/86
10/15/84
10/15/84
10/15/84
10/15/84
10/15/84
10/15/84
10/15/84
10/15/84
10/15/84
6/24/88
6/10/86
6/10/86
8/16/89
6/24/88
6/24/88
7/29/91
9/23/87
1/27/88
1/8/90
3/31/92
40/89
2/21/90
9/27/88
11/2/90
1/17/89
9/23/87
9/21/92
9/30/87
9/28/84
101/92
9/21/88
3/14/90
9/29/88
9/23/92
3/23/90
8/7/92
1/2/90| 7/13/87
301/92
2/28/90
3/27/92
6/22/90
9/30/92
9OO/91
9/29/92
900/92
2/15/86
11/15/88
9/28/90
3/1/88
5/22/86
9/24/92
11/28/90
6/26/87
12/1/86
501/86
3O/87
9/28/90
9/26/90
2/27/87
9/28/84
4/1/91
3/28/90
9OO/8J)
.9/23/87
11/23/92
6/15/88
9/24/91 1 4/29/88
6/24/91
4/2/91
12/7/92J
| 10/14/91
9/23/87
1/27/88
11/19/90
3O1/92
3/1/89
5/29/92
2/24/92
4/11/91
8O1/90
9/21/92
9/29/87
1/7/91
5/1/92
9/25/90
8/10/88
3/1 4/90
9OO/91
9/23/92
8/7/92
.
9OO/81
5/15/86
8/15/85
9/1/86
7/13/87
9/24/85
3/17/88
9/27/90
9/28/90
9/30/91
6/24/91
9/29/89
9/28/90
9/21/90
9OO/85
9/29/88
9/30/89
9/28/92
9/27/90
9/25/84
9/30/92
3/29/91
9/28/92
9/29/89
9OO/92
9/27/91
9/25/87
9/25/87
9OO/87
3/29/91
3/29/91
9OO/92
12/29/89
3/29/91
4/17/90
4/5/90
4/17/90
3/22/90
4/18/90
4/17/90
2/2/90
7/18/90
4/17/90
3/23/90
4/4/90
4/17/90
5/30/90
2/27190
4/5/90
8/1/90
3/23/90
4/17/90
3/21/90
4/5/90
4/17/90
3/20/90
4/11/90
3/20/90
3/21/90
4/18/90
4/11/90
4/16/90
4/18/90
4/16/90
4/18/90
3/21/90
4/11/90
3/21/90
6/24/90
5/2/90
4/5/90
12/2/92
12/1/92
5/1 5/92
8/8/91
12/1/92
12/1/92
12/9/92
8/26/91
2/5/93
12/2/92
12/3/91
6/23/92
2/2/93
8/8/91
8/8/91
9/5/91
8/12/91
11/29/91
12/3/92
8/8/91
12/1/92
8/12/91
8/12/91
8/1 2/91
8/12/91
9/5/91
12/1/92
12/9/92
12/2/92
.8/8/91
8/19/91
2/9/93
8/12/91
12O/92
6/22/92
8/8/91
12/3/92
7/1 2/85
11/14/89
9/29/87
8/12/91
10/30/84
7/15/86
10/29/86
5/1/90
9/6/90
9/15/88
4/18/89
11/19/90
7/17/87
7/21/89
8/12/85
2/23/90
5/11/92
10/30/84
2/23/90
11/6/90
7/27/90
3/29/91
9/28/89
10/1/90
1/17/92
4/14/90
6/12/90
-------
Event Counts and Dates
05-Sep-93
Dates
Cerclis
Number
NF Start NFEnd NP Start NPEnd RA Start RAEnd RD Start RO End Rl Start RIEnd RO Start ROEnd RS Start RSEnd RV Start RVEnd
NYD991 292004
PR41 70027383
PRD090282757
PRD090370537
PRD980301154
PRD980509129
PRD98O512362
PRD980640965
PRD980763775
PRD980753783
VID9S2272S69
6/10/86
10/4/89
9/8/83
9/8/83
9/21/84
9/8/83
9/8/83
9/8/83
9/21/84
9/21/84
10/15/84
6/24/88
12/30/82
12/30/82
9/8/83
12/30/82
12/30/82
12/30/82
9/8/83
9/8/83
2/7/92
500/91
4/19/89
9/1 8/92
9/30/88
5/9/89
12/21/92
8/19/92
4/27/89
9/25/92
7/30/91
2/11/92
3/19/92
4/24/92
9/30/88
9/30/88
9/24/91
9/30/91
9/29/87
9/30/91
3/23/90
7/11/90
7/12/90
7/12/90
7/10/90
7/11/90
7/11/90
7/1 2/90
7/10/90
12/21/92
9/23/92
9/24/92
9/24/92
9/24/92
9/23/92
9/23/92
9/24/92
9/23/92
9/1/82
9/30/91
9/2/87
9/1/83
10/21/91
5/31/90
-------
TAG Dates
05-Sep-93
Cerclis Number
NJD001502517
NJD002173276
NJD002365930
NJD980505366
NJD980505416
NYD000511576
NYD000606947
NYD010959757
NYD091972554
NYD980421176
NYD980506679
NYD980593099
NYD980654206
NYD980664361
NYD982272734
TG Dates
9/29/89
12/22/92
9/26/91
3/15/90
1/16/87
3/30/89
9/30/88
2/9/89
9/29/89
9/26/91
2/21/91
9/30/88
9/30/92
5/13/91
9/30/90
-------
APPENDIX TABLES
-------
Table A-l
29-Aug-93
Event Code
Number of Events per Site Distribution
Number of Events
Number of Sites
AR
1
2
3
4
5
81
26
3
5
2
AS
1
2
68
2
CO
1
2
3
4
5
6
7
8
9
10
21
97
64
23
8
2
1
1
1
1
1
1
CR
1
2
3
96
15
4
CT
1
1
DA
1
2
17
3
DS
1 200^
ED
-------
29-Aug-93
Event Code
Number of Events per Site Distribution
Number of Events
1
2
Number of Sites
45
9
FP
1
2
12
1
FS
1
2
16
1
GS
1
2
4
1
HR
1
102
IM
1
2
3
10
1
1
IR
1
2
3
5
17
3
1
1
LR
1
2
41
4
MA
1
2
3
4
95
22
2
1
ND
NF
16
-------
29-Aug-93
Event Code
NP
OF
OH
OM
OS
PA
PD
RA
Number of Events per Site Distribution
Number of Events
1
2
1
1
1
2
6
1
2
3
1
1
2
1
1
2
3
4
5
7
8
9
10
Number of Sites
198
1
201
1
18
10
1
59
4
1
2
192
7
3
79
59
24
13
5
1
4
1
2
RD
1 78
-------
29-Aug-93
Event Code
Number of Events per Site Distribution
Number of Events
2
3
4
5
7
8
9
Number of Sites
58
24
13
5
2
4
2
RI
1
2
10
1
RM
1
18
RO
1
2 .
3
4
5
6
8
10.
21
110
63
17
1
3
1
2
1
1
RS
1
2
3
.1.
184
3
RV
1
2
3
4
5
7
37
25
6
1
1
1
-------
29-Aug-93
Event Code
Number of Events per Site Distribution
Number of Events
Number of Sites
SI
1
2
121
76
TA
1
2
3
25
4
2
TG
1
16
TS
1
2
22
6
WP
1
2
3
Grand Total : 373
75
8
1
3044
-------
Cerclis Number
NJ01 700221 72
NJ1891 837980
NJ221 0020275
NJ321 0020704
NJ71 70023744
NJ9690510020
NJD000565531
NJD000607481
NJD001502S17
NJDO02005106
NJD002141190
NJD0021 68748
NJD002173276
NJD002349058
NJD00236270S
NJD002365930
NJD002385664
NJD002493054
NJD002517472
NJD01 1717584
NJD030253355
NJD041 743220
NJD041 828906
NJ0045653854
NJD046644407
NJDO47321443
NJD0476844S1
NJD048044325
NJD048798953
NJD049860836
NJD053102232
NJD053280160
NJD053292652
NJD054981337
NJ0061 843249
NJD06315715O
NJD0631 60667
NJD064263817
NJD070281175
NJD070415005
AR
I
2
',
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Cerclis Number
NJD070565403
NJD073732257
NJD078251675
NJD094966611
NJD097400998
NJD980484653
NJD980504997
NJD980505176
NJD980505283
NJD980505341
NJD980505366
NJO980505382
NJD980505416
NJD980505424
NJ0960505648
NJD980505671
NJ0980505754
NJD980505762
NJD980505S79
NJ09S05058S7
NJD980528889
NJD980528996
NJD980529002
NJO98052908S
NJD980529143
NJD980529192
NJD980529408
NJD960529416
NJ0980529598
NJDS80529713
NJD980529739
NJD980529762
NJD980529879
NJD980529887
NJD980529945
NJ0980530109
NJ0980530596
NJO980530679
NJD980532808
NJ0980532816
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1
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Cerclis Number
NJD980532824
NJD980532840
NJD980654099
NJD980654107
NJD960654115
NJD980654131
NJD980654149
NJD9806S4156
NJ0980654164
NJD980654172
NJD980654180
NJD980654198
NJ0980654214
NJ0980654222
NJD980755623
NJD980761357
NJD98076136S
NJD980761373
NJ0980761399
NJD980766828
NJD980769301
NJ0980769350
NJD980785633
NJ0980785646
NJOS80785653
NJ09811 78411
NJD981 179047
NJD981 490261
NY0213820830
NY4571924451
NY4571924774
NY7890008975
NYD000337295
NYD000511360
NYD000511451
NYD000511493
NYD000511576
NYD000511659
NYD000511733
NYD000511857
AR
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-------
Cerclis Number
NYD000512459
NYD000514257
NYD000606947
NYD000813428
NYD000831644
NYD001 485226
NYD001533165
NYD001667872
NYD002044584
NYD002050110
NYD002059517
NYD002066330
NYD002232957
NYD002920312
NYD01 0959757
NYD01 096801 4
NYD01 3468939
NYD048148175
NYD072366453
NYD073675514
NYD075784165
NYD091 972554
NYD980421176
NYD980506679
NYD980506810
NY0980506901
NYD980506927
NYD98O507677
NYD980507693
NYD980507735
NYD980509285
NYD980509376
NYD980528335
NYD980528475
NYD980528657
NYD980531727
NYD980534556
NYD980535124
NYD980535165
NYD980535181
AR
2
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2
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1
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1
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1
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1
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1
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2
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2
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2
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2
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1
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1
1
1
1
1
1
-------
Cerclfs Number
NYD980535215
NYD980535652
NYD980593099
NYD980650667
NYD980651087
NYD980652259
NYD980652267
NYD9806S2275
NYD980654206
NYD980664361
NYD980762520
NYD980763767
NYD980763841
NYO980768683
NYD980768691
NYD980768717
NYD980768774
NYD980780670
NYD980780746
NYD980780779
NYD980780795
NYD980780878
NYD980785661
NYD9811 84229
NYD981 486947
NYD981 486954
NYD981 560923
NYD981561954
NYD982272734
NY0986882660
NYD991292004
PR4170027383
PR0090282757
PRD090370537
PR0980301154
PRD980509129
PRD980512362
PRD980640965
PRD980763775
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1
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1
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1
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1
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1
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1
1
1
1
1
1
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1
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1
1
1
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t
2
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2
2
2
2
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2
2
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I
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t
1
1
2
2
1
1
1
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1
1
1
1
1
1
1
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-------
Cerclis Number
V1D982272569
AR
AS
CO
1
CR
1
CT
DA
DS
ED
1
FP
FS
GS
HR
IM
(R
LR
MA
ND
NF
NP
1
OF
OH
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PD
RA
1
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1
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1
RS
RV
3
SI
TA
TO
TS
WP
-------
Appendix H. 4.
LIST OF OTHER INTERIM REPORTS AND ANALYSES
Environmental Equity Study
1. February 22, 1993. Treatment of boundary issues in some recent environmental equity
court cases, 5 pp.
2. March 16, 1993. An Environmental Equity Study for Hazardous Waste Sites Superfund
Program, U.S. EPA, Region 2. Scope of Work (peer-reviewed). 15 pp.
3. March 16, 1993. Summary of Environmental Equity Studies in the U.S. EPA and
Selected Other Federal Agencies, 11 pp.
4. March 31, 1993. Comments on the Incineration 2000 equity study and implications for
Region 2 equity study.
5. April 1993. Preparation of Sample Data Extractions Using Census Tract Level Data to
test Methodology.
a. April 6, 1993. Example of an Extraction of Census Tracts around Selected NPL
Sites within a 4-Mile Radius of the Sites (Using Longitude/Latitude Delineations
from the "SNAP" Files). 11 pp.
b. April 14, 1993. NPL Sites for which Racial and Ethnic Minority Populations Exceed
5% at 1, 2, 3, and 4 Mile Radii Around the Sites, New Jersey and New York.
Census Tract Level Data. 19 pp.
6. June 14, 1993. Comparison of the Census Centroid Method (CC) vs. the OPM Census
Tiger File Polygon/Uniform Density Method (OPM-UD) for Extracting Populations
Around NPL Sites (Based on OPM data received May 10, 1993). Report to Tom
Scheckells, Director, Office of Program Management.
7. July 1993. NPL Sites at State Boundaries (within 4 miles of another state).
8. July-August, 1993. Data Manuals (cumulative socioeconomic'data for seven distances
around each NPL site):
a. July 26, 1993. Selected Racial and Ethnic Composition of Populations Around NPL
Sites. New Jersey and New York. 1990 Census Data. 50 pp.
b. July, 1993. Selected Housing Characteristics for Populations Around NPL Sites.
New Jersey and New York. 1990 Census Data. 50 pp.
c. August 9, 1993. Selected Age Characteristics for Populations Around NPL Sites.
Puerto Rico. 1990 Census Data. 3 pp.
d. August 9, 1993. Population Density Around NPL Sites. New Jersey, New York and
Puerto Rico 1990 Census Data. 59 pp.
9. October 26-29, 1993. National EPA CIS/Environmental Justice Forum, Participant and
Presenter on Panel on Programmatic Applications of Environmental Justice.
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