c/EPA
GROUND-WATER VULNERABILITY ASSESSMENT
IN SUPPORT OF THE FIRST STAGE
OF THE NATIONAL PESTICIDE SURVEY
-------
GROUND-WATER VULNERABILITY ASSESSMENT
IN SUPPORT OF THE FIRST STAGE
OF THE NATIONAL PESTICIDE SURVEY
by
W. Joseph Alexander
Susan K. Liddle
Robert E. Mason
and
William B. Yeager
Research Triangle Institute
EPA Contract No. 68-01-6646
Work Assignment No. 22
RTI Project No. 255U-3362/3486
EPA Technical Monitor: Stuart Z. Cohen
Office of Pesticide Programs
U. S'. Environmental Protection 'Agency
Washington, DC 20460
February 14, 1986
-------
CONTENTS
Section Page
Acknowledgments ii
1.0 Introduction and Qualifications 1
2.0 Conclusions 4
3.0 Methodology 8
3.1 Agricultural DRASTIC System 8
3.2 Training Program 9
3.3 Study Approaches and Sources of Information 10
3.3.1 Roy F. Weston Consultants 10
3.3.2 Woodward-Clyde Consultants 11
3.4 Form Processing and Data Base Construction 11
3.5 Quality Assurance Program 13
3.6 Data Base Evaluation 14
3.7 Adjustments to Data Base 15
4.0 Results 16
4.1 Classifications of Ground-Water Vulnerability 16
4.2 Comparison of Scores to Ground-Water Regions 17
4.3 Data Confidence and Variability 19
5.0 References ' 22
Appendices
A. Training Manual for Using DRASTIC Hydrogeologic Factors
in Conducting a National Ground-Water Vulnerability
Assessment
8. Description of Quality Assurance Program and Related Data
C. State and County Listing of Weighted and Total Scores for
Originally Coded Data Base with Adjustments
D. Listing of Adjusted VARSCORES in Ascending Order
E. Description of Measurement Error Analysis
F. Description and Results of Qualitative Check
G. Description of Adjustments to Originally Coded Data Base
FIGURES
1. Categories of Ground-Water Vulnerability; Conterminous
United States 5
2. Categories of Ground-Water Vulnerability; Alaska and Hawaii . 6
3. Distribution of VARSCORES by Ground-Water Regions 18
-------
ACKNOWLEDGMENTS
This project was jointly funded by the U.S. EPA Office of
Pesticide Programs and the Office of Drinking Water under Contract No.
68-01-6646. This stage of the project required the input of many
persons from various organizations over a short time, many of whom are
acknowledged below.
U.S. EPA: Dr. Stuart Z. Cohen, Technical Monitor, is acknowledged
for his enthusiastic involvement in the project,
interagency coordination, and arrangement of peer
reviews.
Roy F. Weston, Consultants: Mr. John B. Robertson, Project
Manager, is acknowledged for assembling an effective
team for coding counties in the Eastern United States
and meeting the schedule requirements. Ms. Loren Lasky
is also acknowledged for her leadership of the team,
which included the following participants:
Lisa Arters
Robert Caprara
Peter Ciotoli
Mike Wierman
Bill Miller
Jody Roud
Linda Shanley
Woodward-Clyde Consultants: Mr. David Dean, Project Manager, is
acknowledged for coordination of professionals from
several western offices of Woodward-Clyde who met the
scheduling requirements for coding counties in the
Western United States. Mr. John Pelka is acknowledged
for his coordination efforts, as are the following
professionals:
Jim Dederick
Charles Franks
Richard Kent
Charles Lehotsky-
Melitta Rorty
National Water Well Association: Dr. Jay H. Lehr and Ms. Linda
Aller are acknowledged for their participation in the
training program, the qualitative check of six
counties, and constructive suggestions.
-------
U.S. Geological Survey: Dr. Jack M. Fischer is acknowledged for
his participation in several peer reviews, along with
other members of the Survey, and for directing the
District Offices' participation in the qualitative
check of six counties.
Other Professionals: Mr. Ralph C. Heath and Mr. Harry E. LeGrand
are acknowledged for their overview of various aspects
of the project and participation in the qualitative
check and quality assurance process, respectively.
Research Triangle Institute: The late Dr. Douglas J. Drummond was
instrumental in initiating this stage of the project
through his coordination with U.S. EPA. Ms. Bonnie S.
Barbee is acknowledged for management of the data
editing process, assistance with project administration
and report preparation. RTI's data processing staff is
acknowledged for their efforts in keying and verifying
the data. Ms. Kathleen B. Mohar is acknowledged for
the report editing. Mr. Steven L. Winters and Mr.
Richard W. Pratt are also acknowledged for their
participation in the data reevaluation process.
fii
-------
1.0 INTRODUCTION AND QUALIFICATIONS
This report supports the National Pesticide Survey (NPS) being
conducted jointly by the Environmental Protection Agency's Office of
Pesticide Programs (OPP) and Office of Drinking Water (ODW). The
primary objectives of the NPS sampling design are:
1. to characterize nationally, the distribution of pesticide
residues in community system and rural household water wells,
and
2. to assess the associations between the agricultural use of
pesticides, the distribution of agricultural pesticide
residues in well water, and hydrogeologic factors that
influence ground-water contamination.
Community system water wells and rural household water wells will be
treated differently in the NPS sampling design. The community system
water wells will be treated as a stratified, one or two-stage sample,
while three stages of sampling are planned for the rural household well
component. Stratification will be imposed on the first two stages of
the rural household well component.
The aspect of the sample stratification described in this report
pertains to the first stage of the NPS sampling design. Other aspects
of the first-stage sample stratification will include consideration of
agricultural pesticide usage and possibly the size of community water
well systems. Details of these other aspects will be incorporated as
part of a later task of this stage of stratification and are not
included in this report.
The objective of the study reported herein was to classify all
3,144 counties of the United States into three categories of
ground-water vulnerability (high, medium, or low). The purpose of the
classification is to focus the NPS into areas of the country where
pesticide contamination of well water is most likely to occur. From
these results a samp-le of 100 to 200 counties will be selected for
detailed classification at the subcounty level.
-------
Counties were specified as the basis for classification units in
this study. The use of county units will allow the ground-water
vulnerability .data base to be merged with pesticide application and
related data bases. The assignment of a single numerical score to a
county allows a more reliable classification in some areas of the
country than in others. The averaging effects necessary for a
county-based hydrogeologic classification scheme did allow localized,
highly vulnerable areas within some counties to be overshadowed by the
predominance of surrounding lower vulnerability areas. Such averaging
effects are necessary, however, in focusing the NPS to the more highly
vulnerable counties.
The DRASTIC classification system was selected for use on this
project by EPA, following the consideration of several other classifi-
cation systems. DRASTIC is an acronym for a numerical system capable
of evaluating the vulnerability of aquifers to pollution beneath a
particular land area. It is intended as a manag-ement tool for the
allocation of resources and environmental protection decision making on
a county basis. An application of a modified DRASTIC system was
implemented by the Agency's three contractors (Research Triangle
Institute, Roy F. Weston Consultants, and Woodward-Clyde Consultants)
to meet the objectives of this ground-water vulnerability assessment.
A county with an area of approximately 1,000 square miles could be
reasonably well classified using the DRASTIC system as defined by the
National Water Well Association (NWWA) and would require about 200 to
300 person-hours of effort. Variations in the amount of time required
would depend on the complexity of the regional geology, the county
size, and the availability of data. The use of such a scheme for the
classification of all 3,144 United States counties would have required
300 to 450 person-years of effort, and was not considered necessary to
achieve the objective of this study. The reader should contrast this
with the fact that the level of effort applied to the original coding
and quality assurance efforts for this stage of the NPS averaged only a
few person-hours per county and thus required an application of a
modified DRASTIC system.
-------
Measurement errors were anticipated in this study and are known to
exist within the data base constructed for the first stage of the NPS.
These measurement errors and inconsistencies in the data base result
from differences in opinion when generalizing hydrogeologic conditions
over areas as large as counties. As a result, some counties could be
considered as being misclassified between adjacent categories of
vulnerability. Some misclassifications can be tolerated at this level
of the sampling design, as long as they are not extensive. The
likelihood of extensive misclassifications in the adjusted data base is
considered to be minimal. Although a number of inconsistencies in the
data base still exist, the subsequent stratification needs of the NPS
are sti11 achieved.
Peer reviews of the NPS were conducted by individuals within
private organizations and State and Federal agencies. Many of the
comments received from the peer reviews have been incorporated into
this document. As with any study, the results of this data base
compilation are anticipated to be controversial when taken out of the
context of the study's true objectives. It should be noted that the
weight of evidence presented in this report clearly shows that the
proposed classification reflects county level differences in
ground-water vulnerabiIty. The data may not, however, support uses
other than those intended.
Research Triangle Institute (RTI) managed Stage 1 of the project
for EPA. The overall approach used to meet the objectives of the first
stage included:
t Development of a training program, using the services of the
NWWA, for individuals involved in obtaining hydrogeologic data;
• Data collection efforts by Woodward-Clyde Consultants for
counties in the Western United States and Roy F. Weston
Consultants for counties in the Eastern United States;
• Design and implementation of a quality assurance program; and
• Data base construction, evaluation, adjustment, and reporting.
Conclusions are presented in Section 2.0 and a general description
of the methodology used on the project is provided in Section 3.0. The
primary results are presented in Section 4.0 and details of the
methodology and results are provided in the Appendices.
-------
2.0 CONCLUSIONS
The distribution of the county-weighted numerical scores (termed
VARSCORES) derived for this stage of the National Pesticide Survey
appears to coincide reasonably well with known hydrogeologic conditions
in certain ground-water regions. As anticipated, the highest VARSCORES
are generally found within the Southeast and Atlantic Coastal Plains
where shallow ground-water depths and high recharge rates prevail
(Figure 1). Lowest VARSCORES are generally found in the western
ground-water regions where deeper ground-water depths and lower natural
recharge rates are generally more common. Low VARSCORES were also
derived for some sections of Alaska and high VARSCORES are associated
with the islands of Hawaii (Figure 2).
Several approaches can be used to establish categories of ground-water
vulnerability. For this stage of the survey, selected vulnerability
categories were based on a 10/60/30 percent distribution of the data base
of VARSCORES for low, moderate, and high vulnerability, respectively.
This approach results in a high vulnerability category when considering
VARSCORES of greater than 143 and a moderate vulnerability category for
VARSCORES greater than 102 (Figures 1 and 2).
The-use of a relatively large percentage of the data base for the
moderate vulnerability category minimizes the likelihood of counties
being misclassified between the high and low vulnerability categories.
As with the .selection of any vulnerability categories, some counties
identified on'the upper and lower bounds of the moderate vulnerability
range could be considered as high or low vulnerability, respectively.
The likelihood of extensive misclassifications in the adjusted data base
is considered to be minimal.
Two subsamples of counties were selected and independently rescored as a
part of the quality assurance process. The analysis of the subsample
information suggests that the variable measurement error in determining
county-level VARSCORES is appreciable, but not large enough to obscure
actual differences in vulnerability among counties.
-------
EXPLANATION
BSD High vulnerability county based on VARSCORE .-• 143
Moderate vulnerability county-based on 102 • VARSCORE • 142
Low vulnerability county-based on VARSCORE < 101
Figure 1. Categories of ground-water vulnerability; conterminous United States.
-------
Islands of Hawaii
Election Districts of Alaska
EXPLANATION
High vulnerability area based on VARSCORE > 143
Moderate vulnerability area based on 102 - VARSCORE < 142
Low vulnerability area based on VARSCORE < 101
Figure 2. Categories of ground-water vulnerability; Alaska and Hawaii.
-------
The qualitative check of the six counties, although not a direct part of
the quality assurance process with respect to determining measurement
error, indicated that the standard error in VARSCORES between nationally
recognized hydrogeologists is similar to the standard error in scores for
the same counties coded by the contractors.
Computations of the coefficient of variation between multiple codings of
a randomly selected 3 percent sample of the data base suggest that
average variations in VARSCORES are probably less than 13 percent of a
given mean VARSCORE value.
The originally coded data base was adjusted to remove some of the major
inconsistencies revealed by the quality assurance process and by
subsequent reevaluations of selected factors used to derive the
VARSCORES. The results of the data base, including adjustments, are
reflected in Figures 1 and 2. Although some inconsistencies still exist
in the data base, the improved data base can be used for the subsequent
stratification needs of the National Pesticide Survey. The data may not,
however, support uses other than those intended.
-------
3.0 METHODOLOGY
The ground-water vulnerability assessment (Stage 1) was conducted
between June and December 1985. The training program was held in June
and the primary data collection efforts were performed in July, August,
and September 1985. Data base construction and evaluation efforts were
performed concurrent with data collection efforts. The quality
assurance process was conducted in September and October 1985. A first
draft report of the findings was submitted to EPA in November, and a
reevaluation of selected DRASTIC factors was performed in December
1985. A second draft report of the findings was submitted for peer
review in early January 1986. Pertinent comments from these reviews
have been incorporated in this final report. The following subsections
describe the Stage 1 methodology.
3.1 AGRICULTURAL DRASTIC SYSTEM
Several authors have dealt with the problem of generalizing and
categorizing ground-water resources across the United States (Meinzer,
1923; Ries and Watson, 1904: Fuller, 1905; Thomas, 1952; and Heath
1984). A number of empirical methods have also been developed to
evaluate the environmental impacts of waste disposal practices. The
National-Water Well Association (NWWA) reviewed the existing classifi-
cation systems and determined that although there are a number of
methods applicable to site-specific situations or to the evaluation of
the pollution potential of existing sites, a planning tool was needed
for the site-specific methods to be most efficiently employed. NWWA,
under contract to EPA, recently developed a systematic approach to
evaluating the potential for ground-water pollution (Aller et al.,
1985). Their scheme is based on seven factors considered to be most
significant in affecting the ground water pollution potential and
employs the ground-water regions of Heath (1984). The seven factors
were derived using a modified Delphi technique and form the acronym
DRASTIC:
0 - Depth of water;
R - Recharge (net);
A - Aquifer media;
S - Soil media;
T - Topography (slope);
I - Jmpact of vadose zone; and
C - Conductivity (hydraulic) of the aquifer.
8
-------
A numerical scoring system (DRASTIC Index) was devised by NWWA
based on the weights, ranges, and ratings of the seven DRASTIC factors.
High scores were selected to represent areas where physical character-
istics afforded little protection from ground-water pollution. An
Agricultural DRASTIC Index was designed by NWWA for which the specific
concern was the application of pesticides to the land surface. The
Agricultural DRASTIC Index differs from the DRASTIC Index only in the
assignment of different weights for the seven factors. The Agricul-
tural DRASTIC Index places a higher weighting on topography (percent
slope) and soil media than does the DRASTIC Index. Topography is
important when considering the application of pesticides to the land
surface because slope controls the likelihood that a pollutant will run
off or remain on the land surface in one area long enough to
infiltrate. The attenuating processes (filtration, biodegradation,
sorption, or volatilization) that may be available in the upper few
feet of the soil media can also influence the migration of pesticides.
Although the DRASTIC system was designed by NWWA as a screening
tool for county-size areas down to a resolution of approximately 100
acres, an application of a modified system was considered suitable for
the county-level assessment needed to meet the first stage of stratifi-
cation of the National Pesticide Survey. Recognizing the physical
variation that exists in areas as large as counties, RTI designed a
county data collection form that allowed each factor to be coded on a
percentage basis (Appendix A; Figure 2).
3.2 TRAINING PROGRAM
A training program was developed by RTI in conjunction with NWWA
at the start of the Stage 1 effort. The purpose of the training
program was to provide sufficient information to those professionals
involved in the project so that consistency could be achieved in
obtaining DRASTIC factors on a national scale. Separate training
programs were held for Weston's personnel and for Woodward-Clyde's
personnel in which they were provided a training manual and the
following information:
• An introduction and overall approach to be used on the project
• A history of the DRASTIC system and definitions of the key
hydrogeologic factors
-------
• Step-by-step instructions for completing the county data forms
along with a discussion of the level of effort that should be
used
• Suggestions for data sources
• Instructions for classifying the seven DRASTIC factors, the
sensitivity of the scoring process, and how to indicate the
level of confidence in the data
• Instructions for resolving questions and transmitting completed
data forms.
A portion of the training manual is included in Appendix A along
with a listing of participants. Copies of the training manual were
also distributed to individuals participating in the quality assurance
program as part of the qualitative check described in Section 3.5.
3.3 STUDY APPROACHES AND SOURCES OF INFORMATION
Each of RTI's subcontractors used a slightly different approach in
completing the form coding task. The approaches differed primarily in
the division of labor between coders at each firm. The study
approaches and sources of information were openly discussed between
both subcontractors. In addition to the procedures outlined in the
training manual, it was agreed that emphasis should be given to
significant shallow potable aqu-ifer systems, where present, and that
areas of significant artificial recharge should be accounted for in the
assignment of values for net recharge.
3.3.1 Roy F. Weston Consultants
Roy F. Weston Consultants divided the seven hydrogeologic factors
for each county among several coders so that a few coders ranked the
factors dealing with geologic criteria (depth to water, aquifer media,
impact of the vadose -zone media, and hydraulic conductivity), others
ranked soils, and others ranked the topography and net recharge. The
composite numerical score for each county coded by Weston may have,
therefore, resulted from the combined efforts of at least three
individuals. This division of labor was intended to promote
consistency in the coding of specific hydrogeologic factors between
counties.
10
-------
Hydrogeologic factors such as depth to water, aquifer media, soil
media, and vadose zone media were ranked by Weston based on literature
review and conversations with State and regional representatives (e.g.,
State Geological Surveys, U.S. Geological Survey, and Soil Conservation
Service). Net recharge was ranked based on a formula derived by Weston
to incorporate information on surface water baseflow, published
recharge rates, precipitation, topography, and soil media. Topographic
slope was determined using USGS 2° Topographic Maps (1:250,000) and a
standardized slope estimation technique. Hydraulic conductivity
estimates for each county were based on literature review, the
contractor's knowledge of the aquifer media, and general ranges of
hydraulic conductivity provided in Freeze and Cherry (1979).
3.3.2 Woodward-Clyde Consultants
Woodward-Clyde Consultants assigned entire States to individual
coders. Each coder was therefore responsible for ranking all seven
hydrogeologic factors for every county within a State. Such an
approach was used to promote overall consistency among the VARSCORES
of all the counties within a State.
Six of the seven DRASTIC factors (all but topography) were ranked
by Woodward-Clyde based on literature review, discussions with State
and regional representatives, and the applicable field experience of
the coder. Topographic slope was estimated based on information from
USGS 2" Topographic Maps and from individual State General Soils Maps.
3.4 FORM PROCESSING AND DATA BASE CONSTRUCTION
The county data forms originally coded by the subcontractors were
sent to RTI where they were initially counted, logged in by State and
subcontractor, and compared with scheduling requirements. Status
reports were issued regularly to the subcontractors indicating the
number- of forms outstanding and requirements needed for completion of
the forms. Sources of information used to complete the forms were
indicated for the majority of the counties; however, the compilation of
this information into a comprehensive bibliography was not considered
feasible or necessary for this stage of the project.
11
-------
The forms were then edited by RTI for completeness, correctness,
and legibility. The majority of the fields on the forms were found to
be complete but occasional omissions were not uncommon for a given
DRASTIC factor. The forms were examined for correctness with respect
to the percentage allotted for a given factor (refer to Section 3.4 in
Appendix A). It was a fairly common error of the coders for percentage
distributions of DRASTIC factors to exceed 100 percent. Most of the
'j
forms were considered legible, with the exception of a single coder.
Editorial problems on the forms typically were discussed by telephone
with the subcontractors and corrected directly on the original data
form. Some forms were returned to the subcontractors to resolve
editorial issues or inconsistencies.
Following the editing process, the incoming forms were screened
for reasonableness. Total numerical scores were compared with those of
adjacent counties or States, and the individual DRASTIC factors were
compared for reasonable agreement on a given form. General agreement
between aquifer media, the impact of the vadose zone, and the hydraulic
conductivity was found in many instances. Completed forms were
occasionally reviewed for reasonableness with respect to known regional
hydrogeologic conditions by independent -reviewers (Mr. Ralph C. Heath
and Mr. Harry E. LeGrand). Where significantly different interpre-
tations of DRASTIC factors could be derived, the original coder was
contacted and the rationale for coding was discussed. For the most
part, the original codings of the subcontractors remained unchanged in
the originally coded data base. Are^as where interpretational
differences were significant became apparent in the quality assurance
program and were subsequently reevaluated and adjusted as described in
Section 3.7.
Upon completion of the editing and preliminary review process, the
forms were batched and keyed to disk on RTI's VAX/VMS system. All
fields on the forms were keyed and the forms were verified by the key
operators. The keyed data were copied to tape for transport to EPA's
National Computer Center (NCC) at Research Triangle Park. Data were
read from tape into Statistical Analysis System (SAS) data sets.
12
-------
Numerical scores were calculated from the data set, including the
following three scores differentiated in this report:
TOTAL SCORE - An unweighted score corresponding to the
summation of the highest percentage ratings for the seven
DRASTIC factors. (Refer to Section 3 in Appendix A).
WEIGHTED SCORE - A weighted score corresponding to the summation
of all percentage ratings for the seven DRASTIC factors. The
weighted score accounts for the variability that occurs in
areas as large as counties. In counties with little
variability, little difference exists between the total score
and weighted score.
VARSCORE - Represents the weighted score including (+ or -) the
index of variability, where used. The index of variability and
its intended usage, is defined in Section 3.6 of Appendix A.
The VARSCORE, or the adjusted VARSCORE further defined in
Section 3.7, is considered to be the most appropriate score for
the purposes of this project in that it best accounts for
intracounty variability.
3.5 QUALITY ASSURANCE PROGRAM
Receding of selected counties was included as part of the ground-
water vulnerability assessment to permit analysis of the variable
measurement errors associated with the coding process. Seven percent
of the counties within each of the nation's recognized ground-water
regions were chosen by RTI for receding by the subcontractors. A
subsample of 3 percent of these counties (3/7 of the 7 percent) was
selected for coding by RTI. As a qualitative check, six counties with
widely varying hydrogeologic conditions were also selected for coding
by nationally recognized hydrogeologists.
The selection of counties to be included in the quality assurance
process was a six-step procedure. The first two steps involved
refining the boundaries of the national ground-water regions and
designating a region for each county. The third step involved the
creation of a data base containing the Federal Information Processing
Standards .(FIPS) code and designated ground-water region for each
county. The fourth and fifth steps involved sorting the data set by
ground-water region and randomly choosing the 7 and 3 percent samples.
The final step involved the selection of a few counties to be coded as
part of the qualitative check. The six steps are described in detail
along with data related to the quality assurance program in Appendix B.
13
-------
3.6 DATA BASE EVALUATION
Data consistency and reasonableness were evaluated by RTI through-
out the data base construction effort. Prior to computer entry, many
of the total scores were compared with adjacent counties, and general
trends across States and ground-water regions were reviewed. Following
the completion of data entry for all of the county information from a
given State, two types of computers-generated maps were produced for
initial evaluation purposes. The first map displayed the VARSCORE for
each county in a given State using a scale of 12 patterns to cover the
entire range of scores for that State. The increments of this scale
varied from State to State and were found to be useful in determining
VARSCORE trends and unusual VARSCORE values within a State. The second
map displayed the VARSCORE for each county in a given State using a
uniform scale of patterns that was applied to all States. This scale
used a 20-point increment ranging from a low midpoint score of 30 (the
midpoint value of the 20-39 VARSCORE category) to a high midpoint score'
of 250. This form of map was found to be useful when comparing scores
and trends across State boundaries. Uniform-scale maps of the entire
United States were produced several times during the course of the data
base construction effort in order to compare VARSCORE trends on a
regional scale and to check for missing data or unusual scores.
Comparisons of the ranges of scores within and between ground-water
regions were tabulated as part of the overall data base evaluation
effort. In addition, several tabulations of county VARSCORES sorted in
ascending order were produced. These maps and tabulations were of
assistance in both the analyses of data base completeness and
consistency and in the establishment of preliminary vulnerability
categories.
Preliminary maps of ground-water vulnerability were produced from
the originally coded data base. Several areas on these preliminary
maps stood out in obvious contrast to surrounding states and/or
counties as a result of differences in opinion between coders on the
ranking of individual Agricultural DRASTIC factors. It was also noted
when evaluating the individual components of all receded county
measurements that three factors (depth to water, net recharge, and
impact of the vadose zone media) contributed to the greatest variation
14
-------
in the VARSCORES. It was, therefore, apparent that some of the areas
of greatest contrast needed to be adjusted prior to any further
refinement of the categories of ground-water vulnerability or use of
the data base for stratifying the National Pesticide Survey sampling
frame.
3.7 ADJUSTMENTS TO DATA BASE
A reevaluation process was conducted by RTI in December 1985 with
the following objectives:
• to develop and evaluate computer-generated maps of the
individual Agricultural DRASTIC factors on a regional scale in
an attempt to further identify unusual areas or areas of
greatest contrast;
t to attempt to resolve, from a regional perspective, some of the
areas of greatest contrast;
• to adjust portions of the originally coded data base in an
attempt to improve the general reliability of the data base.
The adjustments made to the data base are further described in
Section 4.3. The adjusted VARSCORES were used in -the final selection
of ground-water vulnerability categories.
15
-------
4.0 RESULTS
The primary results of the total 3,144-county data base are the
originally determined numerical scores listed by State and county in
Appendix C. The different numerical scores previously defined in
Section 3.4 are provided for each county in Appendix C and, when
compared, give a relative indication of the variability that exists
within a given county. Counties that have large variability in
hydrogeologic conditions display a greater difference between the total
score and weighted score.
4.1 CLASSIFICATION OF GROUND-WATER VULNERABILITY
Several approaches can be used to establish categories of ground-
water vulnerability. Once the data set has been sorted in order of
VARSCORE values, any number of vulnerability limits may be selected.
For this stage of the survey, the selected vulnerability categories
were based on a 10/60/30 percent distribution of the data base of
adjusted VARSCORES for low, moderate, and high vulnerability,
respectively (Figures 1 and 2). The entire data set was sorted by
ascending adjusted VARSGORES for the identification of the selected
distribution (Appendix D). This approach results in a high vulner-
ability category, when considering VARSCORES of greater than 143 and a
low vulnerability category for1 VARSCORES of less Mian 101. Moderate
vulnerability would therefore correspond to VARSCORE values between 102
and 142.
This selected distribution, while not greatly different from other
vulnerability categories considered, did offer-fewer contrasts in
scores between adjacent political boundaries and appeared to be the
most reasonable with respect to known hydrogeologic conditions of a
number of different ground-water regions. The use of a relatively
large percentage of the data base for the moderate vulnerability
category (1,892 counties) also minimizes the likelihood of counties
being misclassified between the high and low vulnerability categories.
As with the selection of any vulnerability categories, some counties
identified on the upper and lower bounds of the moderate vulnerability
range could be considered as high or low vulnerability, respectively.
16
-------
Several other similar approaches toward establishing preliminary
ground-water vulnerability categories were examined for this project.
One of the first approaches was based on the distribution of originally
coded county-level VARSCORES that fell between the first and third
quartiles of the data set. For this approach a county exhibiting low
vulnerability corresponded to a VARSCORE of less than or equal to 116
and high vulnerability corresponded to a VARSCORE of 148 or more. One
assumption of such an approach is that it is desirable to classify 50
percent of the data (1572 counties) within the moderate category, and
25 percent (786 counties) each in the high and low categories.
In another approach the entire data base was sorted by ascending
VARSCORES and vulnerability limits were drawn such that 1,048 counties
would fall within each category. In this case, counties with VARSCORE
values less than 121 were assigned a low vulnerability, VARSCORE values
between 121 and 141 were assigned a moderate vulnerability, and
counties with VARSCORE values greater that 141 were assigned a high
vulnerability. The results of this second approach did not appear to
be importantly different than the previous approach, recognizing the
nearness of the two moderate vulnerability categories.
4.2 COMPARISON OF SCORES TO GROUND-WATER REGIONS
The distribution of adjusted VARSCORES by ground-water regions is
arranged by the first and thjrd quartiles in Figure 3. As anticipated,
the highest scores are associated with Region 11 (Southeast Coastal
Plain), Region 12 (Hawaii), and portions of Region 10 (Atlantic and
Gulf Coastal Plain). The lowest VARSCORES are associated with Region
13 (Alaska) largely as a result of the coder's assumption that the vast
permafrost areas would significantly limit the degree of pesticide
infiltration into the ground water. The VARSCORES for the remainder of
the ground-water regions are narrowly banded in the first and third
quartile range of about 110 to 140 (Figure 3). Some associations
between VARSCORES and ground-water regions are apparent, such as the
Piedmont/Coastal Plain contact in the eastern U.S., when the ground-
water vulnerabilty map of the conterminous U.S. is evaluated
(Figure 1).
17
-------
50
r—• 1
I—*—I
I
100
150
VARSCORE
200
GROUND WATER REGION
1 - Western Mountain Ranges
2 - Alluvial Basins
3 - Columbia Lava Plateau
4 - Colorado Plateau and Wyoming Basin
5 - High Plains
6 - Nonglaciated Central
7 - Glaciated Central
8 - Piedmont and Blue Ridge
9 - Northeast
10 - Atlantic and Gulf Coastal Plain
11 - Southeast Coastal Plain
12 - Hawaii
13 - Alaska
250
Explanation
• | Indicates distribution of VARSCORES
-j 2,»i -j,a by 1st through 3rd quamles based
Ou.«uii s on revisions 10 the originally coded
data set The point (2nd quartile)
represents the average VARSCORE
for the ground water region
Figure 3. Distribution of VARSCORES by Ground-Water Regions
18
-------
4.3 DATA CONFIDENCE AND VARIABILITY
The relative confidence of the coder in the data used in
determining the original VARSCORE was keyed into the data base. In
reviewing the distribution of all data confidence listings, the
following percentages were determined for the respective.data
confidence levels:
High data confidence - 17 percent of the counties
Medium data confidence - 80 percent of the counties
Low data confidence - 3 percent of the counties
The high data confidence category was intended for counties where there
was good coverage of published or unpublished data and the professional
coding the form felt fairly confident about the scores cited. Personal
experience in the county or the conveyed experience of another profes-
sional knowledgeable of the conditions in the county, were also
criteria for a high data confidence rating. Low data confidence was
only intended for those remote counties where little if any hydrogeo-
logic information was available and the coder had no experience in the
general area of the county.
These results indicate that, for the majority of the counties, the
individual coding the form felt reasonably confident about the general
hydrogeologic conditions known at the county level. The ability of
another professional to reproduce numerical scores for a given county,
however, is a greater -concern when assessing the validity of such a
subjective data base.
The 3,144 counties were stratified by Heath regions. The sample
of 223 counties described in Appendix B is referred to as the large
sample and the sample of 96 counties as the small sample. Both samples
were allocated proportionally to the strata. Sample counties were
selected from within strata with equal probability and with replace-
ment. The small sample is an equal probability, with replacement
subsample of the large sample.
Due to the contractural constraints of the first stage of the NFS,
all counties in the large sample were receded in only one week. An
argument exists, therefore, that a valid measurement error assessment
is not provided by the large sample since the level of effort was not
comparable to that expended during the original coding process. The
small sample, however, suffers less from scheduling problems.
19
-------
Estimates of the measurement error for the small sample, while
sizeable, do not compromise the numerical scores derived for the
purpose of the first stage stratification effort. A detailed descrip-
tion of the measurement error analysis is provided in Appendix E.
One modification that might have improved the reproducibility of
the numerical score assigned to a given county would have been the
clear designation of the aquifer(s) being considered in the scoring.
For example, a shallow aquifer system may have been considered an
important source of ground water in one portion of a county (perhaps
where a significant glacial drift aquifer was present) and a deeper
consolidated rock aquifer important in other areas of the county
(perhaps where the drift was thin, less permeable, or absent).
Although these variations might sometimes be inferred from the aquifer
media and depth to water percentage differences noted on the coding
forms, the coding format, as implemented, did not lend itself well to
incorporating this kind of variability into the analysis. A more exact
means of distinguishing the areal percentage of a county that
corresponded to each aquifer being considered would have been
beneficial to this analysis.
As discussed in Section 3.5, six counties were selected for
separate coding by several nationally recognized hydrogeologists. This
coding effort was included in the study only to display qualitatively
the range of variability that might be expected among experts. As
such, it is not a statistically valid sample representing the total
data base. A detailed discussion of this qualitative check, along with-
results, is provided in Appendix F.
Computations of the coefficient of variation between multiple
codings of small samples of the data base were performed to gain an
estimate of the average variations in VARSCORES attributable to
individual opinion. The-coefficient of variation for a given county is
defined as the standard deviation divided by the mean, times 100. For
the 6 qualitative check counties, estimates of the coefficient of
variation values ranged from 9 to 21 percent. The average coefficient
of variation for these 6 counties, with 6 observations per county,
would be 13 percent (Appendix F, Table F-2). Computation of the
20
-------
coefficient of variation for the small sample (96 counties coded by the
3 contractors) yielded values ranging from 1 to 26 percent. An average
value for this sample of 96 counties with 3 observations per county
would be 12 percent.
It should be noted that measurement error and coefficient of
variation estimates have been calculated only for the originally coded
data base. These estimates do not reflect later adjustments made to
the data base to improve its general reliability. It is anticipated
that these adjustments, described in detail in Appendix G, should
substantially reduce the measurement error associated with the
originally coded data base.
21
-------
5.0 REFERENCES
Aller, L., T. Bennett, J. H. Lehr and R. J. Petty, 1985. DRASTIC: A
Standardized System For Evaluating Ground Water Pollution Potential
Using Hydrogeologic Settings. U.S. Environmental Protection Agency,
385p.
Freeze, R. A. and J. Cherry, 1979. Groundwater, Prentice-Hall, 604p.
Fuller, M. L., 1905. Underground Waters of the Eastern United States,'*'
U.S. Geologial Survey Water-Supply Paper 114.
Heath, R. C., 1984. Ground-Water Regions of the United States. U.S.
Geological Survey Water Supply Paper 2242, 78p.
Meinzer, 0. E., 1923. The Occurrence of Ground Water in the United
States, U.S. Geological Survey Water-Supply Paper 489.
Raisz, E., 1957. Landforms Map of the United States.
Ries, H., and T. L. Watson, 1914. Engineering Geology, John Wiley and
Sons, Inc., New York.
Thomas, H. E., 1952. Ground-Water Regions of the United States --
Their Storage Facilities and the Physical and Economic Foundation of
Natural Resources, U.S. 83rd Congress, House Interior and Insular
Affairs Committee, Volume 3.
22
-------
APPENDIX-A
TRAINING MANUAL FOR USING DRASTIC HYDROGEOLOGIC FACTORS IN
CONDUCTING A NATIONAL GROUND-WATER VULNERABILITY ASSESSMENT
-------
TRAINING PROGRAM PARTICIPATION
Roy F. Weston Consultants
The Weston training program was held in Washington, DC, on
June 24, 1985, and was attended by:
Weston:
Jack Robertson
Lisa Arters
Robert Caprara
Peter Ciotoli
Loren Laski
Mike Wierman
Bill Miller
EPA:
Stuart Cohen
Ron Hoffer
George DeBuchananne
Jay Lehr
Linda Aller
Geraghty & Miller: Bill Doucette
NWWA:
RTI:
Joe Alexander
Bob Mason
Woodward-Clyde Consultants:
The Woodward-Clyde training program was held in San Francisco, CA,
on June 27, 1985, and was attended by:
Woodward-Clyde: David Dean
Jim Dederick
Charles Franks
Richard Kent
Charles Lehotsky
John Pelka
Melitta Rorty
NWWA: Linda Aller
RTI: Joe Alexander
A-1
-------
SECTION 1
INTRODUCTION
1.1 Background
The Office of Pesticide Programs (OPP) and the Office of Drinking
Water (ODW) are jointly designing a statistical, stratified national
survey of pesticides in ground water. The goals of the survey are to
characterize the occurrence of selected pesticides in ground water; to
determine the relatfoqship between the uses of pesticides, pesticide
characteristics, and ffeld conditions to ground and drinking water con-
tamination; and to estimate human exposure to these chemicals via
drinking water contaminated by normal pesticide usage. One way the
United States will be stratified will be to rank the ground-water vul-
nerability of all counties into categories of high, medium, and low
based on specific hydrogeologic factors (Stage 1). Subsequent stages
of the survey will involve selection of county segments (Stage 2) and
selection of potable wells for ground-water sampling and analysis
(Stage 3).
1.2 Overall Approach
Research Triangle Institute (RTI) will manage the first stage of
the project for EP.A. A significant portion of this first stage is
being performed by Woodward-Clyde Consultants and Weston Consultants
for data collection efforts in the western and eastern United States,
respectively. Seven key hydrogeologic factors, and a system for
ranking them with respect to ground-water pollution potential by pesti-
cides, have recently been developed by the National Water Well Associa-
tion (NWWA). These factors are referred to as DRASTIC by NWWA and will
serve as the basis for data collection in this work assignment. The
ranking and weighting system developed by NWWA for the DRASTIC factors
will be used to determine the relative vulnerability of. each county to
ground-water contamination by pesticides used in agricultural practice.
The data assembled in this effort may also be useful in other related
studies.
1.3 Purpose of Training Program
Brief training programs are scheduled in two locations for repre-
sentatives of Woodward-Clyde and Weston. The purpose of this training
program 'is to provide sufficient information to those individuals
involved in the first stage so that consistency can be achieved in
obtaining DRASTIC factors on a national scale. A number of hydrogeo-
logists will be using their judgement and experience to complete the
information for selected counties, or relying on information obtained
from other professionals knowledgeable of the hydrogeologic conditions
of a particular area. It is the intent of this training program to
provide the professionals in your firm with:
A-2
-------
0 An introduction and overall approach to be used on the project
0 A history of the DRASTIC system and definitions of the key
hydrogeologic factors
0 Step-by-step instructions for completing the county data forms
along with a discussion of the level of effort that should be
used.
0 Suggestions for data sources
0 Instructions for determining the DRASTIC score, the sensitivity
of the scoring process, and how to indicate the level of confi-
dence in the data
0 Instructions for resolving questions and transmitting completed
data forms
0 Guidance in completing data forms for several selected counties
during the training program.
A-3
-------
SECTION 2
BACKGROUND INFORMATION
Two references (Heath 19841; and Aller et al. 19852) serve as
the framework for this work effort and applicable portions of each have
been incorporated in this training program with the permission of the
authors.
2.1 Ground-Water Regions
A current basis for grouping aquifers by their water-bearing char-
acteristics within certain geographic areas is contained in Heath
(1984). The ground-water regions that will be used for the purposes of
this project (modified from Heath 1984) include:
Region 1 Western Mountain Ranges
2 Alluvial Basins
3 Columbia Lava Plateau
4 Colorado Plateau and Wyoming Basin
5 High Plains
6 Nonglaciated Central
7 Glaciated Central
8 Piedmont and Blue Ridge
9 Northeast and Superior Uplands
10 Atlantic and Gulf Coastal Plain
11 Southeast Coastal Plain
12 Hawai1
13 Alaska
^•Heath, R. C. 1984. Ground-Water Regions of the United States. U.S.
Geological Survey Water Supply Paper 2242. 78 pp.
2Aller, L. T. Bennett, J. H. Lehr, and R. J. Petty. 1985. DRASTIC: A
Standarized System for Evaluating Ground Water Pollution Potential
Using Hydrgeologic Settings. U.S. Environmental Protection Agency.
384 pp.
A-4
-------
General information on the hydrogeologic conditions of each of these
regions has been summarized in the Appendix. In addition, these
ground-water regions have been generalized on a more detailed county
map of the United States (scale 1:17,000,000) that will be provided
along with this manual.
2.2 Hydrogeologic Settings
Within the framework of the ground-water regions, NWWA has devel-
oped the concept of hydrogeologic settings, defined as mappable units
with common hydrogeologic characteristics and common vulnerability to
contamination by pollutants. Approximately 86 hydrogeologic settings
have been identified within the ground-water regions of the United
States. Brief descriptions of each of these settings, along with a
schematic block drawing, are provided in the Appendix (from Aller et
al, 1985).
2.3 Agricultural DRASTIC
A history of the DRASTIC system will be provided by NWWA as a part
of this training program. Seven key hydrogeologic factors are consid-
ered to control .ground water pollution potential. They include the
following and form the acronym DRASTIC:
D - Depth of water
R - Recharge (net)
A - Aquifer media
S - Soil Tiedia
T - Topography (slope)
I - Impact of vadose zone
C -. Conductivity (hydraulic) of the aquifer
A numerical ranking system to assess relative ground-water pollution
potential has been devised by NWWA considering weights, ranges, and
ratings of the DRASTIC factors. An Agricultural DRASTIC system has
been designed, and is appropriate for the purposes of this work assign-
ment, where the specific concern is the application of pesticides to
the land surface. A typical numerical ranking system for Agricultural
DRASTIC factors has been incorporated as a constant in the data forms
that will be used in this work assignment. Additional background
information on the ranking system will be provided during the training
program; it is therefore not considered necessary to describe the
ranking system further in this manual.
A-5
-------
2.4 Depth to Water
For the purposes of this manual, depth to water refers either to
the depth to the water surface in an unconfined aquifer, or to the top
of the aquifer where the aquifer is confined. In most cases the upper-
most aquifer should be selected as it is most susceptible to contamina-
tion by pesticides and may serve as a source of recharge to underlying
aquifers. Depth to water does not include saturated zones that have
insufficient permeability to yield significant quantities of water to
be considered an aquifer.
The depth to water is important primarily because it determines
the thickness of material through which a contaminant must travel
before reaching the aquifer, and it may help to determine the amount of
time during which contact with the surrounding media is maintained.
The depth to water is also important because it provides the maximum
opportunity for oxidation by atmospheric oxygen. In general, there is
a greater chance for attenuation to occur as the depth to water
increases because deeper water levels infer longer travel times. The
ranges in depth to water as defined in the DRASTIC system have been
determined based on presumed depths where the potential for pollution
changes.
2.5 Net Recharge
The primary source of qround water is precipitation, which infil-
trates through the surface of the ground and percolates to the water
table. Net' recharge indicates the amount of water per unit area of
land that penetrates the ground surface and reaches the water table.
This recharge water is thus available to transport a contaminant verti-
cally to the water table where it can then migrate within the aquifer.
In addition, the quantity of water available for dispersion and dilu-
tion in the vadose zone and in the saturated zone is controlled by this
parameter. In areas where the aquifer is unconfined, recharge to the.
aquifer usually occurs more readily and the pollution potential is
generally greater than in areas with confined aquifers.
Confined aquifers are partially protected from contaminants intro-
duced at the surface by layers of low permeability media that retard
water movement to the confined aquifer. In some parts of some confined
aquifers, head distribution is such that movement of water is through
the confining bed from the confined aquifer into the unconfined
aquifer. In this situation, there is little opportunity for local
contamination of the confined aquifer. The principal recharge area for
the confined aquifer is often many miles away. Many confined aquifers
are not truly confined and are partially recharged by the migration of
water through the confining layers. The more water that leaks through,
the greater the potential for recharge to carry pollution into the
aquifer. Recharge water, then, is a principal vehicle for leaching and
transporting solid or liquid contaminants to the water table. There-
fore, the greater the recharge, the greater the potential for pollu-
tion.
A-6
-------
One additional factor that must be considered is augmentation of
natural recharge through artificial recharge or by irrigation. When a
range for net recharge is assigned, these additional sources of water
must be considered.
2.6 Aquifer Media
Aquifer media refers to the consolidated or unconsolidated medium
that will yield sufficient quantities of water for use. Water is held
by aquifers in the pore spaces of granular and clastic rock and in the
fractures and solution openings of nonclastic and nongranular rock.
Rocks that yield water from pore spaces have primary porosity; rocks
that have water held in solution openings or fractures created after
the rock was formed have secondary porosity. The aquifer medium exerts
the major control over the route and path length that a contaminant
must follow. In general, the larger the grain size and the more frac-
tures or openings within the aquifer, the higher the permeability and
the lower the attenuation capacity; consequently, the greater the pol-
lution potential.
For purposes of this manual, aquifer media have been designated by
descriptive names. Each medium is listed below in order of increasing
pollution potential. A description of each .medium follows the name and
standard abbreviation that is used on the data form:
1. Massive Shale (SH). Thick-bedded shales and Gladstone or clays
that typically yield only small quanitities of water from frac-
tures and have a low pollution potential. Pollution potential
is influenced by the degree of fracturing.
2. Metamprphic/Igneous (META/IGN). Consolidated bedrock of meta-
morphic or iqneous origin that contains little or no primary
porosity and yields water only from fractures within the rock.
Typical yields" are low and the relative pollution potential is
a function of the degree of fracturing.
3. Weathered (WTHD) Metamorphic/Igneous. Unconsolidated material,
commonlytermedregolithor saprolite, that is derived by
weathering of the underlying consolidated bedrock and contains
primary porosity. The pollution potential is largely influ-
enced by the amount of clay material present: the higher the
clay content, the lower the pollution potential.
4. Bedded Sandstone (SS), Limestone (IS), and Shale (SH). Tvpi-
cally thin-bedded sequences of sedimentary rocks that contain
primary porosity. The controlling factor in determining pollu-
tion potential is the degree of fracturing.
A-7
-------
5. Massive Sandstone (SS). Consolidated sandstone bedrock that
contains both primary and secondary porosity and is typified by
thicker deposits than those of the bedded sandstone, limestone,
and shale sequences. Pollution potential is largely controlled
by the degree of fracturing and the primary porosity of the
sandstone.
6. Massive Limestone (LS). Consolidated limestone or dolomite
bedrock that is characterized by thicker deposits than bedded
sandstone, limestone, and shale sequences. Pollution potential
is largely affected by the degree of- fracturing and the amount
of solution of the limestone.
7. Sand and Gravel (SA & GVL). Unconsolidated mixtures of sand-
to gravel-sized particles that contain varying amounts of fine
materials. Sands and/or gravels that have only small amounts
of fine material are termed "clean." In general, the cleaner
and more coarse-grained the aquifer media, the greater the
pollution potential.
8. Basalt. Consolidated extrusive igneous bedrock that contains
beddinq planes, fractures, and vesicular porosity. The term is
used herein in a generic sense, even though it is actually a
type of rock. Pollution potential is influenced by the amount
of interconnected openings that are present in the lava flow
materials.
9. Karst Limestone (LS). Consolidated limestone bedrock that has
been dissolved to the point where larqe, open, interconnected
cavities and fractures are present. This is a special case of
massive limestone.
2.7 Soil Media
Soil media refers to that uppermost portion of the vadose zone
characterized by significant biological activity. For purposes of this
manual, soil is commonly considered the upper weathered zone of the
Earth that averages 3 feet or less. Soil has a significant impact on
the amount of recharge that can infiltrate into the ground and hence on
the ability of a contaminant to move vertically into the vadose zone.
Moreover, where the soil zone is fairly thick, the attenuation
processes of filtration, biodegradation, sorption, and volatilization.
may be quite significant. Thus, for certain on-land surface practices,
such as agricultural applications of pesticides, soil can be a primary
influence on pollution potential. In general, the pollution potential
of a soil is largely affected by the type of clay present, the
shrink/swell potential of that clay and the grain size of the soil. In
general, the less the clay shrinks and swells and the smaller the grain
size, the less the pollution- potential. The quantity of organic
material present in the soil may also be an important factor.
Descriptions of soil media in order of decreasing pollution potential
follows with the names and standard abbreviations that are used on the
data forms:
A-8
-------
1. Nonshrinkinq Clay (CL). Illitic or kaolinitic clays that do
not contract with the removal of water and therefore do not
form vertical secondary permeability, thus increasing the pol-
lution potential.
2. Clay (CL) Loam. A soil textural classification characterized
by 15 to 55 percent silt, 27 to 40 percent clay, and 20 to 45
percent sand (Figure 1). Because of the high amounts of clay
and restrictive permeabilities, it has a low pollution poten-
tial.
3. Si Ity (SI) Loam. A soil texture classification characterized
by 50 to 85 percent silt, 12 to 27 percent clay, and 0 to 50
percent sand (Figure 1). The pollution potential is still low,
but higher than a clay loam because of typically lower percent-
ages of clay.
4. Loam. A soil textural classification characterized by 25 to 50
percent silt, 7 to 27 percent clay, and 0 to 50 percent sand
(Figure 1). The pollution potential is still low, but higher
than a si Ity loam because of lower percentages of clay and
silt.
5. Sandy (SA) Loam. A soil textural classification characterized
by 0 to 50 percent silt,' 0 to 20 percent clay, and 15 to 50
percent sand (Figure 1). The pollution potential is greater
than a loam due to the higher percentage of sand.
6. Shrinking- Clay. Characterized by montmorillonitic clays or
smectities that have an expanding lattice that swells and
contracts with alternating wetting and drying. Although the
cracks formed up on drying swell as the clay hydrates, the
ability of pollutants to move rapidly upon intitial wetting is
documented. This medium, can have a seemingly high pollution
potential based on the secondary vertical permeability created
by the cracking of the media upon drying even though it is
usually of low permeability.
7. Sand (SA). A size-based delineation of angular or round parti-
cles ranging from 1/16 mm to 2 mm. Sands that are free of
silts or clays have a high pollution potential.
8. Gravel (GVL). A particle-based size classification typified by
particles larger than 2 mm and commonly a mixture of sand,
silt, clay, and gravel with a preponderance of large-sized
particles. Permeability is rapid and pollution potential is
high.
9. Thin or Absent. If a soil layer is not present or if the layer
is so thin as to be considered ineffective, the pollution
potential is very high.
4-9
-------
Si Slht (O£ff1 )5S?r5593M55555k
9«i% IA^QIII *ywTy **/yy- "//ft
T*rinrYVTTr rr'r •• i1 i"r T\ TT T ¥Vi*i*i*i\\A/tj*7
percent sand
{From Soil Conservation Service,
1951 as reported in Aller et al
1935)
Figure 1. Soil t.extural classification chart.
A-10
-------
2.8 Topography
As used here, "topography" refers to the slope and slope variabil-
ity of the land surface. Basically, topography helps control the like-
lihood that a pollutant will run off or remain on the surface in one
area long enough to infiltrate. This is particularly important in
activities such as the application of pesticides where the effect of
the contaminant tends to be cumulative. Therefore, the greater the
chance of infiltration, the higher the pollution potential associated
with the slope. Topography influences soil development and therefore
has an effect on attenuation. Topography is also significant from the
standpoint that the gradient and direction of flow can often be
inferred for water table conditions from the general slope of the land.
Typically, steeper slopes signify higher ground-water velocity.
The ranges of percent slope have assumed that 0 to 2 percent slope
provides the greatest opportunity for a pollutant to infiltrate because
neither the pollutant nor much precipitation exits in the area as run-
off. Conversely, 18+ percent slope affords a high runoff capacity and
therefore a lesser probabi'lity of infiltration with subsequent lower
pollution potential. However, steep slopes are more conducive to rapid
erosion and contamination of surface water.
2.9 Impact of Vadose Zone
The vadose zone is defined as the unsatuated zone above the water
table. For purposes of this document, this rigid definition can be
applied to all water table aquifers. However, when evaluating a
confined aquifer, the "impact" of the vadose zone is expanded to
include both the vadose zone and any saturated zones that overlie the
aquifer. The significantly restrictive zone above the aquifer that
forms the confining layer is used as the type of media having the most
significant impact.
The type of vadose zone media determines the attenuation charac-
teristics of the material below the typical soil horizon and above the
water table. Biodegradation, neutralization, mechanical filtration,
chemical reaction, volatilization, and dispersion are all processes
that may occur within the vadose zone with a general lessening of bio-
degradation and volatilization with depth. The media also control the
path length and routing, thus affecting the time available for attenua-
tion and the quantity of material encountered. Routing is strongly
influenced by any fracturing present. The materials at the top of the
vadose zone also exert an influence on soil development.
A-11
-------
Vadose zone media have been designated by descriptive names. Each
medium, listed in order of increasing pollution potential, is described
below; the standard abbreviation used on the data forms is included:
1. Silt/Clay (SI/CL). A deposit of silt and clay-sized particles
that serve as a barrier to retard movement of liquids. The
high clay content provides a low pollution potential.
Shrinking clays and higher silt concentrations increase the
pollution potential.
2. Shale (SH). A consolidated thick-bedded clay rock th-at may be
fractured. Pollution potential is low but increases with the
degree of fracturing.
3. Limestone (LS). Consolidated massive limestone or dolomite
that typically contains fewer bedding planes than bedded
l--;.estone, sandstone, and shale sequences (see "e" below).
Pollution potential is influenced by the degree of fracturing,
with a low density of fracturing allowing less chance for
pollutant migration.
4. Sandstone (SS). A consolidated sand rock that contains both
primary and secondary porosity and is typified by thicker
bedding than compared to bedded limestone, sandstone, and shale
sequences (see "e" below). Pollution potential is largely
controlled by the degree of fracturing and the primary porosity
of the sandstone.
5. Bedded Limestone (LS). Sandstone (SS), and Shale (SH).
Typically thin-bedded sequences of sedimentary rocks that
contain primary porosity, but where the controlling factor in
determining pollution potential is the degree of fracturing.
6. Sand and Gravel with Significant Silt and Clay (SA & GVL w/ SI
& CL).Unconsolidated mixtures of sand and gravel that contain
an appreciable amount of fine material affecting pollution
potential by having a high concentration of clay or by reducing
the permeability of the deposit. These deposits are commonly
referred to as "dirty" and have a lower pollution potential
than "clean" sands and gravels. In general, finer-grained
"dirtier" sands have a lower pollution than coarser-grained
"dirtier" gravels.
7. Metamorphic/Igneous (META/IGN). Consolidated rock of metamor-
phic or igneous origin that contain no significant primary
porosity and permit movement of liquids through fractures. The
relative pollution potential is a function of the degree of
fracturing.
-------
8. Sand and Gravel (SA & GVL). Unconsolidated mixtures of sand to
gravel-sized particles that contain only small amounts of fine
materials. The range in rating reflects principally a grain
size distribution where poorly sorted small-grained deposits
have a lower pollution potential and well-sorted, large-grained
deposits have a higher pollution potential.
9. Basalt. Consolidated extrusive igneous bedrock that-contains
bedding planes, fractures, and vesicular porosity. This is a
special case of Metamorphic/Igneous. The term is used herein
in a generic sense, even though it is actually a type of rock.
Pollution potential is influenced by the number and amount of
interconnected openings present in the lava flow materials.
Pollution potential is typically high because there is little
chance for attenuation once a pollutant enters the fracture
system.
10. Karst Limestone (LS). Consolidated limestone bedrock that has
beendissolvedtothe point where large open interconnected
cavities and fractures are present. This is a special case of
limestone where pollution potential is high based on the amount
of open area in the rock.
2.10 Hydraulic Conductivity
Hydraulic conductivity refers to the ability of the aquifer mate-
rials to transmit water or pollutants, under a given hydraulic gradi-
ent. Hydraulic conductivity is controlled by the amount and intercon-
nection of void spaces within the aquifer that may. occur as a conse-
quence of intergranular porosity, fracturing, or bedding planes. For
purposes of this manual, hydraulic conductivity is divided into ranges
where high hydraulic conductivities are associated with higher pollu-
tion potential. This is because the pollutant has the potential for
moving quickly away from the point in the aquifer where it is intro-
duced. Ranges of hydraulic conductivity, along with conversion
factors, are provided in Table 1.
A-13
-------
Table 1. Ranges of Hydraulic Conductivity
and Conversion Factors1'
Range of Values of Hydraulic Conductivity
and Permeability
u _
I
1
1
1
Rocks
Un consolidated k k K K K
deposits __ (darcy) (cm2) (cm/s) (m/s) {gal/day/
5 "5 i
-
/I
Sjj
i«
"3-0
gSl
i
C
_ •—
» 0
?
'J
^
tj
o
u:
Ul
"1
h.
y
1-*
O o
a c
P o
1 '
J
o o
_ •
£-0-0
_j C
t
i
c
1
I
£
A
V)
p
™
(
^.
rio5 rio~3 rio2
T
a
X
C.
J
5
a
en
C
a
r
_ t
3 *•
3
•)
5
j
j
en
a
CO —
|
i
c
•a o JJ
S 0 O
^
ai
3
a>
^
u "a. *" i
III1
— '
E ~
^
>
-
a
>• 3
"oi5
at
c
I
-IO4
-IO3
-IO2
-10
-1
-to-1
-io-2
-ID'3
-to-4
-ID'5
-1C"6
-io-7
-ID"8
-io-4
-io-5
-io-6
-io-7
-ID'8
-io-9
-10"°
- IO"1
- 1Q-'2
- IO"13
-io-14
-io-15
-io-6
-10
-1
-10"
-tO'2
-io-3
-io-4
-io-5
-10"6
-ID'7
HO'6
-ID'9
- 10"°
-IO"1
-1
.IO6
-10"
HO'2
-io-3
-io-4
-io-5
-io-6
-1C'7
-io-8
-ID'9
-10"°
-io-11
- 10"2
-IO"3
-IO5
-IO4
-IO3
-IO2
• 10
. 1
Bi
• 10"
-10'2
IV
-10"3
I\J
-io-4
_g
-10
-to-6
,-7
Conversion Factors for Permeability
and Hydraulic Conductivity Units
cm2
ft2
darcy
m/s
ft/s
cm2
1
9.29 x IO2
9.87 x 10-*
1.02 x 10-'
3.11 x 10-*
U.S gal/day/ftJ5.42 x 10'10
Permeability. *•
ft2
1.08 x 10-3
1
1.06 x 10-'i
l.lOx 10-*
3.35 x 10-'
5.83 x 10-13
Hydraulic conductivity. K
darcy
1.01 x 10"
9.42 x 10' °
1
1.04 X I0>
3.15 x 10*
5.49 x 10"2
m/s
9.80 x IO2
9.11 x 10*
966 x 10-«
1
3.05 x I0-i
4.72 x ID'7
ft/s U S. gal/day/ft'
3 22 x 103
2.99 x 10<
3.17 x 10-'
3.28
1
1.55 x 10-«
1.85 x 10»
1.71 x 10i2
1.82 x Ifli
2.12 x 10*
6.46 x 10'
1
•To obtain k m ft2, multiply k in cmz by 1.08 x 10~3.
1} From Freeze, R. Allan and John A. Cherry, 1979, Groundwater. Prentice Hall, Inc.
A-14
-------
SECTION 3
INSTRUCTIONS FOR COMPLETING DATA FORMS
3.1 Introduction
The information that will be compiled in this survey will be use-
ful for future studies and you are encouraged to maintain notes and
reference materials during the course of this project. RTI has devel-
oped a form that should be convenient for compiling the necessary
information for each county in this survey (Figure 2). Once the data
are assembled for the county, the form itself should not require more
than 15 minutes to complete. For convenience, the basic header infor-
mation (FIPS Coding, State, and county name) has been prelabeled for
each form. The form has been designed to indicate estimated percent-
ages of each hydroqeoloqical factor with respect to the total land area
of the county as a means of indicating the variable conditions in land
areas of this size. Step-by-step instructions for completing each por-
tion of the form are provided in the following sections.
3.2 Ground-Water Region
It is anticipated that many counties in the United States will be
located in only one ground-water region. Space for as many as four
ground- water regions are provided on the forms. Spaces for the esti-
mated percentage of the county occupied by the corresponding ground-
water regions are also provided.
Example 1
A county is located entirely within the Piedmont and Blue Ridge
ground-water region (Region 8). The form should be completed as:
GROUND WATER REGION 08 ESTIMATED % OF COUNTY 99
Note that only two digits are built into the percent column of the data
form and that 99 percent will be regarded as 100 percent.
A-15
-------
FTPS CODING
STATE COUNTY
s333=a33ss3B33ssssB:
GROUND-WATER REGION
GA
GB
GC
GD
HYDROGEOLOGIC SETTING
_^ HA
HB
HC
HD
HE
HF
:3=333=3S3333S=3333=3333333SS3
AGRICULTURAL
RANGES IN WEIGHTED 'i
DRASTIC FACTORS RATINGS '
BB3sas3333333SB33S333:
ESTIMATED 7. OF COUNTY
ESTIMATED 7. OF COUNTY
33333B3333
ESTIMATED
7. OF
COUNTY I
/ RANGES IN
DRASTIC FACTORS
:=33BB3a33B3B3BS3B8333 =?:
AGRICULTURAL ESTIMATED
WEIGHTED 7. OF
RATINGS COUNTY
SB===33BB3B=B=========S33=BBB:
DEPTH TO
WATER (FT)
0-5
5-10
10
15
30
50
75
- 15
- 30
- 50
- 75
-100
NET
100 +
:3333B3B3 = 3BB3:
RECHARGE (IN)
0-2
2-4
4-7
7 -10
10 +
50
45
40
35
25
15
10
5
AQUIFER MEDIA
SH (MASSIVE)
META/IGN
META/IGN (WTHD)
SS, LS, SH (TN BED)
SS (MASSIVE)
LS (MASSIVE)
SA £ GVL
BASALT
LS (KARST)
SOIL MEDIA
THIN OR ABSENT
GVL
SA
CL (SHRINKING)
LOAM (SA)
LOAM
LOAM (SI)
LOAM (CL)
CL CNONSHRINKING)
4
12
24
32
36
6
9
12
18
18
18
24
27
30
50
50
45
35
30
25
20
15
5
DA
DB
DC
DD
DE
np
Ur
DG
DH
3 = 333
RA
RB
RC
RD
RE
3333 =
AA
AB
AC
AD
AE
AF
AG
AH
AI
33333
SA
SB
SC
SD
SE
SF
SG
SH
SI
TOPOGRAPHY (3 SLOPE)
0-2 30
2-6 27
6-12 15
12-18 9
18+ 3
IMPACT OF VADOSE ZONE MEDIA
SI/CL 4
SH 12"
LS 24
SS 24
LS, SS. SH (BEDDED) 24
SA £ GVL WXSI £ CL 2*
META/IGN 16
SA £ GVL 32
BASALT 36
LS (KARST) «*0
333333333333333333333333333333S3SS3
HYDRAULIC CONDUCTIVITY (GPDXS2 FT)
1 - 100 2 .
100 - 300 4
300 - 700 8
700 - 1000 12
1000 - 2000 16
2000+ 20
S333333333333333S333333333333333333
AGRICULTURAL DRASTIC SCORE
======================= ============
INDEX VARIABILITY + -
=53333333333333333S=333333=333SB3=3
DATA CONFIDENCE _
(3 = HIGH 2 = MEDIUM. 1 = LOW)
3=333333S33====3=====S3=B========33
BY
FIRM DATE 85
===================================
DATA SOURCES OR COMMENTS ON BACK
TA
TB
TC
TD
TE
IA
IB
1C
ID
IE
IF
IG
IH
II
IJ
BBSS
CA
CB
CC
CD
CE
CF
3=3 =
3SSS
3333
333 S
33=3
Figure 20 Data Collection Form
A-16
-------
Example 2
A county adjacent to that in Example 1 is primarily within Region
8, but also within the Atlantic and Gulf Coastal Plain (Region 10).
In evaluating geologic maps of the county it is estimated that aporoxi-
mately 75 percent of the county is in Region 8 and the remainder of the
county is in Region 10. The form should be completed as:
GROUND-WATER REGION 08 ESTIMATED % OF COUNTY 75
In the unlikely case where more than four ground-water regions are
encountered within a large county, it would not be necessary to indi-
cate the ground-water region that occupies the lowest percentage of the
county.
3.3 Hydrogeologic Setting
Spaces for coding as many as six.'hydrogeologic settings (and cor-
responding percentages of the county) are provided on the data form.
Information about each hydrogeologic setting is provided in the Appen-
dix. It will be necessary to indicate the ground-water region code
with the hydrogeologic setting as shown in the following examples.
Example 3
A county is located entirely within the Western Mountain Ranges
ground-water region (Region 1). Upon viewing more detailed information
about the county it is determined that the county can be subdivided by
predominantly Western Mountain Slopes (lAb West; Appendix 1) and some
Western Alluvial Mountain Valleys (IBb West; Appendix 1). The form
should be completed as:
HYDROGEOLOGIC SETTING 1 Ab ESTIMATED % OF COUNTY 90
-Tib" ~TU~
A-17-
-------
Example 4
A county is located in two ground-water regions (based on Example
2) and four hydrogeologic settings are present as shown by the follow-
ing schematic drawing:
8D
lOAb
Referring to the hydrogeologic settings in the Appendix (Sections 8 and
10), the form should be completed as:
HYDROGEOLOGIC SETTING
8 D
~ST
TO Ab
TO" BE
ESTIMATED % OF COUNTY 65
10
20
5
A-l:*
-------
It is important to note that in this example, the sum of the percent
associated with the hydrogeologic settings of one ground-water region
should equate to the percent of the ground-water region previously
reported for the county as shown below:
GROUND-WATER REGION 08 ESTIMATED % OF COUNTY 75
10 25
HYDROGEOLOGIC SETTING 8 D 65
8 E 10
TO" AB" ~20"
TO"Bb~ T
3.4 Agricultural DRASTIC Factors
Each of the seven DRASTIC factors on the form are arranged under
the following three headings:
AGRICULTURAL ESTIMATED
RANGES IN WEIGHTED ' % OF
DRASTIC FACTORS RATINGS COUNTY
Descriptions of DRASTIC factors were previously defined in Section 2.
Each DRASTIC factor has been divided into significant ranges. The
ranges of the factors are presented in Examples 5 through 11 later in
this section. The corresponding agricultural weighted rating is a
numerical constant that will be used in determining the DRASTIC score.
The corresponding percent of county allows the hydrogeologist to show
the variation of conditions that exists in the particular county. The
following instructions apply to each of the DRASTIC factors:
1. Select the one range or description of DRASTIC factors that
best represents the largest area of the county.
2. Circle only one corresponding agricultural weighted rating
3. Enter the corresponding estimated percentage that applies to
the largest county area.
4. Enter the remaining percentages of the county that apply to
other factors. It is not necessary for the percent of the
county to add up to 100 percent (80 to 90 percent is accept-
able).
A-I9
-------
These instructions are best explained by examples provided in the fol-
lowing subsections. For convenience, each example is assumed to be in
the same ground-water region.
3.4.1 Depth to Water
The county is located in the Piedmont and Blue Ridge ground-water
region, a thick regolith hydrogeologic setting (Region 8; Appendix 8)
An unconfined aquifer is the primary source of drinking water supply.
The depth to the water table in most of the county is in the range of
15 to 30 feet below land surface. A few major streams are present in
the county and the depth to the water table near the streams ranges
from 5 to 15 feet, depending on the prjoximity to the streams. In the
actual floodplain of the stream, the water table is less than 5 feet
below land surface (although this reoresenr,s a very small area of the
total county). The form would be complef-:d as follows:
Example 5
RANGES
IN
DRASTIC FACTORS
DEPTH TO
WATER (FT)
0-5
5-10
10-15
15-30
30-50
50-75
75-100
100+
AGRICULTURAL
WEIGHTED
RATINGS
50
45
15
10-
5
ESTIMATED
% OF
COUNTY
05
~05~
05
"85"
In this example the agricultural weighted rating of 35 would be used in
determining the DRASTIC score (explained in Section 3.5) since it
represents the majority of the area in the county.
-------
3.4.2 Net Recharge
The same county is known to have approximately 50 inches of rain-
fall annually. Approximately 30 inches are known to be lost to evapo-
transpiration. After evaluating other important considerations that
influence net recharge (such as type of surficial soils, vegatative
cover, slopes, and runoff) it is determined that approximately 5 inches
are available for recharge to the unconfined aquifer. The rainfall and
recharge is fairly evenly distributed throughout the county, within the
ranges indicated in the example shown below. The form would be
completed as follows:
Example 6
RANGES IN
DRASTIC FACTORS
NET RECHARGE (IN)
0-2
2-4
4-7
7-10
10+
AGRICULTURAL
WEIGHTED
RATINGS
4
12
32
36
ESTIMATED
% OF
COUNTY
"99
3.4.3 Aquifer Media
The county is known to have an aquifer composed predominantly of
weathered metamorphic and igneous rocks. The alluvium near some of the
major streams may also supply ground water,/'although this would repre-
sent a small percentage of the county area. The form would be com-
pleted as follows:
Example 7
AGRICULTURAL ESTIMATED
RANGES IN WEIGHTED % OF
DRASTIC FACTORS RATINGS COUNTY
AQUIFER MEDIA
SH (MASSIVE) 6
META/IGN 9
META/IGN (WTHD) (g) 90
SS, LS, SH, (TN BED) IS
SS (MASSIVE) 18
LS (MASSIVE) 18
SA & GVL 24 ~0?
BASALT 27
LS (KARST) 30
Note that in this example it is not necessary to account for the entire
percentage of the county.
A-21
-------
3.4.4 Soil Media
The shallow soils in most of the county are derived from the in-
place weathering of the igneous and metamorphic rocks. These residual
soils are classified as silty loams or clayey loams, depending on loca-
tion. Sand and gravel are known to be present in small quantities as
alluvial deposits near the major streams. The form would be completed
as follows:
Example 8
RANGES IN
DRASTIC FACTORS
SOIL MEDIA
THIN/ABSENT
GVL
SA
CL (SHRINKING)
LOAM (SA)
LOAM
LOAM (SI)
LOAM (CL)
CL (NONSHRINKING)
AGRICULTURAL
WEIGHTED
RATINGS
50
50
45
35
30
25
5
ESTIMATED
% OF
COUNTY
~OT
03
~50
JET
It should be noted that in this example there is little differentiation
between the percent of silty and clayey loams in the county. It is
necessary however, to show only one soil type as predominant (Refer to
numbers 2 and 3 in the instructions, in Section 3.4). It is also
important to note in this example that the difference in the agricul-
tural weighted ratings between these two soil types is not significant
(5 points) relative to the total' range of this factor (5 to 50
points).
A-?2
-------
3.4.5 Topography
The percent slope of most of the county is in the range of 6 to 12
percent. The slope decreases to less than 6 percent in the valley of
the major streams. The form would be completed as follows:
Example 9
RANGES
IN
DRASTIC FACTORS
TOPOGRAPHY
(% SLOPE)
0-2
2-6
6-12
12-18
18+
AGRICULTURAL
WEIGHTED
RATINGS
30
27
COb
9
3
ESTIMATED
% OF
COUNTY
05
~05"
"90
3.4.6 Impact of Vadose Zone
The-predominant impact the vadose zone in most of the county is
that of silt or clay-sized particles. The weathered profile of the
underlying metamorphic and igneous rocks is irregular and in some areas
is in'cluded within the vadose zone. The form would be completed as
follows:
Example 10
AGRICULTURAL
RANGES IN WEIGHTED
DRASTIC FACTORS RATINGS
IMPACT OF VADOSE ZONE MEDIA
SI/CL
SH
LS
ss
LS, SS, SH (BEDDED)
SA & GVL W/SI & CL
META/IGN
SA & GVL
BASALT
LS (KARST)
©
12
24
24
24
24
16
32
36
40
ESTIMATED
% OF
COUNTY
75
JQT
-------
3.4.7 Hydraulic Conductivity
The hydraulic conductivity within the fractured metamorphic/
igneous rocks in the county is low, typically less than 50 gpd/ft2.
The form would be completed as follows:
Example 11
RANGES IN
DRASTIC FACTORS
AGRICULTURAL
WEIGHTED
RATINGS
HYDRAULIC CONDUCTIVITY (GPD/FT2)
1-100 ©
100-300 4
300-700 8
700-1,000 12
1,000-2,000 16
2,000+ 20
ESTIMATED
% OF
CnJNTY
99
3.5 Agricultural DRASTIC Score
The Total Agricultural DRASTIC score is determined by simply
adding the seven circled agricultural weighted ratings. In the
previous examples (5' through 11) the total score would be:
[D = 35] + [R = 24] + [A = 12] + [S = 20] + [T = 15] + [I = 4] + [C = 2]
=
AGRICULTURAL
DRASTIC
SCORE
112
The total score, or a percentage-weighted score, will be used to set
vulnerability limits when the study is near completion. Recognizing
the range of total scores that is possible (29 to 256), the total score
in the above example (112) would likely end up being considered as
moderate vulnerability. The usefulness of the total score at this
point in the survey will be for the hydrogeologist coding the forms
when evaluating adjacent counties for overall reasonableness and to be
able to account for differences in total scores.
A -24
-------
3.6 Index Variability
The Agricultural DRASTIC score that was determined in Section 3.5
is based on typical values that do not account for heterogeneities such
as degree of fracturing in the aquifer media or the vadose zone. A
term referred to as "index variability" can be used to indicate they
hydrogeologist's opinion on the variability that might be associated
with the previously determined score. It is anticipated that the
aquifer media and the impact of the vadose zone are the primary DRASTIC
factors where index variability may apply. Table 3 has been designed
to show specific values that could be considered by RTI later in the
study for addition or subtraction to the score for the aquifer media
(Table 3, Part I) or the impact of the vadose zone (Table 3, Part II).
The space provided on the form for the index variability should be
coded as a 2-digit, oositive or negative value, as shown in the
following example.
Example 12
The aquifer media of the county was coded as massive shale, con-
tributing a typical value of 6 to the total Agricultural DRASTIC score
(Refer to the agricultural weighted rating for shale in Figure 2). The
shale is highly fractured in the county, however, and the
hydrogeologist feels that a higher DRASTIC score should be recognized.
Referring to Table 3 (Part I), three specific values could be selected
for massive shale (3, 6, or 9). In the case of massive shale that is
highly fractured, a value of 9 would be more appropriate for the score,.
increasing the reported Agricultural DRASTIC score by 3 points. The
increase would be noted on the form as:
Index Variability +3_
Similarly, the typical value that would have been coded for the
impact of the vadose zone is 12 (Refer to the agricultural weighted
rating for shale in Figure 2). If the variability was also considered
significant for the impact of the vadose zone media (Table 3, Part II),
an additional +8 could be combined with the increased variability of
the aquifer media (+3) for a total reported Index Variability of +11.
It is possible for the index variability of one factor to be a
positive value (an increase in the total score) and another factor to
be a negative value (a decrease in the total score). Only one value
(an absolute number) should be coded for index variability. It is
important to account for each component used to derive the index
variability on the back of the data form, along with other notes or
comments described in Section 3.9. If it is felt that other DRASTIC
factors could have contributed to a different Agricultural DRASTIC
score (such as soil type), arbitrary values can be factored into the
index variability as long as they are noted on the back of the form.
Using Example 12, the back of the form could be noted as follows:
Index Variability: +3 Aquifer media media highly fractured
+8 Vadose zone also fractured
+TT
A-25-
-------
Table 3. Values for Index Variability
Part I
Aquifer Media
Values
Massive Shale
Metamorphic/Igneous
Weathered Metamorphic/Igneous
Thin Bedded Sandstone, Limestone, Shale Sequences
Massive Sandstone
Massive Limestone
Sand and Gravel
Basalt
Karst Limestone
3, 6, 9
6, 9, 12, 15
9, 12, 15
15, 18, 21, 24, 27
12, 15, 18, 21,
24, 27
12, 15, L3-, 21,
24, 27
18, 21, 24, 27
6, 9, 12, 15, 18,
" 21, 24, 27, 30
27, 30
Part II
Impact of Vadose Zone Media
Values
Silt/Clay
Shale
Limestone
Sandstone
Bedded Limestone, Sandstone, Shale
Sand and Gravel with significant Silt and Clay
Metamorphic/Igneous
Sand and Gravel
Basalt
Karst Limestone
4, 8
8, 12, 16, 20
8, 12, 16, 20, 24,
28
16, 20, 24, 28, 32
16, 20, 24, 28, 32
16, 20, 24, 28, 32
8, 12, 16, 20, 24,
28, 32
24, 28, 32, 36
8, 12, 16, 20, 24,
28, 32, 36, 40
32, 36., 40
-------
3.7 Data Confidence
This section of the form is intended to be used to show the rela-
tive confidence in the available data used in determining the Agricul-
tural DRASTIC score. The low confidence category is only intended for
those remote counties, where little, if any, geologic mapping has been
performed and/or little is known about the subsurface conditions. In
these cases where-little published information is available and experi-
enced professionals in the region know little about the county's hydro-
geology, then a number 1 should be entered in the blank as follows:
DATA CONFIDENCE
3 = HIGH 2 = MEDIUM
1 = LOW 1
At the other extreme, high data confidence (3) would be indicated for a
county where there was a good coverage of data (published and/or unpub-
lished) and the hydrogeologist coding the form felt fairly confident of
the range of DRASTIC factors indicated because of his or her personal
experience in the county, or the conveyed experience of another profes-
sional knowledgeable of the conditions in the county.
3.8 Identification
This section of the
her initials, and the
abbreviation for the firm
prelabeled for each form.
form allows the hydrogeologist to place his/
date that the form was completed. Th,e
(WES = Weston, WWC = Woodward-Clyde) has been
The intent of the identification section is
to allow a means of resolving potential
additional -information during the project.
questions or requesting
3.9 Data Sources or Comments
This section of the form provides a blank that can be checked if
data sources or comments are indicated on the back of the form. The
computerized data base will not include the comments on the back, only
an indication that there were (or were not) comments. You are encour-
aged to list primary data sources on the back of the form (printed
using a black ballpoint pen) such as:
Contact with a specific professional about area hydrogeologic
conditions (persons' name, affiliation, and phone number)
Published county report (author, date, and title, using abbrevia-
tions).
A -2 7
-------
Other pertinent comments are also encouraged on the back of the form
such as:
An indication of unusual geologic factors that should be recog-
nized when considering the vulnerability of the county.
A sketch map showing the approximate boundary between ground-
water regions or major hydrogeologic settings that should be
recognized.
When sources of data apply to specific hydrogeologic factors on
the form you are encouraged to use the DRASTIC acronyms for conven-
ience, as shown in the following example:
Example 13 (Back of form)
D - Geology and Ground-Water Resources of this county, 1985,
State Publication No. 1
R - Plates 2 and 13 - Water Atlas, Geraghty et al. 1975
A - Communication with R.C. Heath, retired USGS, (919) 856-4510
(7/1/85)
S - SCS of this county
T - 1:250,000 quad sheet of this area, 1985
1 'I
VPersonal experience with studies in this county.
C -J
3.10 Resolving Questions
It is anticipated that a number of questions will arise after this
training program and during the course of completing the county data
forms. All questions should be initially directed to your firm's
project manager. The project manager should also provide guidance to
the hydrogeologists with respect to sources of data, ^including contacts
in specific areas knowledgeable about local hydrogeologic conditions.
Unresolved questions should be communicated from your firm's project
manager to RTI's project leader.
3.11 Transmlttal of Completed Forms
When the data form has been completed, including any comments on
the back of the form, a copy of the form should be filed by the hydro-
geologist for future reference. The original of the form should be
transmitted to your firm's project manager who will review the forms
for completeness and reasonableness prior to transmitting them to RTI.
Schedules for form transmittals have already been established with your
firm as part of this work assignment.
A-2'3
-------
3.12 Sources of DATA
The primary source of information appropriate for this stage of
the survey should be based on the experience and judgement of the
hydrogeologists coding the forms, or from other professionals knowl-
edgeable of the hydrogeologic "conditions of a particular area. You are
encouraged to contact experienced professionals, such as employees of
the US Geological Survey, for their opinion of hydrogeologic conditions
in specific counties or larger areas. The level of effort defined for
this survey will not allow for a detailed review of published data, and
professional judgement in completing this data form is therefore essen-
tial.
General sources of hydrogeologic information familiar to the
hydrogeologist are available for the DRASTIC factors, as indicated in
Table 2. In addition to these sources, the following specific sources
of data should also be considered for use in the level of effort
required for this stage of the project:
Recharge: Geraghty, Miller, van der Leeden, and Troise, 1973,
Water Atlas of the United States. Plate 2 (Distribution
of Precipitation - Average Annual) and Plate 13 (Poten-
tial Evapotranspiration - Average Annual)
Soil media: State Soil Association maps
Topography: 1:250,000 scale USGS Quadrangle maps
-------
Table 2. General Sources of Hydrogeologic Data
Source
U S Geological Survey
Stale Geological Surveys
Slate Department ol Natural/Water Resources
U S Department ol Agriculture- Soil Conservation Service
Stale Department ol Environmental Protection
Clean Water Act "208" and other Regional Planning
Authorities
County and Regional Water Supply Agencies and
Companies (private water suppliers)
Private Consulting Firms (hydrogeologic. engineering)
Related Industry Studies (mining, well drilling.
quarrying, etc )
Professional Associations
(Geological Society ol America.
National Water Well Association.
American Geophysical Union)
Local Colleges and Universities
(Departments ol Geology. Earth Sciences.
Civil Engineering
Other Federal/Slate Agencies
(Army Corps ol Engineers. National Oceanic
and Atmospheric Administration)
Depth to
Water Table
X
X
X
X
X
X
X
X
X
X
X
Nel Recharge
X
X
X
X
X
X
X
X
X
Aquifer Media
X
X
X
X
X
X
X
X
X
X
X
Sod Media
X
Topography
X
X
Impact ol the
VadoM Zone
X
X
X
X
X
X
X
X
X
X
X
Hydraulic Conductivity
ol the Aquller
X
X
X
X
X
X
X
X
X
-------
I^BJB^&nBr Him
IGITALLY
-------
APPENDIX-B
DESCRIPTION OF QUALITY ASSURANCE PROGRAM AND RELATED DATA
-------
APPENDIX-B
QUALITY ASSURANCE PROGRAM
The selection of counties to be included in the quality assurance
process was a six-step procedure, as described below.
Step 1 - Revisions to Ground-Water Regions-Map
The first step of the quality assurance process was to revise the
Nation's ground-water regions reported by Heath (1984) to provide
reasonable delineations of the regions at the county level.
Discussions with Mr. Heath revealed that a primary source of informa-
tion concerning the boundary locations shown on his map was the land-
form map created by Raisz (1957). In order to extend Heath's (1984)
physiography-based map to include more recently defined geologic and
lithologic considerations, the boundaries were compared to structural
and lithologic boundaries shown on Bayer's (1983) map. Consideration
of major lithologic and structural trends was heavily weighted in
construction of the modified ground-water regional boundaries.
Step 2 - Designating Ground-Water Regions
Since the selection of quality assurance counties was dependent
on the percentages of counties within each ground-water region, the
total number of counties in each region was counted using the modified
ground-water regions map from Step 1. Each county was designated to a
ground-water region corresponding to the largest areal percentage of
the region that occurs in that county. For example, Fairfield County,
Utah, was considered to be approximately 65 percent in Region 4 and 35
percent in Region 2, based on the revised boundaries. Therefore,
Fairfield County was assigned to Region 4 for the purposes of the
quality assurance process.
In some instances, the region containing the greatest percentage
of a county was difficult to ascertain. For example, Big Horn and
Washakie Counties, Wyoming, both appear to be roughly 50 percent in
Region 1 and 50 percent in Region 6. In this case, Big Horn County was
assigned to Region 1 and Washakie County to Region 6.
B-l
-------
Step 3 - County Selection Data Base
The information obtained in Step 2 was assembled into a data base
containing the FIPS code and the ground-water region designation for
each county. A computer-generated map of ground-water regions was
produced from the data base and checked against the map produced in
Step 1 for accuracy. Once constructed, the data set was sorted by
ground-water region and the total number of counties in each Region was
counted. The total sample of quality assurance counties was allocated
in proportion to the counts.
Step 4 - Selection of Quality Assurance Counties for Subcontractors
A program was devised to select 7 percent of the counties from
each ground-water region using the random number generator within the
Statistical Analysis System (SAS) package. The counties were randomly
chosen based on achieving a 7 percent selection goal from each
ground-water region. This selection was made without consideration of
the location of the county with respect to the Mississippi River (the
previously determined dividing line between East Coast and West Coast
subcontractors). Because of the random selection process, some States
were not represented in the quality assurance process. A total of 223
counties were selected for the subcontractors; 115 counties in the East
and 108 counties in the West. This data set is referred to as the
large sample in this report.
Counties originally coded by the West Coast contractor (Woodward-
Clyde Consultants) were recoded by the East Coast contractor (Roy F.
Weston Consultants) and vice versa. Reassignments at the level of
individual coders was not a feature of. the design. A total variable
measurement error component (over both contractors and all coders) was
estimated upon completion of the data base.
Step 5 - Selection of Quality Assurance Counties for RTI
The same basic procedures used in the selection of quality
assurance counties for the subcontractors were applied to the selection
of RTI-coded quality assurance counties. These counties were a
randomly chosen subsample of the 7 percent sample, based on achieving a
3 percent selection goal from the ground-water regions (a total of 96
counties). That is, the subcontractor-coded quality assurance
B-2
-------
selection (Step 4) was based on the entire data base of all county
units in the United States. The data base used for RTI's 3 percent
selection was comprised of the counties selected from Step 4. Thus,
the set of counties selected for RTI's coding is a subsample of those
counties selected in Step 4 and is referred to as the small sample.
Step 6 - Selection of Counties for Qualitative Check
Following a suggestion of the U.S. Geological Survey, and at the
request of the EPA project monitor, a sixth step was added to the
quality assurance process to include additional coding of counties by
several nationally recognized hydrogeologists. The same counties were
also coded by the three EPA contractors. While such a coding effort is
not a part of the measurement error assessment described above, it
displays qualitatively the range of variability that might be expected
among the set of experts.
Six counties were chosen for this qualitative check and represent
a small subsample of the 96 counties selected in Step 5. They were
selected primarily to ensure that a maximum number of ground-water
regions would be represented as described in detail in Appendix F.
General Quality Assurance Procedures
The forms used for the 223 counties selected for receding as a
part of the subcontractors' quality assurance process were color-coded
yellow to ensure that they would not be merged inadvertently with the
white original forms. The blank forms used were otherwise identical to
those used for the original coding effort. It should be noted that the
subcontactors could not put the same level of effort into the quality
assurance process as into the original coding effort due to time and
contractural constraints. The subcontractors' quality assurance coding
took place over a 1-week period. Therefore, the level of confidence in
the results of the larger sample is not a,s great as that for the
originally coded data base, as further described in Appendix E.
B-3
-------
The coding of the Weston's quality assurance counties was
accomplished by the same team that coded the original forms;
Woodward-Clyde's quality assurance coding was accomplished by two
individuals. The two Woodward-Clyde coders made use of the pertinent
references that had been collected by Weston for the original coding
effort. Weston independently assembled the necessary references to
complete the coding of their quality assurance counties. The general
study approaches described in Sections 3.3.1 and 3.3.2 were also used
for the quality assurance process.
A somewhat different study approach was taken for the 96-county
RTI quality assurance check (color-coded blue). As a preliminary step
toward assembling information sources, a comprehensive review of the
publication lists for geologic and water resources agencies within the
35 States represented by the 96 selected counties was undertaken and
appropriate State and county publications were obtained. In some areas
where little published data were available, knowledgeable professionals
in the area were contacted for tneir opinions on hydrogeologic
conditions. One individual was assigned the task of consistently
ranking the topographic slope of all 96 counties, based on' a
standardized' slope estimation technique and information obtained from
USGS 2° Topographic Maps. A consultant to RTI, Mr. Harry E. LeGrand,
was asked to determine independently the Agricultural DRASTIC scores of
26 counties of the RTI data set. Separate independent rankings of all
96 counties were made by at least two other RTI hydrogeologists.
Finally, the separate codings of each of the 96 counties were jointly
compared and composited by the hydrogeologists establishing a
consensus-opinion. RTI's coding effort took place over a 1-month
period.
B-4
-------
STATE AND COUNTY LISTING OF VARSCORES
FOR RECODED COUNTY MEASUREMENTS
B-5
-------
EXPLANATION
Numerical scores calculated from the data set are differentiated
as follows:
TOTAL SCORE - An unweighted score corresponding to the
summation of the highest percentage ratings for the seven
DRASTIC factors. (Refer to Section 3 in Appendix A).
WEIGHTED SCORE - A weighted score corresponding to the summation
of all percentage ratings for the seven DRASTIC factors. The
weighted score accounts for the variability that occurs in
areas as large as counties. In counties with little
variability, little difference exists between the total score
and weighted score.
VARSCORE - Represents the weighted score including ( + or -) the
index of variability, where used. The index of variability and
its intended usage, is defined in Section 3.6 of Appendix A.
The VARSCORE is considered to be the most appropriate score for
the purposes of this project in that it best accounts for
intracounty variability.
The values shown in this listing to/Appendix B are VARSCORES.
Denotes counties not selected for coding by RTI.
Denotes subcontractor responsible for original coding of the
county.
B-6
-------
STATE=ALABAMA
COUNTY
BUTLER
COOSA
COVINGTON
MADISON
MARENGO
MOBILE
PERRY
MESTON WOODWARD CLYDE
153
131
174
162
157
170
138
138
124
149
123
136
141
135
RTI
154
124
173
150
158
STATE=ALASKA
COUNTY
BETHEL
BRISTOL BAY DIVISION
UESTON WOODWARD CLYDE
150
146
123
1 16
RTI
1 12
STATE=ARKANSAS
COUNTY
INDEPENDENCE
JOHNSON
NEWTON
PIKE
WESTON WOODWARD CLYDE
122
107
1 19
1 24
127
123
90
130
RTI
138
STATE=CALIFORNIA
COUNTY
LOS ANGELES
MERCED
SUTTER
WESTON MOODWARD CLYDE
10-8
126
162
1 16
149
157
RTI
167
STATE=COLORADO
COUNTY
ALAMOSA
LINCOLN
MINERAL
PHILLIPS
YUMA
WESTON MOODIJARD CLYDE
122
99
98
98
127
164
1 17
140
127
126
RTI
104
1 19
1 14
B-7
-------
STATE CONNECTICUT
COUNTY
HARTFORD
MESTON WOODWARD CLYDE
145 130
RTI
STATE=FLORIDA
COUNTY
MANATEE
MESTON WOODWARD CLYDE
222 177
RTI
192
STATE=GEORGIA
COUNTY
BAKER
3ARTOW
3ERRIEN
3P.ANTLEY
HARALSON
MARION
PIKE
SEMINOLE
TALBOT
TALIAFERRO
WHYNE
WILKES
WILKINSON
MESTON WOODWARD CLYDE
195
175
185
196
160
177
141
192
146
132
203
137
171
170
125
163
169
120
156
122
171
132
124
170
123
157
RTI
1 16
153
159
155
179
STATE=IDAHO
COUNTY
CLARK
STATE=ILLINOIS
COUNTY
BOND
CARROLL
EDWARDS
LIVINGSTON
PUTNAM
WASHINGTON
WESTON WOODWARD CLYDE
109 106
MESTON WOODWARD CLYDE
127
125
131
129
129
133
1 19
1 12
120
1 18
1 16
1 19
RTI
RTI
159
155
164
B-8
-------
STATE=INDIANA
COUNTY
ADAMS
GIBSON
GREENE
MARSHALL
PAP.KE
SWITZERLAND
STATE=IOMA
COUNTY
BREMER
BUENA VISTA
CLAY
CLINTON
JACKSON
JEFFERSON
MADISOH
MUSCATINE
PAGE
UE3STER
WORTH
STATE=KANSAS
COUNTY
BARBER
COMANCHE
ELK
MIAMI
MORRIS
OSAGE
RUSH
STATE=KENTUCKY
COUNTY
BALLARD
CARTER
CUMBERLAND
GARRARD
GRAVES
HANCOCK
KNOTT
MARION
METCALFE
MUHLENBERG
SCOTT
WESTON WOODWARD CLYDE
127
151
127
147
133
130
WESTON WOODWARD
145
138
144
146
145
125
120
148
136
133
153
WESTON WOODWARD
32
32
17
21
29
23
22
WESTON WOODWARD
134
130
172
148
134
147
136
178
187
155
153
147
131
124
158
142
129
CLYDE
126
109
1 1 1
123
134
107
100
122
103
96
126
CLYDE
150
149
130
1 12-
124
1 14
131
CLYDE
133
96
98
107
129
1 17
94
104
109
1 17
104
RTI
135
149
120
RTI
143
1 17
1 15
1 17
RTI
125
1 1 1
138
130
RTI
131
122
136
155
B-9
-------
STATE=LOUISIANA
COUNTY
ASCENSION
BIENVILLE
CONCORDIA
IBERVILLE
ST CHARLES
ST JAMES
ST TAMMANY
MESTON MOODMARD CLYDE
170
149
170
149
169
166
177
167
160
162
149
133
93
147
RTI
144
163
152
163
STATE=MAINE
COUNTY
PENOBSCOT
WESTON WOODWARD CLYDE
153 130
RTI
172
STATE=MARYLAND
COUNTY
BALTIMORE
WESTON WOODWARD CLYDE
136 116
RTI
STATE=MASSACHUSETTS
COUNTY
BARNSTABLE
DUKES
WESTON WOODWARD CLYDE
202
203
170
172
RTI
STATE=MICHIGAN
COUNTY
ANTRIM
JACKSON
LAKE
LUCE
OSCODA
SAGINAW
SCHOOLCRAFT
WESTON WOODWARD CLYDE
129
153
178
162
167
122
168
156
125
154
14 1
136
132
147
RTI
133
127
B-10
-------
STATE=MINNZSOTA
COUNTY
HOUSTON
LINCOLN
MC LEOD
PIPESTONE
WINONA
WESTON MOODMARD CLYDE
164
145
146
138
164
143
1 13
126
106
148
RTI
96
134
STATE MISSISSIPPI
COUNTY
WESTON WOODWARD CLYDE
RTI
ATTALA
FRANKLIN
HOLMES
LAUDERDALE
YALOBUSHA
130
147
129
123
122
130
1 37
123
123
130
143
STATE=MISSOURI
COUNTY
WESTON WOODWARD CLYDE
RTI
CLINTON
DAVIESS
DENT
MILLER
NEWTON
REYNOLDS
SCHUYLER
STONE
WARREN
WAYNE
121
123
101
140
102
94
125
91
138
1 12
33
90
1 17
1 1 1
104
95
80
9-2
93
106
141
140
1 12
139
131
STATE=MONTANA
COUNTY
DEER LODGE
FALLON
GALLATIN
GRANITE
PRAIRIE
RICHLAND
TETON
WESTON WOODWARD CLYDE
106
97
82
89
1 1 1
1 18
123
130
1 13
143
125
1 30
134
1 15
RTI
120
1 18
89
1 18
1 15
B-ll
-------
STATE=NEBRASKA
COUNTY
FRONTIER
KEARNEY
KEYA PAHA
LOGAN
THAYER
THOMAS
WESTON MOODMARD CLYDE
124
157
150
169
138
187
103
1 19
134
123
104
144
RTI
108
138
STATE=NEVADA
COUNTY
HUMBOLDT
WESTON WOODWARD CLYDE
115 129
RTI
121
STATE=NEU JERSEY
COUNTY
ESSEX
OCEAN
WESTON WOODWARD CLYDE
142
203
122
152
RTI
STATE=NEW MEXICO
COUNTY
EDDY
MORA
WESTON WOODWARD CLYDE
121
1 15
124
1 18
RTI
STATE=NEU YORK
COUNTY
LIVINGSTON
WESTON WOODWARD CLYDE
132 127
RTI
STATE=NORTH CAROLINA
COUNTY
CABARRUS
CARTERET
DAVIDSON
FORSYTH
UNION
WILKES
YADKIN
WESTON WOODWARD CLYDE
32
93
31
29
35
45
30
109
144
107
103
105
96
104
RTI
127
195
1 17
127
B-12
-------
STATE=NORTH DAKOTA
COUNTY
OLIVER
SLOPE
MESTON WOODWARD CLYDE
135
140
133
145
RTI
126
120
S.TATE = OHIO
COUNTY
GEAUGA
MORGAN
NOBLE
PERRY
ROSS
VAN UIERT
MESTON WOODWARD CLYDE
122
137
1 15
137
127
121
24
26
28
12
19
13
RTI
122
143
STATE=OKLAHOMA
COUNTY
ATOKA
BRYAN
CADDO
COAL
LATIMER
MC INTOSH
NOWATA
WESTON WOODWARD CLYDE
135
149
120
109
105
1 17
99
131
1 13
135
120
1 18
136
146
RTI
148
106
103
STATE=OREGON
COUNTY
GRANT
LINN
MORROW
WESTON WOODWARD CLYDE
96
127
97
89
140
1 13
RTI
125
STATE=PENNSYLVANIA
COUNTY
MONROE
PERRY
UNION
VENANGO
WESTMORELAND
WESTON WOODWARD CLYDE
144
124
137
136
147
1 10
1 1 1
106
1 16
103
RTI
145
138
146
B-13
-------
STATE=RHODE ISLAND
COUNTY
KENT
MESTON WOODWARD CLYDE
186 163
RTI
STATE=SOUTH DAKOTA
COUNTY
MC PHERSON
WESTON WOODWARD CLYDE
137 106
RTI
STATE=TENNESSEE
COUNTY
BENTOH
CUMBERLAND
MC NAIRY
WESTON WOODWARD CLYDE
142
149
152
131
1 10
134
RTI
STATE=TEXAS
COUNTY
CASS
GRAY
HOUSTON
LAVACA
LIBERTY
PALO PINTO
REEVES
RUSK
SAN AUGUSTINE
SWISHER
TRAVIS
WESTON WOODWARD CLYDE
158
10-4
154
156
160
124
130
148
155
1 15
155
1 18
123
127
125
127
1 17
133
133
140
121
123
RTI
132
133
151
STATE=UTAH
COUNTY
DAGGETT
GARFIELD
IRON
PIUTE
SUMMIT
WASATCH
WAYNE
WESTON WOODWARD CLYDE
100
1 10
104
93
102
102
109
93
103
1 17
94
95
10 1
106
RTI
100
86
98
102
8-14
-------
STATE=VERMONT
COUNTY
CRAIGHEAD
LAMOILLE
ORLEANS
RUTLAND
WINDSOR
STATE=VIRGINIA
COUNTY
ALEXANDRIA CITY
BRISTOL CITY
CAROLINE
CHARLES CITY
.GLOUCESTER
KING WILLIAM
nATHEUS
NORTHAMPTON
PAGE
SHENANDOAH
TAZEUELL
WASHINGTON
WISE
STATE=WASHINGTON-
COUNTY
BENTON
CLALLAM
MASON
WESTON WOODWARD CLYDE
.
149
135
131
133
WESTON WOODWARD
148
163
164
175
181
173
189
187
155
129
127
125
130
WESTON WOODWARD
108
121
151
133
1 1 1
1 14
105
109
CLYDE
131
1 14
130
138
133
137
154
162
1 13
1 10
102
106
100
CLYDE
141
109
1 14
RTI
143
138
RTI
197
160
189
181
176
161
159
131
139
RTI
1 1 1
STATE=WEST VIRGINIA
COUNTY
HARBOUR
BERKELEY
HARDY
TUCKER
UESTON WOODWARD CLYDE
1 19
150
133
126
120
1 13
1 18
RTI
149
B-15
-------
STATE=WISCONSIX
COUNTY MESTON WOODWARD CLYDE RTI
BARRON 157 121
IRON 159 140 165
MANITOWOC 132 112
HARATHON 161 130
MARINETTE 157 144 167
SAWYER 158 139
SHEBOYGAN 133 111
TREMPEALEAU 92 124
STATE=WYOMING
COUNTY WESTON WOODWARD CLYDE RTI
NIOBRARA 108 105 108
TETON 164 120
B-16
-------
REFERENCES
Bayer, K. C., 1983. Generalized Structural, Lithologic. and
Physiographic Privinces in the Fold and Thrust Belts of the United
States, U.S. Geological Survey, Scale 1:2,500,000.
Heath, R.C., 1984. Ground-Water Regions of the United States. U.S.
Geological Survey Water Supply Paper Z242, 78p.
Raisz, E., 1957. Landforms Map of the United States.
B-17.
-------
APPENDIX-C
STATE AND COUNTY LISTING OF WEIGHTED AND TOTAL SCORES FOR
ORIGINALLY CODED DATA BASE WITH ADJUSTMENTS
-------
EXPLANATION
Numerical scores calculated from the data set are differentiated
as follows:
TOTAL SCORE - An unweighted score corresponding to the
summation of the highest percentage ratings for the seven
DRASTIC factors. (Refer to Section 3 in Appendix A).
WEIGHTED SCORE - A weighted score corresponding to the summation
ot al I percentage ratings for the seven DRASTIC factors. The
weighted score accounts for the variability that occurs in
areas as large as counties. In counties with little
variability, little difference exists between the total score
and weighted score.
VARSCORE - Represents the weighted score including (+ or -) the
index of variability, where used. The index of variability anc
its intended usage, is defined in Section 3.6 of Appendix A.
ADJUSTED VARSCORE - Represents adjustments to the originally coded
VARSCORE resulting from RTI's reevaluation of specific .DRASTIC
factors (. denotes no adjustment made to the originally coded
VARSCORE).
The VARSCORE, or adjusted VARSCORE, is considered to be the most
appropriate score for the purposes of this project in that it best
accounts for intracounty variability.
C-l
-------
STATE=AK
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ALEUTIAN ISLANDS . 75 122 135
ANCHORAGE . 110 138 150
ANGOON . 71 96 106
BARROW . 86 116 126
BETHEL . 123 143 124
BRISTOL BAY BOROCTGH . 119 144 150
BRISTOL BAY DIVISION . 116 126 128
CORDOVA-MC CARTHY . 105 120 112
FAIRBANKS . 120 120 111
HAINES . 78 98 106
JUNEAU . 93 108 95
KENAI-COOK INLET . 97 112 95
KETCHIKAN . 81 101 112
KOBUK . 87 112 106
KODIAK . 107 107 75
KUSKOKWIM . 99 109 101
MATANUSKA-SUSITNA . 116 121 110
NOME . 110 110 98
OUTER KETCHIKAN . 70 90 89
PRINCE OF WALES . 95 95 89
SEMARD . 87 97 106
SITKA . 93 108 100
SKAGWAY-YAKUTAT . 78 108 92
SOUTHEAST FAIRBANKS . 109 109 92
UPPER YUKON . 85 115 118
VALDEZ-CHITINA-WHITT . 101 116 106
WADE HAHPTON . 132 132 127
WP.ANGELL-PETERSBURG . 94 109 100
YUKON-KOYUKUK . 99 114 112
C-2
-------
STATE=AL
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
AUTAUGA . 145 149 144
BALDWIN 176 173 177 182
BARBOUR . 153 157 171
BIBB . 142 146 144
BLOUNT . 150 156 152
BULLOCK . 141 145 171
BUTLER . 153 157 159
CALHOUN . 154 148 155
CHAMBERS . 126 126 126
CHEROKEE . 160 154 155
CHILTON . 136 140 147
CHOCTAW . V42 146 147
CLARKE . 136 140 136
CLAY . 126 126 126
CLEBURNE . 124 124 126
COFFEE . 172 176 182
COLBERT . 174 168 163
CONECUH . 170 174 182
COOSA . 131 131 133
COVINGTON . 174 178 182
CRENSHAW . 163 167 171
CULLMAN . 146 152 15-2
DALE : 179 183 186
DALLAS . 155 155 175
DE KALB . 148 154 155
ELMORE . 150 154 159
ESCAMBIA . 164 168 182
ETOWAH . 163 157 162
FAYE7TE . 153 157 164
FRANKLIN . 166 170 163
GENEVA . 162 169 167
GREENE . 157 157 169
HALE . 141 145 159
HENRY . 173 177 182
HOUSTON . 160 167 167
JACKSON . 152 152 158
JEFFERSON . 149 155 152
LAMAR . 144 148 144
LAUDERDALE . 164 158 155
LAURENCE . 158 152 156
LEE . 131 131 121
LIMESTONE . 162 156 153
C-3
-------
STATE=AL
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
LOMNDES . 132 132 107
MACON . 131 135 1t|7
MADISON . 162 156 158
MARENGO . 157 161 167
MARION . 1i*9 153 144
MARSHALL . 149 155 155
MOBILE 174 170 174 182
MONROE . 158 162 159
MONTGOMERY . 130 130 112
MORGAN . 160 160 165
PERRY . 138 142 159
PICKSNS . 146 150 144
PIKE . 174 178 182
RANDOLPH . 125 125 126
RUSSELL . 124 128 147
ST GLAIR . 158 152 163
SHELBY . 141 147 151
SUMTER . 123 123 112
TALLADEGA . 149 143 155
TALLAPOOSA . 128 128 130
TUSCALOOSA . 137 141 136
WALKER . 149 155 152
WASHINGTON . 168 172 167
WILCOX . 134 138 144
WINSTON . 150 156 152
C-4
-------
STATE=AR
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ARKANSAS . 124 124 139
ASHLEY . 136 136, 124
BAXTER . 94 94 76
BENTON . 97 97 103
BOONE . 86 86 76
BRADLEY . 133 133 124
CALHOUN . 138 138 129
CARROLL . 89 89 76
CHICOT 149 129 129 114
CLARK . 135 135 149
CLAY . 146 146 154
CLEBURNE . 98 98 89
CLEVELAND . 150 150 154
COLUMBIA . 150 150 159
CONWAY . 126 126 119
CRAIGHEAD . 151 151 164
CRAWFORD . 109 109 94'
CRITTENDEN 150 132 132 124
CROSS . 123 123 129
DALLAS . 155 155 159
DESHA 154 136 136 133
DREW . 140 140 139
FAULKNER . 124 124 119
FRANKLIN . Ill 111 109
FULTON . 126 126 133
GARLAND . 129 129 123
GRANT . 151 151 159
GREENE . 148 148 149
HEMPSTEAD . 143 143 159
HOT SPRING . 135 135 145
HOWARD . 133 133 143
INDEPENDENCE . 122 122 129
IZARD . 119 119 123
JACKSON . 141 141 145
JEFFERSON . . 139 139 153
JOHNSON . 107 107 109
LAFAYETTE . 132 132 129
LAWRENCE . 140 140 145
LEE 159 133 133 149
LINCOLN . 142 142 153
LITTLE RIVER . 133 133 129
LOGAN . 113 113 109
C-5
-------
STATE=AR
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
LONOKE . 132 132 128
MADISON . 96 96 77
MARION . 103 103 88
MILLER . 133 133 139
MISSISSIPPI 163 148 148 164
MONROE . 142 142 149
MONTGOMERY ': 132 132 138
NEVADA . 151 151 159
NEWTON . 90 90 91
OUACHITA . 157 157 159
PERRY . 117 117 111
PHILLIPS 157 141 141 154
PIKE . 130 130 139
POINSETT . 127 127 129
POLK . 127 127 117
POPE . 94 94 91
PRAIRIE . 130 130 133
PULASKI . 135 135 128
RANDOLPH . 132 132 123
ST FRANCIS . 132 132 149
SALINE . 131 131 123
SCOTT . 111 111 119
SEARCY . 107 107 103
SEBASTIAN . 113 113 109
SEVIER . 143 143 153
SHARP . 119 119 108
STONE . 114 114 118
UNION . 147 147 159
VAN BUREN . 97 97 83
WASHINGTON . 87 87 63
WHITE . 126 126 T*9
WOODRUFF . 146 146 154
YELL . 125 125 VI9
C-6
-------
STATE=AZ
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
APACHE 94 113 109 121
COCHISE 93 108 108 141
COCONINO V03 118 118 133
GIIA 89 104 104 95
GRAHAM 95 110 110 138
GREENLEE 107 89 124 139
MARICOPA 100 115 115 141
MOHAVS 106 121 121 136
NAVAJO 96 111 111 121
PIMA 91 106 106 121
FINAL 99 114 114 106
SANTA CRUZ 92 107 107 115
YAVAPAI 96 111 111 101
YUHA 97 112 112 106
C-7
-------
STATE=CA
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ALAMEDA . 122 122 132
ALPINE . 89 89 69
AMADOR . 88 83 54
BUTTE 135 126 126 129
CALAVERAS . 97 97 54
COLUSA 150 139 139 169
CONTRA COSTA . 123 123 147
DEL NORTE . 107 107 77
EL DORADO . 95 95 86
FRESNO 114 103 103 111
GLENN 145 133 133 139
HUMBOLDT . 103 103 71
IMPERIAL 124 145 145 140
INYO 111 121 121 124
KERN 124 118 118 133
KINGS 159 135 135 143
LAKE . 110 110 88
LASSEN . 99 113 94
LOS ANGELES . 116 116 125
MADERA 116 109 109 84
MARIN . 117 117 92
MARIPOSA . 97 97 69
MENDOCINO . 106 106 71
MERCED 172 149 149 146
MODOC . 112 126 125
MONO . 133 133 102
MONTEREY . 102 102 87
NAPA . 113 113 99
NEVADA . 107 107 86
ORANGE . 110 110 135
PLACER . 97 97 71
PLUMAS . 112 112 69
RIVERSIDE 112 122 122 J42
SACRAMENTO 180 155 155 172
SAN BENITO . 109 109 73
SAN BERNARDINO 122 132 132 137
SAN DIEGO 97. 107 107 75
SAN FRANCISCO . 134 134 135
SAN JOA2UIN 173 147 147 164
SAN LUIS OBISPO . 109 109 99
SAN MATEO . 116 116 73
SANTA BARBARA . 109 109 92
C-8
-------
STATE=CA
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
SANTA CLARA . 125 125 81
SANTA CRUZ . 115 115 77
SHASTA 100 92 106 97
SIERRA . 92 92 69
SISKIYOU . 112 112 69
SOLANO 174 150 150 177
SONOMA . 116 116 101
STANISLAUS 138 126 126 151
SUTTER 179 157 157 172
TEHAflA 121 110 124 138
TRINITY . 92 92 86
TULARE 112 107 107 77
TUOLUP1NE . 94 94 69
VENTURA . 110 110 92
YOLO 172 148 148 172
YUBA 117 105 105 99
C-9
-------
STATE=CO
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ADAMS . 137 137 128
ALAMOSA 136 164 164 178
ARAPAHOE . 133 133 133
ARCHULETA . 117 117 101
BACA . 114 114 107
BENT 120 144" 144 140
BOULDER . 112 112 77
CHAFFEE . 113 113 98
,CHEYENNE . 111 111 104
{CLEAR CREEK . 96 96 83
'CONEJOS 138 164 164 156
COSTILLA . 126 126 93
cr.OWLEY 107 131 131 124
CUSTER . 122 122 113
DELTA . 115 115 101
DENVER . 147 147 133
DOLORES . 101 101 91
DOUGLAS . 115 115 91
EAGLE . 132 132 121
ELBERT 121 148 148 158
EL PASO . 135 135 135
FREMONT . V11 111 77
GARFIZLD . 122 122 81
GILPIN . 107 107 83
GRAND 11t 136 136 131
GUNNISON . 140 140 122
HINSDALE . 139 139 126
HUERFANO . 106 106 81
JACKSON . 115 115 105
JEFFERSON . 128 128 97
KIOUA . 132 132 134
KIT CARSON . 110 110 110
LAKE . 110 110 ,83
LA PLATA . 111 111 91
LARIMER . 113 113 103
LAS ANIMAS . 116 116 100
LINCOLN . 117 117 104
LOGAN . 137 137 144
MESA . 116 116 105
MINERAL . 140 140 126
MOF7AT . 114 114 105
MONTEZUMA 98 118 118 91
C-10
-------
STATE=CO
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
MONTROSE . 115 115 105
MORGAN . 147 147 158
OTERO . 143. 143 130
OURAY . 124 124 91
PARK . 119 119 77
PHILLIPS . 127 127 118
PITKIN . 113 113 91
PROWERS . 136 136 149
PUEBLO . 126 126 105
RIO BLANCO . 108 108 91
RIO GRANDE . 152 152 166
ROUTT . 120 120 91
SAGUACHE 137 164 164 166
SAN JUAN . 126 126 106
SAN MIGUEL . 119 119 115
SEDGWICK . 143 143 140
SUMMIT . 116 116 92
TELLER . 105 105 83
WASHINGTON . 129 129 110
WELD . 141 141 135
YUMA . 126 126 140
C-ll
-------
STATE=CT
COUNTY
FAIRFIELD
HARTFORD
LITCHFTELD
MIDDLESEX
NEW HAVEN
MEU LONDON
TOLLAKD
WINDHAM
ADJUSTED
VARSCORE
VARSCORE
143
145
141
140
150
145
152
157
WEIGHTED
45
47
43
42
52
47
54
59
TOTAL
SCORE
156
146
136
136
15.6
156
164
164
C-12
-------
STATE=DC
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
DISTRICT OF COLUMBIA 119 125 116
C-13
-------
STATE=DE
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
KENT . 178 189 193
NEU CASTLE . 160 159 149
SUSSEX . 178 189 193
C-14
-------
STATE=FL
COUMTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ALACHUA . 199 199 199
BAKER . 213 213 213
BAY . 213 213 213
BRADFORD . 132 182 181
BREVARD . 212 212 217
BROUARD . 245 245 252
CALHOUN . 176 176 164
CHARLOTTE . 207 207 209
CITRUS . 222 222 227
CLAY . 186 186 181
COLLIER . 240 240 252
COLUMBIA . 217 217 213
DADE . 245 245 252
DE SOTO . 222 222 221
DIXIE . 213 213 219
DUVAL . 213 213 213
ESCAMBIA . 183 183 182
FLAGLER . 219 219 221
FRANKLIN . 212 212 213
GADSDEN . 189 189 187
GILCHRIST . 226 226 222
GLADES . 215 215 217
GULF . 209 209 213
HAMILTON . 229 229 239
HARDEE . 222 222 221
HENDRY . 221 221 221
HERNANDO . 224 224 235
HIGHLANDS . 207 207 209
HILLSBOROUGH . 231 231 235
HOLMES . 176 176 174
INDIAN RIVER . 198 198 209
JACKSON . 177 177 174
JEFFERSON . 184 184 182
LAFAYETTE . 215 215 2'13
LAKE . 229 229 .234
LEE . 217 217 213
LEON . 203 203 199
LEVY . 212 212 209
LIBERTY . 200 200 209
MADISON . 206 206 199
MANATEE . 222 222 221
MARION . 227 227 239
C-15
-------
STATE=FL
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
MARTIN . 219 219 221
MONROE . 184 181 174
NASSAU . 196 196 201
OKALOOSA . 220 220 226
OKEECHOBEE . 219 219 217
ORANGE . 210 210 209
OSCEOLA . 218 218 217
PALM BEACH . 216 216 206
PASCO' . 216 216 217
PINELLAS . 235 235 239
POLK . 212 212 204
PUTNAM . 213 213 217
ST JOHNS . 221 221 221
ST LUCIE . 219 219 221
SANTA ROSA . 206 206 199
SARASOTA . 217 217 213
SEMINOLE . 231 231 234
SUMTER . 234 234 231
SUUANNEE . 235 235 239
TAYLOR . 217 217 213
UNION . 185 185 181
VOLUSIA . 212 212 217
WAKULLA . 212 212 213
WALTON . 216 216 222
WASHINGTON . 204 204 194
C-16
-------
STATE=GA
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
APPLING . 179 185 187
ATKINSON . 191 193 187
BACON . 190 196 -187
BAKER . 195 201 210
BALDWIN . 161 166 166
BANKS . 142 142 141
BARROW . 128 131 124
BARTOW . 175 175 181
BEN HILL . 176 182 182
BERRIEN . 185 187 187
BIBB . 151 157 166
BLECKLEY . 177 179 182
BRANTLEY . 196 204 209
BROOKS . 166 168 174
BRYAN . 194 200 215
BULLOCH . 181 183 182
BURKE . 147 153 147
BUTTS . 130 130 129
CALHOUN . 165 171 167
CAMDEN . 188 198- 215
CANDLER . 178 180 182
CARROLL . 152 152 141
CATOOSA . 159 162 172
CHARLTON . 171 173 154
CHATHAM . 180 182 187
CHATTAHOOCHEE . 162 164 174
CKATTOOGA . 171 171 174
CHEROKEE . 149 142 134
CLARKE . 141 141 137
CLAY . 161 167 156
CLAYTON . 135 138 135
CLINCH . 182 178 181
COBB . 156 157 J61
COFFEE . 172 174 174
COL2UITT . 181 183 182
COLUMBIA . 131 131 127
COOK . 192 194 186
COUETA . 142 145 135
CRAWFORD . 148 144 144
CRISP . 181 183 182
DADE . 154 154 151
DAMSON . 136 140 152
C-17
-------
STATE=GA
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
DECATUR . 179 185 167
DE KALB . 140 140 137
DODGE . 175 177 182
DOOLY . 178 180 182
DOUGHERTY . 159 165 159
DOUGLAS . 135 135 129
EARLY . 178 180 182
ECHOLS . 202 210 213
EFFINGHAM . 189 203 207
EL3ERT . 128 128 129
EMANUEL . 180 182 182
EVANS . 196 206 219
FANNIN . 128 124 124
FAYETTE . 141 144 135
FLOYD . 156 159 157
FORSYTH . 151 155 159
FRANKLIN . 128 131 132
FULTON . 145 145 137
GILMER . 120 116 112
GLASCOCK . 131 131 127
GLYNN . 131 187 -172
GORDON . 158 164 171
GRADY . 165 167 174
GREENE . 140 140 137
GMIXNETT . 140 140 137
HA3ERSHAM . 143 144 161
HALL . 141 142 154
HANCOCK . 138 138 129
HARALSON . 160 161 161
HARRIS . 138 138 134
HART . 126 126 126
HEARD . 147 147 141
HENRY . 125 128 127
HOUSTON . 124 130 139
IRMIN . 179 181 182
JACKSON . 127 130 132
JASPER . 138 138 137
JEFF DAVIS . 182 188 187
JEFFERSON . 165 167 174
JENKINS . 178 180 187
JOHNSON . 160 166 159
JONES . 132 132 126
C-18
-------
STATE=GA
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
LAMAR . 140 140 137
LANIER . 183 197 207
LAURENS . 168 170 174
LEE . 184 186 187
LIBERTY . 190 196 187
LINCOLN . 137 137 137
LONG . 191 201 215
LOWNDES . 194 196 186
LUMPKIN . 144 145 158
MC DUFFIE . 150 150 149
MC INTOSH . 183 185 187
MACON . 140 146 147
MADISON . 121 124 127
MARION . 177 181 189
MERIWETHER . 147 147 137
MILLER . 195 197 186
MITCHELL . 183 189 182
MONROE . 142 142 137
MONTGOMERY . 179 181 182
MORGAN . 136 136 137
MURRAY . 143 149 167
NEWTON . 133 133 129
OCONEE . 139 139 137
OGLETHORPE . 135 135 137
PAULDING . 152 156 159
PEACH . 137 143 147
PICKENS . 125 121 124
PIERCE . 184 190 187
PIKE . 141 141 137
POLK . 177 177 181
PULASKI . 173 175 182
PUTNAM . 141 141 137
SUITMAN - . 143 154 156
RABUN . 136 129 138
RANDOLPH . 159 165 156
RICHMOND . 163 165 174
ROCKDALE . 143 146 139
SCHLEY . 139 139 126
SCREVEN . 175 177 187
SEMINOLE . 192 194 186
SPALDING . 134 137 135
STEPHENS . 136 136. 138
C-19
-------
STATE=GA
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
STEWART . 149 155 156
SUMTER . 150 152 149
TALBOT . 146 146 134
TALIAFERRO . 132 132 137
TATTNALL . 196 198 182
TAYLOR . 170 166 138
TELFAIR . 183 185 182
TERRELL . 179 185 187
THOMAS . 158 164 159
TIFT . 182 184 182
TOOM3S . 173 175 182
TOWNS . 133 126 126
TREUTLEN . 161 167 174
TROUP . 145 148 139
TURNER . 178 180 182
TWIGGS . 165 171 173
UNION . 124 120 124
UPSON . 140 140 137
WALKER . 171 171 177
WALTON . 145 145 137
WARE . 155 157 142
WARREN . 150 150 149
WASHINGTON . 165 167 174
WAYNE . 203 203 193
WEBSTER . 182 192 210
WHEELER . 176 178 182
WHITE . 144 145 158
WHITFIELD . 177 177 181
WILCOX ' . 177 179 182
WILKES . 137 137 137
WILKINSON . 171 181 202
WORTH . 181 183 .182
nUSCOGEE . 158 158 153
C-20
-------
STATE=HA
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
HAWAII . 179 179 183
HONOLULU . 173 173 157
KAUAI . 170 170 177
MAUI . 170 170 177
C-21
-------
STATE=IA
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ADAIR 129 100 100 109
ADAMS 130 104 104 109
ALLAMAKEE . 125 125 150
APPANOOSE 131 103 103 116
AUDUBON 127 100 100 109
BENTON 134 115 115 106
BLACK HAWK . 121 121 116
BOONE 140 113 113 128
BREMER . 126| 126 116
BUCHANAN . 124 124 121
BUENA VISTA . 109 109 118
BUTLER . 126 126 121
CALHOUN 139 111 111 128
CARROLL 131 105 105 111
CASS 130 99 99 103
CEDAR 137 118 118 '116
CERRO GORDO . 131 131 134
CHEROKEE . 102 102 111
CHICKASAW . 117 117 114
CLARKE 130 102 102 121
CLAY . 111 111 118
CLAY.TON . 126 126 150
CLINTON . 128 128 129
CRAWFORD 130 104 104 1 1'4
DALLAS 140 116 116 128
DAVIS 117 105 105 116
DECATUR 130 103 103 121
DELAWARE . 132 132 167
DES MOINES 121 108 108 93
DICKINSON . 113 113 128
DUBUQUE . 132 132 162
EMMET . 112 112 128
FAYETTE . 135 135 T70
FLOYD . 134 134 170
FRANKLIN . 106 106 98
FREMONT . 115 115 114
GREENE 136 110 110 124
GRUNDY 133 93 93 85
GUTHRIE 132 106 106 121
HAMILTON 138 100 100 98
HANCOCK . 106 106 114
HARDIN 137 100 100 98
C-22
-------
STATE=IA
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
HARRISON . 118 118 114
HENRY 120 100 100 93
HOWARD . 121 121 114
HUMBOLDT . 98 98 98
IDA 131 103 103 116
IOWA 133 110 110 116
JACKSON . 134 134 162
JASPER 137 111 111 115
JEFFERSON 119 107 107 119
JOHNSON 136 119 119 111
JONES 136 126 126 126
KEOKUK 119 102 102 90
KOSSUTH . 111 111 128
LEE 119 113 113 119
LINN 137 123 123 131
LOUISA 129 131 131 1-57
LUCAS 130 102 102 121
LYON . 104 104 110
MADISON 128 100 100 111
MAHASKA 120 110 110 116
MARION 129 103 103 116
MARSHALL 136 107 107 115
MILLS . 107 107 114
MITCHELL . 132 132 160
MONONA . 136 136 153
MONROE 130 102 102 116
MONTGOMERY 130 105 105 109
MUSCATINE 128 122 122 109
0 BRIEN . 107 107 118
OSCEOLA . 111 111 118
PAGE 129 103 103 109
PALO ALTO . 112 112 128
PLYMOUTH . 108 108 111
POCAHONTAS . 108 108 118
POLK 138 117 117 128
POTTAWATTAMIE . 108 108 114
POWESHIEK 132 90 90 83
RINGGOLD 130 102 102 121
SAC 135 110 110 118
SCOTT 129 126 126 114
SHELBY 132 105 105 114
SIOUX . 110 110 111
C-23
-------
STATE=IA
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
STORY 140 112 112 128
TAMA 135 111 111 111
TAYLOR 129 100 100 109
UNION 126 98 98 111
VAN BUREN 1 19 108 108 119
UAPELLO 120 109 109 116
UARREN 128 102 102 111
WASHINGTON 118 100 100 93
WAYNE 130 102 102 121
WE3STER 138 96 96 98
WINNEBAGO . 104 i04 114
WINNESHIEK . 134 134 162
WOODBURY . 118 118 116
WORTH . 126 126 134
WRIGHT . 97 97 98
C-24
-------
STATE=ID
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ADA . 147 147 162
ADAMS . 103 103 109
BANNOCK . 108 108 126
BEAR LAKE . 124 124 125
BENEWAH . 89 89 76
BINGHAM . 135 135 147
BLAINE . 127 127 139
BOISE . 86 86 69
BONNER . 103 103 36
BONNEVILLE . 120 120 126
BOUNDARY . 89 89 61
3UTTE . 131 131 135
CAMAS . 112 112 92
CANYON . 141 141 152
CARIBOU . 113 113 126
CASSIA . 115 115 108
CLARK . 106 106 104
CLEARUATER . '98 98 82
CUSTER . 93 93 92
ELMORE . 96 96 69
FRANKLIN . 103 103 114
FREMONT . 110 110 116
GEM . 112 112 104
GOODING . 139 139 137
IDAHO . 39 89 71
JEFFERSON . 137 137 142
JEROME . 139 ir9 147
KOOTENAI . 113 113 126
LATAH . 77 98 105
LEMHI . 101 101 69
LEWIS . 98 107 91
LINCOLN . 154 154 177
MADISON . 117 117 334
MINIDOKA . 152 152 150
NEZ PERCE . 81 90 73
ONEIDA . 109 109 116
OWYHEE . 87 87 81
PAYETTE . 119 119 108
POWER . 118 118 100
SHOS-HONE . 84 84 61
TETON . 124 124 136
TWIN FALLS . 87 87 77
C-25
-------
STATE=ID
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
VALLEY . 93 93 71
WASHINGTON . 128 128 123
C-26
-------
STATE=IL
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ADA*MS 107 131 137 132
ALEXANDER 148 183 183 132
BOND 85 127 133 132
BOONE 123 142 148 144
BROUN 101 125 131 132
BUREAU 121 124 130 124
CALHOUN 108 119 125 109
CARROLL 121 125 131 132
CASS 109 133 139 132
CHAMPAIGN 128 136 142 132
CHRISTIAN 87 130 136 132
CLARK 107 134 140 132
CLAY 86 128 134 132
CLINTON 107 133 139 132
COLES 101 127 133 132
COOK 119 149 155 144
CRAWFORD 122 150 156 144
CUMBERLAND 36 132 138 132
DE KALB 136 135 141 144
DE WITT 129 131 137 132
DOUGLAS 127 126 132 132
DU PAGE 104 130 136 132
EDGAR 94 129 135 132
EDWARDS 111 131 137 132
EFFINGHAM 86 130 136 132
FAYETTE 107 133 139 132
FORD 123 127 133 127
FRANKLIN 90 134 140 132
.FULTON 96 121 127 124
GALLATIN 144 153 159 144
GREENE 101 131 137 132
GRUNDY 105 132 138 132
HAMILTON 105 145 151 ,144
HANCOCK 105 129 135 132
HARDIN 157 185 185 191
HENDERSON 121 147 153 144
HENRY 80 126 132 132
IR02UOIS 144 146 152 144
JACKSON 140 147 153 144
JASPER 38 136 142 132
JEFFERSON 88 132 138 132
JERSEY 105 133 139 132
C-27
-------
STATE=IL
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
JO DAVIESS 119 125 131 129
JOHNSON 146 144 150 148
KANE 133 135 141 132
KANKAKEE 122 153 159 154
KENDALL 103 127 133 132
KNOX 77 121 127 121
LAKE 131 129 135 132
LA SALLE 85 122 128 124
LAWRENCE 124 150 155 144
LEE 130 142 143 144
LIVINGSTON 113 129 135 132
LOGAN 129 131 137 1;2
MC DONOUGH 80 122 128 132
MC HENRY 140 140 146 144
MC LEAN 126 125 131 132
MACON 128 129 135 132
MACOUPIN 82 124 130 132
MADISON 106 133 139 132
MARION 86 130 136 132
.MARSHALL 120 123 129 124
MASON 154 143 149 132
MASSAC 158 174 180 182
MENARD 117 133 139 132
MERCER 105 131 137 132
MONROE 120 130 136 132
MONTGOMERY 85 127 133 132
MORGAN 108 130 136 132
MOULTRIE 104 128 134 132
OGLE 91 136 136 126
PEORIA 97 121 127 124
PERRY 86 132 138 132
PIATT 129 127 133 132
PIKE 120 133 139 132
POPE 146 156 156 143
PULASKI 157 175 181 182
PUTNAM 124 129 135 124
RANDOLPH 98 125 131 121
RICHLAND 87 128 134' 132
ROCK ISLAND 103 128 134 129
ST CLAIR 105 135 141 132
SALINE 108 147 153 144
SANGAMON 86 128 134 132
C-28
-------
STATE=IL
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
SCHUYLER 991 125 131 121
SCOTT 108 136 142 132
SHELBY 87 133 139 132
STARK 79 123 129 124
STEPHENSOH 91 133 133 126
TAZEMELL 134 132 138 132
UNION 144 160 160 143
VERMILION 114 133 139 132
UABASH 110 134 140 132
UARREN 84 126 132 132
WASHINGTON 107 133 139 132
WAYNE 87 129 135 132
WHITE 124 150 156 144
WHITESIDE 107 134 140 132
WILL 102 134 140 132
WILLIAMSON 107 146 152 144
WINNEBAGO 125 152 152 138
WOODFORD 122 122 128- 124
C-29
-------
STATE=IN
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ADAMS 110 127 133 132
ALLEN 110 127 133 144
BARTHOLOMEW 150 158 155 151
BENTON 132 136 139 139
BLACKFORD 109 126 132 132
BOONE 144 147 150 151
BROWN 122 141 144 144
CARROLL 130 135 138 139
CASS 138 151 154 151
CLARK 120 143 146 151
CLAY 125 144 147 151
CLINTON 143 147 150 151
CRAWFORD 108 136 139 136
DAVIESS 143 155 158 151
DEARBORN 106 147 150 156
DECATUR 128 143 151 151
DE KALB 109 127 133 132
DELAWARE 132 141 147 144
DUBOIS 120 127 130 136
ELKHART . 148 151 149
FAYE7TE . 152 155 156
FLOYD 130 149 152 159
FOUNTAIN . 138 141 1.39
FRANKLIN 109 130 "133 136
FULTON . 152 155 156
GIBSON 144 151 154 151
GRANT 121 138 144 144
GREENE 119 127 130 124
HAMILTON . 148 151 151
HANCOCK 144 148 151 151
HARRISON . 155 155 157
HENDRICKS 130 134 137 139
HENRY 144 148 151 151
HOWARD 135 146 149 151
HUNTINGTON 106 123 129 132
JACKSON 117 146 149 151
JASPER 152 168 176 186
JAY 122 138 144 144
JEFFERSON 104 144 147 151
JENNINGS 107 148 151 151
JOHNSON . 147 150 151
KNOX 144 155 158 151
C-30
-------
STATE=IN
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
KOSCIUSKO . 155 152 165
LAGRANGE . 153 150 149
LAKE 136 145 151 132
LA PORTE 141 157 154 153
LAWRENCE . 143 143 146
MADISON . 147 150 151
MARION . 135 138 139
MARSHALL . 147 150 144
MARTIN . 123 126 124
MIAMI 107 126 132 132
MONROE . 140 140 134
MONTGOMERY 129 133 136 139
MORGAN 146 151 154 151
NEWTON 140 156 164 164
NOBLE . 145 142 148
OHIO 87 127 130 124
ORANGE 108 129 132 136
OWEN 123 132 135 136
PARKE 127 133 136 139
PERRY 127 135 138 136
PIKE 140 148 151 151
PORTER 137 141 149 144
POSEY 144 T52 155 151
PULASKI 166 173 181 186
PUTNAM 123 130 133 139
RANDOLPH 135 145 148 151
RIPLEY 105 145 148 151
RUSH 144 148 151 151
ST JOSEPH . 148 159 169
SCOTT 130 149 152 151
SHELBY . 151 154 151
SPENCER 137 146 149 148
STARKE 156 177 185 186
STEUBEN . 145 151 144
SULLIVAN 141 151 154 151
SWITZERLAND 89 130 133 136
TIPPECANOE . 134 137 139
TIPTON 144 148 151 151
UNION . 133 136 139
VANDERBURGH 140 148 151 151
VERMILLION 126 135 138 139
VIGO 142 151 154 151
C-31
-------
STATE=IN
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
UABASH 121 137 143 144
WARREN . 129 132 139
WARRICK 139 146 149 151
WASHINGTON . 145 145 149
WAYNE . 139 142 139
WELLS 121 13.8 144 144
WHITE 141 153 161 175
WHITLEY . 134 140 144
C-32
-------
STATE=KA
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ALLEN . 127 127 133
ANDERSON . 111 111 123
ATCHISON . 92 92 98
BARBER 125 150 150 143
BARTON . 145 145 143
BOURBON . 108 108 114
BROWN . 108 108 111
BUTLER . 122 122 118
CHASE . 123 123 118
CHAUTAU2UA . 115 115 118
CHEROKEE . 113 113 103
CHEYENNE 119 130 130 123
CLARK 134 148 148 153
CLAY . 124 124 118
CLOUD . 125 125 128
COFFEY . 132 132 128
COMANCHE 134 149 149 138
COWLEY . 135 135 128
CRAWFORD . 108 108 103
DECATUR 118 129 129 123
DICKINSON . 120 120 118
DONIPHAN . 109 109 104
DOUGLAS . 125 125 128
EDWARDS . 155 155 153
ELK . 130 130 128
ELLIS 120 140 140 143
ELLSWORTH. . 113 113 103
FINNEY . 133 133 123
FORD 119 129 129 123
FRANKLIN . 130 130 133
GEARY . 132 132 128
GOVS 128 141 141 143
GRAHAM 118 129 129 )28
GRANT 116 127 127 123
GRAY 114 114 114 109
GREELEY 111 122 122 123
GREENWOOD . 133 133 128
HAMILTON 121 135 135 133
HARPER 132 133 133 123
HARVEY . 131 131 103
HASKELL 113 112 112 111
HODGEMAN . 130 130 129
C-33
-------
STATE=KA
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
JACKSON . 114 114 118
JEFFERSON . Ill 111 ''108
JEWELL . 90 90 83
JOHNSON . 97 97 103
KEARNY 122 125 125 115
KINGMAN . ISO 150 158
KIOWA 122 133 133 123
LABETTE . 134 134 133
LANE . 132 132 133
LEAVENWORTH . 106 106 105
LINCOLN . 117 117 128
LINN . 105 105 101
LOGAN 120 129 129 120
LYON . 109 109 108
MC PHERSON . 122 122 114
MARION . 121 121 118
MARSHALL . 124 124 128
MEADE 122 132 132 123
MIAMI . 112 112 125
MITCHELL . 119 119 103
MONTGOMERY . 123 123 133
MORRIS . 124 124 118
MORTON 124 135 135 123
NEMAHA . 100 100 94
NEOSHO . 122 122 128
NESS . 129 129 129
NORTON 115 126 126 123
OSAGE . 114 114 118
OS30RNE 119 132 132 133
OTTAWA . Ill 111 103
PAWNEE . 141 141 143
PHILLIPS 123 138 138 143
POTTAWATOMIE . 121 121 J25
PRATT 134 145 145 133
RAWLINS 115 126 126 123
RENO . 155 155 174
REPUBLIC . 86 86 78
RICE . 136 136 139
RILEY . 128 128 128
ROOKS 125 139 139 143
RUSH . 131 131 129
RUSSELL 119 140 140 143
C-34
-------
STATE=KA
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
SALINE . 133 .133 128
SCOTT 108 116 116 115
SEDGUICK . 123 123 114
SEUARD 120 131 131 123
SHAWNEE . 115 115 128
SHERIDAN 117 128 128 123
SHERMAN 115 126 126 123
SMITH 119 133 133 143
STAFFORD 138 149 149 153
STANTON 112 123 123 123
STEVENS 124 135 135 123
SUMNER . 126 126 108
THOMAS 112 123 123 123
TREGO 121 135 135 143
UA3AUNSEE . 114 114 118
WALLACE 118 131 131 123
WASHINGTON . 115 115 118
WICHITA 114 125 125 123
WILSON . 133 133 138
WOODSON . 130 130 133
WYANDOTTE . 107 107 100
C-35
-------
STATE=KY
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ADAIR . 185 185 139
ALLEN . 188 188 189
ANDERSON . 156 163 158
BALLARD 142 134 133 131
BARREN . 187 187 189
BATH . 141 148 163
BELL . 158 156 159
BOONE . 147 154 163
BOURBON . 156 163 163
BOYD . 127 131 128
BOYLE . 158 165 163
BRACKEN . 141 148 147
BREATHITT . 139 140 138
BRECKINRIDGE . 156 154 156
3ULLITT . 140 147 183
BUTLER . 154 152 156
CALDWELL . 160 162 159
GALLOWAY 141 133 132 131
CAMPBELL . 146 153 158
CARLISLE 14-3 135 134 131
CARROLL ' . 135 142 155
CARTER . 130 134 128
CASEY . 182 182 186
CHRISTIAN . 173 171 159
CLARK . 153 160 163
CLAY . 140 144 133
/CLINTON . 178 178 189
CRITTENDEN . 154 153 135
CUMBERLAND . 172 172 f86
DAVIESS . 146 142 141
EDMONSON . 158 159 152
ELLIOTT . 133 134 128
ESTILL . 135 136 V40
FAYETTE . 155 162 163
FLEMING . 146 153 163
FLOYD . 131 135 133
FRANKLIN . 152 159 163
FULTON . 134 133 131
GALLATIN . 145 152 155
GARRARD . 148 155 158
GRANT . 151 158 160
GRAVES 142 134 133 131
C-36
-------
STATE=KY
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
GRAYSON . 159 157 156
GREEN . 182 182 189
GREENUP . 128 132 128
HANCOCK . 147 148 152
HARDIN . 182 180 189
HARLAN . 141 142 140
HARRISON . 150 157 163
HART . 172 172 186
HENDERSON . 142 138 145
HENRY . 152 159 158
HICKMAN 143 135 134 131
HOPKINS . 152 150 159
JACKSON . 133 137 140
JEFFERSON . 143 150 163
JESSAMINE . 155 162 163
JOHNSON . 140 141 138
KENTON . 148 155 158
KNOTT . 136 140 133
KNOX . 132 133 128
LARUE . 180 180 189
LAUREL . 147 151 155
LAWRENCE . 137 138 138
LEE . 130 134 128
LESLIE . 137 141 138
LETCHER . 141 142 138
LEWIS . 146 150 140
LINCOLN . 156 163 163
LIVINGSTON . 150 149 131
LOGAN . 175 175 189
LYON . 174 174 189
MC CRACKEN 145 137 136 131
MC CREARY ' . 148 152 150
MC LEAN . 153 149 145
MADISON . 147 154 158
MAGOFFIN . 135 136 128
MARION . 178 178 189
MARSHALL 142 134 133 131
MARTIN . 149 153 150
MASON . 151 158 163
MEADE . 178 178 189
MENIFEE . 130 134 128
MERCER . 167 174 173
C-37
-------
STATE=KY
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
METCALFE . 187 187 189
MONROE . 185 185 189
MONTGOMERY . 147 154 163
MORGAN . 131 132 128
MUHLENBERG . 155 153 159
NELSON . 160 167 185
NICHOLAS . 152 159 158
OHIO . 157 155 156
OLDHAM . 160 167 175
OWEN . 137 144 147
OUSLEY . 138 139 138
PENDLETON . 142 149 155
PERRY . 136 140 138
PIKE . 147 151 150
POWELL . 134 135 140
PULASKI . 177 177 189
ROBERTSON . 148 155 155
ROCKCASTLE . 150 151 143
ROWAN . 123 .127 120
RUSSELL . 185 185 189
SCOTT . 153 160 163
SHELBY . 166 173 175
SIMPSON . 189 189 189
SPENCER . 164 171 175
TAYLOR . 183 183 189
TODD . 170 171 155
TRIGG . 172 172 189
TRIMBLE . 146 153 155
UNION . 137 136 131
WARREN . 178 178 186
WASHINGTON . 165 172 175
WAYNE . 171 171 174'
WEBSTER . 147 143 T41
WHITLEY . 147 151 155
WOLFE . 136 137 128
WOODFORD . 155 162 163
C-38
-------
STATE=LA
COUNTY ADJUSTED VARSCORE UEIGHTED TOTAL
VARSCORE SCORE
ACADIA 127 163 163 168.
ALLEN 130 172 172 193
ASCENSION 113 167 167 200
ASSUMPTION 111 73 100 71
AVOYELLES . 171 171 195
BEAUREGARD 133 168 163 183
BIENVILLE 151 160 160 180
BOSSIER 153 165 165 192
CADDO 142 172 182 205
CALCASIEU 128 173 173 183
CALDWELL 153 147 147 179
CAMERON 104 88 115 96
CATAHOULA . 175 175 180
CLAIRBORNE 158 155 155 159
CONCORDIA . 162 162 189
DE SOTO 142 148 148 150
EAST BATON ROUGE . 156 156 160
EAST CARROLL . 193 193 204
EAST FELICIANA 125 151 151 164
EVANGELINE 129 166 166 163
FRANKLIN . 173 173 186
GRANT 163 165 165 167
IBERIA 103 105 151 162
I3ERVILLE . 149 149 181
JACKSON 153 147 147 162
JEFFERSON 130 100 127 S6
JEFFERSON DAVIS 129 166 166 168
LAFAYETTE 130 173 173 187
LAFOURCHE 107 49 76 56
LA SALLE 157 152 152 173
LINCOLN 149 138 138 150
LIVINGSTON 125 163 163 208
MADISON . 179 179 1,99
MOREHOUSE . 164 164 196
NATCHITOCHES 147 158 158 192
ORLEANS 108 85 112 56
OUACHITA 158 163 163 180
PL'ASUEMINES 106 48 75 56
POINTS COUPEE . 178 173 200
RAPIDES 128 172 172. 209
RED RIVER 145 165 165 171
RICHLAND . 161 161 182
C-39
-------
STATE=LA
COUNTY
SABINE
ST BERNARD
ST CHARLES
ST HELENA
ST JAMES
ST JOHN THE BAPTIST
ST LANDRY
ST MARTIN
ST MARY
ST TAMMANY
TANGIPAHOA
TENSAS
TERREBONNE
UNION
VERMILION
VERNON
WASHINGTON
U'EBSTER
UEST BATON ROUGE
WEST CARROLL
UEST FELICIANA
UINN
ADJUSTED
VARSCORE
147
106
109
129
103
1 13
179
138
1 12
1 17
131
103
154
102
132
156
172
123
148
VARSCORE
48
133
169
93
182
169
178
125
147
174
168
45
152
1 13
148
171
167
16 1
167
159
142
WEIGHTED
148
75
133
169
139
158
169
178
125
147
174
168
72
152
140
148
171
167
16 1
167
159
142
TOTAL
SCORE
150
96
101
139
147
164
169
195
1 1 1
165
195
204
56
173
1 14
174
208
184
165
190
164
145
C-40
-------
STATE=MA
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
BARNSTABLE . 202 195 221
BERKSHIRE . 140 151 149
BRISTOL . 169 183 177
DUKES . 203 196 210
ESSEX . 192 181 177
FRANKLIN . 149 163 156
HAMPDEN . 147 161 161
HAMPSHIRE . 159 173 178
MIDDLESEX . 155 169 169
NANTUCKET . 209 202 205
NORFOLK . 165 179 177
PLYMOUTH . 204 197 205
SUFFOLK . 174 188 189
WORCESTER . 165 179 174
C-41
-------
STATE=MD
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ALLEGANY . 126 120 114
ANNE ARUNDEL . 175 186 184
BALTIMORE . 136 136 124
CALVERT . 170 184 185
CAROLINE . 186 200 193
CARROLL . 130 127 126
CECIL . 142 148 150
CHARLES . 157 171 178
DORCHESTER . 182 196 198
FREDERICK . 118 115 116
GARRETT . 136 140 138
HARFORD . 123 123 114
HOWARD . 125 125 124
KENT . 161 163 163
MONTGOMERY . 129 126 126
PRINCE GEORGES . 159 173 180
QUEEN ANNES . 172 174 173
ST MARYS . 165 179 175
SOMERSET . 179 193 188
TALBOT . 178 192 193
WASHINGTON . 137 137 156
WICOMICO .. 185 199 198
WORCESTER . 184 198 198
BALTIMORE CITY . 144 152 '137
C-42
-------
STATE=ME
COUNTY ADJUSTED VARSCORE WEIGHTED -TOTAL
VARSCORE SCORE
ANDROSCOGGIN . 150 152 154
AROOSTOOK . 132 134 132
CUMBERLAND . 151 153 154
FRANKLIN . 147 149 139
HANCOCK . 158 160 159
KENNE3EC . 147 149 151
KNOX . 144 146 139
LINCOLN . 146 148 151
OXFORD . 163 165 159
PENOBSCOT . 153 155 144
PISCATA2UIS . 144 146 154
SAGADAHOC . 144 146 151
SOMERSET . 149 151 154
WALDO . 148 150 151
WASHINGTON . 158 160 162
YORK . 159 161 162
C-43
-------
STATE=MI
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORC SCORE
ALCONA . 139 145 124
ALGER . 143 149 149
AILEGAN . 163 166 134
ALPENA . 132 138 129
ANTRIM . 129 135 124
ARENAC . 140 143 154
BARAGA . 110 110 109
BARRY . 128 134 119
BAY . 124 127 126
BENZIE . 144 150 174
BERRIEN . 162 168 182
BRANCH . 158 164 147
CALHOUN . 157 163 162
CASS . 143 149 154
CHARLEVOIX . 123 129 126
CHEBOYGAN . 134 137 131
CHIPPEMA . 158 158 159
CLARE . 147 153 152
CLINTON . 138 144 154
CRAWFORD . 161 167 182
DELTA . 153 159 167
DICKINSON- . 115 115 119
EATON . 125 131 124
EMMET . 123 129 121
GENESEE . 123 129 124
GLADUIN . 136 139 154
GOGEBIC . 97 103 94
GRAND TRAVERSE . 162 165 193
GRATIOT . 138 144 132
HILLSDALE . 127 133 119
HOUGHTOM . 126 126 131
HURON . 123 126 126
INGHAM . 132 138 124
IONIA . 143 149 147
IOSCO . 149 155 152
IRON . 102 108 94
ISABELLA . 135 141 144
JACKSON . 153 159 147
KALAMAZOO . 143 149 154
KALKASKA . 176 173 208
KENT . 138 144 174
KEWEENAW . 122 122 153
C-44
-------
STATE=MI
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
LAKE . 178 175 208
LAPEER . 127 133 124
LEELANAU . 130 136 109
LENAWEE . 106 112 97
LIVINGSTON . 135 141 124
LUCE . 162 162 184
MACKINAC . 154 160 172
MACOMB . 127 130 126
MANISTEE . 167 164 188
MAR2UETTE . 101 107 104
MASON . 157 163 182
MECOSTA . 159 165 147
MENOMINEE . 143 149 137
MIDLAND . 139 142 139
MISSAUKEE . 133 139 119
MONROE . 137 140 139
MONTCALM . 156 162 152
MONTMORENCY . 146 152 147
MUSKEGON . 170 173 184
NEWAYGO . 170 173 208
OAKLAND . 147 153 147
OCEANA . 164 170 179
OGEMAW . 144 150 147
ONTONAGON . 106 112 129
OSCEOLA . 131 137 144
OSCODA . 167 164 183
OTSEGO . 153 159 147
OTTAWA . 148 151 161
PRES2UE ISLE . 151 157 137
ROSCOMMON . 153 158 175
SAGINAW . 122 125 126
ST GLAIR . 121 124 126
ST JOSEPH . 163 169 182
SANILAC . 124 130 124
SCHOOLCRAFT . 168 168 184
SHIAWASSEE . 132 138 132
TUSCOLA . 129 132 126
VAN 3UREN . 144 150 139
WASHTENAW . 138 144 152
WAYNE . 127 130 131
WEXFORD . 157 163 147
C-45
-------
STATE=MN
COUNTY
AITKIN
ANOKA
BECKER
BELTRAMI
BENTON
BIG STONE
BLUE EARTH
BROUN
CARLTON
CARVER
CASS
CHIPPEWA
CHISAGO
CLAY
CLEARWATER
COOK
COTTONMOOD
CROW UING
DAKOTA
DODGE
DOUGLAS
FARIBAULT
FILLMORE
FREE30RN
GOODHUE
GRANT
HENNEPIN
HOUSTON
HUBBARD
ISANTI
ITASCA
JACKSON
KANABEC
KANDIYOHI
KITTSON
KOOCHICHING
LAC 2UI PARLE
LAKE
LAKE OF THE WOODS
LE SUEUR
LINCOLN
LYON
ADJUSTED
VARSCORE
VARSCORE
128
170
156
120
141
132
1 13
122
111
129
160
123
164
1 15
109
131
1 19
166
133
139
133
1 15
152
122
150
1 13
122
143
167
170
131
122
143
133
1 19
109
1 18
134
129
1 17
1 13
1 19
WEIGHTED
128
170
156
120
141
170
1 13
122
141
129
160
123
164
1 15
109
131
1 19
166
138
131
133
1 15
152
122
150
1 13
122
143
167
170
-131
122
144
138
1 19
109
1 18
134
129
1 17
1 13
1 19
TOTAL
SCORE
130
187
164
108
135
164
108
122
145
139
191
1 12
187
108
108
146
1 12
191
1 19
1 16
1 12
108
177
1 16
177
102
1 19
174
191
137
133
132
1,45
134
1 19
108
122
146
138
1 14
122
122
C-46
-------
STATE=MN
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
MC LEOD . 126 126 119
MAHNOMEN . 108 108 108
MARSHALL . 116 116 123
MARTIN . 122 122 112
MEEKER . 143 143 134
MILLE LACS . 141 137 145
MORRISON . 149 149 135
MOWER . 145 137 131
MURRAY . 121 121 122
NICOLLET . 120 120 124
NOBLES . 147 147 144
NORMAN . 113 113 108
OLMSTED . 155 155 177
OTTER TAIL . 159 159 179
PENNINGTON . 110 110 108
PINE . 140 136 140
PIPESTONE . 106 106 98
POLK . 110 110 108
POPE . 132 132 122
RAMSEY . 122 122 119
RED LAKE . 113 113 108
REDWOOD . 122 122 122
RENVILLE . 121 121 112
RICE . 114 114 104
ROCK . 98 98 104
ROSEAU . 118 118 104
ST LOUIS . 140 140 155
SCOTT . 117 117 104
SHERBURNE . 187 187 187
SIBLEY . 119 119 114
STEARNS . 161 161 187
STEELE . 139 131 1-16
STEVENS . 127 127 122
SWIFT . 127 127 T22
TODD ; 149 149 139
TRAVERSE . 102 110 98
WABASHA . 152 152 177
WADENA . 184 184 196
WASECA . 106 106 88
WASHINGTON . 122 122 119
WATONWAN . 122 122 104
WILKIN . 111 111 98
C-47
-------
STATE=HN
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
WINONA . 148 148 174
WRIGHT . 134 134 139
YELLOW MEDICINE . 121 121 122
C-48
-------
STATE=MO
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ADAIR 102 77 77 68
ANDREW . 103 103 75
ATCHISON . 95 95 75
AUDRAIN . 69 69 68
BARRY . 80 80 77
BARTON . 101 101 98
BATES . 98 98 86
BENTON . 100 100 98
BOLLINGER . 103 103 108
BOONE . 125 125 136
BUCHANAN . 101 101 95
BUTLER . 138 138 158
CALDWELL 111 88 88 78
CALLAUAY .- 89 89 78
CAMDEN . 95 95 101
GIRARDEAU . 133 133 130
CARROLL . 97 97 83
CARTER . 87 87 83
CASS . 90 90 98
CEDAR . 95 95 98
CHARITON . 97 97 83
CHRISTIAN . 113 113 123
CLARK . 91 91 83
CLAY . 102 102 83
CLINTON 113 83 83 78
COLE . 95 95 95
COOPER . 99 99 98
CRAWFORD . 98 98 95
DADE '. 92 92 95
DALLAS . 103 108 98
DAVIESS 106 90 90 75
DE KALB 108 86 86 78
DENT . 117 117 126
DOUGLAS . 10U 10U '83
DUNKLIN . 156 156 168
FRANKLIN . 89 89 83
GASCONADE . 89 89 83
GENTRY 114 100 100 78
GREENE . 122 122 126
GRUNDY 108 91 91 78
HARRISON 112 95 95 78
HENRY . 105 105 86
-C-49
-------
STATE=MO
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
HICKORY . Ill 111 98
HOLT . 105 105 78
HOWARD . 91+ 91^ 80
HOUELL . 115 115 123
IRON . 80 80 88
JACKSON . 100 100 83
JASPER . 109 109 100
JEFFERSON . 94 9 <4 83
JOHNSON . 92 92 98
KNOX 97 75 75 68
LACLEDE . 93 93 95
LAFAYETTE . 91 91 88
LAWRENCE . 95 95 95
LEWIS . 91 91 83
LINCOLN . 119 119 137
LINN 112 90 90 83
LIVINGSTON 114 109 109 83
MC DONALD . 100 100 82
MACON 102 77 77 68
MADISON . 78 78 88
MARIES . 89 89 88
MARION . 93 93 83
MERCER 110 95 95 78
MILLER . 111 111 85
MISSISSIPPI 182 159 159 158
MOHITEAU . 92 92 95
MONROE 96 73 73 63
MONTGOMERY . 87 87 78
MORGAN . 100 100 98
NEW MADRID 179 156 156 158
NEWTON . 104 104 88
NODAWAY 108 87 87 75
OREGON . 95 95 95
OSAGE . 92 92 83
OZARK . 93 93 77
PEMISCOT 171 148 148 153
PERRY . 127 127 145
PETTIS . 90 90 98
PHELPS . 112 112 100
P-IKE . 88 88 78
PLATTE . 103 103 98
POLK . 127 127 98
C-50
-------
STATE=HO
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
PULASKI . 102 102 88
PUTNAM 107 87 87 78
RALLS . 85 85 68
RANDOLPH 101 76 76 68
RAY . 96 96 83
REYNOLDS . 95 95 111
RIPLEY . 118 118 113
ST CHARLES . 120 120 103
ST CLAIR . 93 93 98
ST FRANCOIS . 101 101 97
ST LOUIS . 145 145 138
STE GENEVIEVE . 108 108 103
SALINE . 104 104 83
SCHUYLER 105 80 80 68
SCOTLAND 111 90 90 83
SCOTT . 146 146 158
SHANNON . 92 92 82
SHELBY 98 76 76 68
STODDARD . 152 152 158
STONE . 92 92 '77
SULLIVAN 105 84 84 78
TANEY . 87 87 77
TEXAS . 118 108 77
VERNON . 95 95 98
WARREN . 93 93 75
WASHINGTON . 100 100 95
WAYNE . 106 106 38
WEBSTER . 127 117 98
WORTH 107 91 91 78
WRIGHT . 123 115 88
ST LOUIS CITY . 161 161 158
C-51
-------
STATE = I1S
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ADAMS . 135 131 128
ALCORN . 117 120 120
AMITE . 144 144 136
ATTALA . 130 138 158
BENTON . 125 125 136
BOLIVAR 151 148 151 160
CALHOUN . 97 97 88
CARROLL . 114 122 136
CHICKASAU . 108 108 91
CHOCTAU . 110 110 98
CLAI30RNE . 133 129 128
CLARKE . 146 154 169
CLAY . 100 100 91
COAHOMA 149 145 148 145
COPIAH . 151 159 158
COVIHGTON . 156 164 169
DE SOTO . 124 120 119
FORREST . 157 165 169
FRANKLIN . 147 155 158
GEORGE 166 157 165 169
GREENE . 147 155 161
GRENADA . 118 126 136
HANCOCK 152 149 152 155
HARRISON 163 156 164 169
HINDS . 113 121 116
HOLMES . 129 125 119
HUMPHREYS 146 131 134 133
ISSA2UENA 145 130 133 133
ITAUAMBA . 143 151 146
JACKSON 150 145 153 161
JASPER . 130 138 148
JEFFERSON . 143 139 128
JEFFERSON DAVIS . 157 165 166
JONES . 145 153 161
KEMPER . 111 111 98
LAFAYETTE . 121 129 136
LAMAR . 155 163 169
LAUDERDALE . 128 128 98
LAURENCE . 147 155 161
LEAKE . 135 143 158
LEE . 118 118 99
LEFLORE 150 147 150 145
C-52
-------
STATE=MS
COUNTS ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
LINCOLN . 147 155 158
LOWNDES . 133 141 151
MADISON . 110 1J8 136
MARION . 149 157 161
MARSHALL . 127 135 136
MONROE . 133 141 148
MONTGOMERY . 138 146 158
NESHOBA . 139 147 166
NEWTON . 135 143 158
NOXUBEE . 105 105 106
OKTIBBEHA . 91 91 91
PANOLA . 130 126 119
PEARL RIVER . 149 157 161
PERRY . 155 163 169
PIKE . 147 155 161
PONTOTOC . 105 105 88
PRENT1SS . 154 162 166
2UITMAN 150 147 150 145
RANKIN . 117 117 91
SCOTT . 112 112 94
SHARKEY 148 145 148 145
SIMPSON . 142 150 151
SMITH . 138 146 161
STONE 167 158 166 169
SUNFLOWER 149 146 149 145
TALLAHATCHIE 144 129 132 133
TATE . 125 121 119
TIPPAH . 110 110 106
TISHOMINGO . 142 150 146
TUNICA 144 129 132 128
UNION . 123 123 118
WALTHALL . 153 166 169
WARREN . 120 116 T,16
WASHINGTON 144 129 132 133
WAYNE . 142 150 161
WEBSTER . 104 104 98
WILKINSON . 139 147 161
WINSTON . 105 105 98
YALOBUSHA . 122 130 136
YAZOO . 127 123 119
C-53
-------
STATE=MT
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
BEAVERHEAD 112 131 131 120
BIG HORN . 124 124 108
BLAINE . 114 114 88
BROADWATER . 145 145 112
CARBON . 126 126 108
CARTER . 103 103 83
CASCADE . 118 113 96
CHOUTEAU . 114 114 90
CUSTER . 131 131 105
DANIELS . 130 130 111
DAWSON . 135 135 111
DEER LODGE . 130 130 97
FALLON . 113 113 87
FERGUS . 107 107 76
FLATHEAD 128 130 130 94
GALLATIN . 143 143 124
GARFIELD . 121 121 102
GLACIER . 122 122 96
GOLDEN VALLEY . 125 125 96
GRANITE . 125 125 106
HILL . 116 116 96
JEFFERSON . 129 129 77
JUDITH BASIN . 113 113 84
LAKE 132 133 133 106
LEWIS AND CLARK . 121 121 106
LIBERTY -. 112 112 96
LINCOLN 134 136 136 130
MC CONE . 120 120 94
MADISON . 131 131 97
MEAGHER . 127 127 114
MINERAL . 124 124 106
MISSOULA . 133 133 106
MUSSELSHELL . 127 127 1,02
PARK . 138 138 106
PETROLEUM . 112 112 97
PHILLIPS . 112 112 108
PONDERA . 117 117 108
POWDER RIVER . 124 124 107
POWELL . 140 140 127
PRAIRIE . 130 130 109
RAVALLI . 139 139 97
RICHLAND . 134 134 113
C-54
-------
STATE=MT
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ROOSEVELT . 129 129 114
ROSEBUD . 124 124 108
SANDERS 134 135 135 114
SHERIDAN . 131 131 113
SILVER BOW . 134 134 97
STILLWATER . 122 122 108
SWEET GRASS . 130 130 96
TETON . 115 115 96
TOOLS . 118 118 96
TREASURE . 112 112 84
VALLEY . 112 112 108
WHEATLAND . 122 122 108
WI3AUX . 131 131 104
YELLOWSTONE . 116 116 96
YELLOWSTONE NATIONAL 124 146 146 135
C-55
-------
STATE=NC
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ALAMANCE . 129 135 134
ALEXANDER . 140 146 148
ALLEGHANY . 146 143 138
ANSON . 137 143 144
ASHE 136 150 147 143
AVERY 144 158 155 155
BEAUFORT . 138 184 186
BERTIE . 187 183 186
BLADEN . 203 199 194
BRUNSWICK . 202 202 209
BUNCOMBE 146 160 157 150
BURKE . 136 142 136
CABARRUS . 132 138 139
CALDWELL . 135 141 130
CAMDEN . 191 187 186
CARTERET . 193 189 186
CASWELL . 130 136 139
CATAWBA . 129 135 144
CHATHAM . 118 124 126
CHEROKEE 143 157 154' 150
CHOWAN . 205 201 194
CLAY 147 161 158 155
CLEVELAND . 130 136 T44
COLUMBUS . 189 185 186
CRAVEN . 190 186 186
CUMBERLAND . 197 193 189
CURRITUCK , . 204 200 194
DARE . 175 185 176
DAVIDSON . 131 137 139
DAVIE . 132 138 139
DUPLIN . 202 198 209
DURHAM . 120 126 126
EDGECOMBE . 185 181 .181
FORSYTH . 129 135 144
FRANKLIN . 132 138 147
GASTON . 132 138 139
GATES . 192 188 186
GRAHAM 142 156 153 150
GRANVILLE . 130 136 134
GREENE . 185 181 181
GUILFORD . 132 138 144
HALIFAX . 182 178 181
C-56
-------
STATE=NC
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
HARNETT . 186 182 186
HAYWOOD 147 161 158 155
HENDERSON 149 163 160 161.
HERTFORD . 185 181 186
HOKE . 196 192 189
HYDE . 173 183 176
IREDELL . 129 135 144
JACKSON 144 158 155 155
JOHNSTON . 183 179 186
JONES . 192 186 184
LEE . 149 155 134
LENOIR . 199 195 184
LINCOLN . 131 137 144
MC DOWELL 142 152 149 155
MACON 145 159 156 155
MADISON 159 173 170 170
MARTIN . 178 174 171
MECKLENBURG . 131 137 144
MITCHELL 145 159 156 155
MONTGOMERY . 135 141 134
MOORE . 165 168 196
NASH . 170 166 179
NEW HANOVER . 202 202 209
NORTHAMPTON . 184 180 181
ONSLOW . 217 211 219
ORANGE . 129 135 134
PAMLICO . 192 188 186
PASQUOTANK . 187 183 186
PENDER . 198 198 209
PER2UIMANS . 189 185 186
PERSON . 129 135 134
PITT . 187 183 181
POLK " . 126 132 ,124
RANDOLPH . 128 134 139
RICHMOND . 187 190 196
ROBESON . 185 181 181
ROCKINGHAM . 128 134 144
ROWAN . 133 139 139
RUTHERFORD . 129 135 136
SAMPSON . 197 200 199
SCOTLAND . 186 182 181
STANLY . 131 137 144
C-57
-------
STATE=NC
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
STOKES . 122 128 132
SURRY . 120 126 121
SUAIN 144 158 155 155
TRANSYLVAKIA 149 163 160 155
TYRRELL . 186 182 186
UNION . 135 mi 148
VANCE . 132 138 144
WAKE . 136 142 144
WARREN . 132 138 144
WASHINGTON . 186 182 186
WATAUGA 145 159 156 155
WAYNE . 198 194 189
WILKES . 145 142 143
WILSON . 189 135 181
YADKIN . 130 136 144
YANCEY 144 158 155 155
C-58
-------
STATE=ND
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ADAMS 123 147 147 150
BARNES 127 110 110 96
BENSON 128 113 113 111
BILLINGS 119 144 144 145
30TTINEAU 124 14-1 141 121
BOWMAN 120 146 146 147
BURKE 109 136 136 117
3URLEIGH 121 120 120 117
CASS 127 110 110 76
CAVALIER 123 125 125 36
DICKEY 127 110 110 91
DIVIDE 126 143 143 127
DUNN 109 141 141 125
EDDY' 131 117 117 104
EMMONS 102 133 133 135
FOSTER 135 120 120 114
GOLDEN VALLEY 117 132 132 135
GRAND FORKS 136 121 121 96
GRANT 112 150 150 127
GRIGGS 146 131 131 128
HETTINGER 119 149 149 147
KIDDER 134 140 140 166
LA MOURE 129 114 114 91
LOGAN 118 123 123 111
MC HENRY 128 149 149 138
MC INTOSH 117 122 122 114
MC KENZIE 106 134 134 145
MC- LEAN 1 13 143 143 117
MERCER 105 1.36 136 117
MORTON 123 148 148 133
MOUNTRAIL 108 137 137 117
NELSON 128 113 113 96
OLIVER 102 133 133 .125
PEMBINA 124 121 121 114
PIERCE 113 125 125 140
RAMSEY 135 120 120 86
RANSOM 131 115 115 91
RENVILLE 110 148 148 130
RICHLAND 149 134 134 125
ROLETTE 121 123 123 125
SARGENT 134 117 117 91
SHERIDAN 148 148 127
C-59
-------
STATE=ND
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
SIOUX 101 139 139 123
SLOPE 126 145 145 135
STARK 118 151 151 147
STEELE 136 120 120 96
STUTSMAN 120 114 114 103
TOUNER 122 108 108 104
TRAILL 118 113 113 96
WALSH 122 114 114 86
WARD 112 144 144 117
WELLS 112 115 115 111
WILLIAMS 125 142 142 115
C-60
-------
STATE=NE
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ADAMS . 117 117 129
ANTELOPE . 122 122 129
ARTHUR . 134 134 123
BANNER . 109 109 119
BLAINE . 137 137 141
300NE . 119 119 119
BOX BUTTE . 112 112 109
BOYD . 122 122 118
BROUN . 143 143 127
BUFFALO . 119 119 129
3URT . 122 122 131
BUTLER . 119 119 124
CASS . 114 114 98
CEDAR . 123 123 137
CHASE . 128 128 109
CHERRY . 153 153 144
CHEYENNE . 107 107 114
CLAY . 107 107 109
COLFAX . 113 113 117
CUMING . 110 110 117
CUSTER . 110 110 109
DAKOTA . 121 121 133
DAUES . 101 101 119
DAMSON . 111 111 109
DEUEL . 108 108 109
DIXON . 114 114 119
DODGE . 113 113 117
DOUGLAS . 123 123 128
DUNDY . 127 127 131
FILLMORE . 111 111 119
FRANKLIN . 114 1.14 114
FRONTIER . 103 103 109
FURNAS . 105 105 309
GAGE . 106 106 93
GARDEN . 122 122 134
GARFIELD . 143 143 151
GOSPER . 108 108 109
GRANT . 131 131 113
GREELEY . 118 118 109
HALL . 132 132 139
HAMILTON . 117 117 119
HARLAN . 105 105 109
C-61
-------
STATE=NE
COUNTY
ADJUSTED
VARSCORE
VARSCORE
WEIGHTED
TOTAL
SCORE
HAYES
HITCHCOCK
HOLT
HOOKER
HOWARD
JEFFERSON
JOHNSON
KEARNEY
KEITH
KEYA PAHA
KIMBALL
KHOX
LANCASTER
LINCOLN
LOGAN
LOUP
MC PHERSON
MADISON
MERRICK
nORRILL
NANCE
NEMAHA
NUCKOLLS
OTOE
PAWNEE
PERKINS
PHELPS
PIERCE
PLATTE
POLK
RED WILLOW
RICHARDSON
ROCK
SALINE
SARPY
SAUNDERS
SCOTTS BLUFF
SEWARD
SHERIDAN
SHERMAN
SIOUX
STANTON
1 10
129
127
102
1 10
1 19
1 19
134
107
128
1 16
132
123
129
132
108
135
1 19
20
17
09
1 1
09
109
120
1 18
127
1 1 1
104
1 17
146
1 1 1
123
124
1 13
1 12
123
120
108
1 1 1
1 10
104
144
129
127
102
1 10
1 19
1 19
134
107
128
1 16
132
123
129
132
108
135
1 19
120
1 17
109
1 1 1
109
109
120
1 18
127
1 1 1
104
1 17
146
1 1 1
123
124
1 13
1 12
123
120
108
1 1 1
109
109
139
1 13
1 19
88
93
129
134
154
1 14
123
98
134
134
131
123
109
129
1 19"
1 14
1 13
1 19
98
93
109
1 19
129
129
1 14
109
1 13
>27
1 14
1 18
128
1 19
1 14
144
1 19
1 19
1 17
C-62
-------
STATE=ME
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
THAYER . 104 104 94
THOMAS . 144 144 123
THURSTON . 120 120 131
VALLEY . 113 113 109
WASHINGTON . 116 116 116
WAYNE . 108 108 117
WEBSTER . 116 116 129
WHEELER . 143 143 151
YORK . 111 111 119
C-63
-------
STATE=NH
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
BELKNAP . 157 159 152
CARROLL . 154 156 138
CHESHIRE . 153 155 144
COOS . 144 146 138
GRAFTON . 149 151 138
HILLSBOROUGH . 169 171 164
MERRIMACK . 163 165 152
ROCKIXGHAM . 169 171 167
STRAFFORD . 170 172 164
SULLIVAN . 149 151 144
C-64
-------
STATS=NJ
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ATLANTIC . 206 206 216
BERGEN . 141 147 144
BURLINGTON . 189 189 215
CAMDEN . 176 176 196
CAPE MAY . 190 194 -194
CUMBERLAND . 190 194 189
ESSEX . 142 148 149
GLOUCESTER . 178 178 195
HUDSON . 142 148 144
HUNTERDON . 155 149 130
MERCER . 136 136 139
MIDDLESEX . 147 147 154
MONMOUTH . 135 135 99
MORRIS . 148 154 156
OCEAN . 203 203 216
PASSAIC . 128 134 132
SALEM . 151 151 167
SOMERSET . 121 115 111
SUSSEX . 148 154 156
UNION . 125 131 137
UARREN . 158 164 159
C-65
-------
STATE=NM
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
BERNALILLO . 130 130 116
CATRON 123 140 140 121
CHAVES . 127 127 111
COLFAX . 113 113 110
CURRY . 130 130 118
DE BACA . 115 115 111
DONA ANA 131 143 143 130
EDDY . 124 124 136
GRANT 128 140 140 141
GUADALUPE . 107 107 81
HARDING . 118 118 116
HIDALGO 119 132 132 125
LEA . 129 129 122
LINCOLN . 113 113 86
LOS ALAMOS . 138 138 121
LUNA 120 133 133 120
MC KINLEY . 109 109 110
MORA . 118 118 100
OTERO . 126 126 116
2UAY . 121 121 107
RIO ARRIBA . 107 107 86
ROOSEVELT . 130 130 125
SANDOVAL . 119 119 116
SAN JUAN . 117 117 -130
SAN MIGUEL . 99 99 110
SANTA FE . 119 119 111
SIERRA 123 133 133 130
SOCORRO 121 130 130 116
TAOS . 121 121 107
TORRANCE . 113 113 111
UNION . 115 115 101
VALENCIA . 123 123 131
C-66
-------
STATE=NV
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
CHURCHILL 117 139 139 161-
CLARK 121 127 127 145
DOUGLAS . 117 117 108
ELKO 112 12S 125 87
ESMERALDA 122 139 139 97
EUREKA 120 139 139 161
HUMBOLDT . 129 129 141
LANDER 117 138 138 141
LINCOLN 119 126 126 125
LYON . 133 133 117
MINERAL 117 132 132 114
NYE 120 136 136 131
ORMSBY . 110 110 69
PERSHING 119 136 136 137
STOREY . 90 104 107
WASHOE . 113 127 107
WHITE PINE 121 138 138 141
CARSON CITY CITY . 168 168 160
C-&7
-------
STATE=NY
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ALBANY . 133 139 134
ALLEGANY . 132 138 134
BRONX . 123 129 124
BROOME . 131 137 134
CATTATAUGUS . 135 141 134
CAYUGA . 145 151 154
CHAUTAU2UA . 139 145 136
CHEMUNG . 132 138 129
CHENANGO . 134 140 134
CLINTON . 143 149 152
COLUMBIA . 138 144 129
CORTLAND . 142 148 146
DELAWARE . 130 136 134
DUTCHESS . 139 145 134
ERIE . 146 152 157
ESSEX . 109 115 86
FRANKLIN . 147 143 140
FULTON . 131 137 129
GENESEE . "153 159 157
GREENE . 114 120 116
HAMILTON . 130 130 137
HERKIMER . 146 142 130
JEFFERSON . 153 159 147
KINGS . 171 164 158
LEWIS . 162 168 176
LIVINGSTON . 132 133 122
MADISON . 136 14/2 134
MONROE . 157 163 161
MONTGOMERY . 147 143 142
NASSAU . 200 193 187
NEW YORk . 111 117 112
NIAGARA . 147 153 147
ONEIDA . 156 162 154
ONONDAGA . 137 143 1*46
ONTARIO . 145 151 157
ORANGE . 131 137 136
ORLEANS . 154 160 161
OSWEGO . 158 164 156
OTSEGO . 132 138 134
PUTNAM . 141 137 117
QUEENS . 177 170 162
RENSSELAER . 132 138 134
C-68
-------
STATE=NY
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
RICHMOND . 111 117 112
ROCKLAND . 149 155 158
ST LAWRENCE . 146 142 140
SARATOGA . 131 137 129
SCHENECTADY . 155 161 154
SCHOHARIE . 129 135 123
SCHUYLER . 130 136 136
SENECA . 148 154 157
STEUBEN . 131 137 129
SUFFOLK . 197 190 187
SULLIVAN . 131 137 134
TIOGA . 131 137 134
TOHPKINS . 130 136 134
ULSTER . l"28 134 129
WARREN . 117 113 92
WASHINGTON . 114 110 117
WAYNE . 157 163 161
WESTCHESTER . 125 131 129
WYOMING . 152 158 154
YATES . 142 148 142
C-69
-------
STATE=OH
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ADAMS . 122 122 133
ALLEN 104 125 131 132
ASHLAND . 121 127 122
ASHTA3ULA . 114 114 109
ATHENS . 142 142 138
AUGLAIZE 104 121 127 127
BELMONT . 121 121 133
BROUN . 110 110 109
BUTLER . 130 130 126
CARROLL . 125 121 106
CHAMPAIGN 139 147 153 149
CLARK 140 148 154 149
CLERMONT . 108 108 109
CLINTON 117 125 131 132
COLUMBIANA . 148 148 158
COSHOCTON . 132 128 118
CRAWFORD . 122 128 127
CUYAHOGA . 116 122 122
DARKE 125 142 148 149
DEFIANCE . Ill 117 114
DELAWARE . 113 119 119
ERIE . 1.25 131 132
FAIRFIELD . 119 125 122
FAYETTE 106 123 129 129
FRANKLIN . 126 132 129
FULTON . 128 134 142
GALLIA . 123 123 126
GEAUGA . 122 122 121
GREENE 139 146 152 144
GUERNSEY . 107 107 105
HAMILTON . 120 120 106
HANCOCK 103 124 130 132
HARDIN 99 120 126 -132
HARRISON . 101 101 93
HENRY . 122 128 122
HIGHLAND . 118 118 121
HOCKING . 130 130 126
HOLMES . 132 128 118
HURON . 118 124 122
JACKSON . 143 143 150
JEFFERSON . 119 119 121
KNOX . 142 148 144
C-70
-------
STATE=OH
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
LAKE . 126 126 131
LAWRENCE . 124 124 126
LICKING . 129 135 122
LOGAN 103 120 126 127
LORAIN" . 119 125 122
LUCAS . 124 130 122
MADISON 106 123 129 129
MAHONING . 126 126 116
MARION 96 117 123 124
MEDINA . 117 123 122
MEIGS . 150 150 150
MERCER 104 121 127 127
MIAMI 136 144 150 149
MONROE . 129 129 133
MONTGOMERY . 145 151 149
MORGAN . 137 137 136
MORROW i 116 122 119
MUSKINGUM . 122 118 118
NOBLE . 115 115 121
OTTAWA 99 121 127 122
PAULDING 86 109 115 114
PERRY . 137 133 141
PICKAWAY . 134 140 129
PIKE . 132 132 118
PORTAGE . 128 134 122
PRE3LE 135 142 148 149
PUTNAM 89 111 117 114
RICHLAND . 140 ' 146 134
ROSS . 127 127 118
SANDUSKY 97 120 126 122
SCIOTO . 126 126 112
SENECA 104 125 131 132
SHELBY 104 121 127 127
STARK . 143 149 .144
SUMMIT . 123 129 129
TRUMBULL . 120 120 126
TUSCARAWAS . 129 125. 118
UNION 96 113 119 119
VAN 'JERT 100 121 127 132
VINTON . 139 139 150
WARREN . 114 114 114
WASHINGTON . 128 128 133
C-71
-------
STATE=OH
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
WAYNE . 139 145 144
WILLIAMS . 112 118 119
WOOD 89 111 117 114
WYANDOT 103 124 130 127
C-72
-------
STATE=OK
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ADAIR . 113 113 105
ALFALFA . 144 144 154
ATOKA . 131 131 143
BEAVER . 141 141 152
BECKHAM . 135 135 149
BLAINE . 133 133 143
BRYAN . 113 113 123
CADDO . 135 135 145
CANADIAN . 129 129 128
CARTER . 131 131 133
CHEROKEE . 113 113 121
CHOCTAW . 128 128 158
ClilARRON . 130 130 152
CLEVELAND . 113 113 128
COAL . 120 120 104
COMANCHE . 125 125 138
COTTON . 140 140 138
CRAIG . 133 133 148
CREEK . 124 124 143
CUSTER . 118 118 113
DELAWARE . 114 114 111
DEWEY . 136 136 138
ELLIS . 160 160 176
GARFIELD . 135 135 133
GARVIN . 118 118 104
GRADY . 130 130 133
GRANT . 139 139 128
GREER . 140 140 149
HARMON . 127 127 94
HARPER . 138 138 133
HASKELL . 116 116 104
HUGHES . 126 126 135
JACKSON . 123 123 124
JEFFERSON . 132 132 128
JOHNSTON . 123 123 143
KAY . 132 132 128
KINGFISHER . 142 142 158
KIOWA . 120 120 109
LATIMER . 118 118 107
LE FLOP.E . 141 141 133
LINCOLN . 127 127 148
LOGAN . 138 138 143
C-73
-------
STATE=OK
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
LOVE . 154 154 163
MC CLAIN . 116 116 104
MC CURTAIN . 140 140 148
MC INTOSH . 136 136 138
MAJOR . 151 151 154
MARSHALL . 108 108 94
MAYES . 134 134 133
MURRAY . 124 124 138
MUSKOGEE . 128 128 128
NOBLE . 129 129 133
NOWATA . 146 146 153
OKFUSKEE . 146 146 158
OKLAHOMA . 132 132 143
OKMULGEE . 139 139 133
OSAGE . 131 131 138
OTTAWA . 117 117 103
PAWNEE . 120 120 113
PAYNE . 130 130 123
PITTSBURG . 110 110 104
PONTOTOC . 121 121 108
PO-TTAWATOMIE . 110 110 116
PUSHMATAHA . 131 131 131
ROGER MILLS . 134 134 146
ROGERS . 135 135 128
SEMINOLE . 126 126 140
SEQUOYAH . 128 128 128
STEPHENS . 133 133 143
TEXAS . 125 125 122
TILLMAN . 136 136 139
TULSA . 136 136 133
WAGONER . 137 137 133
WASHINGTON . 143 143 143
WASHITA . 135 135 1,33
WOODS . 142 142 128
WOODWARD . 145 145 153
C-74
-------
STATE=OR
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
BAKER . 99 99 54
BENTON . 134 134 139
CLACKAMAS . 126 126 114
CLATSOP . 133 133 109
COLUMBIA . 115 130 102
COOS . 91 91 76
CROOK . 98 98 69
CURRY . 89 89 81
DESCHUTES . 95 101 99
DOUGLAS . 104 104 84
GILLIAM . 111 111 91
GRANT . 89 95 82
HARNEY . 112 112 104
HOOD RIVER . 118 108 101
JACKSON . 107 107 77
JEFFERSON . 95 95 59
JOSEPHINE . 84 84 64
KLAMATH . 107 100 81
LAKE . 109 123 126
LANE . 127 127 95
LINCOLN . 122 122 109
LINN . 140 140 149
MALHEUR . 103 103 75
MARION . 129 129 140
MORROW . 113 113 91
MULTNOMAH . 135 135 149
POLK . 122 122 111
SHERMAN . 102 102 92
TILLAMOOK. . 89 128 122
UMATILLA . 111 111 67
UNION . 102 102 82
WALLOWA . 97 97 70
WASCO . 110 110 '79
WASHINGTON . 108 114 85
WHEELER . 90 99 89
YAMHILL . 116 116 122
C-75
-------
STATE=PA
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ADAMS . 132 129 138
ALLEGHENY . 118 112 118
ARMSTRONG . 130 124 103
BEAVER . 106 103 81
BEDFORD . 126 130 108
BERKS . 137 131 108
BLAIR . 121 118 92
BRADFORD . 156 164 166
BUCKS . 142 142 138
BUTLER . 120 116 106
CAMBRIA . 114 111 98
CAMERON . 126 130 122
CARSON . 125 122 105
CENTRE . 141 141 154
CHESTER . 119 116 102
CLARION . 127 131 122
CLEARFIELD . 132 136 128
CLINTON . 134 138 128
COLUMBIA . 147 151 14-6
CRAUFORD . 174 170 166
CUMBERLAN-D . 146 153 178
DAUPHIN . 141 138 116
DELAUARE . 134 131 122
ELK . 141 145 151
ERIE . 145 141 131
FAYETTE . 145 149 151
FOREST . 146 150 148
FRANKLIN . 142 149 190
FULTON . 114 111 100
GREENE . 136 140 138
HUNTINGDON . 120 114 95
INDIANA . 153 157 163
JEFFERSON . 139 143 1,48
JUNIATA . 128 125 124
LACKAUANNA . 162 170 171
LANCASTER . 155 152 143
LAURENCE . 129 125 118
LEBANON . 151 145 125
LEHIGH . 148 155 180
LUZERNE . 146 154 158
LYCOMIMG . 141 149 146
MC KEAN . 136 140 132
C-76
-------
STATE=PA
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
MERCER . 140 136 130
MIFFLIN . 117 111 111+
MONROE . 144 152 163
MONTGOMERY . 142 142 138
MONT.OUR . 150 158 161
NORTHAMPTON . 146 146 138
NORTHUMBERLAND . 149 146 138
PERRY . 124 118 128
PHILADELPHIA . 123 120 102
PIKE . 154 162 163
POTTER . 132 140 132
SCHUYLKILL . 127 121 130
SNYDER . 134 131 130
SOMERSET . 141 149 163
SULLIVAN . 147 155 163
SUSQUEHANNA . 149 157 158
TIOGA . 136 144 143
UNION . 137 134 120
VENAHGO . 136 144 136
WARREN . 139 147 145
WASHINGTON . 138 146 143
WAYNE . 154 162 163
WESTMORELAND . 147 155 163
WYOMING . 149 157 166
YORK . 134 131 140
C-77
-------
STATE=RI
COUMTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
BRISTOL . 164 166 147
KENT . 186 179 192
NEWPORT . 112 144 139
PROVIDENCE . 166 180 177
WASHINGTON . 183 176 182
C-78
-------
STATE=SC
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ABBEVILLE . 142 148 148
AIKEN . 185 188 184
ALLENDALE . 193 194 194
ANDERSON . 142 148 148
3AMBERG . 198 194 189
3ARNWELL . 192 195 196
BEAUFORT . 152 163 149
BERKELEY . 186 182 186
CALHOUN . 192 188 201
CHARLESTON . 157 165 149
CHEROKEE . 120 126 124
CHESTER . 138 144 136
CHESTERFIELD . 173 176 188
CLARENDON . 197 193 189
COLLETON . 186 132 186
DARLINGTON . 194 190 189
DILLON . 195 191 189
DORCHESTER . 186 182 186
EDGEFIELD . 128 134 124
FAIRFIELD . 140 146 148
FLORENCE . 196 192 189
GEORGETOWN . 183 179 186
GREENVILLE . 147 153 148
GREENWOOD . 135 141 144
HAMPTON . 203 203 213
HORRY . 207 203 198
JASPER . 185 181 181
KERSHAW . 178 181 199
LANCASTER . 136 142 134
LAURENS . 142 148 148
LEE . 196 192 189
LEXINGTON . 167 170 176
MC CORMICK . 131 137 144
MARION . 195 191 189
MARLBORO . 194 190 189
NEWBERRY . 141 147 148
OCONEE . 141 147 130
OP.ANGEBURG . 196 192 189
PICKENS . 134 140 136
RICHLAND . 169 172 188
SALUDA . 105 111 106
SPARTANBURG . 141 147 148
C-79
-------
STATE=SC
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
SUHTER . 197 193 189
UNION . 129 135 132
WILLIAMSBURG . 196 192 19t
YORK . 131 137 132
C-80
-------
STATE=SD
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
AURORA . 96 96 90
BEADLE . 118 118 124
BENNETT . 116 116 109
BON HOHUE . 96 96 90
BROOKINGS . 134 134 175
BROUN . 115 115 114
3RULE . 82 82 85
BUFFALO . 97 97 97
BUTTE . 98 98 91
CAMPBELL . 130 130 156
CHARLES MIX . 95 95 82
CLARK . 99 99 101
CLAY . 118 118 142
CODINGTON . 110 110 94
CORSON . 102 102 93
CUSTER . 108 108 112
DAVISON . 114 114 110
DAY . 105 105 106
DEUEL . 103 103 101
DEUEY . 99 99 91
DOUGLAS . 96 96 82
EDMUNDS . 96 96 90
FALL RIVER . 104 104 93
FAULK . 92 92 90
GRANT . 101 101 77
GREGORY . 105 105 91
HAAKON . 93 93 91
HAMLIN . 112 112 104
HAND . 112 112 134
HANSON . 97 97 88
HARDING . 109 109 111
HUGHES . 90 90 .87
HUTCHINSON . 98 98 .90
HYDE . 105 105 90
JACKSON . 107 107 91
JERAULD . 101 101 87
JONES . 97 97 91
KINGSBURY . 92 92 90
LAKE . 99 99 87
LAURENCE . 115 115 114
LINCOLN . 98 98 104
LYMAN . 96 96 91
C-81
-------
STATE=SD
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
MC COOK . 90 90 88
MC PHERSON . 106 106 90
MARSHALL . 105 105 101
MEADE . 101 101 91
MELLETTE . 99 99 91
MINER . 117 117 134
MINNEHAHA . 99 99 75
MOODY . 100 100 77
PENNINGTON . 107 107 100
PERKINS . 103 103 101
POTTER . 92 92 82
ROBERTS . 98 98 94
SAMBORN . 120 120 124
SHANNON . 122 122 126
SPINK . 93 93 80
STANLEY . 99 99 91
SULLY . 92 92 85
TODD . 114 114 126
TRIPP . 104 104 91
TURNER . 100 100 80
UNION . 117 117 139
UALWORTH . 100 10.0 97
UASHABAUGH . 106 106 94
YANKTON . 123 123 152
ZIEBACH . 95 95 91
C-82
-------
STATE=TN
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ANDERSON . 145 139 138
BEDFORD 173 185 189 199
BENTON 132 142 141 148
BLEDSOE 140 148 145 150
BLOUNT . 144 138 128
BRADLEY . 133 127 132
CAMPBELL 134 143 140 148
CANNON 146 158 162 186
CARROLL 141 149 148 149
CARTER . 141 135 131
CHEATHAM 121 133 139 126
CHESTER 140 148 147 156
CLAI30RNE . 133 127 114
CLAY 125 137 143 131
COCKE . 121 118 106
COFFEE 129 141 147 141
CROCKETT 142 150 149 149
CUMBERLAND 137 149 146 142
DAVIDSON . 179 183 207
DECATUR 128 139 138 133
DE KALB 144 154 158 182
DICKSON 110 122 128 126
DYER . 145 144 141
FAYETTE 141 149 148 149
FEXTRESS 131 141 141 148
FRANKLIN 122 134 140 141
GIBSON 140 148 147 149
GILES 154 166 170 186
GRAINGER . 129 123 125
GREENE . 131 128 132
GRUNDY -137 149 146 140
HAMBLEN . 137 137 140
HAMILTON . 135 129 T32
HANCOCK . 127 121 119
HARDEMAN 136 144 143 134
HARDIN 136 147 146 138
HAWKINS . 139 133 127
HAYWOOD 142 150 149 149
HENDERSON 140 148 147 146
HENRY 140 148 147 146
HICKMAN 122 134 140 120
HOUSTON 111 123 129 126
C-83
-------
STATE=TN
COUNTY
HUMPHREYS
JACKSON
JEFFERSON
JOHNSON
KNOX
LAKE
LAUDERDALE
LAURENCE
LEWIS
LINCOLN
LOUDON
MC MINN
MC HAIRY
MACON
MADISON
MARION
MARSHALL
MAURY
MEIGS
MONROE
MONTGOMERY
MOORE
MORGAN
OBION
OVERTON
PERRY
PICKET7
POLK
PUTNAM
RHEA
ROANE
ROBERTSON
RUTHERFORD
SCOTT
SESUATCHIE
SEVIER
SHELBY
SMITH
STEWART
SULLIVAN
SUMNER
TIPTON
ADJUSTED
VARSCORE
120
145
121
1 17
149
1 15
138
139
178
165
19
63
37
31
21
19
129
1 19
183
136
135
1 16
148
VARSCORE
132
157
135
132
141
147
147
129
129
161
131
130
152
127
146
151
190
177
145
143
131
175
149
148
143
133
131
148
141
152
140
131
195
148
147
122
151
190
128
131
160
148
WEIGHTED
138
161
135
129
135
143
146
135
135
165
125
124
151
133
145
143
194
181
139
140
137
179
146
147
143
139
137
145
141
146
134
137
199
145
' 144
1 19
150
194
134
125
166
147
TOTAL
SCORE
126
186
128
126
140
145
149
144
130
176
132
130
146
126
149
146
187
192
137
134
141
136
142
149
152
130
138
140
120
140
133
138
202
146
142
112
153
186
126
120
138
149
C-84
-------
STATE=TN
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
TROUSDALE 183 195 199 20.4
UNICOI . 132 129 126
UNION . 130 121 120
VAN '3UREN 139 151 148 154
WARREN 119. 131 137 120
WASHINGTON . 150 144 145
WAYNE 119 131 137 136
WEAKLEY 137 145 144 146
WHITE 122 134 140 138
WILLIAMSON 151 163 167 192
WILSON 181 193 197 199
C-85
-------
STATE=TX
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ANDERSON 126 120 120 118
ANDREWS . 153 153 159
ANGELINA 132 124 121 123
ARANSAS 129 128 128 113
ARCHER . 112 112 108
ARMSTRONG . 130 110 98
ATASCOSA 125 115 115 113
AUSTIN 131 131 131 103
BAILEY . 133 133 150
BANDERA . 100 100 95
BASTROP 128 126 126 113
BAYLOR . 158 158 161
3ES 119 110 110 108
3ELL . 133 133 113
3EXAR 131 130 130 138
BLANCO . 123 123 98
30RDEN . 135 135 132
30S2UE . 118 118 123
BOWIE 158 154 134 115
BRAZORIA 119 111 111 93
BRAZOS 132 130 130 103
BREWSTER . 96 96 71
BRISCOE . 128 108 96
BROOKS 151 155 155 164
BROUN . 111 111 113
3URLESON 111 139 139 115
3URNET . 115 115 103
CALDWELL 126 123 123 113
CALHOUN 125 114 114 103
CALLAHAN . 131 131 133
CAMERON 130 1-2 8 128 129
CAMP 153 148 128 108
CARSON . 117 117 1.28
CASS 121 118 118 108
CASTRO . 120 120 124
CHAMBERS 132 131 131 110
CHEROKEE 134 130 130 148
CHILDRESS . 114 114 123
CLAY . 100 100 89
COCHRAN . 139 139 141
COKE . 114 114 103
COLEMAN . 122 122 110
C-86
-------
STATE=TX
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
COLLIN . 112 112 108
COLLINGSUORTH . 108 108 133
COLORADO 139 137 137 108
COMAL . 132 132 113
COMANCHE . 113 113 103
CONCHO . 120 120 98
COOKE . 133 113 103
CORYELL . 118 118 128
COTTLE . 143 137 118
CRANE . 123 123 113
CROCKETT . 110 110 108
CROSBY . 119 119 126
CULBERSON . 103 103 93
DALLAM . 35 105 93
DALLAS . 113 113 103
DAMSON . 150 150 171
DEAF SMITH . 121 121 124
DELTA . 116 116 108
DENTON . 119 119 118
DE MITT 134 133 133 115
DICKENS . 105 105 121
DIMMIT 117 111 111 103
DONLEY . 130 120 111
DUVAL 117 1.11 111 102
EASTLAND . 138 138 133
ECTOR . 126 126 119
EDWARDS . 112 112 103
ELLIS . 129 129 108
EL PASO . 135 135 137
ERATH . 116 116 109
FALLS 125 12.3 123 118
FANNIN . 118 118 103
FAYETTE 131 129 129 -113
FISHER . 132 132 134
FLOYD . 119 119 113
FOARD . 133 127 128
FORT 3EN.D 124 114 114 103
FRANKLIN . 133 108 98
FREESTONE 138 132 132 136
FRIO 133 136 136 116
GAINES . 140 140 151
GALVESTON 128 122 122 107
C-87
-------
STATE=TX
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
GARZA . 135 135 143
GILLSSPIE . 115 115 105
GLASSCOCK . 117 117 130
GOLIAD 120 112 112 108
GONZALES 129 125 125 113
GRAY . 123 123 119
GRAYSON . 1<48 128 108
GREGG 131 130 130 136
GRIMES 127 122 122 108
GUADALUPE 126 122 122 108
HALE . 114 114 114
HALL . 112 112 108
HAMILTON . 127 127 136
HANSFORD . 98 108 95
HARDEMAN . 110 110 123
HARDIN 122 116 116 110
HARRIS . 121 111 111 91
HARRISON 134 134 134 145
HARTLEY . 90 105 98
HASKELL . 160 160 160
HAYS . 132 132 110
HEMPHILL . 132 132 129
HENDERSON 137 138 138 141
HIDALGO 135 130 130 120
HILL . 128 . .128 124
HOCKLEY . 127 127 136
HOOD . 131 131 138
HOPKINS . 125 115 101
HOUSTON 129 127 127 121
HOWARD . 135 125 115
HUDSPETH . 105 105 105
HUNT . 122 122 108
HUTCHINSON . 143 123 fl6
IRION . 126 126 10.8
JACK . 119 119 118
JACKSON 118 109 109 101
JASPER 124 118 118 110
JEFF DAVIS . 91 91 65
JEFFERSON 132 131 131 110
JIM HOGG 135 134 134 150
JIM WELLS 120 112 112 110
JOHNSON . 126 126 108
C-88
-------
STATE=TX
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
JONES . 146 m6 160
KARNES 117 115 115 115
KAUFMAN . 125 125 118
KENDALL . 125 125 110
KENEDY 144 148 148 159
KENT . 133 133 163
KERR . 122 122 100
KIMBLE . 107 107 98
KING . 127 121 108
KINNEY . 125 125 108
KLZBERG 125 116 116 91
KNOX . 166 166 168
LAMAR . 125 125 108
LAMB . 125 125 129
LAMPASAS . 120 120 113
LA SALLE 129 124 124 103
LAVACA 128 125 125 108
LEE 124 119 119 113
LEON 134 133 133 143
LIBERTY 133 127 127 93
LIMESTONE 131 128 128 126
LIPSCOMB . 89 109 98
LIVE OAK 128 121 121 98
LLANO . 101 101 86
LOVING . 124 124 115
LUBBOCK . 128 128 132
LYNN . 145 145 164
MC CULLOCH . 101 101 81
MC LENNAN . 136 136 137
MC MULLEN 114 112 112 98
MADISON 120 112 112 108
MARION 116 109 109 108
MARTIN . 122 122 '125
MASON . 100 100 73
MATAGORDA 132 125 125 101
MAVERICK 133 130 130 123
MEDINA 131 132 132 115
MENARD . 115 115 98
MIDLAND . 128 128 132
MILAM 139 139 139 115
MILLS . 117 117 113
MITCHELL . 122 122 124
C-89
-------
STATE=TX
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
MOKTAGUE . 126 126 133
MONTGOMERY 119 116 116 110
MOORE . 128 128 1 1V
MORRIS 158 143 123 101
MOTLEY . 105 105 100
NACOGDOCHES 135 130 130 143
NAVARRO 125 124 121 133
NEUTON 124 118 118 110
NOLAN . 132 132 139
NUECES 127 118 118 93
OCHILTREE . 88 108 96
OLDHAM . 113 113 131
ORANGE 121 116 116 105
PALO PINTO . 117 117 108
PANOLA 155 156 156 164
PARKER . 115 115 108
PARKER . 118 118 124
PECOS . 107 107 93
POLK 139 139 139 133
POTTER . 123 123 112
PRESIDIO . 102 102 78
RAINS . 116 116 113
RANDALL . 113 113 129
REAGAN . 113 113 108
REAL . 114 114 108
RED RIVER . 133 133 123
REEVES . 133 133 149
REFUGIO 130 122 122 101
ROBERTS . 124 94 69
ROBERTSON 141 140 140 135
ROCKUALL . 130 130 118
RUNNELS . 116 116 110
RUSK 134 133 133 123
SABINE 144 144 144 141
SAN AUGUSTINE 140 140 140 121
SAN JACINTO 121 112 112 115
SAN PATRICIO 126 118 118 88
SAN SABA . 115 115 109
SCHLEICHER . 108 108 98
SCURRY . 122 122 121
SHACKELFORD . 115 115 103
SHELBY 145 143 143 156
C-90
-------
STATE=TX
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
SHERMAN . 81 101 100
SMITH 121 119 119 113
SOMERVELL . 132 132 125
STARR 125 120 120 98
STEPHENS . 108 -108 98
STERLING . 120 120 108
STONEWALL . 117 117 102
SUTTON . 109 109 103
SWISHER . 121 121 129
TARRANT . 119 119 113
TAYLOR . 139 139 130
TERRELL . 101 101 105
TERRY . 136 136 142
THROCKMORTON . 135 135 128
TITUS 138 '126 106 96
TOM GREEN . 127 127 119
TRAVIS . 123 123 110
TRINITY 119 115 115 121
TYLER 141 137 137 167
UPSHUR 122 Tl7 117 120
UPTON . 114 114 114
UVALDE . 122 122 113
VAL VERDE . 118 118 103
VAN ZANDT 124 122 122 118
VICTORIA 134 131 131 113
WALKER 123 115 115 108
WALLER 120 110 110 98
WARD . 129 129 134
WASHINGTON 126 124 124 108
WEBB 123 119 119 103
WHARTON 130 130 130 103
WHEELER . 149 149 141
WICHITA ' . 150 150 139
WILBARGER . 152 152 145
WILLACY 122 113 113 98
WILLIAMSON . 140 140 124
WILSON 131 126 126 113
WINKLER . 113 113 113
WISE . 123 123 118
WOOD 122 120 120 123
YOAKUM . 131 131 134
YOUNG . 106 106 103
C-91
-------
STATE=TX
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ZAPATA 136 133 133 110
ZAVALA 130 126 126 113
C-92
-------
STATE=UT
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
BEAVER 94 104 104 78
BOX ELDER 92 102 102 101
CACHE . 107 107 87
CARBON . 98 98 57
DAGGETT . 93 93 92
DAVIS . 135 135 157
DUCHESNE . 102 102 86
EMERY . 107 107 86
GARFIELD . 103 103 106
GRAND . 96 96 82
IRON 107 117 117 109
JUAB 92 102 102 74
KANE . 101 101 106
MILLARD 100 110 110 84
MORGAN . 93 93 86
PIUTE 84 94 94 64
RICH . 107 107 98
SALT LAKE . 134 134 157
SAN JUAN . 114 114 106
SANPETE . 107 107 87
SEVIER . 98 98 59
SUMMIT . 95 95 86
TOOELE 101 111 111 105
UINTAH . 89 89 71
UTAH . 125 125 102
UASATCH . 101 101 86
WASHINGTON 94 104 104 89
WAYNE . 106 106 86
WEBER . 123 123 90
C-93
-------
STATE=VA
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ACCOMACK . 185 185 189
AL3EMARLE 147 127 124 125
ALLEGHANY 133 113 111 100
AMELIA 133 125 131 131
AMHERST 130 110 116 107
APPOMATTOX 132 124 130 131
ARLINGTOK . 131 137 126
AUGUSTA 154 134 134 134
BATH 126 106 108 100
BEDFORD 132 124 130 131
BLAND 144 124 122 100
BOTETOURT 149 129 129 128
BRUNSWICK 132 124 130 131
BUCHANAN . 130 127 125
BUCKINGHAM 131 123 129 131
CAMPBELL 130 122 128 131
CAROLINE . 164 167 176
CARROLL 1.46 126 123 125
CHARLES CITY . 175 178 179
CHARLOTTE 132 124 130 131
CHESTERFIELD 149 141 135 131
CLARKE 178 170 170 179
CRAIG 135 115 113 100
CULPEPER 128 120 126 131
CUMBERLAND . 124 130 131
DICKZNSON . 130 127 125
DINWIDDIE 143 138-' 144 131
ESSEX . 167 170 168
FAIRFAX 137 129 135 131
FAU2UIER 131 123 129 131
FLOYD 148 128 125 125
FLUVANNA 134 126 132 131
FRANKLIN 129 109 115 107
FREDERICK 144 124 122 106
GILES 148 128 126 106
GLOUCESTER . 181 184 184
GOOCHLAND . 126 132 131
GRAYSON 147 127 124 125
GREENE 152 144 141 137
GREENSVILLE . 157 160 179
HALIFAX 128 120 126 131
HANOVER 157 152 155 168
C-94
-------
STATE=VA
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
HENRICO . 161 164 171
HENRY 126 118 124 119
HIGHLAND 137 117 115 100
ISLE OF WIGHT . 179 182 184
JAMES CITY . 172 175 176-
KING AND 2UEEN . 175 178 184
KING GEORGE . 174 177 181
KING UILLIAM . 173 176 179
LANCASTER . 181 184 184
LEE 146 138 136 112
LOUDOUN 141 133 139 141
LOUISA 133 125 131 131
LUNENBERG 133 125 131 131
MADISON 138 130 136 119
MATHEUS . 189 189 189
MECKLENBURG 130 122 128 131
MIDDLESEX . 180 183 184
MONTGOMERY 146 126 126 128
NELSON 144 124 121 125
NEW KENT . 186 186 186
NORTHAMPTON . 187 187 189
NORTHUMBERLAND . 180 183 181
NOTTOUAY 133 125 131 131
ORANGE 131 123 129 131
PAGE 163 155 155 158
PATRICK 133 125 131 119
PITTSYLVANIA 127 119 125 131
POWHATAN 135 127 133 131
PRINCE EDWARD 132 124 130 131
PRINCE GEORGE . 174 177 179
PRINCE WILLIAM 130 111 111 108
PULASKI 145 125 125 134
RAPPAHANNOCK 137 129 135 131
RICHMOND 177 176 179 181
ROANOKE 146 126 123 119
ROCKBRIDGE 163 143 143 152
ROCKINGHAM 154 134 134 128
RUSSELL 152 132 129 112
SCOTT 140 120 118 95
SHENANDOAH 149 129 127 106
SMYTH 141 121 119 95
SOUTHAMPTON 173 176 179
C-95
-------
STATE=VA
COUNTY
SPOTSYLVANIA
STAFFORD
SURRY
SUSSEX
TAZEUELL
UARREX
WASHINGTON
WESTMORELAND
WISE
WYTHE
YORK
ALEXANDRIA CITY
BEDFORD CITY
BRISTOL CITY
BUSNA VISTA CITY
CHARLOTTESVILLS CITY
CHESAPEAKE CITY
CLIFTON FORGE CITY
COLONIAL HEIGHTS GIT
COVINGTON CITY
DANVILLE CITY
EMPORIA CITY
FAIRFAX CITY
FALLS CHURCH CITY
FF.ANXLIN CITY
FREDERICKS3URG CITY
GALAX CITY
HAMPTON CITY
HARRISON3URG CITY
HOPEWELL CITY
LEXINGTON CITY
LYNCHBURG CITY
MANASSAS
MANASSAS PARK
MARTINSVILLE CITY
NEWPORT NEWS CITY
NORFOLK CITY
NORTON CITY
PETERSBURG CITY
P02UDSON
PORTSMOUTH CITY
RADFORD CITY
ADJUSTED
VARSCORE
136
147
163
145
146
VARSCORE
128
137
177
170
127
155
125
176
130
126
183
148
1 10
163
161
1 1 1
130
106
137
123
1 10
162
108
108
167
137
139
178
162
162
163
107
106
106
1 10
171
175
130
161
175
175
162
WEIGHTED
134
143
180
173
124
152
125
179
127
126
186
151
1 16
163
161
1 17
180
104
140
121
1 16
165
1 14
1 14
170
140
136
173
162
165
163
1 13
106
106
1 16
174
175
128
164
175
175
162
TOTAL
SCORE
131
136
179
179
112
137
128
184
125
134
184
156
122
167
167
122
189
106
159
121
122
171
1 19
1 19.
176
156
140
181
167
171
167
1 19
tl 1
1 1 1
122
176
181
133
171
181
181
167
C-96
-------
STATE=VA
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
RICHMOND CITY . 121 127 122
ROANOKE CITY . 151 148 140
SALEM CITY . 138 135 110
SOUTH BOSTON CITY . 105 111 117
STAUNTON CITY . 161 161 167
SUFFOLK CITY . 180 180 189
VIRGINIA BEACH CITY . 177 177 189
MAYNES30RO CITY . 162 162 167
UILLIAMSBURG CITY . 167 170 176
WINCHESTER CITY . 162 162 167
C-97
-------
STATE=VT
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ADDISON . 111 1<43 147
BENNINGTON . 137 139 122
CALEDONIA . 119 151 144
CHITTENDEN . 152 154 144
ESSEX . 154 156 144
FRANKLIN . 150 152 144
GRAND ISLE . 152 154 149
LAMOILLE . 149 151 144
ORANGE . 134 136 132
ORLEANS . 135 137 122
RUTLAND . 131 133 116
WASHINGTON . 146 148 144
WINDHAM . 147 149 144
WINDSOR . 133 135 126
C-98
-------
STATE=WA
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ADAnS . 142 142 147
ASOTIN . 87 108 97
BENTON . 141 141 134
CHELAN . 92 92 71
CLALLAM . 109 109 116
CLARK . 133 133 175
COLUMBIA . 90 111 117
COULITZ . 93 93 83
DOUGLAS . 143 143 148
FERRY . 99 99 81
FRANKLIN . 135 135 132
GARFIELD . 81 102 97
GRANT . 141 141 138
GRAYS HARBOR . 109 109 116
ISLAND . 127 127 132
JEFFERSON . 109 109 92
KIHG . 112 112 130
KITSAP . 151 151 166
KITTITAS . 100 121 107
KLICKITAT . 104 125 123
LE'-JIS . 109 109 73
LINCOLN . 112 121 99
MASON . 114 114 132
OKANOGAN . 101 101 83
PACIFIC . 111 111 107
PEND OREILLE . 111 111 134
PIERCE . 139 139 154
SAN JUAN . 1 1'4 114 123
SKAGIT . 93 93 73
SKAMANIA . 87 87 77
SNOHOMISH . 102 102 82
SPOKANE . 139 139 133
STEVENS . 101 101 '88
THURSTON . 141 141 166
WAHKIAKUH . 110 110 114
WALLA WALLA . 124 124 138
MHATCOM . 92 92 88
WHITMAN . 123 123 138
YAKIMA . 120 120 117
C-99
-------
STATE=UI
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ADAMS . 179 183 187
ASHLAND '. 153 149 151
3ARROX . 157 153 141
BAYFISLD . mi 141 129
BROUN . 132 132 124
BUFFALO . 9O 86 76
BURNETT . 183 191 204
CALUMET . 130 130 124
CHIPPEWA . 154 150 151
CLARK . 129 125 129
COLUMBIA . 131 127 124
CRAWFORD . 36 82 76
DAKS . 113 109 114
DODGE . 139 -135 136
DOOR . 120 123 123
DOUGLAS . 160 156 141
DUNN . 110 106 106
EAU CLAIRE . 116 112 111
FLORENCE . 152 160 159
FOND DU LAC . 123 119 114
FOREST . 150 158 159
GRANT . 82 78 76
GREEN . 87 83 76
GREEN LAKE . 137 133 136
IOUA . 82 78 76
IRON . 159 167 179
JACKSON . 96 92 91
JEFFERSON . 139 135 136
JUNEAU . 162 162 184
KENOSHA . 134 130 129
KEUAUNEE . 136 132 124
LA CROSSE . 39 35 76
LAFAYETTE . 83 79 76
LANGLADE . 158 166 179
LINCOLN . 162 170 174
MANITOWOC . 132 128 124
MARATHON . 161 161 161
MARINETTE . 157 165 179
MAR2UETTE - . 159 163 179
MENOMINEE . 163 171 179
MILWAUKEE . 123 119 114
MONROE . 104 100 86
C-99A
-------
STATE=WI
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
OCONTO . 159 155 151
ONEIDA . 184 192 202
OUTAGAMIE . 139 139 129
OZAUKEE . 109 105 106
PEPIN . 111 107 108
PIERCE . 99 95 96
POLK . 156 164 159
PORTAGE . 168 172 179
PRICE . 155 151 146
RACINE . 145 141 136
RICHLAND . 81 77 76
ROCK . 141 137 136
RUSK . 154 150 141
ST CROIX . 114 122 127
SAUK . 89 85 76
SAUYER . 158 154 151
SHAUANO . 150 158 179
SHE30YGAN . 133 129 124
TAYLOR . 162 158 146
TREMPEALZAU . 92 88 88
VEP.XON . 80 76 76
VILAS . 179 187 187
UALUORTH . 122 126 126
UASHBURN . 169 177 179
WASHINGTON . 125 121 126
UAUKESHA . 129 137 154
WAUPACA . 149 157 179
UAUSHARA . 160 164 179
UIXNE3AGO . 135 135 129
MOOD . 158 158 166
C-100
-------
STATE=WV
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
3ARBOUR . 119 115 106
BERKELEY . 150 140 112
BOONE . 92 88 80
BRAXTON . 108 104 92
BROOKE . 88 84 80
CABELL . 96 92 80
CALHOUN . 101 97 92
CLAY . 112 108 105
DODDRIDGE . 100 96 92
FAYETTE . 117 113 106
GILMER . 102 98 92
GRANT . 126 116 100
GREENBRIER . 158 162 168
HAMPSHIRE . 128 118 108
HANCOCK . 110 106 98
HARDY . 133 123 117
HARRISON . -113 109 106
JACKSON . 95 91 77
JEFFERSON . 153 147 147
KANAUHA . 120 116 105
LEWIS . 113 109 106
LINCOLN . 103 99 85
LOGAH . 89 85 74
MC DOWELL . 93 89 85
MARION . 114 110 100
MARSHALL . 92 88 80
MASON . 107 103 98
MERGER . 117 113 106
MINERAL . 130 120 117
MINGO . 88 84 80
MONONGALIA . 119 115 120
MONROE . 169 173 193
MORGAN . 134 124 ,V1 3
NICHOLAS . 119 115 .106
OHIO . 115 111 92
PENDLETON . 135 125 107
PLEASANTS . 119 115 98
POCAHONTAS . 143 147 162
PRESTON . 139 129 108
PUTNAM . 106 102 92
RALEIGH . 119 115 105
RANDOLPH . 145 135 102
C-101
-------
STATE=WV
COUNTY ADJUSTED VARSCORE . WEIGHTED TOTAL
VARSCORE SCORE
RITCHIE . 122 118 106
ROANE . 101 97 92
SUMMERS . 107 103 91
TAYLOR . 119 115 108
TUCKER . 141 131 102
TYLER' . 96 92 80
UPSHUR . 120 116 106
WAYNE . 98 94 85
WEBSTER . 106 102 92
WETZEL . 101 97 80
WIRT . 110 106 92
WOOD . 108 104 97
WYOMING . 98 94 85
C-102'
-------
STATE=WY
COUNTY ADJUSTED VARSCORE WEIGHTED TOTAL
VARSCORE SCORE
ALBANY . 99 99 96
BIG HORN . 103 103 81
CAMPBELL . 99 99 38
CARSON . 113 118 130
CONVERSE . 108 108 93
CROOK . 103 103 83
FREMONT . 111 111 81
GOSHEN . 117 117 111
HOT SPRINGS . 109 109 81
JOHNSON . 101 101 83
LAP.AMIE . 112 112 101
LINCOLN . 110 110 91
NATP.ONA . 100 100 105
NIOBRAP.A . 105 105 93
PARK . 124 124 107
PLAITS . 117 117 117
SHERIDAN . 95 95 76
SUBLETTE . 104 104 96
SUEETWATER . 110 110 110
TETON . 120 120 107
UINTA . 113 113 98
WASHAKIE . 100 100 105
WESTON . 98 98 93
C-103
-------
APPENDIX-D
LISTING OF ADJUSTED VARSCORES IN ASCENDING ORDER
-------
EXPLANATION
Numerical scores calculated from the data set are differentiated
as follows:
TOTAL SCORE - An unweighted score corresponding to the
summation of the highest percentage ratings for the seven
DRASTIC factors. (Refer to Section 3 in Appendix A).
WEIGHTED SCORE - A weighted score corresponding to the summation
of all percentage ratings for the seven DRASTIC factors. The
weighted score accounts for the variability that occurs in
areas as large as counties. In counties with little
variability, little difference exists between the total score
and weighted score.
VARSCORE - Represents the weighted score including (+ or -) the
index of variability, where used. The index of variability and
its intended usage, is defined in Section 3.6 of Appendix A.
The VARSCORE, or the adjusted VARSCORE further defined in
Section 3.7 is considered to be the most appropriate score for
the purposes of this project in that it best accounts for
.intracounty variability.
The values shown in this listing to Appendix D include adjusted
VARSCORES, when adjusted, or originally coded VARSCORES if no adjust-
ments were needed.
D-l
-------
OBS STATE COUNTY VARSCORE
1 MO AUDRAIN 69
2 AK OUTER KETCHIKAN 70
3 AK ANGOON 71
1 AK ALEUTIAN ISLANDS 75
5 ID LATAH 77
6 IL KNOX 77
7 AK HAINES 78
8 AK SKAGUAY-YAKUTAT 78
9 MO MADISON 78
10 IL STARK 79
11 IL HENRY 80
12 IL MC DONOUGH 80
13 MO BARRY 80
11 MO IRON 80
15 WI VERNON 80
16 AK KETCHIKAN 81
17 ID NEZ PERCE 81
18 TX SHERnAN 81
19 WA GARFIELD 81
20 UI RICHLAND 81
21 IL HACOUPIN 82
22 SD BRULE 82
23 UI GRANT 82
21 UI IOUA 82
25 UI LAFAYETTE 83
26 ID SHOSHONE 81
27 IL UARREN 84
28 OR JOSEPHINE 81
29 UT PIUTE 81
30 AK UPPER YUKON 85
31 IL BOND 85
32 IL LA SALLE 85
33 IL nONTGOMERY 85
31 MO RALLS 85
35 TX DALLAtt 85
36 AK BARROU 86
37 AR BOONE 86
38 ID BOISE 86
39 IL CLAY 86
10 IL CUnBERLAND 86
11 IL EFFINGHAn 86
42 IL ttARION 86
13 IL PERRY 86
11 IL SANGAnON 86
15 KA REPUBLIC 86
16 OH PAULDING 86
17 UI CRAUFORD 86
18 AK KOBUK 87
19 AK SEUARD 87
50 AR UASHINGTON 87
51 ID OUYHEE 87
52 ID TUIN FALLS 87
53 IL CHRISTIAN 87
51 IL RICHLAND 87
D-2
-------
OBS STATE COUNTY VARSCORE
55 IL SHELBY 87
56 IL WAYNE 87
57 IN OHIO 87
58 MO CARTER 87
59 MO MONTGOMERY 87
60 MO TANEY 87
61 UA ASOTIN 87
62 UA SKAMANIA 87
63 MI GREEN 87
64 CA AMADOR 88
65 IL JASPER 88
66 IL JEFFERSON 88
67 MO PIKE 88
68 TX OCHILTREE 88
69 MV BROOKE 88
70 MV MINGO 88
71 AZ GILA 89
72 AR CARROLL 89
73 CA ALPINE 89
71 ID BENEMAH 89
75 ID BOUNDARY 89
76 ID IDAHO 89
77 IN SWITZERLAND 89
78 MO CALLAUAY 89
79 MO FRANKLIN 89
80 MO GASCONADE 89
81 MO MARIES 89
82 OH PUTNAM 89
83 OH MOOD 89
81 OR CURRY 89
85 OR GRANT 89
86 OR TILLAMOOK 89
87 TX LIPSCOMB 89
88 UT UINTAH 89
89 MV LOGAN 89
90 MI LA CROSSE 89
91 MI SAUK 89
92 AR NEWTON 90
93 IL FRANKLIN 90
91 KA JEMELL 90
95 MO CASS 90
96 MO PETTIS 90
97 NV STOREY 90
98 OR MHEELER 90
99 SD HUGHES 90
100 SD MC COOK 90
101 TX HARTLEY 90
102 MA COLUMBIA 90
103 MI BUFFALO 90
104 AZ PIMA 91
105 IL OGLE 91
106 IL STEPHENSON 91
107 MS OKTIBBEHA 91
108 MO CLARK 91
D-3
-------
OBS STATE COUNTY VARSCORE
109 MO LAFAYETTE 91
110 MO LEWIS 91
111 OR COOS 91
112 TX JEFF DAVIS 91
113 AZ SANTA CRUZ 92
114 CA SIERRA 92
115 CA TRINITY 92
116 KA ATCHISON 92
117 HO DADE 92
118 no JOHNSON 92
119 no nONITEAU 92
120 no OSAGE 92
121 HO SHANNON 92
122 HO STONE 92
123 SD FAULK 92
124 SD KINGSBURY 92
125 SD POTTER 92
126 SD SULLY 92
127 UT BOX ELDER 92
128 UT JUAB 92
129 MA CHELAN 92
130 UA WHATCOn 92
131 MV BOONE 92
132 UV HARSHALL 92
133 WI TREHPEALEAU 92
134 AK JUNEAU 93
135 AK SITKA 93
136 AZ COCHISE 93
137 ID CUSTER 93
138 ID V-ALLEY 93
139 HO LACLEDE 93
140 HO HARION 93
141 HO OZARK 93
142 HO ST CLAIR 93
143 HO WARREN 93
144 SD HAAKON 93
145 SD SPINK 93
146 UT DAGGETT 93
147 UT HORGAN 93
148 WA COWLITZ 93
149 WA SKAGIT 93
150 WV HC DOWELL 93
151 AK WRANGELL-PETERSBURG 94
152 AZ APACHE 94
153 AR BAXTER 94
154 AR POPE 94
155 CA TUOLUHNE 94
156 IL EDGAR 94
157 HO HOWARD 94
158 HO JEFFERSON 94
159 UT BEAVER 94
160 UT WASHINGTON 94
161 AK PRINCE OF WALES 95
162 AZ GRAHAH 95
D-4
-------
OBS STATE COUNTY VARSCORE
163 CA EL DORADO 95
164 MO ATCHISON 95
165 MO CAMDEN 95
166 MO CEDAR 95
167 MO COLE 95
168 MO LAMREKCE 95
169 MO OREGON 95
170 MO REYNOLDS 95
171 MO VERNON 95
172 OR DESCHUTES 95
173 OR JEFFERSON 95
174 SD CHARLES MIX 95
175 SD ZIEBACH 95
176 UT SUMMIT 95
177 MV JACKSON 95
178 UY SHERIDAN 95
179 AZ NAVAJO 96
180 AZ YAVAPAI 96
181 AR MADISON 96
182 CO CLEAR CREEK 96
183 ID ELMORE 96
184 IL FULTON 96
185 MO MONROE 96
186 MO RAY 96
187 OH MARION 96
188 OH UNION 96
189 SD AURORA 96
190 SD BON HOMME 96
191 SD DOUGLAS 96
192 SD EDMUNDS 96
193 SD LYMAN 96
194 TX BREMSTER 96
195 UT GRAND 96
196 MV CABELL 96
197 MV TYLER 96
198 MI JACKSON 96
199 AK KENAI-COOK INLET 97
200 AZ YUMA 97
201 AR BENTON 97
202 AR VAN BUREN 97
203 CA CALAVERAS 97
204 CA MARIPOSA 97
205 CA PLACER 97
206 CA SAN DIEGO 97
207 IL PEORIA 97
208 IA MRIGHT 97
209 KA JOHNSON 97
210 MI GOGEBIC 97
211 MS CALHOUN 97
212 MO CARROLL 97
213 MO CHARITON 97
214 MO KNOX 97
215 OH SANDUSKY 97
21.6 OR UALLOMA 97
D-5
-------
OBS STATE COUNTY VARSCORE
217 SD BUFFALO 97
218 SD HANSON 97
219 SD JONES 97
220 AR CLEBURNE 98
221 CO MONTEZUMA 98
222 ID CLEARUATER 98
223 ID LEWIS 98
224 IL RANDOLPH 98
225 IA HUMBOLDT 98
226 MN ROCK 98
227 MO BATES 98
228 HO CRAWFORD 98
229 MO SHELBY 98
230 OR CROOK 98
231 SD BUTTE 98
232 SD HUTCHINSON 98
233 SD LINCOLN 98
234 SD ROBERTS 98
235 TX HANSFORD 98
236 UT CARBON 98
237 UT SEVIER 98
238 WV WAYNE 98
239 MV WYOMING 98
240 UY UESTON 98
241 AK KUSKOKWIM 99
242 AK YUKON-KOYUKUK 99
243 AZ FINAL 99
244 CA LASSEN 99
245 IL SCHUYLER 99
246 MO COOPER . 99
247 NM SAN MIGUEL 99
248 OH HARDIN 99
249 OH OTTAWA 99
250 OR BAKER 99
251 SD CLARK 99
252 SD DEWEY 99
253 SD LAKE 99
254 SD MELLETTE 99
255 SD MINNEHAHA 99
256 SD STANLEY 99
257 WA FERRY 99
258 WI PIERCE 99
259 WY ALBANY 99
260 WY CAMPBELL 99
261 AZ MARICOPA 100
262 CA SHASTA 100
263 KA NEMAHA 100
264 MS CLAY 100
265 MO BENTON 100
266 MO JACKSON 100
267 MO MC DONALD 100
268 MO MORGAN 100
269 MO WASHINGTON 100
270 OH VAN WERT 100
D-6
-------
OBS STATE COUNTY VARSCORE
271 SD MOODY 100
272 SD TURNER 100
273 SD UALUORTH 100
274 TX BANDERA 100
275 TX CLAY 100
276 TX MASON 100
277 UT MILLARD 100
278 WA KITTITAS 100
279 UV DODDRIDGE 100
280 MY NATRONA 100
281 MY MASHAKIE 100
282 AK VALDEZ-CHITINA-MHITT 101
283 CO DOLORES 101
284 ID LEMHI 101
285 IL BROUN 101
286 IL COLES 101
287 IL GREENE 101
288 MI MAR2UETTE 101
289 MO BARTON 101
290 MO BUCHANAN 101
291 MO RANDOLPH 101
292 MO ST FRANCOIS 101
293 NE DAMES 101
294 OH HARRISON 101
295 SD GRANT 101
296 SD JERAULD 101
297 SD MEADE 101
298 TX LLAHO 101
299 TX MC CULLOCH 101
300 TX TERRELL 101
301 UT KANE 101
302 UT TOOELE 101
303 UT MASATCH 101
304 MA OKANOGAN 101
305 MA STEVENS 101
306 MV CALHOUN 101
307 MV ROANE 101
308 MV METZEL 101
309 MY JOHNSON 101
310 CA MONTEREY 102
311 IL MILL 102
312 IA CHEROKEE 102
313 LA VERMILION 102
314 MI IRON 102
315 MN TRAVERSE 102
316 MO ADAIR 102
317 MO CLAY 102
318 MO MACON 102
319 MO PULASKI 102
320 NE JEFFERSON 102
321 ND EMMONS 102
322 ND OLIVER 102
323 OR SHERMAN 102
324 OR UNION 102
D-7
-------
OBS STATE COUMTY VARSCORE
325 SD 'CORSON 102
326 TX PRESIDIO 102
327 UT DUCHESKE 102
328 UA SNOHOMISH 102
329 WV GILMER 102
330 AZ COCONIKO 103
331 AR MARION 103
332 CA HUMBOLDT 103
333 ID ADAMS 103
334 ID BONNER 103
335 ID FRANKLIN 103
336 IL KENDALL 103
337 IL ROCK ISLAND 103
338 LA IBERIA 103
339 LA ST JAMES 103
340 LA TERREBONNE 103
341 MO ANDREW 103
342 MO BOLLINGER 103
343 MO PLATTE 103
344 MT CARTER 103
345 NE FRONTIER 103
346 OH HANCOCK 103
347 OH LOGAN 103
348 OH WYANDOT 103
349 OR MALHEUR 103
350 SD DEUEL 103
351 SD PERKINS 103
352 TX CULBERSON 103
353 UT GARFIELD 103
354 WV LINCOLN 103
355 WY BIG HORN 103
356 WY CROOK 103
357 IL DU PAGE 104
358 IL MOULTRIE 104
359 IN JEFFERSON 104
360 IA LYON 104
361 IA UINNEBAGO 104
362 LA CAMERON 104
363 MS WEBSTER 104
364 MO DOUGLAS 104
365 MO NEWTON 104
366 MO SALINE 104
367 NE HITCHCOCK 104
368 NE RED WILLOW 104
369 NE THAYER 104
370 ND SIOUX 104
371 OH ALLEN 104
372 OH AUGLAIZE 104
373 OH MERCER 104
374 OH SENECA 104
375 OH SHELBY 104
376 OR DOUGLAS 104
377 SD FALL RIVER 104
378 SD TRIPP 104
D-8
-------
OBS STATE COUNTY VARSCORE
379 UA KLICKITAT 104
380 UI MONROE 104
381 MY SUBLETTE 104
382 AK CORDOVA-nC CARTHY 105
383 CO TELLER 105
384 IL GRUNDY 105
385 IL HAMILTON 105
386 IL HANCOCK 105.
387 IL JERSEY 105
388 IL MERCER 105
389 IL ST CLAIR 105
390 IN RIPLEY 105
391 KA LINN 105
392 MS NOXUBEE 105
393 MS PONTOTOC 105
394 MS WINSTON 105
395 MO HENRY 105
396 MO HOLT 105
397 MO SCHUYLER 105
398 MO SULLIVAN 105
399 NE FURNAS 105
400 NE HARLAN 105
401 ND ' MERCER 105
402 SC SALUOA 105
403 SD DAY 105
404 SD GREGORY 105
405 SD HYDE 105
406 SD MARSHALL 105
407 TX DICKENS 105
408 TX HUDSPETH 105
409 TX MOTLEY 105
410 VA SOUTH BOSTON CITY 105
411 UY NIOBRARA 105
412 AZ MOHAVE 106
413 CA MENDOCINO 106
414 CO HUERFANO 106
415 ID CLARK 106
416 IL MADISON 106
417 IN DEARBORN 106
418 IN HUNTINGTON 106
419 IA FRANKLIN 106
420 IA HANCOCK 106
421 KA LEAVENUORTH 106
422 LA PLA2UEMINES 106
423 LA ST BERNARD 106
424 MI LENAUEE 106
425 MI ONTONAGON 106
426 MN PIPESTONE 106
427 MN UASECA 106
428 MO DAVIESS 106
429 MO WAYNE 106
430 NE GAGE 106
431 ND MC KENZIE 106
432 OH FAYETTE 106
D-9
-------
OBS STATE COUNTY VARSCORE
433 OH MADISON 106
431 PA BEAVER 106
435 SD MC PHERSON 106
436 SO WASHABAUGH 106
437 TX YOUNG 106
438 UT WAYNE 106
439 VA CLIFTON FORGE CITY 106
440 VA MANASSAS 106
441 VA MANASSAS PARK 106
442 MV PUTNAM 106
443 UV WEBSTER 106
444 AK KODIAK 107
445 AZ GREENLEE 107
446 AR JOHNSON 107
447 AR SEARCY 107
448 CA DEL NORTE 107
449 CA NEVADA 107
450 CO CROWLEY 107
451 CO GILPIN 107
452 IL ADAMS 107
453 IL CLARK 107
454 IL CLINTON 107
455 IL FAYETTE 107
456 IL WASHINGTON 107
457 IL WHITESIDE 107
458 IL WILLIAMSON 107
459 IN JENNINGS 107
460 IN MIAMI 107
461 IA MILLS 107
462 IA 0 BRIEN 107
463 KA WYANDOTTE 107
464 LA LAFOURCHE 107
465 MO PUTNAM 107
466 MO WORTH 107
467 MT FERGUS 107
468 NE CHEYENNE 107
469 NE CLAY 107
470 NE KIMBALL 107
471 NM GUADALUPE 107
472 NM RIO ARRIBA 107
473 OH GUERNSEY 107
474 OR JACKSON 107
"475 OR KLAMATH 107
476 SD JACKSON 107
477 SD PENNINGTON 107
478 TX KIMBLE 107
479 TX PECOS 107
480 UT CACHE 107
481 UT EMERY 107
482 UT IRON 107
483 UT RICH 107
484 UT SANPETE 107
485 VA LYNCHBURG CITY 107
486 WV MASON 107
D-10
-------
OBS STATE COUNTY VARSCORE
487 UV SUMMERS 107
488 CO RIO BLANCO 108
489 ID BANNOCK 108
490 IL CALHOUN 108
491 IL MORGAN 108
492 IL SALINE 108
493 IL SCOTT 108
494 IN CRAWFORD 108
495 IN ORANGE 108
496 IA PLYMOUTH 108
497 IA POCAHONTAS 108
498 IA POTTAUATTAMIE 108
499 KA BOURBON 108
500 KA BROUN 108
501 KA CRAWFORD 108
502 KA SCOTT 108
503 LA ORLEANS 108
504 MN MAHNOMEN 108
505 MS CHICKASAW 108
506 MO DALLAS 108
507 MO DE KALB 108
508 MO GRUNDY 108
509 MO NODAWAY 108
0 MO STE GENEVIEVE 108
1 NE DEUEL 108
2 NE GOSPER 108
3 NE MADISON 108
4 NE SIOUX 108
5 NE WAYNE 108
6 ND MOUNTRAIL 108
7 OH CLERMONT 108
8 OK MARSHALL 108
9 OR WASHINGTON 108
520 SD CUSTER 108
521 TX COLLINGSWORTH 108
522 TX SCHLEICHER 108
523 TX STEPHENS 108
524 VA FAIRFAX CITY 108
525 VA FALLS CHURCH CITY 108
526 WV BRAXTON 108
527 WV WOOD 108
528 WY CONVERSE 108
529 AK SOUTHEAST FAIRBANKS 109
530 AR CRAWFORD 109
531 CA SAN BENITO 109
532 CA SAN LUIS OBISPO 109
533 CA SANTA BARBARA 109
534 ID ONEIDA 109
535 IL CASS 109
536 IN BLACKFORD 109
537 IN DE KALB 109
538 IN FRANKLIN 109
539 IA BUENA VISTA 109
540 KA DONIPHAN 109
D-ll
-------
OBS STATE COUNTY VARSCORE
541 KA LYON 109
542 LA ST CHARLES 109
543 UN CLEARWATER 109
544 MN KOOCHICHING 109
545 MO JASPER 109
546 NE BANNER 109
547 NE NUCKOLLS 109
548 NE PAWNEE 109
549 NE PERKINS 109
550 NM MC KINLEY 109
551 NY ESSEX 109
552 ND BURKE 109
553 ND DUNN 109
554 OR LAKE 109
555 SD HARDING 109
556 TX SUTTON 109
557 MA CLALLAM 109
558 MA GRAYS HARBOR 109
559 MA JEFFERSON 109
560 MA LEWIS 109
561 HI OZAUKEE 109
562 MY HOT SPRINGS 109
563 AK ANCHORAGE 110
564 AK NOME 110
565 CA LAKE 110
566 CA ORANGE 110
567 CA VENTURA 110
568 CO KIT CARSON 110
569 CO LAKE 110
570 ID FREMONT 110
571 IL MABASH 110
572 IN ADAMS 110
573 IN ALLEN 110
574 IA SIOUX 1 TO
575 MI- BARAGA 1 10
576 MN PENNINGTON 110
577 MN POLK 110
578 MS CHOCTAM 110
579 MS MADISON 110
580 MS TIPPAH 110
581 MO MERCER 110
582 NE- CUMING 1 10
583 NE CUSTER 1 10
584 NE HAYES 1 10
585 NE JOHNSON 1 10
586 NV ORHSBY 1 10
587 ND RENVILLE 1 10
588 OH BROMN 1 10
589 OK PITTSBURG 110
590 OK POTTAMATOMIE 110
591 OR MASCO 110
592 SD CODINGTON 110
593 TN DICKSON 110
594 TX CROCKETT 110
0-12
-------
OBS STATE COUNTY VARSCORE
595 TX HARDEMAN 1 10
596 VA BEDFORD CITY 110
597 VA DANVILLE CITY 110
598 VA MARTINSVILLE CITY 110
599 MA WAHKIAKUM 110
600 WV HANCOCK 110
601 WV MIRT 110
602 UI DUNN 110
603 MY LINCOLN 110
604 WY SMEETWATER 110
605 AR FRANKLIN 111
606 AR SCOTT 111
607 CA INYO 111
608 CO CHEYENNE 111
609 CO FREMONT 111
610 CO GRAND 111
6 t 1 CO LA PLATA 1 1 1
612 IL EDWARDS 111
613 IA CLAY 1 1 1
614 IA KOSSUTH 1 1 1
615 IA OSCEOLA 111
616 KA ANDERSON 111
617 KA GREELEY 111
618 KA JEFFERSON 111
619 KA OTTAWA 111
620 LA ASSUMPTION 111
621 MN WILKIN 11 1
622 MS KEMPER 111
623 MO CALDWELL 111
624 MO HICKORY 111
625 MO MILLER 111
626 MO SCOTLAND 1 1 1
627 NE DAWSON 1 1 1
628 NE FILLMORE 111
629 NE OTOE 111
630 NE POLK 111
631 NE SALINE 111
632 NE STANTON 111
633 NE YORK 1 11
634 NY NEW YORK 1 1 1
635 NY RICHMOND 1 1 1
636 OH DEFIANCE 111
637 OR GILLIAM 111
638 OR UMATILLA 111
639 TN HOUSTON 111
640 TX BROWN 111
641 VA CHARLOTTESVILLE CITY 111
642 WA PACIFIC 111
643 WA PEND OREILLE 111
644 WI PEPIN 111
645 WY FREMONT 1 1 1
646 CA MODOC 112
647 CA PLUMAS 112
648 CA RIVERSIDE 112
D-13
-------
OBS STATE COUNTY VARSCORE
649 CA SISKIYOU 112
650 CA TULARE 112
651 CO BOULDER 112
652 ID CAMAS 112
653 ID GEM 112
651 IA EMMET 112
655 IA PALO ALTO 112
656 KA MIAMI 112
657 KA STANTON 112
658 KA THOMAS 112
659 LA ST MARY 112
660 MS SCOTT 112
661 MO HARRISON 112
662 MO LINN 112
663 MO PHELPS 112
664 MT BEAVERHEAD 112
665 MT LIBERTY 112
666 MT PETROLEUM 112
667 MT PHILLIPS 112
668 MT TREASURE 112
669 MT VALLEY 112
670 NE BOX BUTTE 112
671 NE SEMARD 112
672 NV ELKO 112
673 ND GRANT 112
674 ND WARD 112
675 ND WELLS 112
676 OH WILLIAMS 112
677 OR HARNEY 112
678 SD HAMLIN 112'
679 SD HAND 112
680 TX ARCHER 112
681 TX COLLIN 112
682 TX EDWARDS 112
683 TX HALL 112
684 WA KING 1 12
685 WA LINCOLN 1 12
686 WV CLAY 112
687 WY LARAMIE 112
688 AR LO-GAN 113
689 AR SEBASTIAN 113
690 CA NAPA 113
691 CO CHAFFEE 113
692 CO LARIMER 113
693 CO PITKIN 113
694 ID CARIBOU 113
695 ID KOOTENAI 113
696 IL LIVINGSTON 113
697 IA DICKINSON 113
698 KA CHEROKEE 113
699 KA ELLSWORTH 113
700 KA HASKELL 113
701 LA ASCENSION 113
702 LA ST JOHN THE BAPTIST 1.13
D-14
-------
OBS STATE COUNTY VARSCORE
1 13
1 13
1 13
1 13
1 13
1 13
1 13
703
704
705
706
707
708
709
710
71 1
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
•740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
MN
MN
MN
MN
MN
MS
MO
MO
MT
MT
NE
NE
NE
NE
NV
NM
NM
NM
ND
ND
OH
OK
OK
OK
OK
OR
TX
TX
TX
TX
TX
TX
MV
MV
WI
MY
AR
CA
CO
CO
IL
KA
KA
KA
KA
KA
MN
MS
MO
MO
MT
MT
NE
NE
BLUE EARTH
GRANT
LINCOLN
NORMAN
RED LAKE
HINDS
CHRISTIAN
CLINTON
FALLON
JUDITH BASIN
COLFAX
DODGE
SCOTTS BLUFF
VALLEY
UASHOE
COLFAX
LINCOLN
TORRANCE
MC LEAN
PIERCE
DELAUARE
ADAIR
BRYAN
CHEROKEE
CLEVELAND
MORROW
COMANCHE
DALLAS
OLDHAM
RANDALL
REAGAN
MINKLER
HARRISON
LEWIS
DANE
UINTA
STONE
FRESNO
BACA
MOFFAT
VERMILION
GRAY
JACKSON
OSAGE
UABAUNSEE
WICHITA
RICE
CARROLL
GENTRY
LIVINGSTON
BLAINE
CHOUTEAU
CASS
DIXON
1 13
1 13
1 13
1 13
1 13
1 13
1 13
1 13
1 13
1 13
1 13
1 13
1 13
1 13
1 13
1 13
1 13
1 13
1 13
1 13
1 13
1 13
1 13
1 14
1 14
1 14
1 14
1 14
1 14
1 14
1 14
1 14
1 14
1 14
1 14
1 14
1 14
1 14
1 14
1 14
114
D-15
-------
OBS STATE COUNTY VARSCORE
757 NE FRANKLIN 114
758 NY GREENE 114
759 NY WASHINGTON 114
760 OH ASHTABULA 114
761 OH WARREN 114
762 OK DELAWARE 114
763 PA CAMBRIA 114
764 PA FULTON 114
765 SD DAVISON 114
766 SD TODD 114
767 TX CHILDRESS 114
768 TX COKE 114
769 TX HALE 114
770 TX MC MULLEN 1 14
771 TX REAL 114
772 TX UPTON 114
773 UT SAN JUAN 114
774 WA MASON 114
775 WA SAN JUAN 114
776 WV MARION 114
777 WI ST CROIX 114
778 CA SANTA CRUZ 115
779 CO DELTA 115
780 CO DOUGLAS 115
781 CO JACKSON 115
782 CO MONTROSE 115
783 ID CASSIA 115
784 IA FREMONT 115
785 KA CHAUTAU2UA 115
786 KA NORTON 115
787 KA RAWLINS 115
788 KA SHAWNEE 115
789 KA SHERMAN 115
790 KA WASHINGTON 115
791 MI DICKINSON 115
792 MN CLAY 115
793 MN FARIBAUL-T 115
794 MO HOWELL 115
795 MT TETON 115
796 NM DE BACA 115
797 NM UNION 115
798 OH NOBLE 115
799 OR COLUMBIA 115
800 SD BROWN 115
801 SD LAWRENCE 115
802 TN MACON 115
803 TX BURNET 115
804 TX GILLESPIE 115
805 TX MENARD 115
806 TX PARKER 115
807 TX SAN SABA 115
808 TX SHACKELFORD 115
809 WV OHIO 115
810 AK BRISTOL BAY DIVISION 116
D-15
-------
OBS STATE COUNTY VARSCORE
811 AK MATANUSKA-SUSITNA 116
812 CA LOS ANGELES 116
813 CA MADERA 1 16
814 CA SAN MATED 1 16
815 CA SONOMA 1 16
816 CO LAS ANIMAS 1 16
817 CO MESA 1 16
818 .po SUMMIT 1 16
819 KA GRANT 116
820 MN MARSHALL 116
821 MT HILL 116
822 MT YELLOWSTONE 116
823 NE LANCASTER 1 16
824 NE WASHINGTON 116
825 NE WEBSTER 1 16
826 OH CUYAHOGA 1 16
827 OH MORROW 116
828 OK HASKELL 1 16
829 OK MC CLAIM 1 16
830 OR YAMHILL 1 16
831 SD BENNETT 116
832 TN STEWART 1 16
833 TX DELTA 1 16
834 TX ERATH 1 16
835 TX MARION 1 16
836 TX RAINS 1 16
837 TX RUNNELS 116
838 WI EAU CLAIRE 1 16
839 AR PERRY 117
840- CA- MARIN 1 17
841 CA YUBA 117
842 CO ARCHULETA 117
843 CO LINCOLN 1 17
844 ID MADISON 1 17
845 IL MEN-ARD 117
846 IN JACKSON 1 17
847 IA CHICKASAW 117
848 IA DAVIS 117
849 KA LINCOLN 1 17
850 KA SHERIDAN 117
851 LA ST TAMMANY 117
852 MN LE SUEUR 1 17
853 MN SCOTT 1 17
854 MS ALCORN 1 17
855 MS RANKIN 1 17
856 MO DENT 117
857 MT PONDERA 1 17
858 NE ADAMS 1 17
859 NE HAMILTON 117
860 NE NEMAHA 117
861 NE RICHARDSON 117
862 NV CHURCHILL 117
863 NV DOUGLAS 1 17
864 NV LANDER T 17
-------
OBS STATE COUNTY VARSCORE
865 NV MINERAL 117
866 NM SAN JUAN 117
867 NY WARREN 117
868 ND GOLDEN VALLEY 117
869 ND MC INTOSH 117
870 OH CLINTON 117
871 OH MEDINA 117
872 OK OTTAWA 117
873 PA MIFFLIN 117
874 SD MINER 117
875 SD UNION 117
876 TN LEWIS 117
877 TX CARSON 117
878 TX DIMMIT 117
879 TX DUVAL 117
880 TX GLASSCOCK 117
881 TX KARNES ' 117
882 TX MILLS 117
883 TX PALO PINTO 117
884 TX STONEWALL 117
885 WV FAYETTE 117
886 WV MERCER 117
887 WY GOSHEN 117
888 WY PLATTE 117
889 ID POWER 118
890 IA HARRISON 118
891 IA WASHINGTON 118
892 IA WOODBURY 118
893 KA DECATUR 118
894 KA GRAHAM 118
895 KA WALLACE 118
896 MD FREDERICK 118
897 MN LAC 2UI PARLE 118
898 MN ROSEAU 118
899 MS GRENADA 118
900 MS LEE 118
901 MO RIPLEY 1
902 MO TEXAS 1
903 MT CASCADE 1
904 MT TOOLE 1 8
905 NE GREELEY 1 8
906 NE PIERCE 118
907 NM HARDING 118
908 NM MORA 118
909 NC CHATHAM 118
910 ND LOGAN 118
911 ND STARK 118
912 ND TRAILL 118
913 OH HIGHLAND 118
914 OH HURON 118
915 OK* CUSTER 118
916 OK GARVIN 118
917 OK LATIMER 118
913 OR HOOD RIVER 118
D-18
-------
OBS STATE COUNTY VARSCORE
919 PA ALLEGHENY 118
920 SO BEADLE 118
921 SD CLAY 118
922 TX BOSBUE 1 18
923 TX CORYELL 118
924 TX FANNIN 1 18
925 TX JACKSON 1 18
926 TX FARMER 118
927 TX VAL VERDE 1 18
928 MY CARBON 118
929 AK BRISTOL BAY BOROUGH 119
930 AR IZARD 119
931 AR SHARP 119
932 CO PARK 119
933 CO SAN MIGUEL 119
934 DC DISTRICT OF COLUMBIA 119
935 ID PAYETTE 1 19
936 IL. COOK 119
937 IL JO DAVIESS 119
938 IN GREENE 119
939 IA JEFFERSON 119
940 IA KEOKUK 119
941 IA LEE 119
942 IA VAN BUREN 1 19
943 KA CHEYENNE 1 19
944 KA FORD 119
945 KA MITCHELL 119
946 KA OSBORNE 119
947 KA RUSSELL 1 19
948 KA SMITH 1 19
949 MN COTTONUOOD 119
•950 MN KITTSON 1 19
951 MN LYON 119
952 MN SIBLEY 119
953 MO LINCOLN 119
954 NE BOONE 119
955 NE BUFFALO 119
956 NE BUTLER 119
957 NE KEARNEY 119
958 NE KEITH 119
959 NE MORRILL 119
960 NV LINCOLN 119
961 NV PERSHING 119
962 NM HIDALGO 119
963 NM SANDOVAL 119
964 NM SANTA FE 119
965 ND BILLINGS 119
966 ND HETTINGER 119
967 OH FAIRFIELD 1 19
968 OH JEFFERSON 119
969 OH LORAIN 119
970 PA CHESTER 119
971 TN MONTGOMERY 119
972 TN PICKETT 119
D-19
-------
OBS STATE COUNTY VARSCORE
973 TN ROBERTSON 119
974 TN WARREN 119
975 TN WAYNE 119
976 TX BEE 119
977 TX BRAZORIA 119
978 TX CROSBY 1 19
979 TX DENTON 119
980 TX FLOYD 1 19
981 TX JACK 119
982 TX MONTGOMERY 119
983 TX TARRANT 1 19
984 TX TRINITY 119
985 WV BARBOUR 1 19
98ii WV MONONGALIA 119
n37 WV NICHOLAS 119
988 WV PLEASANTS 119
989 WV RALEIGH 119
990 WV TAYLOR 119
991 AK FAIRBANKS 120
992 CO BENT 120
993 CO ROUTT 120
994 GA GILMER 120
995 ID BONNEVILLE 120
996 IL MARSHALL 120
997 IL MONROE 120
998 IL PIKE 120
999 IN CLARK 120
1000 IN DUBOIS 120
1001 IA HENRY 120
1002 IA MAHASKA 120
1003 IA WAPELLO 120
1004 KA DICKINSON 120
1005 KA ELLIS 120
1006 KA LOGAN 120
1007 KA SEWARD 120
1008 MN BELTRAMI 120
1009 MN NICOLLET 120
1010 MS UARREN 120
1011 MO ST CHARLES 120
1012 MT MC CONE 120
1013 NE NANCE 120
1014 NE PHELPS 120
1015 NE SHERMAN 120
1016 NE THURSTON 120
1017 NV EUREKA 120
1018 NV NYE 120
1019 NM LUNA 120
1020 NC DURHAM 120
1021 NC SURRY 120
1022 ND BOWMAN 120
1023 ND STUTSMAN 120
1024 OH HAMILTON 120
1025 OH TRUMBULL 120
1026 OK COAL 120
D-20
-------
OBS STATE COUNTY VARSCORE
1027 OK KIOUA 120
1028 OK PAWNEE 120
1029 PA BUTLER 120
1030 PA HUNTINGDON 120
1031 SC CHEROKEE 120
1032 SD SANBORN 120
1033 TN HUMPHREYS 120
1031 TX CASTRO 120
1035 TX CONCHO 120
1036 TX GOLIAO 120
1037 TX JIM WELLS 120
1038 TX LAMPASAS 120
1039 TX MADISON 120
1040 TX STERLING 120
1041 TX WALLER 120
1042 WA YAKIMA 120
1043 WV KANAWHA 120
1044 WV UPSHUR 120
1045 WI DOOR 120
1046 WY TETON 120
1047 CA TEHAMA 121
1048 CO ELBERT 121
1049 GA MADISON 121
1050 IL BUREAU 121
1051 IL CARROLL 121
1052 IL HENDERSON- 121
1053 IN GRANT 121
1054 IN WABASH 121
105? IN WELLS 121
1056 IA BLACK HAWK 121
1057 IA DES MOINES 121
1058 IA HOWARD 121
1059 KA HAMILTON 121
1060 KA MARION 121
1061 KA POTTAWATOMIE 121
1062 KA TREGO 121
1063 MI ST CLAIR 121
1064 MN MURRAY 121
1065 MN RENVILLE 121
1066 MN YELLOW MEDICINE 121
1067 MS LAFAYETTE 121
1068 MT GARFIELD 121
1069 MT LEWIS AND CLARK 121
1070 NE DAKOTA 121
1071 NV CLARK 121
1072 NV WHITE PINE 121
1073 NJ SOMERSET 121
1074 NM QUAY 121
1075 NM SOCORRO 121
1076 NM TAOS 121
1077 ND BURLEIGH 121
1078 ND ROLETTE 121
1079 OH ASHLAND 121
1080 OH BELMONT 121
D-21
-------
DBS STATE COUNTY VARSCORE
1081 OK PONTOTOC 121
1082 PA BLAIR 121
1083 TN CHEATHAM 121
1081 TN COCKE 121
1085 TN LAWRENCE 121
1086 TN PERRY 121
1087 TX CASS 121
1088 TX DEAF SMITH 121
1089 TX HARRIS 121
1090 TX ORANGE 121
1091 TX SAN JACINTO 121
1092 TX SMITH 121
1093 TX SUISHER 121
109" VA RICHMOND CITY 121
1095 AP. INDEPENDENCE 122
1096 CA ALAMEDA 122
1097 CA SAN BERNARDINO 122
1098 CO CUSTER 122
1099 CO GARFIELD 122
1100 IL CRAWFORD 122
1101 IL KANKAKEE 122
1102 IL WOODFORD 122
1103 IN BROWN 122
1104 IN JAY 122-
1105 KA BUTLER 122
1106 KA KEARNY 122
1 107 KA KIOWA 122
1108 KA MC PHERSON 122
1109 KA MEADE 122
1110 KA NEOSHO 122
1111 MI KEWEENAW 122
1112 MI SAGINAW 12.2
1113 MN BROWN 122
1114 MN FREEBORN 122
1115 MN HENNEPIN 122
1116 MN JACKSON 122
1117 MN MARTIN 122
1118 MN RAMSEY 122
1119 MN REDWOOD 122
1120 MN WASHINGTON 122
1121 MN WATONWAN 122
1122 MS YALOBUSHA 122
1123 MO GREENE 122
1124 MT GLACIER 122
1125 MT STILLWATER 122
1126 MT WHEATLAND 122
1127 NE ANTELOPE 122
1128 NE BOYD 122
1129 NE BURT 122
1130 NE GARDEN 122
1131 NV ESMERALDA 122
1132 NC STOKES 122
1133 ND TOWNER 122
1134 ND WALSH 122
D-22
-------
OBS STATE COUNTY VARSCORE
122
122
122
122
122
122
122
122
122
122
122
122
122
122
122
122
122
122
122
122
122
122
.122
122
122
123
123
123
123
123
123
.123
123
123
123
123
123
123
123
123
123
123
123
123
123
123
123
123
123
123
123
123
123
123
D-23
1 135
1 36
1 37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
1 155
1 156
1 157
1 158
1 159
1 160
1161
1 162
1 163
1 64
1 65
1 66
1 67
1 68
1169,
1 170
1 171
1 172
1 173
1 174
1 175
1 176
1 177
1 178
1 179
1 180
1 181
1 182
1 183
1 184
1 185
1 186
1 187
1 188
OH
OH
OH
OH
OH
OR
OR
SO
TN
TN
TN
TN
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
UV
MI
AL
AK
AR
CA
IL
IL
IN
IN
IN
KA
KA
KA
KA
KY
LA
MD
MI
MI
MI
MI
MN
MS
MO
NE
NE
NE
NE
NE
NM
ADAMS
CRAWFORD
GEAUGA
HENRY
MUSKINGUM
LINCOLN
POLK
SHANNON
FRANKLIN
HICKMAN
SEVIER
WHITE
COLEMAN
HARDIN
HUNT
KERR
MARTIN
MITCHELL
SCURRY
UPSHUR
UVALDE
UILLACY
WOOD
RITCHIE
WALWORTH
SUMTER
BETHEL
CROSS
CONTRA COSTA
BOONE
FORD
MARTIN
OWEN
PUTNAM
CHASE
MONTGOMERY
PHILLIPS
SEDGWICK
ROWAN
WEST FELICIANA
HARFORD
CHARLEVOIX
EMMET
GENESEE
HURON
CHIPPEWA
UNION
WRIGHT
CEDAR
DOUGLAS
LOGAN
SARPY
SHERIDAN
CATRON
-------
OBS STATE COUNTY VARSCORE
1189 NM SIERRA 123
1190 NM VALENCIA 123
1191 NY BRONX 123
1192 ND ADAMS 123
1193 ND CAVALIER 123
1194 ND MORTON 123
1195 OH GALLIA 123
1196 OH SUMMIT 123
1197 OK JACKSON 123
1198 OK JOHNSTON 123
1199 PA PHILADELPHIA 123
1200 SD YANKTON 123
1201 TX BLANCO 123
1202 TX CRANE 123
1203 TX GRfl/ 123
1204 TX POTTER 123
1205 TX TRAVIS 123
1206 TX WALKER 123
1207 TX WEBB 123
1208 TX WISE 123
1209 UT WEBER 123
1210 -VA COVINGTON CITY 123
1211 WA WHITMAN 123
1212 WI FOND DU LAC 123
1213 WI MILWAUKEE 123
1214 -AL CLEBURNE 124
1215 AL RUSSELL 124
1216 AR ARKANSAS 124
1217 AR FAULKNER 124
1218 CA IMPERIAL 124
1219 CA KERN 124
1220 CO OURAY 124
1221 GA HOUSTON 124
1222 GA UNION 124
1223 ID BEAR LAKE 124
1224 ID TETON 124
1225 IL LAWRENCE 124
1226 IL PUTNAM 124
1227 IL WHITE 124
1228 IA BUCHANAN 124
1229 KA CLAY 124
1230 KA MARSHALL 124
1231 KA MORRIS 124
1232 KA MORTON 124
1233 KA STEVENS 124
1234 MI BAY 124
1235 MI SANILAC 124
1236 MS DE SOTO 124
1237 MT BIG HORN 124
1238 MT MINERAL 124
1239 MT POWDER RIVER 124
1240 MT ROSEBUD 124
1241 MT YELLOWSTONE NATIONAL 124
1242 NE SAUNDERS 124
D-24-
-------
OBS STATE COUNTY VARSCORE
124
124
124
124
124
124
124
124
124
124
124
124
124
124
124
124
124
124
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
D-25
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
NM
NO
ND
OH
OH
OK
OK
PA
TX
TX
TX
TX
TX
TX
TX
VA
UA
UY
AL
AR
CA
GA
GA
IL
IN
IA
KA
KA
KA
KA
LA
LA
MD
MI
MS
MS
MO
MT
MT
NJ
NY
ND
OH
OH
OH
OK
OK
PA
TN
TX
TX
TX
TX
TX
EDDY
BOTTINEAU
PEMBINA
LAURENCE
LUCAS
CREEK
MURRAY
PERRY
FORT BEND
JASPER
LEE
LOVING
NEWTON
ROBERTS
VAN ZANDT
CUMBERLAND
WALLA WALLA
PARK
RANDOLPH
YELL
SANTA CLARA
HENRY
PICKENS
UINNEBAGO
CLAY
ALLAMAKEE
BARBER
CLOUD
DOUGLAS
ROOKS
EAST FELICIANA
LIVINGSTON
HOWARD
EATON
BENTON
TATE
BOONE
GOLDEN VALLEY
GRANITE
UNION
WESTCHESTER
WILLIAMS
CARROLL
DARKE
ERIE
COMANCHE
TEXAS
CARBON
CLAY
ATASCOSA
CALHOUN
FALLS
HOPKINS
KAUFMAN
-------
OBS STATE COUKTY VARSCORE
125
125
125
125
125
125
125
125
125
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
D-26
1297
1298
1299
1300
130 1
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1 320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
TX
TX
TX
TX
TX
TX
TX
UT
MI
AL
AL
AR
AR
AR
CO
CO
CO
CO
GA
IL
IN
IA
IA
IA
IA
IA
KA
no
MI
MN
MT
NM
MC
NO
ND
OH
OH
OH
OH
OK
OK
OR
PA
PA
TX
TX
TX
TX
TX
TX
TX
TX
TX
VA
KENDALL
KINNEY
KLEBERG
LAMAR
LAMB
NAVARRO
STARR
UTAH
WASHINGTON
CHAMBERS
CLAY
CONUAY
FULTON
WHITE
COSTILLA
PUEBLO
SAN JUAN
YUMA
HART
MC LEAN
VERMILLION
•BREMER
BUTLER
CLAYTON
UNION
WORTH
SUMNER
ALLEGANY
HOUGHTON
MC LEOD
CARBON
OTERO
POLK
DIVIDE-
SLOPE
FRANKLIN
LAKE
MAHONING
SCIOTO
HUGHES
SEMINOLE
CLACKAMAS
BEDFORD
CAMERON
ANDERSON
CALDUELL
ECTOR
GUADALUPE
IRION
JOHNSON
MONTAGUE
SAN PATRICIO
WASHINGTON
BATH
-------
OBS STATE COUNTY VARSCORE
126
126
126
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
127
128
128
128
128
D-27
1351
1352
1353
1354
1355
356
357
358
359
360
36 1
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
139 1
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
VA
VA
WV
AR
AR
CO
GA
ID
IL
IN
IN
IA
KA
KY
LA
MI
MI
MI
MI
MN
MN
MS
MS
MO
MO
MO
MT
MT
NE
NE
NE
NM
ND
ND
ND
OH
OK
OK
OR
PA
PA
TN
TX
TX
TX
TX
TX
TX
VA
UA
AL
CO
GA
GA
GOOCHLAND
HENRY
GRANT
POINSETT
POLK
PHILLIPS
JACKSON
BLAINE
DOUGLAS
PARKE
PERRY
AUDUBON
ALLEN
BOYD
ACADIA
HILLSDALE
LAPEER
MACOMB
WAYNE
STEVENS
SWIFT
MARSHALL
YAZOO
PERRY
POLK
WEBSTER
MEAGHER
MUSSELSHELL
DUNDY
HOWARD
PLATTE
CHAVES
BARNES
CASS
DICKEY
ROSS
HARMON
LINCOLN
LANE
CLARION
SCHUYLKILL
HANCOCK
GRIMES
HAMILTON
HOCKLEY
KING
NUECES
TOM GREEN
PITTSYLVANIA
ISLAND
TALLAPOOSA
JEFFERSON
BARROW
ELBERT
-------
OBS STATE COUNTY
VARSCORE
1<405
1406
1407
1408
1,409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
142 1
1422
1423
1424
1425
1426
1427
1428
1429
1430.
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
GA
GA
ID
IL
IL
IN
IA
IA
IA
IA
KA
KA
KY
LA
LA
MI
MN
MS
nT
NE
NE
NJ
NM
NY
NC
NC
ND
ND
ND
OH
OH
OH
OK
OK
OK
PA
SC
TN
TX
TX
TX
TX
TX
TX
TX
TX
TX
VA
VA
WV
AR
CO
IL
IL
FANNIN
FRANKLIN
WASHINGTON
CHAMPAIGN
MACON
DECATUR
CLINTON
MADISON
MUSCATINE
WARREN
GOVE
RILEY
GREENUP
CALCASIEU
RAPIDES
BARRY
AITKIN
LAUDERDALE
FLATHEAD
CHASE
KNOX
PASSAIC
GRANT
ULSTER
RANDOLPH
ROCKINGHAM
BENSON
MC HENRY
NELSON
FULTON
PORTAGE
WASHINGTON
CHOCTAW
MUSKOGEE
SE2UOYAH
JUNIATA
EDGEFIELD
DECATUR
BASTROP
BRISCOE
GALVESTON
HILL
LAVACA
LIVE OAK
LUBBOCK
MIDLAND
MOORE
CULPEPER
HALIFAX
HAMPSHIRE
GARLAND
WASHINGTON
DE WITT
LOGAN
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
128
129
129.
129
129
D-28
-------
OBS STATE COUNTY VARSCORE
1459 IL PIATT 129
1460 IN MONTGOMERY 129
1461 IN WARREN 129
1462 IA ADAIR 129
1463 IA LOUISA 129
1464 IA MARION 129
1465 IA PAGE 129
1466 IA SCOTT 129
1467 IA TAYLOR 129
1468 KA NESS 129
1469 LA EVANGELINE 129
1470 LA JEFFERSON DAVIS 129
1471 LA ST HELENA 129
1472 MD MONTGOMERY 129
1473 MI ANTRIM 129
1474 MI TUSCOLA 129
1475 MN CARVER 129
1476 MN LAKE OF THE MOODS 129
1477 MS HOLMES 129
1478 MT JEFFERSON 129
1479 MT ROOSEVELT 129
1480 NE HOOKER 129
1481 NE LOUP 129
1482 NV HUMBOLDT 129
1483 NM LEA 129
1484 NY SCHOHARIE 129
1485 NC ALAMANCE 129
1486 NC CATAUBA 129
1487 NC FORSYTH 129
1488 NC IREDELL 129
1489 NC ORANGE 129
1490 NC PERSON 129
1491 NC RUTHERFORD 129
1492 ND LA MOURE 129
1493 OH LICKING 129
1494 OH MONROE 129
1495 OH TUSCARAUAS 129
1496 OK CANADIAN 129
1497 OK NOBLE 129
1498 OR MARION 129
1499 PA LAWRENCE 129
1500 SC UNION 129
1501 TN COFFEE 129
1502 TN GRAINGER 129
1503 TN PUTNAM 129
1504 TX ARANSAS 129
1505 TX ELLIS 129
1506 TX GONZALES 129
1507 TX HOUSTON 129
1508 TX LA SALLE 129
1509 TX WARD 129
1510 VA FRANKLIN 129
1511 MI CLARK 129
1512 MI MAUKESHA 129
D-29
-------
OBS STATE COUNTY VARSCORE
1513 AL MONTGOMERY 130
1514 AR PIKE 130
1515 AR PRAIRIE 130
1516 GA BUTTS 130
1517 IL LEE 130
1518 IN CARROLL 130
1519 IN FLOYD 130
1520 IN HENDRICKS 130
1521 IN SCOTT 130
1522 IA ADAMS 130
1523 IA CASS 130
1524 IA CLARKE 130
1525 IA CRAWFORD 130
1526 IA DECATUR 130
1527 IA LUCAS 130
1528 IA MONROE 130
1529 IA MONTGOMERY 130
1530 IA RINGGOLD 130
1531 IA UAYNE 130
1532 KA ELK 130
1533 KA FRANKLIN 130
1534 KA HODGEMAN 130
1535 KA WOODSON 130
1536 KY CARTER 130
1537 KY LEE 130
1538 KY MENIFEE 130
1539 LA ALLEN 130
1540 LA JEFFERSON 130
1541 LA LAFAYETTE 130
1542 MD CARROLL 130
.1543 MI LEELANAU 130
1544 MS ATTALA 130
1545 MS JASPER 130
1546 MS PANOLA 130
1547 MT DANIELS 130
1548 MT DEER LODGE 130
1549 MT PRAIRIE 130
1550 MT SWE-ET GRASS 130
1551 NM BERNALILLO 130
1552 NM CURRY 130
1553 NM ROOSEVELT 130
1554 NY DELAWARE 130
1555 NY HAMILTON 130
1556 NY SCHUYLER 130
1557 NY TOMPKINS 130
1558 NC CASMELL 130
1559 NC CLEVELAND 130
1560 NC GRANVILLE 130
1561 NC YADKIN 130
1562 OH BUTLER 130
1563 OH HOCKING 130
1564 OK CIMARRON 130
1565 OK GRADY 130
1566 OK PAYNE 130
0-30
-------
OBS STATE COUNTY VARSCORE
1567 PA ARMSTRONG 130
1568 SD CAMPBELL 130
1569 TN MC MINN 130
1570 TN UNION 130
1571 TX ARMSTRONG 130
1572 TX CAMERON 130
1573 TX DONLEY 130
1574 TX REFUGIO 130
1575 TX ROCKWALL 130
1576 TX MHARTON 130
1577 TX ZAVALA 130
1578 VA AMHERST 130
1579 VA BUCHANAN 130
1580 VA CAMPBELL 130
1581 VA DICKENSON 130
1582 VA MECKLENBURG 130
1583 VA PRINCE WILLIAM 130
1584 VA WISE 130
1585 VA NORTON CITY 130
1586 WV MINERAL 130
1587 WI CALUMET 130
1588 AL COOSA 131
1589 AL LEE 131
1590 AL MACON 131
1591 AR SALINE 131
1592 GA COLUMBIA 131
1593 GA GLASCOCK 131
1594 ID BUTTE 131
1595 IL LAKE 131
1596 IA APPANOOSE 131
1597 IA CARROLL 131
1598 IA CERRO GORDO 131
1599 IA IDA 131
1600 KA HARVEY 131
1601 KA RUSH 131
1602 KY FLOYD 131
1603 KY MORGAN 131
1604 LA TANGIPAHOA 131
1605 MI OSCEOLA 131
1606 MN COOK 131
1607 MN ITASCA 131
1608 MT CUSTER 131
1609 MT MADISON 131
1610 MT SHERIDAN 131
1611 MT WIBAUX 131
1612 NE GRANT 131
1613 NM DONA ANA 131
1614 NY BROOME 131
1615 NY FULTON 131
1616 NY ORANGE 131
1617 NY SARATOGA 131
1618 NY STEUBEN 131
1619 NY SULLIVAN 131
1620 NY TIOGA 131.
D-31
-------
OBS STATE COUNTY VARSCORE
1621 NC DAVIDSON 131
1622 NC LINCOLN 131
1623 NC MECKLENBURG 131
1624 NC STANLY 131
1625 ND EDDY 131
1626 ND RANSOM 131
1627 OK ATOKA 131
1628 OK CARTER 131
1629 OK OSAGE 131
1630 OK PUSHMATAHA 131
1631 SC FIG CORMICK 131
1632 SC YORK 131
1633 TN FENTRESS 131
1634 TN GREENE 131
1635 TN IOUDON 131
1636 TN OVERTON 131
1637 TN SULLIVAN 131
1638 TX AUSTIN 131
1639 TX BEXAR 131
1640 TX CALLAHAN 131
1641 TX FAYETTE 131
1642 TX GREGG 131
1643 TX HOOD 131
1644 TX LIMESTONE 131
1645 TX MEDINA 131
1646 TX WILSON 131
1647 TX YOAKUM 131
1648 VT RUTLAND 131
1649 VA 'ARLINGTON 131
1650 VA BUCKINGHAM 131
1651 VA FAU2UIER 131
1652 VA ORANGE 131
1653 MI COLUMBIA 131
1654 AL LOUNDES 132
1655 AK UADE HAMPTON 132
1656 AR LAFAYETTE 132
1657 AR LONOKE 132
1658 AR MONTGOMERY 132
1659 AR RANDOLPH 132
1660 AR ST FRANCIS 132
1661 CO EAGLE 132
1662 CO KIOUA 132
1663 GA JONES 132
1664 GA TALIAFERRO 132
1665 IN BENTON 132
1666 IN DELAWARE 132
1667 IA DELAWARE 132
1668 IA DUBU2UE 132
1669 IA GUTHRIE 132
1670 IA MITCHELL 132
1671 IA POMESHIEK 132
1672 IA SHELBY 132
1673 KA COFFEY 132
1674 KA GEARY 132
D-32
-------
OBS STATE COUNTY VARSCORE
1675 KA HARPER 132
1676 KA LANE 132
1677 KY KNOX 132
1.678 LA WASHINGTON 132
1679 ME AROOSTOOK 132
1680 MI ALPENA 132
1681 MI INGHAM 132
1682 MI SHIAUASSEE 132
1683 MN POPE 132
1684 MT LAKE 132
1685 NE HALL 132
1686 NE LINCOLN 132
1687 NE MC PHERSON 132
1688 NY ALLEGANY 132
1689 NY CHEMUNG 132
1690 NY LIVINGSTON 132
1691 NY OTSEGO 132
1692 NY RENSSELAER 132
1693 NC CABARRUS 132
1694 NC DAVIE 132
1695 NC FRANKLIN 132
1696 NC GASTON 132
1697 NC GUILFORD 132
1698 NC VANCE 132
1699-NC WARREN 132
1700 OH COSHOCTON 132
1701 OH HOLMES 132
1702 OH PIKE 132
1703 OK JEFFERSON £32
1704 OK KAY 132
1705 OK OKLAHOMA 132
1706 PA ADAMS 132
1707 PA CLEARFIELD 132
1708 PA POTTER 132
1709 TN BENTON 132
1710 TN JOHNSON 132
1711 TN UNICOI 132
1712 TX ANGELINA 132
1713 TX BRAZOS 132
1714 TX CHAMBERS 132
1715 TX COMAL 132
1716 TX FISHER 132
1717 TX HAYS 132
1718 TX HEMPHILL 132
1719 TX JEFFERSON 132
1720 TX MATAGORDA 132
1721 TX NOLAN 132
1722 TX SOMERVELL 132
1723 VA APPOMATTOX 132
1724 VA BEDFORD 132
1725 VA BRUNSWICK 132
1726 VA CHARLOTTE 132
1727 VA PRINCE EDWARD 132
1728 WI BROWN 132
D-33
-------
OBS STATE COUNTY VARSCORE
132
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
133
D-34
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
WI
AR
AR
AR
AR
CA
CO
GA
GA
IL
IN
IA
IA
KA
KA
KA
KA
KY
KY
LA
MI
MN
ns
MS
MS
MO
MT
NV
NY
NC
OK
OK
OK
OR
TN
TN
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
VT
VA
VA
VA
VA
VA
VA
MANITOMOC
BRADLEY
HOWARD
LITTLE RIVER
MILLER
MONO
ARAPAHOE
NEWTON
TOWNS
KANE
UNION
GRUNDY
IOWA
FINNEY
GREENWOOD
SALINE
WILSON
ELLIOTT
JACKSON
BEAUREGARD
MISSAUKEE
DOUGLAS
CLAIBORNE
LOWNDES
MONROE
GIRARDEAU
MISSOULA
LYON
ALBANY
ROWAN
BLAINE
CRAIG
STEPHENS
CLATSOP
BRADLEY
CLAIBORNE
BAILEY
BELL
COOKE
FOARD
FRANKLIN
FRIO
KENT
LIBERTY
MAVERICK
RED RIVER
REEVES
WINDSOR
ALLEGHANY
AMELIA
LOUISA
LUNENBERG
NOTTOWAY
PATRICK
-------
OBS STATE COUNTY VARSCORE
1783 MA CLARK 133
1784 WV HARDY 133
1785 MI SHEBOYGAN 133
1786 AL MILCOX 134
1787 CA SAN FRANCISCO 134
1788 GA SPALDING 134
1789' IL TAZEUELL 134
1790 IN TIPPECANOE 134
1791 IN WHITLEY 134
1792 IA BENTON 134
1793 IA FLOYD 134
1794 IA JACKSON 134
1795 IA MINNESHIEK 134
1796 KA CLARK 134
1797 KA COMANCHE 134
1798 KA LABETTE 134
1799 KA PRATT 134
1800 KY FULTON 134
1801 KY POWELL 134
1802 MI CHEBOYGAN 134
1803 MN LAKE 134
1804 MN WRIGHT 134
1805 m LINCOLN 134
1806 MT RICHLAND 134
1807 FIT SANDERS 134
1808 MT SILVER BOW 134
1809 NE ARTHUR 134
1810 NE KEYA PAHA 134
1811 NY CHENANGO 134
1812 ND KIDDER 134
1813 ND SARGENT 134
1814 OH PICKAWAY 134
1815 OK HAYES 134
1816 OK ROGER MILLS 134
1817 OR BENTON 134
1818 PA CLINTON 134
1819 PA DELAWARE 134
1820 PA SNYDER 134
1821 PA YORK 134
1822 SC PICKENS 134
1823 SD BROOKINGS 134
1824 TN CAMPBELL 134
1825 TX CHEROKEE 134
1826 TX DE WITT 134
1827 TX HARRISON 134
1828 TX LEON 134
1829 TX RUSK 134
1830 TX VICTORIA 134
1831 UT SALT LAKE 134
1832 VT ORANGE 134
1833 VA FLUVANNA 134
1834 WV MORGAN 134
1835 WI KENOSHA 134
1836 AR CLARK 135
D-35
-------
OBS STATE COUNTY VARSCORE
1837 AR HOT SPRING 135
1838 AR PULASKI 135
1839 CA BUTTE 135
18140 CO EL PASO 135
1841 GA CLAYTON 135
1842 GA DOUGLAS 135
1843 GA OGLETHORPE 135
1844 ID BINGHAH 135
1845 IN HOWARD 135
1846 IN MARION 135
1847 IN RANDOLPH 135
1848 IA FAYETTE 135
1849 IA SAC 135
1850 IA TAMA 135
1851 KA COWLEY 135
1852 KY CARROLL 135
1853 KY ESTILL 135
1854 KY MAGOFFIN 135
1855 MI ISABELLA 135
1856 MI LIVINGSTON 135
1857 MS ADAMS 135
1858 MS LEAKE 135
1859 MS NEWTON 135
1860 MT DAWSON 135
1861 NE MERRICK 135
1862 NJ MONMOUTH 135'
1863 NY CATTATAUGUS 135
1864 NC CALDWELL 135
1865 NC MONTGOMERY 135
1866 NC UNION 135
1867 ND FOSTER 135
1868 ND RAMSEY 135
1869 OH PREBLE 135
1870 OK BECKHAM 135
1871 OK CADDO 135
1872 OK GARFIELD 135
.1873 OK ROGERS 135
1874 OK MASHITA 135
1875 OR MULTNOMAH 135
1876 SC GREENWOOD 135
1877 TN HAMILTON 135
1878 TN JEFFERSON 135
1879 TN SE2UATCHIE 135
1880 TX BORDEN 135
1881 TX EL PASO 135
1882 TX GARZA 135
1883 TX HIDALGO 135
1884 TX HOWARD 135
1885 TX JIM HOGG 135
1886 TX NACOGDOCHES 135
1887 TX THROCKMORTON 135
1888 UT DAVIS 135
1889 VT ORLEANS 135
1890 VA CRAIG 135
D-36
-------
OBS STATE COUNTY VARSCORE
135
135
135
135
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
.136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
137
137
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
190 1
1902
1903
1904
1905
1906
1907
1908
1909
1910
19 1 1
19 12
1913
1914
1915
19 16
19 17
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
19.32
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
VA
MA
WV
UI
AL
AL
AR
CO
CO
GA
GA
GA
GA
IL
IN
IA
IA
IA
IA
IA
KA
KY
KY
KY
MD
MD
MI
NJ
NY
NC
NC
NC
ND
NO
OH
OK
OK
OK
OK
PA
PA
PA
PA
SC
TN
TN
TN
TX
TX
TX
VA
UI
AL
CO
POUHATAN
FRANKLIN
PENDLETON
UINNEBAGO
CHILTON
CLARKE
ASHLEY
ALAMOSA
PROUERS
DAUSON
MORGAN
RABUN
STEPHENS
DE KALB
LAKE
GREENE
JOHNSON
JONES
MARSHALL
MONONA
RICE
KNOTT
PERRY
WOLFE
BALTIMORE
GARRETT
GLADUIN
MERCER
MADISON
ASHE
BURKE
MAKE
GRAND FORKS
STEELE
MIAMI
DEUEY
MC INTOSH
TILLMAN
TULSA
GREENE
MC KEAN
TIOGA
VENANGO
LANCASTER
HARDEMAN
HARDIN
SCOTT
MC LENNAN
TERRY
ZAPATA
SPOTSYLVANIA
KEUAUNEE
TUSCALOOSA
ADAMS
D-37
-------
OBS STATE COUNTY VARSCORE
1945 CO LOGAN 137
1946 CO SAGUACHE 137
1947 GA LINCOLN 137
1948 GA PEACH 137
1949 GA WILKES 137
1950 ID JEFFERSON 137
1951 IN PORTER 137
1952 IN SPENCER 137
1953 IA CEDAR 137
1954 IA HARDIN 137
1955 IA JASPER 137
1956 IA LINN 137
1957 KY LAURENCE 137
1958 KY LESLIE 137
1959 KY OWEN 137
1960 KY UNION 137
1961 MD WASHINGTON 137
1962 MI MONROE 137
1963 NE BLAINE 137
1964 NY ONONDAGA 137
1965 NC ANSON 137
1966 OH MORGAN 137
1967 OH PERRY 137
1968 OK WAGONER 137
1969 PA BERKS 137
1970. PA UNION' 137
1971 TN CUMBERLAND 137
1972 TN GRUNDY 137
1973 TN HAMBLEN 137
1974 TN MORGAN 137
1975 TN WEAKLEY 137
1976 TX HENDERSON 137
1977 VT BENNINGTON 137
1978 VA FAIRFAX 137
1979 VA HIGHLAND 137
1980 VA RAPPAHANNOCK 137
1981 VA STAFFORD 137
1982 VA COLONIAL HEIGHTS CIT -137
1983 VA FREDERICKSBURG CITY 137
1984 WI GREEN LAKE 137
1985 AL PERRY 138
1986 AR CALHOUN 138
1987 CA STANISLAUS 138
1988 CO CONEJOS 138
1989 GA HANCOCK 138
1990 GA HARRIS 138
1991 GA JASPER 138
1992 IN CASS 138
1993 IN FOUNTAIN 138
1994 IA HAMILTON 138
1995 IA POLK 138
1996 IA WEBSTER 138
1997 KA STAFFORD 138
1998 KY OWSLEY 138
D-38
-------
OBS STATE COUNTY
VARSCORE
1999
2000
2001
2002
2003
2004
2005
2006
2007
2003
2009
2010
201 1
2012
2013
20 14
2015
20 16
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
LA
ni
MI
MI
MI
MX
MN
MS
MS
MO
MT
NM
NY
OK
OK
PA
SC
TN
TX
TX
TX
VA
VA
AR
CO
GA
GA
ID
ID
IN
IN
IA
KY
MI
MI
MN
MN
MS
MS
MT
NY
NY
OH
OH
OH
OH
OK
OK
PA
PA
TN
TN
TN
TX
ST MARTIN
CLINTON
GRATIOT
KENT
WASHTENAW
DAKOTA.
KANDIYOHI
MONTGOMERY
SMITH
BUTLER
PARK
LOS ALAMOS
COLUMBIA
HARPER
LOGAN
WASHINGTON
CHESTER
MADISON
EASTLAND
FREESTONE
TITUS
MADISON
SALEM CITY
JEFFERSON"
HINSDALE
OCONEE
SCHLEY
GOODING
JEROME
MARRICK
WAYNE
CALHOUN
B'REATHITT
ALCONA
MIDLAND
DODGE
STEELE
NESHOBA
UILKINSON
RAVALLI
CHAUTAU2UA
DUTCHESS
CHAMPAIGN
GREENE
VINTON
WAYNE
GRANT
OKMULGEE
JEFFERSON
WARREN
HAWKINS
MARION'
VAN BUREN
COCHRAN
138
138
138
138
138
138
138
138
138
138
138
138
138
138
138
138
138
138
138
138
138
138
138
139
139
139
139
139
139
139
139
139
139
139
139
139
139
139
139
139
139
139
139
139
139
139
139
139
139
139
139
139
139
139
D-39
-------
OBS STATE COUNTY
VARSCORE
2053
2054
2055
2056
2057
2058
2059
2060
206 1
2062
2063
2064
2065
2066
2067
2068
2069
2070
207 1
2072
2073
2071
2075
2076
2077
2078
2079
2080
208 1
2082
2083
2084
2085
2086
2087
2088
2089
2090
209 1
2092
2093
2094
2095
2096
2097
2098
2099
2100
210 1
2102
2103
2104
2105
210.6
TX
TX
TX
TX
VA
WA
UA
MV
MI
MI
UI
AR
AR
CO
CO
CT
GA
GA
GA
GA
GA
GA
IL
IL
IN
IN
IN
IN
IA
IA
IA
KY
KY
KY
MA
MI
MN
MN
FIT
NC
OH
OH
OK
OK
OK
OR
PA
sc
TN
TN
TN
TN
TN
TN
COLORADO
MILAn
POLK
TAYLOR
GALAX CITY
PIERCE
SPOKANE
PRESTON
DODGE
JEFFERSON
OUTAGAMIE
DREU
LAURENCE
GUNNISON
MINERAL
MIDDLESEX
DE KALB
GREENE
GMINNETT
LAMAR
MACON
UPSON
JACKSON
MC HENRY
MONROE
NEUTON
PIKE
VANDERBURGH
BOONE
DALLAS
STORY
BULLITT
CLAY
JOHNSON
BERKSHIRE
ARENAC
PINE
ST LOUIS
POWELL
ALEXANDER
CLARK
RICHLAND
COTTON
GREER
MC CURTAIN
LINN
MERCER
FAIRFIELD
BLEDSOE
CHESTER
GIBSON
HENDERSON
HENRY
ROANE
139
139
139
139
'139
139
139
139
139
139
139
140
140
140
140
140
140
140
140
140
140
140
140
140
140
140"
140
140
140
140
140
140
140
140
140
140
140
140
140
140
140
140
140
140
140
140
140
140
140
140
140
140
140
140
D-40
-------
OBS STATE COUNTY VARSCORE
140
140
140
140
141
141
141
141
141
141
141
141
141
141
141
141
141
141
14 1
141
141
141
141
141
141
141
141
141
141
141
141
141
141
141
141
141
141
141
141
141
141
141
141
141
141
141
141
141
141
141
141
141
141
141
D-41
2107
2108
2109
21 10
2111
2112
21 13
2114
2115
2116
2 17
2 18
2 19
2 20
2 21
2 122
2 123
2 124
2125
2 26
2 27
2 28
2 29
2 30
2131
2132
2 133
2134
2 135
2 1 36
2 137
2138
2139
2140
2 41
2 42
2 43
2 44
2 45
2 46
2 47
2 48
2 49
'2 50
2151
2152
2153
2154
2155
2156
2157
2 158
2159
2160
TX
TX
TX
VA
AL
AL
AL
AR
CO
CT
GA
GA
GA
GA
GA
ID
IN
IN
IN
KA
KY
KY
KY
KY
KY
UN
MN
MN
NJ
NY
OK
OK
PA
PA
PA
PA
PA
SC
SC
SC
TN
TN
TN
TN
TX
TX
TX
VT
VA
VA
UA
MA
UA
WV
GAINES
SAN AUGUSTINE
WILLIAMSON
SCOTT
BULLOCK
HALE
SHELBY
JACKSON
WELD
LITCHFIELD
CLARKE
FAYETTE
HALL
PIKE
PUTNAM
CANYON
LA PORTE
SULLIVAN
WHITE
PAWNEE
BATH
BRACKEN
CALLOWAY
HARLAN
LETCHER
BENTON
CARLTON
MILLE LACS
BERGEN
PUTNAM
BEAVER
LE FLORE,
CENTRE
DAUPHIN
ELK
LYCOMING
SOMERSET
NEWBERRY
OCONEE
SPARTANBURG
CARROLL
CARTER
FAYETTE
KNOX
BURLESON
ROBERTSON
TYLER
ADDISON
LOUDOUN
SMYTH
BENTON.
GRANT
THURSTON
TUCKER
-------
OBS STATE COUNTY VARSCORE
141
141
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
143
143
143
143
143
143
143
143
143
143
143
143
143
D-42
2161
2162
2163
2164
2 165
2 166
2 167
2 168
2169
2170
2 171
2172
2 173
2174
2 175
2176
2177
2178
2179
2180
2181
2182
2183'
2184
2185
2186
2187
2138
2189
2190
2191
2192
2 193
2 194
2195
2 196
2197
2198
2199
2200
220 1
2202
2203
2204
2205
2206
2207
2208
2209
2210
22 1 1
22 12
2213
2214
MI
UI
AL
AL
AR
AR
GA
GA
GA
IN
KY
KY
KY
KY
KY
LA
LA
MD
MS
MS
MS
NJ
NJ
NY
NY
NC
NC
OH
OH
OK
OK
PA
PA
PA
RI
SC
SC
SC
TN
TN
UA
AR
AR
CO
CO
CT
GA
GA
GA
IN
IN
IN
KY
KY
BAYFIELD
ROCK
BIBB
CHOCTAM
LINCOLN
MONROE
BANKS
COMETA
MONROE
VIGO
BALLARD
GRAVES
HENDERSON
MARSHALL
PENDLETON
CADDO
DE SOTO
CECIL
SIMPSON
TISHOMINGO
WAYNE
ESSEX
HUDSON
CORTLAND
YATES
GRAHAM •
MC DOMELL
ATHENS
KNOX
KINGFISHER
UOODS
BUCKS
FRANKLIN
MONTGOMERY
NEWPORT
ABBEVILLE
ANDERSON .
LAURENS
CROCKETT
HAYWOOD
ADAMS
HEMPSTEAD
SEVIER
OTERO
SEDGUICK
FAIRFIELD
HABERSHAM
MURRAY
ROCKDALE
CLINTON
DAVIESS
LAWRENCE
CARLISLE
HICKMAN
-------
OBS STATE COUNTY VARSCORE
2215 KY JEFFERSON 143
2216 MI ALGER 143
2217 MI CASS 143
2218 MI IOHIA 143
2219 MI KALAMAZOO 143
2220 MI MENOMINEE 143
2221 MN HOUSTON 143
2222 MN MEEKER 143
2223 MS ITAMAMBA 143
2224 MS JEFFERSON 143
2225 MT GALLATIN 143
2226 NE BROUN 143
2227 NE GARFIELD 143
2228 NE WHEELER 143
2229 NY CLINTON 143
2230 NC CHEROKEE 143
2231 OH JACKSON 143
2232 OH STARK 143
2233 OK WASHINGTON 143
2234 TN MONROE 143
2235 TX COTTLE 143
2236 TX HUTCHINSON 143
2237 VA DINMIDDIE 143
2238 WA DOUGLAS 143
2239 WV POCAHONTAS 143
2240 AL LAMAR 144
2241 GA LUMPKIN 144
2242 GA WHITE 144
2243 IL GALLATIN 144
2244 IL IROQUOIS 144
2245 IL UNION 144
2246 IN 'BOONE 144
2247 IN GIBSON 144
22'48 IN HANCOCK 144
2249 IN HENRY 144
2250 IN KNOX 144
2251 IN POSEY 144
2252 IN RUSH 144
2253 IN TIPTON 144
2254 ME KNOX 144
2255 ME PISCATABUIS 144
2256 ME SAGADAHOC 144
2257 MD BALTIMORE CITY 144
2258 MI BENZIE 144
2259 MI OGEMAM 144
2260 MI VAN BUREN 144
2261 MS AMITE 144
2262 MS TALLAHATCHIE 144
2263 MS TUNICA 144
2264 MS WASHINGTON 144
2265 NE HOLT 144
2266 NE THOMAS 144
2267 NH COOS 144
2268 NC AVERY 144
D-43
-------
OBS STATE COUNTY
VARSCORE
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
229 1
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
2307
2308
2309
2310
231 1
2312
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
NC
NC
NC
OK
PA
TN
TN
TN
TX
TX
VA
VA
VA
AL
CA
CT
CT
GA
GA
GA
IN
IN
IN
KA
KY
KY
LA
MN
MS
MS
MO
MT
NY
NY
NC
NC
NC
NC
OH
OK
PA
PA
TN
TN
TN
TN
TX
TX
VA
VA
UV
MI
AL
AL
JACKSON
SUAIN
YANCEY
ALFALFA
MONROE
BLOUNT
DE KALB
MC NAIRY
KENEDY
SABINE
BLAND
FREDERICK
NELSON
AUTAUGA
GLENN
HARTFORD
NEW LONDON
FULTON
TROUP
WALTON
NOBLE
STEUBEN
WASHINGTON
BARTON
GALLATIN
MC CRACKEN
RED.RIVER
MOWER
ISSA2UENA
JONES
ST LOUIS
•BROADWATER
CAYUGA
ONTARIO
MACON
MITCHELL
WATAUGA
WILKES
MONTGOMERY
WOODWARD
ERIE
FAYETTE
ANDERSON
DYER
JACKSON
MEIGS
LYNN
SHELBY
PULASKI
WASHINGTON
RANDOLPH
RACINE
CULLMAN
PICKENS
144
144
144
144
144
144
144
144
144
144
144
144
144
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
145
146
146
D-44
-------
OBS STATE COUNTY VARSCORE
2323 AR CLAY 146
2324 AR WOODRUFF 146
2325 GA TALBOT 146
2326 XL JOHNSON 146
2327 IL POPE 146
2328 IN MORGAN 146
2329 KY CAMPBELL 146
2330 KY DAVIESS 146
2331 KY FLEMING 146
2332 KY LEWIS 146
2333 KY TRIMBLE 146
2334 ME LINCOLN 146
2335 MI MONTMORENCY 146
2336 MS CLARKE 146
2337 MS HUMPHREYS 146
2338 MO SCOTT 146
2339 NE ROCK 146
2340 NY ERIE 146
2341 NY HERKIMER 146
2342 NY ST LAWRENCE 146
2343 NC ALLEGHANY 146
2344 NC BUNCOMBE 146
2345 ND GRIGGS 146
2346 OK NOWATA 146
2347 OK OKFUSKEE 146
2348 PA CUMBERLAND 146
2349 PA FOREST 146
2350 PA LUZERNE 146
2351. PA NORTHAMPTON 146
2352 TN CANNON 146
2353 TX JONES 146
2354 VT WASHINGTON 146
2355 VA CARROLL 146
2356 VA LEE 146
2357 VA MONTGOMERY 146
2358 VA ROANOKE 146
2359 VA WYTHE 146
2360 AR UNION 147
2361 CO DENVER 147
2362 CO MORGAN 147
2363 GA BURKE 147
2364 GA HEARD 147
2365 GA MERIWETHER 147
2366 ID ADA 147
2367 IN JOHNSON 147
2368 IN MADISON 147
2369 IN MARSHALL 147
2370 KY BOONE 147
2371 KY HANCOCK 147
2372 KY LAUREL 147
2373 KY MADISON 147
2374 KY MONTGOMERY 147
2375 KY PIKE 147
2376 -KY WEBSTER 147
D-45
-------
OBS STATE COUNTY
VARSCORE
2377
2378
2379
2380
2381
2382
2383
2384
2385
2386
2387
2388
2389
2390
239 1
2392
2393
2394
2395
2396
2397
2398
2399
2400
240 1
2402
2403
2404
2405
2406
2407
2408
2409
2410
241 1
2412
2413
2414
2415
2416
2417
2418
2419
2420
242 1
2422
2423
2424
2425
2426
2427
2428
2429
2430
KY
LA
LA
ME
ME
MA
MI
MI
MN
MS
MS
MS
MS
MS
NJ
NY
NY
NY
NC
NC
PA
PA
PA
SC
TN
TN
'VT
VA
VA
VA
AL
AR
GA
GA
IL
IN
IN
IN
KY
KY
KY
KY
LA
LA
ME
MI
MN
MN
MS
NJ
NJ
NY
ND
OH
MHITLEY
NATCHITOCHES
SABINE
FRANKLIN
KENNEBEC
HAMPDEN
CLARE
OAKLAND
NOBLES
FRANKLIN
GREENE
LAURENCE
LINCOLN
PIKE
MIDDLESEX
FRANKLIN
MONTGOMERY
NIAGARA
CLAY
HAYUOOD
COLUMBIA
SULLIVAN
WESTMORELAND
GREENVILLE
LAKE
LAUDERDALE
UINDHAM
ALBEMARLE-
GRAYSON
TAZEWELL
DE KALB
GREENE
CRAWFORD
QUITMAN
ALEXANDER
ELKHART
HAMILTON
ST JOSEPH
GARRARD
KENTON
MC CREARY
ROBERTSON
VERNON
WINN
WALDO
OTTAWA
KANABEC
WINONA
SHARKEY
MORRIS
SUSSEX
SENECA
SHERIDAN
COLUMBIANA
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147.
147
147
147
147
147
148
148
148
148
148
148
148
148
148
148
148
148
148
148
148
148
148
148
148
148
148
148
148
148
D-46
-------
OBS STATE COUNTY
VARSCORE
2131
2432
2433
2434
2435
2436
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
.2452
2453
2454
2455
2456
2457
2458
2459
2460
246 1
2462
2463
2464
2465
2466
2467
2468
2469
2470
247 1
-2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
2483
2484
PA
TH
TN
TN
TN
TX
VA
VA
VA
AL
AL
AL
AL
AL
AR
GA
GA
KY
LA
LA
ME
MA
MI
MN
MN
MS
MS
MS
MS
NH
NH
NY
NC
NC
NC
ND
PA
PA
PA
TN
TX
VT
VT
VA
VA
VA
WI
AL
AL
AL
AR
AR
AR
CA
LEHIGH
OBION
POLK
SUMNER
TIPTON
GRAYSON
FLOYD
GILES
ALEXANDRIA CITY
JEFFERSON
MARION
MARSHALL
TALLADEGA
WALKER
CHICOT
CHEROKEE
STEWART
MARTIN
IBERVILLE
LINCOLN
SOMERSET
FRANKLIN
IOSCO
MORRISON
TODD
COAHOMA
MARION
PEARL RIVER
SUNFLOWER
GRAFTON
SULLIVAN
ROCKLAND
HENDERSON
LEE
TRANSYLVANIA
RICHLAND
NORTHUMBERLAND
SUS2UEHANNA
WYOMING
LINCOLN
WHEELER
CALEDONIA
LAMOILLE
BOTETOURT
CHESTERFIELD
SHENANDOAH
WAUPACA
BLOUNT
ELMORE
WINSTON
CLEVELAND
COLUMBIA
CRITTENDEN
COLUSA
148
148
148
148
148
148
148
148
148
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
150
150
150
150
150
150
150
D-47
-------
OBS STATE COUNTY
VARSCORE
2185
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
250 1
2502
2503
2504
2505
2506
2507
2 5-0' 8
2509
2510
251 1
2512
2513
2514
2515
2516
2517
2518
2519
2520
2521
2522
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
2535
2536
2537
2518
CT
GA
GA
GA
IN
KA
KY
KY
KY
ME
MN
CIS
MS
ns
OH
PA
TN
TX
TX
VT
WV
UI
MI
AR
AR
AR
GA
GA
IN
KY
KY
LA
ME
MI
ns
ns
NJ
OK
PA
TN
TN
TX
VA
UA
AL
CO
CT
GA
GA
ID
IN
IN
IN
KY
NEW HAVEN
MC DUFFIE
SUMTER
WARREN
BARTHOLOMEW
KINGMAN
HARRISON
LIVINGSTON
ROCKCASTLE
ANDROSCOGGIN
GOODHUE
JACKSON
LEFLORE
2UITMAN
MEIGS
MONTOUR
WASHINGTON
DAMSON
WICHITA
FRANKLIN
BERKELEY
FOREST
SHAWANO
CRAIGHEAD
GRANT
NEVADA
BIBB
FORSYTH
SHELBY
GRANT
MASON
BIENVILLE
CUMBERLAND
PRES2UE ISLE
BOLIVAR
COPIAH
SALEM
MAJOR
LEBANON
SHELBY
WILLIAMSON
BROOKS
ROANOKE CITY
KITSAP
JACKSON
RIO GRANDE
TOLLAND
CARROLL
PAULDING
MINIDOKA
FAYETTE
FULTON
JASPER
FRANKLIN
150
150
150
150
150
150
150
150
150
150
150
150
150
150
150
150
150
150
150
150
150
150
150
151
151
151
151
151
151
151
151
151
151
151
151
151
151
151
151
151
151
151
151
151
152
152
152
152
152
152
152
152
152
152
D-48
-------
OBS STATE COUNTY
VARSCORE
2539
2540
2541
2542
2543
2544
2545
2546
2547
2548
2549
2550
2551
2552
2553
2554
2555
2556
2557
2558
2559
2560
2561
2562
2563
2564
2565
2566
2567
2568
2569
2570
257 1
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
2583
2584
2585
2586
2587
2588
2589
2590
2591
2592
KY
KY
KY
MN
MN
MS
no
NY
SC
TN
TX
VT
VT
VA
VA
UI
AL
AL
AL
IN
KY.
KY
KY
LA
LA
LA
ME
MI
MI
MI
MI
NE
NH
NY
NY
PA
TX
TX
WV
UI
AL
AR
GA
ID
IL
KY
KY
LA
MI
MS
NH
NY
OK
PA
HENRY
HOPKINS
NICHOLAS
FILLMORE
UABASHA
HANCOCK
STODDARD.
WYOMING
BEAUFORT
RHEA
UILBARGER
CHITTENDEN
GRAND ISLE
GREENE
RUSSELL
FLORENCE
BARBOUR
BUTLER
FAYETTE
LAGRANGE
CLARK
MC LEAN
SCOTT
BOSSIER
CALDUELL
JACKSON
PENOBSCOT
DELTA
JACKSON
OTSEGO
ROSCOMMON
CHERRY
CHESHIRE
GENESEE
JEFFERSON
INDIANA
ANDREWS
CAMP
JEFFERSON
ASHLAND
CALHOUN
DESHA
DADE
LINCOLN
MASON
BUTLER
CRITTENDEN
UNION
MACKINAC
PRENTISS
CARROLL
ORLEANS
LOVE
PIKE"
D-49
152
152
152
152
152
152
152
152
152
152
152
152
152
152
152
152
153
153
153
153
153
153
153
153
153
153
153
153
153
153
153
153
153
153
153
153
153
153
153
153
154
15-4
154
154
154
154
154
154
154
154
154
154
154
154
-------
OBS STATE COUNTY
VARSCORE
2593
2594
2595
2596
2597
2598
2599
2600
2601
2602
2603
2604
2605
2606
2607
2608
2609
26 10
26 1 1
26 12
26 13
26 14
26 15
2616
2617
26 18
26 19
2620
,2621
2622
2623
2624
2625
2626
2627
2628
2629
2630
2631
2632
2633
2634
2635
2636
2637
2638
2639
2640
2641
2642
2643
2644
2645
2646
PA
TN
VT
VA
VA
WI
MI
AL
AR
GA
IN
IN
KA
KA
KY
KY
KY
KY
MA
MN
MS
MS
NJ
NY
PA
TX
UI
GA
GA
IN
KY
KY
KY
KY
LA
LA
MI
MN
MS
MO
NY
PA
UI
AL
AL
AR
AR
CT
IL
IL
KY
LA
MD
MI
WAYNE
GILES
ESSEX
AUGUSTA
ROCKINGHAM
CHIPPEMA
RUSK
DALLAS
DALLAS
MARE
HARRISON
KOSCIUSKO
EDWARDS
RENO
FAYETTE
JESSAMINE
MUHLENBERG
UOODFORD
MIDDLESEX
OLMSTED
LAMAR
PERRY
HUNTERDON
SCHENECTADY
LANCASTER
PANOLA
PRICE
COBB
FLOYD
STARKE
ANDERSON
BOURBON
BRECKINRIDGE
LINCOLN
EAST BATON ROUGE
WEBSTER
MONTCALM
BECKER
COVINGTON
DUNKLIN
ONEIDA
BRADFORD
POLK
GREENE
MARENGO
OUACHITA
PHILLIPS
WINDHAM
HARDIN
PULASKI
OHIO
LA SALLE
CHARLES
CALHOUN
154
154
154
154
154
'154
154
155
155
155
155
155
155
155
155
155
155
155
155
155
155
155
155
155
155
155
155
156
156
156
156
156
156
156
156
156
156
156
156
156
156
156
156
157
157
157
157
157
157
157
157
157
157
157
D-50
-------
OBS STATE COUNTY
VARSCORE
2647
2643
2649
2650
2651
2652
2653
2654
2655
2656
2657
2658
2659
2660
266 1
2662
2663
2664
2665
2666
2667
2668
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
2680
2681
2682
2683
2684
2685
2686
2687
2688
2689
2690
2691!
2692
2693
2694
2695
2696
2697
2698
2699
2700
MI
MI
MS
MS
NH
NY
NY
SC
VA
VA
MI
WI
AL
AL
AL
GA
GA
GA
IL
KY
KY
KY
LA
LA
ME
ME
MI
MI
MS
NJ
NY
TX
TX
TX
UV
MI
HI
MI
AR
CA
GA
GA
GA
KY
ME
MD
MA
MI
MN
NC
MI
MI
MI
AL
MASON
HEXFORD
FORREST
JEFFERSON DAVIS
BELKNAP
MONROE
MAYNE
CHARLESTON
GREENSVILLE
HANOVER
BARRON
MARINETTE
LAMRENCE
MONROE
ST CLAIR
GORDON
THOMAS
MUSCOGEE
MASSAC
BELL
BOYLE
EDMONSON
CLAIRBORNE
OUACHITA
HANCOCK
MASHINGTON
BRANCH
CHIPPEMA
HALTHALL
MARREN
OSMEGO
BAYLOR
BOMIE
MORRIS
GREENBRIER
LANGLADE
SAMYER
MOOD
LEE
KINGS
CATOOSA
DOUGHERTY
RANDOLPH
GRAYSON
YORK
PRINCE GEORGES
HAMPSHIRE
MECOSTA
OTTER TAIL
MADISON
IRON
MAR2UETTE
OCONTO
CHEROKEE
157
157
157
157
157
157
157
157
157
157
157
157
158
158
158
158
158
158
158
158
158
158
158
158
158
158
158
158
158
158
158
158
158
158
158
158
158
158
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
160
D-51
-------
OBS STATE COUNTY
VARSCORE
2701
2702
2703
2704
2705
2706
2707
2708
2709
27 10
27 1 1
2712
2713
27 14
2715
2716
2717
2718
2719
2720
2721
2722
2723
2724
2725
2726
2727
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743
2744
2745
2746
2747
2748
2749
2750
2751
2752
2753
2754
AL
AL
DE
GA
GA
KY
KY
KY
MN
OK
TX
UI
UI
GA
GA
LA
MD
MI
MN
MO
VA
VA
VA
VA
UI
AL
AL
AL
GA?
/-
LA
MI
MI
MI
NY
PA
VA
VA
VA
VA
VA
VA
UI
UI
UI
AL
AL
AR
GA
LA
ME
MI
MI
MS
NH
HOUSTON
MORGAN
NEW CASTLE
HARALSON
JOHNSON
CALOUELL
NELSON
OLDHAM
CASS
ELLIS
HASKELL
DOUGLAS
UAUSHARA
CLAY
TREUTLEN
RICHLAND
KENT
CRAUFORD
STEARNS
ST LOUIS CITY
HENRICO
BUENA VISTA CITY
PETERSBURG CITY
STAUNTON CITY
MARATHON
GENEVA
LIMESTONE
MADISON
CHATTAHOOCHEE
CONCORDIA
BERRIEN
GRAND TRAVERSE
LUCE
LEUIS
LACKAUANNA
EMPORIA CITY
HARRISONBURG CITY
HOPEUELL CITY
RADFORD CITY
UAYNESBORO CITY
UINCHESTER CITY
JUNEAU
LINCOLN
TAYLOR
CRENSHAU
ETOUAH
MISSISSIPPI
RICHMOND
GRANT
OXFORD
ALLEGAN
ST JOSEPH
HARRISON
MERRIMACK
160
160
160
160
160
160
160
160
160
160
160
160
160
161
161
161
16
16
16
16
16
16
16
161
161
162
162
.162
162
162
162
162
162
162
162
162
162
-162
162
162
162
162
162
162
163
163
163
163
163
163
163
163
163
163
D-52
-------
OBS STATE COUNTY
VARSCORE
2755
2756
2757
2758
2759
2760
2761
2762
2763
2764
2765
2766
2767
2768
2769
2770
277 1
2772
2773
2774
2775
2776
2777
2778
2779
2780
2781
2782
2783
2784
2785
2786
2787
2788
2789
2790
2791
2792
2793
2794
2795
2796
2797
2798
2799
2800
2801
2802
2803
2804
2805
2806
2807
2808
TN
VA
VA
VA
VA
VA
MI
AL
AL
GA
KY
LA
MI
MN
RI
VA
GA
GA
GA
GA
GA
KY
MD
MA
HA
NC
TN
AL
GA
IN
KY
MN
MS
RI
TX
KY
LA
MI
MI
MN
MS
SC
VA
VA
VA
AL
GA
LA
MI
NV
Ml
MA
NH
NH
MOORE
PAGE
ROCKBRIDGE
UARREN
BRISTOL CITY
LEXINGTON CITY
MENOMINEE
ESCAMBIA
LAUDERDALE
BALDWIN
SPENCER
MOREHOUSE
OCEANA
CHISAGO
BRISTOL
CAROLINE
CALHOUN
GRADY
JEFFERSON
TMIGGS
WASHINGTON
WASHINGTON
ST MARYS
NORFOLK
WORCESTER
MOORE
MAURY
FRANKLIN
BROOKS
PULASKI
SHELBY
CROW WING
GEORGE
PROVIDENCE
KNOX
MERCER
WEST CARROLL
MANISTEE
OSCODA
HUBBARD
STONE
LEXINGTON
ESSEX
FRANKLIN CITY
WILLIAMSBURG CITY
WASHINGTON
LAURENS
TENSAS
SCHOOLCRAFT
CARSON CITY CITY
PORTAGE
BRISTOL
HILLSBOROUGH
ROCKINGHAM
163
163
163
163
163
163
163
164
164
164
164
164
164
164
164
164
165
165
165
165
165
165
165
165
165
165
165
166
166
166
166
166
166
166
166
167
167
167
167
167
167
167
167
167
167
168
168
168
168
168
168
169
169
169
D-53
-------
OBS STATE COUNTY
VARSCORE
2809
2810
281 1
2812
28 3
28 4
28 5
28 6
28 7
28 8
28 9
2820
2821
2822
2823
2821
2825
2826
2827
2828
2829
2830
2831
2832
2833
2834
2835
2836
2837
2838
2839
2840
2841
2842
2843
2844
2845
2846
2847
2848
2849
2850
2851
2852
2853
2854
2855
2856
2857
2858
2859
2860
286 1
2862
SC
WV
MI
AL
GA
HA
HA
KY
MD
MI
MI
MN
MN
NH
NC
VA
GA
GA
GA
GA
KY
LA
MO
NY
VA
AL
CA
CA
GA
KY
KY
KY
LA
MD
VA
AL
CA
GA
GA
HA
KY
LA
NC
SC
TN
VA
VA
AL
AL
AL
AL
CA
KY
MA
RICHLAND
MONROE
UASHBURN
CONECUH
TAYLOR
KAUAI
MAUI
TODD
CALVERT
MUSKEGON
NEUAYGO
ANOKA
ISANTI
STRAFFORD
NASH
SUSSEX
CHARLTON
CHATTOOGA
WALKER
WILKINSON
WAYNE
AVOYELLES
PEMISCOT
KINGS
NEWPORT NEWS CITY
COFFEE
MERCED
YOLO
COFFEE
CUMBERLAND
HART
TRIGG
WEST BATON ROUGE
QUEEN ANNES
JAMES CITY
HENRY
SAN JOA2UIN
PULASKI
TOOMBS
HONOLULU
CHRISTIAN
FRANKLIN
HYDE
CHESTERFIELD
BEDFORD
KING WILLIAM
SOUTHAMPTON
COLBERT
COVINGTON
MOBILE
PIKE
SOLANO
LYON
SUFFOLK
D-54
169
169
169
170
170
170
170
170
170
170
170
170
170
170
170
170
171
171
171
171
171
171
171
171
171
172
172
172
172
172
172
172
172
172
172
173
173
173
173
173
173
173
173
173
173
173
173
174
174
174
174
174
174
174
-------
OBS STATE COUNTY
VARSCORE
2863
2864
2865
2866
2867
2868
2869
2870
2871
2872
2873
2874
2875
2876
2877
2878
2879
2830
2881
2882
2883
2884
2885
2886
2887
2888
2889
2890
289 1
2892
2893
2894
2895
2896
2897
2898
2899
2900
2901
2902
2903
2904
2905
2906
2907
2908
2909
2910
291 1
2912
2913
2914
2915
2916
PA
VA
VA
GA
GA
GA
KY
LA
MD
NC
VA
VA
VA
VA
VA
AL
FL
FL
GA
GA
MI
NJ
VA
FL
GA
GA
GA
GA
GA
KY
NY
VA
VA
VA
DE
DE
GA
GA
GA
GA
GA
KY
KY
KY
KY
LA
MD
MI
NJ
NC
SC
TN
VA
VA
CRAWFORD
KING GEORGE
PRINCE GEORGE
BARTOU
DODGE
SCREVEN
LOGAN
CATAHOULA
ANNE ARUNDEL
DARE
CHARLES CITY
KING AND QUEEN
NORFOLK CITY
POBUDSON
PORTSMOUTH CITY
BALDUIN
CALHOUN
HOLMES
BEN HILL
WHEELER
KALKASKA
CAMDEN
WESTMORELAND
JACKSON
BLECKLEY
MARION
POLK
WHITFIELD
UILCOX
PULASKI
QUEENS
RICHMOND
SURRY
VIRGINIA BEACH CITY
KENT
SUSSEX
CANDLER
DOOLY
EARLY
JENKINS
TURNER
CLINTON
MARION
MEADE
MARREN
POINTE COUPEE
TALBOT
LAKE
GLOUCESTER
MARTIN
KERSHAU
MARSHA.LL
CLARKE
HAMPTON CITY
174
174
174
175
175
175
175
175
175
175
175
175
175
175
175
176
176
176
176
176
176
176
176
177
177
177
177
177
177
177
177
177
177
177'
178
178
178
178
178
178
178
178
178
178
178
178
178
178
178
178
178
178
178
178
D-55
-------
OBS STATE COUNTY
VARSCORE
2917
2918
2919
2920
2921
2922
2923
2924
2925
2926
2927
2928
2929
2930
2931
2932
2933
2934
2935
2936
2937
2938
2939
2940
2941
2942
2943
2944
2945
2946
2947
2948
2949
2950
2951
2952
2953
2954
2955
2956
2957
2958
2959
2960
296 1
2962
2963
2964
2965
2966
2967
2968
2969
2970
AL
CA
GA
GA
GA
GA
GA
HA
LA
LA
MD
MO
TN
VA
WI
MI
CA
GA
GA
KY
VA
VA
VA
VA
GA
GA
GA
GA
GA
TN
VA
VA
FL
GA
GA
GA
GA
KY
KY
KY
MD
MM
MO
NC
FL
GA
GA
GA
GA
KY
NC
RI
SC
TN
DALE
SUTTER
APPLING
DECATUR
IRMIN
MONTGOMERY
TERRELL
HAWAII
MADISON
ST LANDRY
SOMERSET
NEW MADRID
DAVIDSON
ISLE OF WIGHT
ADAMS
VILAS
SACRAMENTO
CHATHAM
EMANUEL
LARUE
MIDDLESEX
NORTHUMBERLAND
CHESAPEAKE CITY
SUFFOLK CITY
BULLOCH
COL2UITT
CRISP
GLYNN
WORTH
WILSON
GLOUCESTER
LANCASTER
BRADFORD
CLINCH
JEFF DAVIS
TIFT
WEBSTER
CASEY
GREEN
HARDIN
DORCHESTER
BIG STONE
MISSISSIPPI
HALIFAX
ESCAMBIA
LANIER
MC INTOSH
MITCHELL
TELFAIR
TAYLOR
JOHNSTON
WASHINGTON
GEORGETOWN
RUTHERFORD
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
180
180
180
180
180
180
180
180
181
181
181
181
181
181
181
181
182
182
182
182
182
182
182
182
182
182
182
182
183
183
183
183
183
183
183
183
183
183
D-56
-------
OBS STATE COUNTY
VARSCORE
2971
2972
2973
2974
2975
2976
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
2987
2988
2989
2990
2991
2992
2993
2994
2995
2996
2997
2998
2999
3000
3001
3002
3003
3004
3005
3006
3007
3008
3009
3010
30 1 1
30 12
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
TN
VA
MI
FL
FL
GA
GA
MD
MN
NC
WI
FL
GA
KY
KY
KY
MD
KG
NC
NC
NC
SC
SC
VA
FL
MD
NC
NC
NC
NC
RI
SC
SC
SC
VA
KY
KY
MN
NC
NC
NC
NC
VA
GA
KY
NC
FL
GA
KY
NJ
NC
NC
NC
VA
TROUSDALE
YORK
BURNETT
JEFFERSON
MONROE
LEE
PIERCE
WORCESTER
UADENA
NORTHAMPTON
ONEIDA
UNION
BERRIEN
ADAIR
MONROE
RUSSELL
WICOMICO
EDGECOMBE
GREENE
HERTFORD
ROBESON
AIKEN
JASPER
ACCOMACK
CLAY
CAROLINE
HARNETT
SCOTLAND
TYRRELL
WASHINGTON
KENT
BERKELEY
COLLETON
DORCHESTER
NEU KENT
BARREN
METCALFE
SHERBURNE
BERTIE
PASBUOTANK
PITT
RICHMOND
NORTHAMPTON
CAMDEN
ALLEN
BEAUFORT
GADSDEN
EFFINGHAM
SIMPSON
BURLINGTON
COLUMBUS
PER2UIMANS
WILSON
MATHEWS
183
183
183
184
184
184
184
184
184
184
184
185
185
185
185
185
185
185
185
185
185
185
185
185
186
186
186
186
186
186
186
186
186
186
186
187
187
187
187
187
187
187
187
188
188
188
189
189
189
189
189
189
189
189
D-57
-------
OBS STATE COUNTY
VARSCORE
3025
3026
3027
3028
3029
3030
3031
3032
3033
3034
3035
3036
3037
3033
3039
3040
3041
3042
3043
3044
3045
3046
3047
3048
3049
3050
3051
3052
3053
3054
3055
3056
3057
3058
3059
3060
3061
3062
3063
3064
3065
3066
3067
3068
3069
3070
3071
3072
3073
3074
3075
3076
3077
3078
GA
GA
NJ
NJ
NC
TN
GA
GA
NC
GA
GA
MA
NC
NC
NC
SC
SC
LA
NC
GA
GA
SC
SC
GA
GA.
SC
SC
FL
GA
GA
GA
NC
SC
SC
SC
SC
NY
NC
NC
SC
SC
FL
NC
NC
SC
SC
FL
NC
FL
NY
GA
MA
NC
NC
BACON
LIBERTY
CAPE MAY
CUMBERLAND
CRAVEN
SMITH
ATKINSON
LONG
CAMDEN
COOK
SEMINOLE
ESSEX
GATES
JONES
PAMLICO
BARNUELL
CALHOUN
EAST CARROLL
CARTERET
BRYAN
LOUHDES
DARLINGTON
MARLBORO
BAKER
MILLER
DILLON
MARION
NASSAU
BRANTLEY
EVANS
TATTNALL
HOKE
FLORENCE
LEE
ORANGEBURG
WILLIANSBURG
SUFFOLK
CUMBERLAND
SAMPSON
CLARENDON
SUMTER
INDIAN RIVER
PENDER
WAYNE
ALLENDALE
BAMBERG
ALACHUA
LENOIR
LIBERTY
NASSAU
ECHOLS
BARNSTABLE
BRUNSWICK
DUPLIN
190
190
190
190
190
190
191
191
191
192
192
192
192
192
192
192
192
193
193
194
194
194
194
195
195
195
195
196
196
196
196
196
196
196
196
196
197
197
197
197
197
198
198
198
198
198
199
199
200
200
202
202
202
202
D-58
-------
OBS STATE COUNTY
VARSCORE
3079
3080
3081
3082
3083
30814
3085
3086
3087'
3088
3089
3090
309 1
3092
3093
3094
3095
3096
3097
3098
30-99
3100
310 1
3102
3103
3104
3105
3106
3107
3108
3109
3110
3111
3112
31 13
3114
31 15
3116
31 17
3118
3119
3120
3121
3122
3123
3124
3125
3126
3127
3128
3129
3130
3131
3132
NC
FL
GA
MA
NJ
NC
SC
FL
MA
NC
NC
FL
FL
NJ
FL
FL
SC
FL
MA
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
NC
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
NEW HANOVER
LEON
WAYNE
DUKES
OCEAN
BLADEN
HAMPTON
WASHINGTON
PLYMOUTH
CURRITUCK
CHOUAN
MADISON
SANTA ROSA
ATLANTIC
CHARLOTTE
HIGHLANDS
HORR"Y
GULF
NANTUCKET
ORANGE
BREVARD
FRANKLIN
LEVY
POLK
VOLUSIA
WAKULLA
BAKER
BAY
DIXIE
DUVAL
PUTNAM
GLADES
LAFAYETTE
PALM BEACH
PASCO
WALTON
COLUMBIA
LEE
SARASOTA
TAYLOR
ONSLOW
OSCEOLA
FLAGLER
MARTIN
OKEECHOBEE
ST LUCIE
OKALOOSA
HENDRY
ST JOHNS
CITRUS
DE SOTO
HARDEE
MANATEE
HERNANDO
202
203
203
203
203
203
203
204
204
204
205
206
206
206
207
207
207
209
209
210
212
21.2
212
212
212
2 12
213
213
213
2 13
213
215
215
216
2 16
216
2 17
2 17
217
217
217
218
219
219
2 19
219
220
221
221
222
222
222
222
224
D-59
-------
OBS STATE COUNTY VARSCORE
3133 FL GILCHRIST 226
3134 FL MARION 227
3135 FL HAMILTON 229
3136 FL LAKE 229
3137 FL HILLSBOROUGH 231
3138 FL SEMINOLE 231
3139 FL SUMTER 234
3140 FL PINELLAS 235
3141 FL SUWANNEE 235
3142 FL COLLIER 240
3143 FL BROUARD 245
3144 FL DADE 245
D-60
-------
APPENOIX-E
DESCRIPTION OF MEASUREMENT ERROR ANALYSIS
-------
APPENDIX-E
The 3,144 counties were stratified by Heath regions. The sample
of 223 counties described in detail in Appendix-B is referred to as the
large sample and the sample of 96 counties as the small sample. Both
samples were allocated proportionally to the strata. Sample counties
were selected from within strata with equal probability and with
replacement. The small sample is an equal probability, with
replacement subsample of the large sample.
The distribution of the two samples are shown in Table E-l. Note
that Hawaii is omitted from the large sample and Hawaii, Alaska and the
Colorado Plateau and Wyonihg Basin Region are omitted from the small
sample. Thus, the inference: that can be drawn from each sample are
somewhat different, neither being fully national. The average
VARSCORE computed from the large sample is 132.4 plus or minus 1.92.
That from the small sample is 137.4 plus or minus 3.67 (The VARSCORE
computed for the 3,144 data base was 134.1 plus or minus 1.0). The
plus and minus quantities are twice the standard error of the
estimates.
The sampling variances for each sample receive a contribution
from,
(a) the county to county differences in VARSCORES, and,
(b) some uncertainty associated with determining the scores for
each county.
An assessment of the separate components is complicated by correlations
that arise due to a. single individual scoring several counties. The
sampling design does not allow the correlations to be quantitated.
Consequently, the results are presented for a range of values of the
correlations. County to county differences are maximized relative to
the measurement" error if the correlations are small or zero. Negative
correlations are unlikely and are not considered. Because different
contractors determined the two scores for a county, the correlations
between the pairs of scores are taken as zero.
E-l
-------
TABLE E-l. Distribution of Quality Assurance Samples
By Ground Water Regions
TOTAL
GROUND WATER COUNTIES IN
REGION THE REGION
1
2
3
4
5
6
7
8
9
10
11
12
13
Western Mountain Ranges
Alluvial Basins
Columbia Lava Plateau
Colorado Plateau and Wyoming
Basin
High Plains
Nonglaciated Central
Glaciated Central
Piedmont and Blue Ridge
Northeast and Superior Uplands
Atlantic and Gulf Coastal Plain
Southeast Coastal Plain
Hawaii
Alaska
TOTAL
129
119
65
29
124
895
746
215
161
519
109
4
29
3144
SAMPLE
LARGE
SAMPLE
10
9
5
2
9
63
52
16
12
35b
7
0
2
222
SIZE
SMALL
SAMPLE
4
3
2
0^
4
27
23
7
5
16
3
0
Q3
94
a Sample size regarded as 0 because only 1 county coded.
b Craighead County, Arkansas was recoded but not included in error
calculations because of an incorrect FIPS code.
E-2
-------
The large sample results are considered first. The measurement
error variance is denoted by V£ , and the county to county differences
in VARSCORES are denoted by Vp. Using the extreme values of the
unknown correlation (zero and one) leads to the estimates,
0:806 < VP < 32.911,
Vy <_ 0.106.
The large sample result suggests that ground-water vulnerability strata
constructed using VARSCORES will likely reflect the variability of the
scoring procedure more than actual county differences. A substantially
different interpretation can be qiven :he result, however, if the
schedule associated with the recod-og of the large sample is
considered. All counties in the large sample were receded in only 1
week. An argument exists, therefore, that a valid measurement error
assessment is not provided by the large sample because sufficient time
was not available to accurately apply the numerical coding.
The small sample suffers less from possible scheduling problems
and, indeed, the results from the small sample estimate a smaller
measurement error component. Comparable results for the small .sample
are that,
1.222 < V. < 22.327,
VU 1 2.140.
Alaska and the Colorado Plateau and Wyoming Basin Region, both highly
variable Heath regions, were not included in the small sample. The
results, therefore, are not unequivocable. Table E-2 shows the results
obtained for values of the correlation of 0.0, 0.025, 0.05 and 0.10,
The results indicate that, if the correlations are less than about
0.025, the VARSCORES in the small sample are determined more by real
county differences than by measurement error.
Because the troublesome correlations cannot be estimated from the
data at hand, the measurement error assessment is less definitive than
might be desired. However, the distribution of counties classified as
E-3
-------
TABLE E-2. Measurement Error Variances and County VARSCORE
Differences For a Range of Correlations
VARSOCRE
VALUE OF MEASUREMENT DIFFERENCES
CORRELATION ERROR VARIANCE AMONG COUNTIES
0.0 1.222 2.140
0.025 1.749 1.612
0.05 2.277 1.084
0.10 3.332 0.029
E-4
-------
vulnerable based on their VARSCORES generally agrees with other
assessments of the areas in question. Counties are, of course, quite
variable with respect to hydrogeologic characteristics, making any
attempt to classify whole counties sensitive to measurement error
issues. The scoring procedure used in this assessment, although
inexact at the county level, nonetheless provides useful information
for stratifying the first stage area frame for the National Pesticide
Survey.
E-5
-------
APPENDIX-F
DESCRIPTION'AND RESULTS OF QUALITATIVE CHECK
-------
APPENDIX-F
As discussed in Section 3.5, the six counties selected for
separate coding by several nationally recognized hydrogeologists were
not included as part of the quality assurance program. This coding
effort was included in the study only to display qualitatively the
range of variability that might be expected among a set of experts in
•i
the field of hydrogeology. As such, it is not a statistical sample of
the total data base.
The six counties were selected primarily to ensure that a maximum
number of ground-water regions and diverse hydrogeologic settings would
be represented. A brief description of the hydrogeologic settings of
the six counties follows:
Summit County, Utah
This county lies within the ground-water regions of the Western
Mountain Ranges and the Colorado Plateau and Wyoming Basin. Summit
County is largely mountainous and is characterized by alpine glacial
topography and pre-Cambrian geology, erosional and structurally
controlled alluvial valleys, and large upland areas underlain by
consolidated and fractured sedimentary rocks.
Manatee County, Florida
This county lies entirely within the Southeast Coastal Plain ground-
water region. Manatee-county is characteristic of the Gulf coastal
lowland and is topographically controlled by a series of Pleistocene-
age marine terraces, underlain by a thick sequence of limestone and
dolomite and overlain by sands, clays and marls with interbedded
limestone.
Merced County, California
This county straddles the-Alluvial Basins and Western Mountain Ranges
ground-water regions. Merced County consists predominantly of low
alluvial plains, and coalescing alluvial fans of low relief in the
southwest, with significant dissection of upland topography as well
as river terrace and flood plain development in the northeast.
F-l
-------
Barber County, Kansas
This county lies within the Non-Glaciated Central and the High Plains
ground-water regions. Barber County is characterized by low relief,
thin soil cover, sparse alluvium less than 40 feet thick, and is
underlain by low-permeability shales and fine-grained sandstones.
Caroline County, Virginia
This county falls within the Atlantic and Gulf Coastal Plain and the
Piedmont and Blue Ridge ground-water regions. Caroline County has a
predominantly coastal plain topography of moderately low relief and
is underlain by gently, dipping unconsolidated and semi-consolidated
sand, silt and clay interbeds; in the extreme northwest, the county
is characterized by a thick regolith underlain by fractured bedrock
of sedimentary, igneous, and metamorphic origin.
Daviess County, Missouri
This county lies entirely within the Glaciated Central ground-water
region. Daviess County is generally characterized by dissected till
plains, frequently underlain by bedded sedimentary rocks, or, less
commonly,-glacial outwash.
The coding approaches used by the three groups of hydrogeological
experts varied in both the amount of time devoted to each county and
the level of research necessary to complete each form. Those forms
coded by the U.S. Geological Survey were distributed to appropriate
experts within six District Offices for the following respective
counties:
Summit County Salt Lake City, Utah
Manatee County Tampa, Florida
Merced County Sacramento, California
Barber County Lawrence, Kansas
Caroline County Richmond, Virginia
Daviess County Rolla, Missouri
The coding of all the counties by the U.S. Geological Survey hydroge-
ologists took place during the month of September 1985. Those forms
coded by Dr. Jay Lehr of the National Water Well Association were
completed over a 4-day period in early October 1985. Many of the
rankings made by Dr. Lehr were based on his personal experience with a
given area. Mr. Ralph Heath had constraints on the amount of time he
could devote to the coding exercise. Consequently, nearly all of his
rankings are based on general experience in a given location and from
regional reports.
F-2
-------
Each of the coders had specific comments and recommendations
concerning the use of the numerical scheme for a county-level ranking
effort. The representative of the USGS Salt Lake City District Office
found it inappropriate to come up with one overall Agricultural DRASTIC
score to cover a county as large as Summit County, Utah (1,800 square
miles). Dr. Lehr found it an interesting challenge to assemble all
the information necessary to enable county-wide generalizations of
•?
hydrogeologic conditions. Mr. Heath had some comments questioning the
usefulness of such factors as impact of vadose zone media and hydraulic
conductivity in the overall DRASTIC system and felt that assigning
index variability values to scores that were based on county-level
generalizations was not meaningful.
The experts' ranges of numerical scores for a given county (Table
F-l) varied from a low spread of VARSCORES of 19 points (Manatee
County, Florida) to a larger spread of 58 points (Daviess County,
Missouri). The standard errors (standard deviation divided by the
mean) associated with different scores coded by the experts ranged from
5.8 for Manatee, County Florida to 16.9 for Daviess County, Missouri.
In general, the standard errors in numerical scores between experts are
similar to those for the same counties coded by the EPA contractors
(Table F-l).
Estimates of the coefficient~of variation of the scores for the 6
qualitative check counties coded by the 3 experts and the 3 contractors
are presented in Table F-2. The coefficient of variation for a given
county is defined as the standard deviation divided by the mean, times
100. The estimates of the coefficient of variation values ranged from
9 to 21 percent for the 6 qualitative check counties. The average
coefficient of variation for the qualitative check counties, with 6
observations per county, was 13 percent of the mean for those 6
counties.
F-3
-------
Table F-l. SUMMARY OF QUALITATIVE CHECK
DRASTIC FACTOR
Barber, KA
Carol Ine, VA
Da v less, MO
Manatee, FL
Merced, CA
Summit, UT
EXPERTS
Weighted Components of
Agricultural DRASTIC Scores
USGS LEHR HEATH
125 1240 151
166 178 148
93 151 128
211 207 226
158 129 145
136 102 85
Standard
Mean Error
133 8.8
164 8.7
124 16.9
215 5.8
144 8.4
108 15.0
CONTRACTORS
Weighted Components of
Agricultural' DRASTIC Scores
WES WCC RTI
128 150 128
167 130 158
125 90 140
222 177 192
129 149 170
102 95 99
Standard
Mean Error
135 7.3
152 II. 1
118 14.8
197 13.2
149 11.8
98 2.0
COMPOSITE
Standard
Mean Error
134 5.2
158 6.9
121 10.1
206 7.6
147 6.6
103 7.1
-------
Table F-2. STATISTICAL EVALUATION CONCERNING THE MULTIPLE CODING
OF THE QUALITATIVE CHECK COUNTIES
County
Barber, KA
Caroline, VA
Daviess, MO
Manatee, FL
Merced, CA
Summit, UT
Mean VARSCORE
134
158
121
206
147
103
Standard
Deviation
13
17
25
19
16
17
Coefficient of
Variation %
9
11
21
9
n
17
F-5
-------
A comparison of the components of the numerical scores for each of
the six qualitative check counties is also included in this Appendix
(Tables F-3 to F-8). The county with the highest standard error
associated with scores generated by the experts is Daviess County,
Missouri. Most of the scoring variability in this county is
attributable to differences in opinion on the vadose zone media
(standard error of 11.5), net recharge (standard error of 9.7), and
depth to water (standard error of 8.0). Standard errors for the other
hydrogeologic factors in the county were significantly lower (Table
F-3).
The standard error associated with scores coded by the experts for
Summit County, Utah was the second highest. Most of the scoring
variability in this county (Table F-4) is attributable to differences
in opinion on net recharge (standard error of 4.3), soil media
(standard error of 3.5) and aquifer media (standard error of 3.4) The
standard errors for the other hydrogeological factors in the county
were significantly lower.
The standard errors in VARSCORE values coded by the experts for
Barber County, Kansas, Caroline County, Virginia, and Merced County,
California were similar at 8.8, 8.7 and 8.4, respectively. The
majority of the scoring variability in Barber County, Kansas (Table
F-5) is due to differences in opinion on vadose zone media (standard
error of 5.3), soil media (standard error of 5.3) and depth to water
(standard error of 4.7). The standard errors for the other
hydrogeologic factors in the county ranged from 0.4 to 2.7.
The major factors contributing to standard error in VARSCORE
determinations by the experts in Caroline County Virginia were net
recharge, aquifer media, and vadose zone media, all with a standard
error of 3.2 (Table F-6). The variablity associated with the scores
for Merced County, California pertains to the way the coders accounted
for artificial recharge. The standard error associated with net
recharge in the county is 9.7, while the range in standard errors for
the other hydrogeologic factors is from 0.4 to 5.4 (Table F-7).
The standard errors for the seven factors contributing to the
VARSCORE determinations for Manatee County, Florida are presented in
Table F-8.
F-6
-------
Table F-3. RESULTS OF QUALITATIVE CHECK: Davless County, Missouri
DRASTIC FACTOR
Depth to Water
Net Recharge
Aqul far Media
Soil Media
Topography
Impact of Vadose
Conductivity
Weighted Score
EXPERTS
Weighted Components of
Agricultural DRASTIC Scores
USGS LEHR HEATH
25.5 27.0 40.0
4.8 24.0 12.0
19.8 21.6 18.0
18.8 21.8 19.0
17.4 23.7 27.0
4.0 24.0 4.0
2.5 8.8 8.0
93 151 128
Standard
Mean Error
30.8 4.6
13.6 5.6
19.8 1.0
19.9 1.0
22.7 2.8
10.7 6.7
6.4 2.0
124 16.9
CONTRACTORS
Weighted Components of
Agricultural DRASTIC Scores
WES WCC RTI
35.8 9.5 28.3
12.0 4.0 4.0
24.0 19.2 22.8
18.8 19.0 28.0
27.9 23.0 27.3
4.0 12.0 26.4
2.9 3.2 3.1
125 90 140
Standard
Mean Error
24.5 7.8
6.7 2.7
22.0 1.4
21.9 3.0
26.1 1.5
14.1 6.6
3.1 O.I
118 14.8
COMPOSITE
Standard
Mean Error
27.7 4.3
10. 1 3.2
20.9 0.9
20.9 1.5
24.4 1.6
12.4 4.3
4.8 1.2
121 10. 1
-------
Table F-4. RESULTS OF QUALITATIVE CHECK:' Summit County, Utah
DRASTIC FACTOR
Depth to Water
Net Recharge
Aquifer Media
Soil Media
Topography
Impact of Vadosc
Conductivity
Weighted Score
EXPERTS
Weighted Components of
Agricultural DRASTIC Scores
USGS LEHR HEATH
10.5 12.5 6.0
19.0 II. 0 4.0
20.6 12.8 9.0
44.8 33.3 35.3
12.3 10.2 13.8
23.4 18.0 15.2
5.4 3.7 2.0
136 102 85
Standard
Mean Error
9.7 1.9
11.3 4.3
14.1 3.4
37.8 3.5
12.1 1.0
18.9 2.4
3.7 1.0
108 15.0
CONTRACTORS
Agricultural DRASTIC Scores
WES WCC RTI
15.5 8.0 6.0
8.4 4'. 8 7.6
13.8 19.8 15.6
29.3 27.5 36.3
13.2 8.1 9.0
19.2 20.8 20.4
2.2 6.0 3.6
102 95 99
Standard
Mean Error
9.8 2.9
6.9 I.I
16.4 1.8
31.0 2.7
10. 1 1.6
20.1 0.5
3.9 I.I
98 2.0
COMPOSITE
Standard
Mean Error
9.8 1.6
9.1 2.2
15.3 1.8
34.4 2.5
II. 1 1.0
19.5 I.I
3.8 0.7
103 7.1
00
-------
Table F-5. RESULTS OF QUALITATIVE CHECK. Barber County, Kansas
DRASTIC FACTOR
Depth to Water
Net Recharge
Aquifer Media
Soil Media
Topography
Impact of Vadose
Conductivity
Weighted Score
EXPERTS
Weighted Components of
Agricultural DRASTIC Scores
USGS LEHR HEATH
20.4 15.0 31.0
4.0 4.0 12.0
20.5 19.5 19.2
28.8 32.0 46.0
20.9 25.4 29.4
24.0 24.0 8.0
6.6 4.1 5.6
125 124 151
Standard
Mean Error
22.1 4.7
6.7 2.7
19.7 0.4
35.6 5.3
25.2 2.5
18.7 5.3
5.4 0.7
133 8.8
CONTRACTORS
Weighted Components of
Agricultural DRASTIC Scores
WES WCC RTI
16.5 34.6 31.0
25.6 4.0 7.6
19.8 24.0 13.8
28.0 19.3 24.3
27.0 29.1 21.3
8.0 24.0 24.8
3.0 15.2 5.6
128 150 128
Standard
Mean Error
27.2 5.4
12.4 6.7
19.2 3.0
23.9 2.5
25.8 2.3
18.9 5.5
7.9 3.7
135 7.3
COMPOSITE
Standard
Mean Error
24.7 3.4
9.5 3.5
19.5 1.3
29.7 3.7
25.5 1.5
18.8 3.4
6.7 1.8
134 5.2
I
UD
-------
Table F-6. RESULTS OF QUALITATIVE CHECK: Caroline County, Virginia
DRASTIC FACTOR
Depth to Water
Net Recharge
Aquifer Media
Soil Media
Topography
Impact of Vadosc
Conductivity
*
Weighted Score
EXPERTS
Weighted Components of
Agricultural DRASTIC Scores
USGS LEHR HEATH
30.3 39.2 36.0
27.6 36.0 25.6
19.0 22.8 12.0
33.7 32.8 34.0
29.7 22.2 27.6
19.6 18.8 9.6
6.5 5.8 3.2
166 178 148
Standard
Mean Error
35.2 2.6
29.7 3.2
17.9 3.2
33.5 0.4
26.5 2.2
16.0 3.2
5.2 1.0
164 8.7
CONTRACTORS
Weighted Components of
Agricultural DRASTIC Scores
WES WCC RTI
33.5 8.5 37.5
28.4 16.0 30.4
24.0 21.6 21.6
29.3 20.5 23.5
27.2 28.2 21.7
21.0 24.0 16.0
3.7 II. 0 7.6
167 130 158
Standard
Mean Error
26.5 9.1
24.9 4.5
22.4 0.8
24.4 2.6
25.7 2.0
20.3 2.3
7.4 2.1
152 -ll.l
COMPOSITE
Standard
Mean Error
30.8 4.6
27.3 2.7
20.2 1.8
29.0 2.3
26.1 1.4
18.2 2.0
6.3 1.2
158 6.9
-------
Table F-7. RESULTS OF QUALITATIVE CHECK: Merced County, CalIfornla
DRASTIC FACTOR
Depth to Water
Net Recharge
Aquifer Media
Soil Media
Topography
Impact of Vadosc
Conductivity
Weighted Score
EXPERTS
Weighted Components of
Agricultural DRASTIC Scores
USGS LEHR HEATH
15.5 6.0 24.8
36.0 11.2 4.0
24.0 22.6 23.1
30.0 34.3 33.0
24.5 18.6 27.8
24.0 23.6 22.2
4.2 12.7 10.5
158 129 145
Standard
Mean Error
15.4 5.4
17.1 9.7
23.2 0.4
32.4 1.3
23.6 2.7
23-. 3 0.5
9.1 2.5
144 8.4
CONTRACTORS
Weighted Components of
Agricultural DRASTIC Scores
WES WCC RTI
18.1 35.7 41.3
9.6 9.2 34.4
21.0 23.1 19.7
25.0 25.8 27.8
20.7 24.0 22.9
27.2 18.4 19.6
7.0 12.6 4.2
129 149 170
Standard
Mean Error
31.7 7.0
17.7 8.3
21.3 1.0
26.2 0.8
22.5 1.0
21.7 2.8
7.9 2.5
149 11.8
COMPOSITE
Standard
Mean Error
23.6 5.4
17.4 5.7
22.3 0.7
29.3 1.6
23.1 1.3
22.5 1.3
8.5 1.6
147 6.6
-------
Table F-8. RESULTS OF QUALITATIVE CHECK: Manatee County, Florida
DRASTIC FACTOR
Depth to Water
Net Recharge
Aquifer Media
Soil Media
Topography
Impact of Vadose
Conductivity
Weighted Score
EXPERTS
Weighted Components of
Agricultural DRASTIC Scores
US6S LEHR HEATH
48.0 43.0 44.5
32.8 36.0 36.0
24.0 24.0 30.0
43.7 42.0 37.6
30.0 28.2 30.0
30.4 24.0 32.0
2.0 9.9 16.0
211 207 226
Standard
Mean Error
45.2 1.5
34.9 I.I
26.0 2.0
41.1 1.8
29.4 0.6
28.8 2.4
9.3 4.1
215 5.8
CONTRACTORS
Weighted Components of
Agricultural DRASTIC Scores
WES WCC RTI
50.0 6.5 49.0
36.0 24.8 26.4
24.3 28.2 21.6
45.0 43.0 36.0
30.0 29.8 30.0
31.6 33.6 26.4
4.6 II. 0 2.3
222 177 192
Standard
Mean . Error
35.2 14.3
29.1 3.5
24.7 1.9
41.3 2.7
29.9 0.0
30.5 2.1
6.0 2.6
197 13.2
COMPOSITE
Standard
Mean Error
40.2 6.8
32.0 2.1
25.4 1.3
41.2 1.5
29.7 0.3
29.7 1.5
7.6 2.3
206 7.6
I
I—•
ro
-------
APPENDIX G
DESCRIPTION OF ADJUSTMENTS TO ORIGINALLY CODED DATA BASE
-------
APPENDIX-G
INTRODUCTION
Several preliminary maps of ground-water vulnerability were
produced from the originally coded data base. A number of areas on
these preliminary maps stood out in obvious contrast to surrounding
states and/or counties as a result of- differences in opinion between
coders on the ranking of individual DRASTIC factors. It was apparent
that some of the areas of greatest contrast needed to be resolved prior
to any further refinement of the categories of ground-water vulner-
ability or use of the data base for stratifying the National Pesticide
Survey sampling frame.
A reevaluation process was. conducted by RTI in December 1985 with
the following objectives:
0 to develop and evaluate computer-generated maps of the
individual DRASTIC factors on a regional scale in an attempt to
further identify unusual areas or areas of greatest contrast;
• to attempt to briefly resolve, from a regional perspective,
some of the areas of greatest contrast;
• to revise portions of the originally coded 'data base in an
attempt to improve the general reliability of the data base;
• to assess different categories of ground-water vulnerability
and evaluate how they compare with regionally known hydrogeo-
logic conditions; and
• to determine the most appropriate category of ground-water
vulnerability to serve the needs of the National Pesticide
Survey.
Three categories were selected for each DRASTIC factor for sorting
the originally coded data base and constructing computer-generated
maps. A single weighted rating was selected for each particular
category to reflect the predominant condition coded for each county.
These weighted ratings were grouped into three categories for
convenience in making regional comparisons and, therefore, were not
always true reflections of the actual county conditions, particularly
in counties with highly variable conditions.
6-1
-------
A review of the individual plots of the originally coded DRASTIC
factors revealed several sources of the regional contrasts and
inconsistencies that were observed on the working plots of different
ground-water vulnerability categories. The greatest contrasts are
briefly described by geographical areas in the following subsections.
Specific adjustments that were made to the originally coded data base
are provided in the following sections along with the descriptions for
each geographical area. These adjustments are also listed as adjusted
VARSCORES in Appendices A and C. The most notable changes to the
originally coded data base included the depth to water, net recharge,
and aquifer media factors. Relatively few revisions were made for
hydraulic conductivity, and no changes were made in the original data
base for soil media (one of the more difficult factors to reevaluate in
a short time frame) or topography (regarded as one of the most reliable
factors for the scale of this project).
Sources of information used to make the adjustments are also
indicated and primarily included contacts with hydrologists within
district offices of the U.S.G.S.; the National Water Summary recently
published (1984) by the U.S.G.S.; overviews of the regional
inconsistencies by Mr. Ralph C. Heath and Mr. Harry E. LeGrand; and
personal knowledge or experience about hydrogeologic conditions in
certain regions.
Great Lakes - Midwestern States
The depth to water map indicated a contrast in the originally
coded data base concerning ground-water depths east of the Mississippi
River (Illinois, Indiana, and Ohio) versus west of the Mississippi
River (Iowa and Missouri). A related contrast was also apparent in the
coding of aquifer media in Iowa, Missouri, and Illinois. Gross aquifer
characteristics in this region (Heath, 1984; U.S.G.S., 1984) suggest
that the actual ground-water vulnerability differences due to these
factors is probably not as great as originally coded. It appeared that
the contrasts more reflected the differences of opinion between coders
with regard to the importance of shallow aquifer systems within the
glacial drift in these areas than actual hydrogeologic differences.
G-2
-------
To address this issue, representatives of the U.S.G.S. and several
state agencies were contacted with questions concerning the extent,
thickness, water-bearing characteristics, and potential use of sand and
gravel lenses within the glacial drift in this region (Missouri
Geological Survey, 1985; Illinois Geological Survey, 1985; U.S.
Geological Survey, 1985 a and b). In addition, several national,
regional and state references were employed to gain a better
•;
understanding of the bedrock aquifer conditions (Knight, 1962; Burger
et al., 1971; U.S.G.S., 1984; Todd, 1983; Coble, 1971; Twenter and
Coble, 1965; Piskin and Bergstrom, 1975; Eagon, 1973; Wayne, 1958).
The revisions made to the data base were as follows:
Missouri
• revised depth to water percentages in all counties within the
glacial drift area of northern Missouri to reflect relatively
shallow unconfined aquifers commonly used for farm supply.
• revised depth to water, aquifer media, and impact of the yadose
zone media percentages in several counties along the Mississippi
and Missouri Rivers to reflect shallow ground-water occurrence in
alluvial aquifers.
t revised net recharge in extreme southeastern counties to agree
with recharge revisions to the Mississippi embayment.
Iowa
• revised depth to water in several counties in southern and south-
eastern Iowa to reflect shallow unconfined aquifers in the glacial
drift and along the Mississippi R4ver.
Illinois, Indiana, Western Ohio
• revised the depth to water, aquifer media and impact of vadose
zone in all counties within the glacial drift areas to reflect
relatively shallow unconfined aquifers commonly used for farm
supply.
• revised depth to water, aquifer media and impact of vadose zone
media in all counties with sigificant ground-water supplies in
relatively deep bedrock aquifers.
• revised depth to water, aquifer media and impact of vadose zone
media in counties near rivers or major streams to reflect shallow
ground-water occurrence in alluvial aquifers.
6-3
-------
Southwestern United States
One of the originally coded ground-water vulnerability maps
depicted the southwestern United States (Nevada, Arizona, New Mexico,
southeastern California, and parts of Utah and Colorado) as moderately
vulnerable. Counties in this region fall largely within the Colorado
Plateau or Alluvial Basins ground-water province where permeable
aquifers exist at substantial depths.
A review of the original coding for depth to water and aquifer
media in Arizona, western Utah, Nevada, and southeastern California
revealed that no significant revisions to the DRASTIC factors were
required to bring the scores more into agreement with known
hydrogeologic conditions in the area. Most of the area was coded as
haying permeable aquifers at depths greater than 100 feet. While such
a description is true, it may not adequately portray the degree of
protection provided by substantially deeper water tables. For this
reason, it was felt that some revision to the VARSCORES were
necessary.
Other contrasts that became apparent during the review of the
original coding of this area concerned the aquifer media and hydraulic
conductivity in seven counties in New Mexico and one county in Arizona,
and the depth to water in eight Colorado counties. Revisions to these
counties were based predominantly on a review of published reports
(U.S.G.S., 1984) and on assumptions made in the original coding of the
surrounding counties. A final revision to this region involved the net
recharge values in portions of 16 counties in the Central Valley of
California to reflect artificial ground-water recharge in the area
(Heath, 1985; U.S.G.S., 1984). One apparent inconsistency in the data
base concerns the absence of consideration given to artificial recharge
in additional areas of the Southwest. The counties in this area of the
United States tend to be large in areal extent with only small,
localized pockets of agricultural development. In some of these
localized areas, irrigation and artificial recharge practices are
extensively used. The exact percentage of any given county to which
artificial recharge considerations are pertinent does not appear to be
well documented. In most cases, however, the estimated percentage of
the county to which artificial recharge considerations might be applied
6-4
-------
is generally less than about 40 percent. In many counties, such
consideration would not cause a sufficient change in VARSCORE to cause
a misclassification of the vulnerability category. While some counties
in this area might be misclassified by as much as one category of
vulnerability, (e.g. Yuma, Arizona), no counties appear to be misclas-
sif ied by two categories on the basis of lower net recharge values.
For this reason, in combination with the lack of exact information
•t
concerning the degree of artificial recharge in the area, additional
revisions to the data base for artificial recharge in the Southwest
were considered unnecessary.
The adjustments made to the southwestern United States were:
Arizona
• subtracted 15 points under the Index Variability category for all
counties in order to account for the extremely deep water-table
conditions.
t revised the aquifer media and hydraulic conductivity for Green lee
County to reflect 60 percent of the county dominated by basin-fill
aquifers, 30 percent of the county covered by igneous and
metamorphic rocks, and 10 percent of the county covered by
fractured basalt.
Nevada
t modified the depth to water slightly to emphasize the deeper
aquifers in the 11 counties that fall within the Alluvial Basins
ground-water province.
• subtracted 10 points under the Ind'ex Variability category for the
11 Alluvial Basins counties in order to account for deeper water-
table conditions.
California
• subtracted 10 points under the Index Variability category for five
counties in the Alluvial Basins province and along the Nevada
border. This revision was to account for deeper water-table
conditions in this area.
• modified the depth to water slightly in Imperial County to
emphasize the deeper aquifer system.
• revised the net recharge (to 10 or more inches per year) for
portions of 11 counties in the Central Valley area to account for
artificial recharge.
New Mexico
• revised the aquifer media and hydraulic conductivity in five
counties to reflect a greater percentage of the county dominated
by basin-fill aquifers, and a lesser percentage of the county
covered by fratured basalt aquifers.
G-5
-------
New Mexico (continued)
• revised the aquifer media and hydraulic conductivities for Catron
and Grant counties to reflect a greater percentage of the counties
dominated by igneous and metamorphic rocks and a lesser percentage
of the counties covered by fractured basalt and basin-fill
aquifers.
Colorado
• modified the depth to water to reflect deeper conditions in eight
counties that appeared unusually shallow in the original depth to
water map.
Midwestern States
The map of hydraulic conductivity depicted unusually high values
for western Kansas, in contrast with the lower values sho in Nebraska
and the more moderate values in Colorado, Oklahoma, and Texas. The
dominant aquifer in this area is the Hi-gh Plains aquifer system, a
series of unconfined, unconsolidated clay, sand, silt and gravel
deposits of Cenozoic age (U.S.6.S., 1984).
A reevaluation of the original coding of western Kansas yielded
some questions concerning the aquifer media and the associated
hydraulic conductivity in the counties that border the outcrop area of
the High Plains aquifer in central Kansas. The aquifer media in many
of these counties was originally coded as sand and gravel, but should
coincide with the outcrop area of Cretaceous-aged bedded shales,
sandstones and limestones (U.S.G.S., 1984).
The hydraulic conductivity contrast was resolved based on
discussions with representatives of the Kansas District Office of the
U.S.G.S. (U.S.G.S., 1985c). The revisions made to the hydraulic
conductivity values in the High Plains aquifer in Kansas were in
general agreement with those values coded for the same aquifer in
Colorado, Nebraska, Oklahoma, and Texas. The questions concerning the
aquifer media and conductivity for counties bordering the High Plains
aquifer outcrop area were resolved based on two published reports
(U.S.G.S., 1984; Fader and Stullken, 1978). The revisions made to
Kansas were:
• corrected aquifer media and hydraulic conductivity for 14 counties
in central Kansas to reflect varying percentages of bedded
sedimentary units and alluvial deposits along thre outcrop fringe
of the High Plains aquifer.
G-6
-------
Kansas (continued)
• revised hydraulic conductivity ranges for 33 counties in western
Kansas to reflect typical values for the High Plains aquifer.
Hydraulic conductivity values in this aquifer range from 100 to
300 gallons per day per square foot (gpd/sq. ft.) in northwestern
Kansas to as much as 381 gpd/sq. ft. in some parts of southwestern
Kansas.
Eastern States
Review of the originally coded net recharge map revealed a
contrast along the Tennessee-Kentucky border, and along the North
Carolina-Virginia border. While some change in recharge rates over
such a broad area would be reasonable, such a sharp boundary along
state lines was suspect. Published recharge values for this area
report roughly 8 inches per year (in/yr) in central and western
Virginia, 8 to 16 in/yr in the Piedmont and Blue Ritlge Region of North
Carolina, roughly 5 in/yr in southern Kentucky, a maximum of 8 in/yr
western Tennessee, and 2 in/yr in Ohio (U.S.G.S., 1984). Based on
these values, revisions made to this area were:
Tennessee
• revised the recharge rates in central and western Tennessee,
excluding the counties that border the Mississippi River.
Virginia
0 revised the recharge rates to 7 to 10 in/yr for counties-within
the Valley and Ridge, Blue Ridge, and Piedmont Provinces.
Kentucky
• revised the recharge rates to 7 to 10 in/yr for eight counties
within the Atlantic and Gulf Coastal Plain Province.
North Carolina
t revised the depth to water to reflect deeper conditions" in the
counties of the Blue Ridge that appeared unusually shallow
for mountaineous conditions.
North Central and Western States
The net recharge map indicated unusually high amounts of ground-
water recharge in parts of western North Dakota and southwestern and
southern Montana, relative to the surrounding states. Revisions the
counties were based predominantly on review of published reports
(U.S.G.S., 1984) and on assumptions made in the original coding of the
surrounding counties. The revisions made to North Dakota and Montana
were:
6-7
-------
North Dakota
• modified net recharge in western North Dakota for counties
containing the Fort Union and Hell Creek - Fox Hills aquifer
systems.
• revised recharge for eastern North Dakota to correspond to the
Great Plains (Dakota) aquifer system.
• changed depth to water to reflect deeper conditions in counties
without substantial unconsolidated alluvial aquifers.
Montana
• modified net recharge to reflect more arid conditions uniformly
across the state.
• corrected depth to water to reflect slightly deeper conditions in
extreme northwestern counties.
South-Central States
The Mississippi embayment represents the predominant feature of
the Gulf Coastal Plain, and includes all of Louisiana and Mississippi
and portions of Texas, Arkansas, Alabama, western Tennessee, south-
eastern Missouri, and southwestern Kentucky. The embayment is compose
of unconsolidated sands, gravels, silt and clay transported by stream
from the adjoining uplands. The sediments attain their greatest
thickness along the seaward edge and the axis of embayment. The older
formations crop out along the inner margin'of the embayment in a series
of bands that roughly parallel the coast and axis of the embayment.
Although a number of complexly interbedded aquifer systems exist with
the embayment, they generally have similar hydrogeologic character-
istics where continuous.
A number of contrasts were apparent in the originally coded data
base within the south central states. The depth to water was regarded
as deeper than that of the Atlantic Coastal Plain. Notable contrasts
in net recharge were apparent in Louisiana as compared with adjoining
areas of Texas and Arkansas. The aquifer media was coded as bedded
sedimentary deposits (mostly massive shale) for most of the coastal
areas of Texas and Louisiana. High values of hydraulic conductivity in
south central Louisiana were also in contrast with adjoining areas.
One explanation for some of these contrasts was the deliberate coding
of the lowest ratings for some of the DRASTIC factors, re&ognizing the
limited potable use of certain shallow aquifers in the coastal areas as
a result of brackish ground-water conditions.
6-8
-------
To address these issues, representatives of the U.S.G.S. were
contacted (U.S.G.S., 1985 d, e, and f) and regional and State
references were used to gain a better understanding of the shallow
ground-water potential in the embayment (U.S.G.S., 1984). The
revisions made to the data base regarding these contrasts are as
follows:
Louisiana
• revised depth to water, aquifer media, and impact of vadose zone
media in the immediate coastal parishes to reflect deep
unconsolidated aquifers. Subtracted 10 points under the Index
Variability category to account for predominantly brackish ground
water conditions.
• revised depth to water in several parishes where alluvial aquifer
predominate.
• revised depth to water, -net recharge, and aquifer irfedia in 12
parishes where the Cockfield/Sparta aquifer systems predominate.
• revised depth to water, net recharge, and aquifer media in 23
parishes where the Chicot or deeper aquifer systems predominate.
• revised depth to water and net recharge in several counties where
the Wilcox/Carrizo aquifer systems predominate.
Texas
» revised aquifer media and impact of the vadose zone media for
approximately 100 coastal counties to reflect unconsolidated
aquifers.
Arkansas
• revised depth to water and net recharge in 7 southeastern counties
that parallel the Mississippi River to reflect relatively shallow
ground-water occurrence in alluvial aquifers.
Mississippi
t revi-sed net recharge in 11 northwestern counties to be consi-stency
with alluvial aquifers.
• revised depth to water in 5 southeastern counties for consistency
with coastal conditions.
Alabama
• revised depth to water in several southwestern counties for
consistency with coastal conditions.
Missouri
• revised net recharge in extreme southeastern counties to be
consistent with alluvial aquifers.
Kentucky
t revised net recharge rates for 8 southwestern counties within the
Mississippi embayment.
G-9
-------
New England States
The originally coded depth to water map for this region displayed
an unusual boundary between shallow ground-water conditions in Vermont,
New Hampshire, and Maine, and somewhat deeper ground-water conditions
in New York and Massachusetts. These boundaries are a reflection of
the proximity of several ground-water and geologic provinces that occur
in this region, and display some of the error associated with averaging
geologic conditions over a county-sized area.
Ground-water conditions in central and southwestern Vermont are
characteristic of those in the New England Province where thick,
coarse-grained glacial outwash deposits occupy former valleys, and
thin, silty glacial till deposits overlie fractured metamorphic bedrock
(Wright, 1974; U.S.G.S., 1984; Todd, 1983). While the outwash deposits
are small in area! extent covering only 10 to 25 percent of a county
area, they are the dominant water-supply aquifers of the province.
Given the productive nature of these outwash aquifers, it was felt by
the original coders and subsequently by RTI that they should be ranked
as highly vulnerable. The adjacent Hudson-Champlain Valley Province
extends as a thin band between the New England and Adirondack Provinces
in northwestern Vermont and into eastern New York. The productive
aquifers in this province occur as valley-fill deposits surrounded by
thin, clayey till over sandstones, quartzites, and carbonates.
Immediately to the west lies the Adirondack Province, a circular uplift
of crystalline granitic and gneissic rocks with some alluvium and
glacial outwash present in the valleys.
Most counties within this area straddle at least one of these
provicial boundaries. Thus the effect of any one rock-aquifer unit is
minimized when averaged over an entire county area. A review of the
hydrogeologic conditions across the region yielded depth to water
rankings very similar to those originally coded for the counties in the
area. For this reason, it was decided that no significant revisions to
the data base were necessary for this area.
G-10
-------
REFERENCES
Burger, A. M., J. L. Forsyth, R. S. Nicoll, and W. J. Wayne, 1971.
Geologic Map of the 1" x 2° Muncie Quadrangle, Indiana and Ohio--
Showing Bedrock and Unconsolidated Deposits, Indiana Department of
Natural Resources.
Coble, R. W., 1971. The Water Resources of Southeast Iowa, Iowa
Geological Survey Water Atlas Number 4, 101 p.
•t
Eagon, Herbert B., Jr., 1971. Report of Ground-Water Investigations:
Ground-Water Levels in the Vicinity of Van Wert, Van Wert County,
Ohio. Ohio Department of Natural Resources Division of Geological
Survey.
Fader, S. W. and L. E. Stullken, 1978. Geohydrology of the Great Bend
Prairie, South-Central Kansas, Irrigation Series 4, Kansas
Geological Survey, 19 p.
Heath, R. C., 1985. Personal Communication, December 11, 1985.
Illinois Geological Survey, 1985. Telephone Conversation with Bill
Dickson, Illinois State Geological Survey, December 16, 1985.
Knight, Robert D., 1962. Groundwater Areas of Missouri, Missouri
Geological Survey and Water Resources.
Missouri Geological Survey and Water Resources, 1985. Telephone
Conversation with Don Miller, Head of the Ground Water Section,
December 10, 1985.
Piskin, K. and R. E. Bergstrom, 1975. Glacial Drift in Illinois:
Thickness and Character. Illinois State Geological Survey
Circular 490, 35 p.
Todd, K. K., 1983. Ground-Water Resources of the United States,
Premier Press, Berkeley, CA, 749 p.
Twenter, F. R. and R. W. Coble, 1965. The Water Story in Central Iowa,
Iowa Geological Survey Water Atlas Number 1, 89 p.
U.S. Geological Survey, 1984. National Water Summary, Water-Supply
Paper 2275.
U.S. Geological Survey, 1985a. Telephone Conversation with Leo Emit,
Missouri District Office, December 10, 1985.
U.S. Geological Survey, 1985b. Telephone Conversation with Marvin
Sherril, Illinois District Office, December 16, 1985.
U.S. Geological Survey, 1985c. Telephone Conversation with Lloyd
Stullken, Kansas District Office, Garden City, Kansas,
December 19, 1985.
6-11
-------
REFERENCES (Continued)
U.S. Geological Survey, 1985d. Teleohone Conversation with Angel
Martin, Louisiana District Office, December 18, 1985.
U.S. Geological Survey, 1985e. Telephone Conversation with Hayes
Grubb, 1985e. Texas District Office, December 19, 1985.
U.S. Geological Survey, 1985f.- Telephone Conversation with Dan
Ackerman, Arkansas District Office, December 20, 1985.
Wayne, W. J., 1958. Glacial Geology of Indiana, Atlas of Mineral
Resources of Indiana, Map No. 10, Indiana Geological Survey.
Wright, F. M., Ill, 1974. Geology for Environmental Planning in the
Johnson-Hardwick Region, Vermont, Environmental Geology No. 4,
Vermont Geological Survey, 59 p.
G-12
------- |