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Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 1 of 99
STATE OF NORTH CAROLINA
IN THE GENERAL COURT OF JUSTICE
COUNTY OF WAKE
SUPERIOR COURT DIVISION
11 CVS 16896
11 CVS 16940
MARGARET DICKSON, et al.,
Plaintiffs,
v.
ROBERT RUCHO, in his official capacity
only as the Chainnan of the North
Carolina Senate Redistricting
Committee, et al.,
Defendants.
SECOND AFFIDAVIT OF DAVID W.
PETERSON, Ph.D.
NORTH CAROLINA STATE CONFERENCE
OF BRANCHES OF THE NAACP et aI.,
Plaintiffs,
v.
STATE OF NORTH CAROLINA et aI.,
Defendants.
Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 2 of 99
I, David Peterson, being first duly sworn, depose and say:
1.
I am over 18 years of age, legally competent to give this affidavit and have personal
knowledge of the facts set forth in this affidavit. I am a statistician retained by counsel for Plaintiffs
to assist with statistical aspects of this case. For more than twenty years I taught statistical theory
and applications at Duke University, first as a member of the business school faculty and later as a
member of the statistics faculty. During that time I also taught statistics courses in Duke's
department of health administration, school of forestry and the law school. I am co-author ofthe
book Use ofStatistics in Equal Employment Opportunity Litigation, and author or co-author of
numerous articles in professional journals dealing with the use of statistics in litigation. One of
these articles addresses uses and misuses of scientific evidence in court, and another critiques the
Federal Judicial Center's Reference Manual on Scientific Evidence. I am the author of a book
outlining the elements of forensic decision analysis, a general method for determining
empirically the reasons that past decisions were made the way they were. I have advised
hundreds oflegal teams, both plaintiff and defendant, on the use of statistical evidence. The U.S.
Supreme Court has cited my work favorably on several occasions. My resume is attached as
Appendix A. For the work leading up to and including the preparation of this report, I am being
paid $6,000. The cases in which I have testified recently are listed in Appendix B.
Charge
2. I am asked by counsel for Plaintiffs in this matter to verify and interpret the results of
a "Segment Analysis"l of North Carolina's 12th Congressional Voting District defined by
"Rucho-Lewis Congress 3,,2, an analysis performed by staff at the Southern Coalition for Social
Justice under the direction of Mr. Chris Ketchie, designed to test whether the boundary of that
district appears to have been chosen more on the basis of racial considerations than on political
considerations.
1 Segment Analysis is described in Peterson, David W., "On Forensic Decision Analysis," Journal of
. Forensic Economics, Vol. XVIII, No.1, Winter 2005, pp. 11-62, and also in Peterson, David W.,
Why Did They Do That? An Introduction to Forensic Decision Analysis, Lulu Press, 2007. Segment
Analysis was used by defendants in the North Carolina redistricting litigation arising from the 1990
census (Hunt, Governor ofNorth Carolina, et al. v. Cromartie et aI., 526 U.S. 541 (1999) and
Easley, Governor ofNorth Carolina, v. Cromartie, et al., 532 U.S. 234 (2001)).
2 "Rucho-Lewis Congress 3" was enacted as Session Law 2011 -403 by the North Carolina General
Assembly on July 28th, 2011.
2
Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 3 of 99
Conclusions
3. I reviewed the steps undertaken in the Segment Analysis and determined that the
calculations were correctly done. The analysis indicates that racial considerations better account
for the boundary definition of the 12th NC Congressional Voting District than do party affiliation
considerations. There is no indication that party affiliation dominated racial considerations.
Sources
4. The information on which my opinion is based is primarily District_12.csv, a data file
created and conveyed to me by Chris Ketchie on November 28,2011. The file was created by a
computer script originally written by Damian Maddelena, but modified by me just before Mr.
Ketchie used it to create District_12.csv. The information contained in the data file is a table,
each row of which pertains to a segment of the boundary of the 12th District, and indicates,
among other things, the fraction of the people residing in the precinct just outside the 12th
District who are black, as well as the fraction ofthe population who are democrats. The
analogous information is provided for people living in the neighboring precinct just inside the
12th District. The pertinent parts of the file are printed out in Appendix C. I also rely on 23
maps provided to me by Mr. Ketchie, which I used to identifY instances in which the precincts
involved in this study touch one another at just a single point.
Review
5. I have studied the data and computer program mentioned above, discussed them at
length with Mr. Ketchie, and verified a sample of the calculations. I believe they properly
execute the studies described below.
Segment Analysis Rationale
6. Segment Analysis rests on the observation that if the boundary of a voting district is
chosen with the object of encompassing large numbers of black residents, then at least some
portion of that boundary must separate a geographic region with a large representation of black
residents from a region with a smaller representation, the region with the larger representation
being included within the voting district. The analogous observation holds with respect to
political affiliation - a voting district defined with the object of collecting democrats within must
3
Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 4 of 99
on at least some portion of its boundary separate a geographic region with a large representation
of democrats from one with a smaller representation, the area with the larger representation
being inside the voting district. Segment analysis breaks down the border of a voting district into
many pieces, and examines whether, based on the race and political behavior of residents just
inside and outside each segment, the overall pattern suggests that, as between race and political
affiliation, one consideration dominated the other in the process that defined the voting district.
Analysis
7. The boundary of District 12 was divided into the segments corresponding to the
precincts inside and out that form its border. Each such segment separates a precinct inside the
district from a precinct outside the district. Map 1 depicts the precincts involved in this process.
For each segment, we noted whether the proportion of residents of the inside precinct who are
black is greater than the proportion of residents of the outside precinct who are black. We called
segments for which this relationship holds "Type B". We also, for each segment, noted whether
the proportion of residents of the inside precinct who are democrats is greater than the proportion
of residents of the outside precinct who are democrats. We called segments for which this
relationship holds "Type D".3
8. If a segment is of Type B, it lends support to the proposition that it was chosen at least
in part because it serves to aggregate black people into the
Ii" District.
Similarly, a Type D
segment lends support to the proposition that it was chosen at least in part because it serves to
aggregate democrats into the District. A segment that is both of Type B and of Type D, lends
support to both propositions, and therefore is of no help in distinguishing which consideration
may have dominated. Likewise, a segment that is neither of Type B nor of Type D reveals
nothing about which of the two propositions may have dominated in the choice of that segment
by the legislature.
9. The remaining segments are either a) Type B and not Type D or else b) Type D and
not Type B. A segment of the first sort supports the proposition (the Race Hypothesis) that it
3 Included in the study are all segments having positive length; all segments of zero length (which occur
where an inside precinct touches an outside precinct at only a single point) are excluded.
4
Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 5 of 99
was chosen at least in part because it serves to collect blacks into the 12th District, and it militates
against the proposition (the Political Hypothesis) that the segment was chosen because it serves
to collect democrats into the District. We call such a segment a Race (or Type R) segment,
because it supports the Race Hypothesis over the Political Hypothesis.
10. A segment of the second sort (Type D and not Type B) has an analogous
interpretation. Such a segment supports the proposition (the Political Hypothesis) that it was
chosen at least in part because it serves to collect democrats into the 12th District, and it militates
against the proposition (the Race Hypothesis) that the segment was chosen because it serves to
collect blacks into the District. We call such a segment a Party (or Type P) segment.
11. In all, there are 330 segments to the border of the 12th District.4 But whether a given
segment is of Type R, of Type P, or of neither type depends on just how one measures the racial
composition of residents in a precinct, as well as how one measures the party preferences of a
precinct's residents.
12. We used three different measures of the racial composition of the residents of each
precinct:
a the proportion of people living in the precinct who, in the 2010 US Census, reported
their race as black or partially black;
b. the proportion of the people of voting age living in the precinct who, in the 2010 US
Census, reported their race as black or partially black; and
c. the proportion of registered voters living in the precinct who are registered as blacks.
13. We used four different measures of party preference for the residents of each
precinct:
a. the proportion of registered voters living in the district who are registered as
democrats;
4 While these 330 segments encompass very nearly the entire boundary of the 12th District, there are a few
gaps. These occur when the district line cuts through a precinct rather than following the precinct
boundary. These gaps could not be included in the analysis because data on voting behavior are not
available at the sub-precinct level.
5
Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 6 of 99
b. the proportion of people living in the district and voting for Governor in 2008 who
voted for the democratic gubernatorial candidate;
c. the proportion of people living in the district and voting for President in 2008 who
voted for the democratic presidential candidate; and
d. the proportion of people living in the district and voting for US Senator in 2010 who
voted for the democratic senatorial candidate.
14. We used each of the three measures of race cited in '\[12 above in conjunction with
each of the four measures of party preference cited in '\[13 above, producing a total of twelve
different segment analyses of the boundary of District 12. The results are summarized in Table 1
and graphed in Figure 1.
Table 1. Tallies of District 12 Segments by Race and Party Types
Black Population
Registered
Democrat
Race
Party
6
8
Voted fur Democrat:
2008 President
2008 Governor
Race
Race
Party
Party
5
7
4
7
2010 US Senate
Race
Party
10
6
Black Voting Age Population
7
7
6
6
8
3
11
5
Black Registered Voters
4
6
4
6
6
3
11
7
Source: D,strict_12 DWP Edlt.xlsx
15. In four of the twelve studies the number of segments supporting the Political
Hypothesis exceeds the number of segments supporting the Race Hypothesis. There are two
studies in which there are equal numbers of Type R and Type P segments. In the other six
studies, there is more support for the Race Hypothesis than for the Political Hypothesis, and in
each of these six, the imbalance is more pronounced than in any of the four studies favoring the
Political Hypothesis.
16. While the classification of a segment as Type R or Type P depends on just how one
characterizes its precincts' racial and political populations, there are just two segments which are
unequivocal across all twelve studies - one of these is invariably of Type R, the other of Type P.
6
Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 7 of 99
17. The studies above may be compared with a similar study undertaken of North
Carolina's 12th Congressional District in the wake of the 1990 census and the ensuing litigation
cited in Footuote 1 above. In that case, the dozen studies analogous to those depicted in Table 1
resulted in seven instances favoring the Political Hypothesis, three favoring the Race Hypothesis,
and two ties. Thus, while this earlier study on balance favored the Political Hypothesis, the
results in Table 1, in contrast, favor the Race Hypothesis.
Conclusions
18. I reviewed the steps undertaken in the Segment Analysis and determined that the
calculations were correctly done. The analysis indicates that racial considerations better account
for the boundary definition of the 12th NC Congressional Voting District than do party affiliation
considerations. There is no indication that party affiliation dominated racial considerations.
This, the
'\~h day of
l.Yll.I..l>«"J
, 2012.
Da;id eterS01l
I, 1l.G.""~
1C!At-\
certifY that hMio.
~~e.~
\1.\. ~~h
,a Notary Public of the County and State aforesaid, hereby
personally known to me to be the affiant in the
foregoing affidavit, personally appeared before me this day and having been by me duly sworn
deposes and says that the facts set forth in the above affidavit are true and correct.
Wituess my hand and official seal this the __~.:.:\-_~__ day of
(SEAL)
1.". . . .1'
'-I> lei-
Kl'N~Ub~~1A )
OFFICIAL SEAL
KATHY JEAN HERMEYER
NOTArw PUBLIC, STATE OF ILLINOIS
MYGOMMISSION fXPIRES 07/30/2012
My Commission expires:
-0'1- I -s..- I
.;\0 I do-
•
7
Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 8 of 99
Map 1.
NC 12th Congressional District
r.··:.)J Inside Boundary Precincts
_
Outside Boundary Precincts
_
Split Boundary Precincts
o_____10
~
00
i:::====~__________.c=========:JiMiles
Map Created By: Chris Ketchie, Policy Analyst, Southern Coalition for Social Justice
Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 9 of 99
APPENDIX A
DAVID WEST PETERSON
1942 Rock Rest Road
Pittsboro, North Carolina 27312
Home: 919-542-6937
Office: same
Higher Education:
B.S., University of Wisconsin at Madison, 1962
M.S., Stanford University, 1963
Ph.D., Stanford University, 1965, all in Electrical Engineering
Employment History:
1960
1961-62
Engineering Trainee, General Electric Company
Research Assistant, Computer Laboratory, Department of
Electrical Engineering, University of Wisconsin
Member, Technical Staff, Hughes Aircraft Company
1962-63
Research Assistant, Systems Laboratory, Stanford University
1963-65
1965-67
Mathematician and Hybrid Simulation Project Officer, U.S.
Army Electronics Command, Fort Momnouth, N.J.
1967-70
Assistant Professor of Quantitative Methods, Northwestern
University Graduate School of Management
Associate Professor of Managerial Economics and Decision
1970-73
Sciences, Northwestern University Graduate School of
Management
1971-72
Research Fellow, International Institute of Management, Berlin
1973
Visiting Lecturer, Systems Engineering, University of Illinois at
Chicago Circle (spring quarter)
Professor, Graduate School of Business Administration, Duke
1973-84
University, Durham, N.C.
1979-2000
President, PRJ Associates, Durham, N.C.
1982-86
Senior Lecturer, Duke Law School
Adjunct Professor, Graduate School of Business Administration,
1984-89
Duke University, Durham, N.C.
1989-94
Adjunct Professor, Institute for Statistics and Decision Sciences,
Duke University, Durham, N.C.
Senior Vice President, Peopleclick, Inc., Raleigh, N.C.
2000-02
2002-present Independent Consultant
Various consulting activities undertaken for the U.S. Public Health Service, U.S. Army
Electronics Command, and numerous private corporations, law firms and governmental
agencies, largely on matters related to the use of statistics in litigation.
Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 10 of 99
David W. Peterson
Languages:
English (native)
German (working knowledge)
Some French, Russian and Mandarin
Professional Memberships:
Institute for Electrical and Electronic Engineers
The American Statistical Association
Professional Publications:
Technical articles published in internationally circulatedjoumals, treating topics in the
theory and application of mathematical modeling in areas such as radio propagation, control of
economic systems, optimization of static and dynamic systems, statistical decision making, the
measurement of employment opportunity equality, and the detection of computer code theft.
Professional Speaking Engagements:
Technical papers read at meetings of the IEEE Man, Systems and Cybernetics Group, the
Econometric Society, The Institute for Management Sciences and the American Statistical
Association. Many semi-technical engagements in the U.S., Europe and the Middle East,
generally pertaining to mathematical modeling applications in management. Speaker at seminars
for lawyers dealing with statistical applications in litigation.
General Background:
While at Stanford University I was involved in a project whose chief aim was to analyze
radar return data to discriminate among different types of vehicles entering the atmosphere.
Problems of primary concern in this project were data processing speed and discrimination
accuracy.
While at Fort Momnouth I was involved in two major projects. The fIrst was the
construction and analysis of a mathematical model describing very-low-frequency
electromagnetic propagation in the earth-atmosphere-ionosphere system, and another model for
such propagation in the lithosphere.
The second major project on which I worked while at Fort Momnouth was the simulation
of various helicopter fIre control systems on a large scale hybrid computer. In this project I was
responsible for the construction of a mathematical model of a fIre control computer, for the
stochastic subroutines associated with the simulation, and for various subroutines involving the
generation of certain artifIcial images for the benefIt of the pilot. The system simulated was
comprehensive in that it included the pilot and a gunner (both of them live) and a cockpit with a
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Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 11 of 99
David W. Peterson
visual display consisting of a television-scanned terrain belt on which were superimposed
artificially-generated data relating target size and location to the trajectories of tracer rounds.
The challenge in this task was to simulate the aircraft flight dynamics, the tracer round
trajectories and the feel of the aircraft on the pilot and co-pilot controls, to within acceptable
tolerances, subject to limitations on computer memory and computational speed.
At Northwestern I taught courses in mathematical progrannning, elementary probability
and statistics, computer progrannning and applications, and optimal control to graduate students
in management, attracting some students from economics, computer science and industrial
engineering.
My early research interests were in establishing a logical-mathematical foundation for
information theory, and the construction and analysis of dynamic econometric models. A year
spent at the International Institute of Management in Berlin enabled me to bring to publishable
form the results of several investigations in these areas, as well as to make personal and
professional acquaintances in several European and Middle Eastern communities.
While at Duke my activities in the early years were directed toward improving the quality
and volume of research of junior faculty, to developing an expanded Ph.D. program, to revising
the MBA curriculum, and to exploring and developing bases on which Graduate School of
Business Administration faculty and students can interact with faculty and administrators in
various other departments. I developed a special interest in the application of statistical methods
to the measurement of the equality with which an employer extends employment opportunities to
employees of differing age, sex or ethnicity. These activities led to several publications,
speaking engagements and consulting assigmnents, and to the formation of PRJ Associates.
PRJ Associates' main business was statistical consultation, though it also designed,
developed and sold software that employers used to help manage their affIrmative action
activities. Our consultations usually were with attorneys involved in litigation,· and the subject
matter spanned a wide variety of issues, including political redistricting, census-taking,
employment discrimination and high-tech intellectual property disputes.
In August 2000 I sold PRJ Associates to PeopleClick, Inc. Leaving PeopleClick in 2002, I
have since consulted as a sole proprietor with a variety of clients, aided on occasion by an
informal network of colleagues.
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David W. Peterson
Other Work Experience:
a.
the fonnulation of a plan for a national health data infonnation center, and for its
process of creation
b.
the design of a computer-based inventory management system for a $50M per
year mail-order finn
c.
the provision of statistical advice to researchers studying the effects on costs and
services of a merger of nine hospitals in Arizona
d.
the provision of criticism, advice and encouragement to researchers establishing a
methodology for evaluating the effects of different types of care extended to
elderly Americans
e.
consultation with legal teams on the structuring of statistical data presented at
judicial proceedings involving employment discrimination, jury selection, antitrust damages, political redistricting processes, census taking, and high tech
intellectual property issues
f.
fonnation of PRI Associates, Inc., providing statistical consultation services on
matters pertaining to the use of statistical methods in litigation, and on matters
related to software development
Bibliography:
I.
lit-Inverse LaPlace Transfonn, IBM 1620 Digital Computer Program, IBM Program
Infonnation Department Library File Number 6.0.164, September, 1964.
2.
Discriminant Functions - Properties, Classes, and Computational Techniques, Ph.D. thesis,
Rept. SV-SEL-021, Technical Report 6761-2, Stanford Electronics Laboratories, Stanford,
California, April 1965.
3.
A Theorem on Decision Boundaries, Proceedings ofthe 12th Annual Conference ofArmy
Mathematicians, Dartmouth College, Hanover, New Hampshire, June 22-23, 1966 with K. A.
Belser.
4.
A Method of Finding Linear Discriminant Functions for a Class ofPerfonnance Criteria, IEEE
Transactions on Information Theory, IT-12, No.3, July, 1966, pp. 380-387, with R. L. Mattson.
5.
A Theorem on Single Sample Confidence Intervals, 13th Annual Conference of Anny
Mathematicians, Fort Monmouth, New Jersey, June 7-8, 1967. Also, Proceedings ofthe IEEE,
Vol. 55, No.9, September 1967, pp. 1637-1638, (Correspondence).
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David W. Peterson
6.
The Mathematics ofInformation - A Critique, paper read at the u.s. Army Electronics
Command Advanced Planning Briefmg and Technical Symposium, Fort Monmouth, New
Jersey, March 7, 1968.
7.
A Model for Electromagnetic Propagation in the Lithosphere, Proceedings of the IEEE, Vol. 56,
No.5, May 1968, pp. 799-804, with F. H. Schwering and S. B. Levin.
8.
A Proposed Method for Predicting the Phase Behavior of a VLF Radio Signal, Journal of
Atmospheric and Terrestrial PhySiCS, Vol. 31,1969, pp. 225-232.
9.
Using the Maximum Principle and a Hybrid Computer for Production Planning, with Robert R.
Gann, Proceedings of the American Institute for Decision Sciences Meeting, New Orleans,
Louisiana, October 1969.
10. Some Convergence Properties of a Nearest Neighbor Decision Rule, Record of the IEEE
Systems Science and Cybernetics Conference, October, 1968, San Francisco, also IEEE
Transactions on Information Theory, Vol.lT-16, No.1, January 1970, pp. 26-31.
11. A Stabilizing Transformation for Numerical Solution of Maximum Principle Problems, with R.
Gann, IEEE Transactions on Automatic Control (correspondence), Vol. 15, No.6, December
1970, pp. 686-687.
12. A Sufficient Maximum Principle, IEEE Transactions on Automatic Control
(correspondence), February 1971, Vol. 16, No.1, pp. 85-86.
13. Optimal Control and Monetary Policy, with E. M. Lerner, International Economic Review, Vol.
12, No.2, June 1971.
14. The Response of Prices and Income to Monetary Policy: An Analysis Based Upon a
Differential Phillips Curve, with E. M. Lerner and E. J. Lusk, Journal ofPolitical Economy,
Vol. 19, No.4, July/August 1971, pp. 857-866.
15. Equitability in Multi-Agent Dynamic Systems: The Case of Two Agents and Four States,
presented at the European Econometric Society Meeting, Barcelona, September 1971, published
in revised form in the Nigerian Journal of Quantitative Economics, Vol. 1, No.1, March 1975,
pp.33-58.
16. Equitability in Multi-Agent Dynamic Systems: The Case of m Agents and nm States,
Proceedings of the Joint Conference on Major Systems, Sponsored by IEEE Systems, Man and
Cybernetics Group and by ORSA, Anaheim, California, October 1971.
17. Comments on "Economics of Information Systems," by Jacob Marschak, in Frontiers of
Quantitative Economics, M. Intriligator, ed., North-Holland Publishing Company, Amsterdam,
1971, pp. 107-108.
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David W. Peterson
18. The Economic Significance of Auxiliary Functions in Optimal Control, presented at the
Econometric Society North American Meeting, August 1971,International Economic Review,
Vol. 14, No.1, February 1973, pp. 1-19.
19. A Review of Constraint Qualifications in Finite Dimensional Spaces, SIAM Review, Vol. 15,
No.3, July 1973, pp. 639-654.
20. Some Relationships Between Hierarchical Systems Theory and Certain Optimization Problems,
with Y. M. I. Dirickx and L. P. Jennergren, presented at the IEEE Systems, Man and
Cybernetics Group Conference, Washington, D. C., October 1972; IEEE Transactions on
Systems, Man and Cybernetics, Fall 1973.
21. On Sensitivity in Optimal Control Problems, Journal of Optimization Theory and Applications,
Vol. 13, No.1, January 1974, pp. 56-73.
22. Toward a Mathematical Definition of Information, Proceedings of the Sixth Annual
Southeastern Symposium on System Theory, February 1974.
23. On Dynamic Behavior of the Regulated Firm, with James Vander Weide, presented at the
Econometric Society Winter Meetings, 1974, revised March 1975.
24. Transferring Ideas from Engineering to the Social Sciences, Proceedings of the IEEE, Vol. 63,
No.3, pp. 354-359, March 1975.
25. Trader-Co=odity Parity Theorems, with D. Graham, P. Jennergren, and R. Weintraub, Journal
ofEconomic Theory, Vol. 12, No.3, June 1976.
26. A Note on the Optimal Investment Policy of the Regulated Firm, with J. H. Vander Weide,
Atlantic Economic Journal, Vol. IV, No.3, Fall 1976, pp. 51-55.
27. A Strategy which Maxinrizes the Geometric Mean Retnrn on Portfolio Investments, with S. F.
Maier and J. H. Vander Weide, Management Science, Vol. 23, No. 10, June 1977, pp. 11171123.
28. A Monte Carlo Investigation of Characteristics of Optimal Geometric Mean Portfolios, with
Steven F. Maier and James H. Vander Weide, invited paper, presented at a joint session of the
Econometric Society and the American Finance Association Winter Meeting, 1974, revised and
published in the Journal ofFinancial and Quantitative Analysis, June 1977, pp. 215-233.
29. Quadraticity and Neutrality in Discrete Time Stochastic Linear Quadratic Control, with Carole
Aldrich, Automatica, Vol. 13, 1977, pp. 307-312.
30. The Coordination of Short-Run Decision Making with Long-Range Planning, with D.
Loughridge and W. Damon, Omega, Vol. 4, No.6, 1977, pp. 1-12.
31. On the Estimation of the Racial and Sexual Composition of the Labor Force Available to an
Employer, in Perspectives on Availability, Equal Employment Advisory Council, August 1978.
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David W. Peterson
32. A Review of Direct Sufficiency Conditions in Optimal Control Theory, with J. Zalkind,
International Journal of Control, Vol. 28, No.4, 1978, pp. 589-610.
33. An Analytic Framework for Evaluating Rolling Schedules, with K. Baker, Management Science,
Vol. 25, No.4, April 1979, pp. 341-351.
34. Use ofStatistics in Equal Employment Opportunity Litigation, with Walter B. Connolly, Jr.,
New York Law Journal Seminars Press, February 1980 (1982,1983,1985,1987,1988,1989,
1991,1992,1994,1995,1996,1997, 1998, 1999,2000,2001,2002,2003,2004,2005,2007).
35. Pitfalls in the Use of Regression Analysis for the Measurement of Equal Employment
Opportunity, Journal on Policy Analysis and Information Systems, Vol. 5, No.1, March 1981,
pp.43-65.
36. An Empirical Bayes Estimate of Market Risk, with S. F. Maier and J. H. Vander Weide,
Management Science, Vol. 28, No.7, July 1982, pp. 728-737.
37. Measurement Error, Regression and Equal Employment Opportunity, in Statistical Evidence of
Discrimination, D. H. Kaye and M. Aickin, eds., Marcel Dekker, New York, 1986.
38. Measuring Pass-Fail Employment Test Impact Disparities, presented at the joint National
Meeting ofORSNTIMS, October 1982.
39. A Regression Specification Test Based on Observation Exchanges, presented at the American
Statistical Association meetings, August 1984, Philadelphia, PA. Revised June 1985.
40. Law and Contemporary Problems, Vol. 46, Autumn 1983, No.4, Special Editor for the
Symposium on Statistical Inference in Litigation.
41. Data Acquisition and Analysis, in Statistical Evidence in Litigation, David W. Barnes and John
Conley, Little, Brown, Boston 1986.
42. Trial by Regression: Detecting and Measuring Disparate Treatment in Employment
Discrimination Litigation, presented at the American Statistical Association meetings, August
1986, Chicago, IL.
43. The Role of Experts in Software Infringement Cases, with John M. Conley, Georgia Law
Review, Vol. 22, No.2, Winter 1988, pp. 425-468. Reprinted in Computer Law & Practice,
Vol. 5, No.3, pp. 99-110 (part 1) and Vol. 5, No.4, pp. 147-153 (Part 2).
44. Court-Imposed Methodological Constraints: An Employment Discrimination Example, with
John M. Conley, presented at the American Statistical Association meetings, August 6-9, 1990.
45. The Employment Discrimination Case of Bayes v. Fisher, presented at the Second International
Conference on Forensic Statistics, Arizona State University, Tempe, AZ, May 19-21, 1993.
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Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 16 of 99
David W. Peterson
46. When Ethical Systems Collide: The Social Scientist and the Adversary Process, with John M.
Conley, in Kniffka, Hannes, Recent Developments in Forensic Linguistics, Peter Lang, Frankfurt
am Main, 1996, pp. 345-358.
47. Review of Daniel L. Rubinfeld's Reference Guide on Multiple Regression in the Federal Judicial
Center's 1994 Reference Manual on Scientific Evidence, Jurimetrics, Vol. 36, No.2, Winter
1996, pp. 213-216.
48. Science of Gatekeeping: The Federal Judicial Center's New Reference Manual on Scientific
Evidence, with John M. Conley, North Carolina Law Review, Vol. 74, No.4, April 1996, pp.
1183-1223.
49. Pay Discrimination Models, Journal o/Forensic Economics, 12(2), 1999, pp. 111-124.
50. Of Cherries, Fudge and Onions: Science and its Courtroom Perversions, with John M. Conley,
Law and Contemporary Problems, Vol. 64, No.4, Autumn 2001, pp. 213-240.
51. In Quest ofthe Perfect P-Value, Journal o/Forensic Economics, 15(1),2002, pp. 75-80.
52. Cohort Analysis: A Regression Plain and Fancy, Journal 0/Forensic Economics, 16(2),2003,
pp. 153-176. Correction, JFE, 18(2-3),2005, p. 263. Reply to comment, JFE, 19(3),2006, pp.
325-332.
53. On Forensic Decision Analysis, Journal o/Forensic Economics, 18(1),2005, pp. 11-62.
54. A Fresh Look at Pay Discrimination, Chance, Vol. 19, No.2, Spring 2006.
55. Why Did They Do That? An Introduction to Forensic Decision Analysis, Lulu Press, Morrisville
NC,2007.
56. Putting Chance to Work: Reducing the Politics in Political Redistricting, Chance, Vol. 21, No.
I, 2008, pp. 22-26.
57. Review of Statistics in the Law by Joseph B. Kadane, Journal o/the American Statistical
Association, 104(486), June 2009, p. 868.
Newsletter Articles:
1.
Measurement of Age Discrimination, Personnel Research Report, Vol. 1, No.1, July 1981.
2.
Measurement Error, Regression, and Equal Employment Opportunity, Personnel Research
Report, Vol. I, No.2, October 1981.
3.
Notes on Statistical Proof: Rebuttal and Cumulative Impact, Personnel Research Report, Vol.
I, No.3, January 1982.
A- 8
Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 17 of 99
David W. Peterson
4.
Age Profiles and Workforce Reductions: Some Basic Relationships, Personnel Research
Report, Vol. 2, No. I, July 1982.
5.
Statistical Models and Employer Discretion, Personnel Research Report, Vol. 2, No.2, October
1982.
6.
Binomial v. Hypergeometric Employee Selection Models, Personnel Research Report, Vol. 2,
No.4, April 1983.
7.
Preponderance of Evidence, P-values and Standard Deviations, Personnel Research Report, Vol.
3, No.1; October 1983.
8.
Age Patterns in Employee Flow, Personnel Research Report, Vol. 3, No.2, April 1984.
9.
Testing the Plausibility of A Regression, Personnel Research Report, Vol. 3, No.3, July 1984.
10. Workforce Reductions: A Time for Preventive Statistics, PRJ Report, Vol. 4, No.3, October
1985.
II. Data Acquisition for Litigation (part 1 & II), PRJ Report, Vol. 5, No.1, April 1986, Vol. 5, No.
3, March 1987.
12. Underutilization: The Small Group and Large Group Problems, and a Proposed Solution to
Both, PRJ Report, Vol. 5, No.2, July 1986.
13. Calculating Mitigated Lost Earnings, PRJ Report, Vol. 5, No.4, June 1987.
14. Using Computers to Prepare Evidence, PRJ Report, Vol. 6, No. I, October 1987.
15. Samples, Populations and the Whole Universe, PRJ Report, Vol. 6, No.2, July 1988.
16. Lost Future Income: Calculating Expected Present Values, PRJ Report, Vol. 6, No.3, October
1988.
17. Detecting Discrimination in Peremptory Challenges, PRJ Report, Vol. 6, No.4, December 1990.
18. One Tail or Two? Or Does it Really Matter?, PRJ Report, Vol. 7, No. I, June 1991.
19. The Worst ofTen is Pretty Bad, PRJ Report, Vol. 8, No. I, July 1997.
20. Standard Deviation Calculations: A Refinement for Small Numbers, PRJ Report, Vol. 8, No.3,
May 1998.
21. What Does a Regression Analysis Really Show?, PRJ Report, Vol. 8, No.4, November 1998.
22. Compensation Analysis Ii la OFCCP, PRJ Report, Vol. 9, No.2, March 2000.
A-9
Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 18 of 99
David W. Peterson
23. Compensation Analysis: Accounting for Employer Latitude in Setting Pay, The Report, Vol. 1
No.1, February 200l.
24. A Regression Example for Those Who Still Believe in it, The Report, Vol. 1 No.3, August 2001.
25. Normal Equivalent Standard deviations, The Report, Vol. 1 No.4, March 2002.
Patents
1. Verifiable, Auditable Voting System Maintaining Voter Privacy, U.S. Patent 7,451,928 B2,
Granted November 18, 2008.
2. Automated Voting District Generation Using Preexisting Geopolitical Boundaries, Filed January
24,2007, (with Claire Ellis Osgood), Pending.
November 9,2010
Pittsboro NC
A -10
Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 19 of 99
APPENDIXB
Cases in which David W. Peterson has Testified at Trial or by Deposition
Since January 1, 2005
Case Name
Depo or Trial
Date
Venue
DAG Petroleum Suppliers, LLC v.
BP p.l.c. and BP Products North
America, Inc. .
Deposition
7/26/06
Chicago, IL
O'Neal, et al. v. Wackenhut
Services, et al.
Deposition
Deposition
6/16/05
4/3/06
Raleigh, NC
Raleigh, NC
8/6/09
Washington, DC
Anniemarie Harrison-Gray and
Deposition
Beverly Hatcher, Class Agents, v.
Eric K. Shinseki, Secretary,
U.S. Department of Veterans Affairs,
Agency
Updated 12/20/2011
Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 20 of 99
P-16
P-16
Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 21 of 99
STATE OF NORTH CAROLINA
COUNTY OF WAKE
IN THE GENERAL COURT OF JUSTICE
SUPERIOR COURT DIVISION
11 CVS 16896
11 CVS 16940 MARGARET DICKSON, et al.,
Plaintiffs,
v.
ROBERT RUCHO, in his official capacity
only as the Chairman of the North
Carolina Senate Redistricting
Committee, et al.,
FOURTH AFFIDAVIT OF PLAINTIFFS’
STATISTICAL EXPERT
DAVID W. PETERSON, PhD
Defendants.
______________________________________
FIRST CONGRESSIONAL DISTRICT
SEGMENT ANALYSIS
NORTH CAROLINA STATE CONFERENCE
OF BRANCHES OF THE NAACP, et
al.,
Plaintiffs,
v.
STATE OF NORTH CAROLINA, et al.,
Defendants.
I, David Peterson, being first duly sworn, depose and say:
1. I am over 18 years of age, legally competent to give this affidavit and have personal
knowledge of the facts set forth in this affidavit. My qualifications and recent testimony are set
forth in each of my First and Second Affidavits in this case.
Charge
2. I am asked by counsel for Plaintiffs in this matter to verify and interpret the results of
a “Segment Analysis”1 of North Carolina’s 1st Congressional Voting District defined by “Rucho 1
Segment Analysis is described in Peterson, David W., “On Forensic Decision Analysis,” Journal of
Forensic Economics, Vol. XVIII, No. 1, Winter 2005, pp. 11-62, and also in Peterson, David W.,
1 Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 22 of 99
Lewis Congress 3”2, an analysis performed by staff at the Southern Coalition for Social Justice
under the direction of Mr. Chris Ketchie, designed to test whether the boundary of that district
appears to have been chosen more on the basis of racial considerations than on political
considerations.
Conclusions
3. I reviewed the steps undertaken in the Segment Analysis and determined that the
calculations were correctly done. The analysis indicates that racial considerations better account
for the boundary definition of the 1st NC Congressional Voting District than do party affiliation
considerations. There is no indication that party affiliation dominated racial considerations.
Sources
4. The information on which my opinion is based is primarily District_1.csv, a data file
created and conveyed to me by Chris Ketchie on May 8, 2012. The file was created by a
computer script originally written by Damian Maddelena, but modified by me before Mr.
Ketchie used it to create District_1.csv. The information contained in the data file is a table,
each row of which pertains to a segment of the boundary of the 1st District, and indicates, among
other things, the fraction of the people residing in the precinct just outside the 1st District who are
black, as well as the fraction of the population who are democrats. The analogous information is
provided for people living in the neighboring precinct just inside the 1st District. The pertinent
parts of the file are printed out in Appendix A. I also rely on a map provided to me by Mr.
Ketchie, which I used to identify instances in which the precincts involved in this study touch
one another at just a single point.
Why Did They Do That? An Introduction to Forensic Decision Analysis, Lulu Press, 2007. Segment
Analysis was used by defendants in the North Carolina redistricting litigation arising from the 1990
census (Hunt, Governor of North Carolina, et al. v. Cromartie et al., 526 U.S. 541 (1999) and
Easley, Governor of North Carolina, v. Cromartie, et al., 532 U.S. 234 (2001)).
2
“Rucho-Lewis Congress 3” was enacted as Session Law 2011-403 by the North Carolina General
Assembly on July 28th, 2011.
2 Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 23 of 99
Review
5. I have studied the data and computer program mentioned above, discussed them with
Mr. Ketchie, and verified a sample of the calculations. I believe they properly execute the
studies described below.
Segment Analysis Rationale
6. Segment Analysis rests on the observation that if the boundary of a voting district is
chosen with the object of encompassing large numbers of black residents, then at least some
portion of that boundary must separate a geographic region with a large representation of black
residents from a region with a smaller representation, the region with the larger representation
being included within the voting district. The analogous observation holds with respect to
political affiliation – a voting district defined with the object of collecting democrats within must
on at least some portion of its boundary separate a geographic region with a large representation
of democrats from one with a smaller representation, the area with the larger representation
being inside the voting district. Segment analysis breaks down the border of a voting district into
many pieces, and examines whether, based on the race and political behavior of residents just
inside and outside each segment, the overall pattern suggests that, as between race and political
affiliation, one consideration dominated the other in the process that defined the voting district.
Analysis
7. The boundary of District 1 was divided into the segments corresponding to the
precincts inside and out that form its border. Each such segment separates a precinct inside the
district from a precinct outside the district. Map 1 depicts the precincts involved in this process.
For each segment, we noted whether the proportion of residents of the inside precinct who are
black is greater than the proportion of residents of the outside precinct who are black. We called
segments for which this relationship holds “Type B”. We also, for each segment, noted whether
the proportion of residents of the inside precinct who are democrats is greater than the proportion
of residents of the outside precinct who are democrats. We called segments for which this
relationship holds “Type D”.3
Included in the study are all segments having positive length; all segments of zero length (which occur
where an inside precinct touches an outside precinct at only a single point) are excluded.
3
3 Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 24 of 99
8. If a segment is of Type B, it lends support to the proposition that it was chosen at least
in part because it serves to aggregate black people into the 1st District. Similarly, a Type D
segment lends support to the proposition that it was chosen at least in part because it serves to
aggregate democrats into the District. A segment that is both of Type B and of Type D, lends
support to both propositions, and therefore is of no help in distinguishing which consideration
may have dominated. Likewise, a segment that is neither of Type B nor of Type D reveals
nothing about which of the two propositions may have dominated in the choice of that segment
by the legislature.
9. The remaining segments are either a) Type B and not Type D or else b) Type D and
not Type B. A segment of the first sort supports the proposition (the Race Hypothesis) that it
was chosen at least in part because it serves to collect blacks into the 1st District, and it militates
against the proposition (the Political Hypothesis) that the segment was chosen because it serves
to collect democrats into the District. We call such a segment a Race (or Type R) segment,
because it supports the Race Hypothesis over the Political Hypothesis.
10. A segment of the second sort (Type D and not Type B) has an analogous
interpretation. Such a segment supports the proposition (the Political Hypothesis) that it was
chosen at least in part because it serves to collect democrats into the 1st District, and it militates
against the proposition (the Race Hypothesis) that the segment was chosen because it serves to
collect blacks into the District. We call such a segment a Party (or Type P) segment.
11. In all, there are 253 segments to the border of the 1st District.4 But whether a given
segment is of Type R, of Type P, or of neither type depends on just how one measures the racial
composition of residents in a precinct, as well as how one measures the party preferences of a
precinct’s residents.
4
While these 253 segments encompass very nearly the entire boundary of the 1s District, there are a few
gaps. These occur when the district line cuts through a precinct rather than following the precinct
boundary. These gaps could not be included in the analysis because data on voting behavior are not
available at the sub-precinct level.
4 Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 25 of 99
12. We used three different measures of the racial composition of the residents of each
precinct:
a. the proportion of people living in the precinct who, in the 2010 US Census, reported
their race as black or partially black;
b. the proportion of the people of voting age living in the precinct who, in the 2010 US
Census, reported their race as black or partially black; and
c. the proportion of registered voters living in the precinct who are registered as blacks.
13. We used four different measures of party preference for the residents of each
precinct:
a. the proportion of registered voters living in the district who are registered as
democrats;
b. the proportion of people living in the district and voting for Governor in 2008 who
voted for the democratic gubernatorial candidate;
c. the proportion of people living in the district and voting for President in 2008 who
voted for the democratic presidential candidate; and
d. the proportion of people living in the district and voting for US Senator in 2010 who
voted for the democratic senatorial candidate.
14. We used each of the three measures of race cited in ¶12 above in conjunction with
each of the four measures of party preference cited in ¶13 above, producing a total of twelve
different segment analyses of the boundary of District 1. The results are summarized in Table
P5.1 and graphed in Figure P5.1.
15. In two of the twelve studies the number of segments supporting the Political
Hypothesis exceeds the number of segments supporting the Race Hypothesis. There are two
studies in which there are equal numbers of Type R and Type P segments. In the other eight
5 Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 26 of 99
Table P5.1. Tallies of District 1 Segments by Race and Party Types
Black Population
Registered
Democrat
Race
Party
15
5
Voted for Democrat:
2008 Governor
2008 President
Race
Party
Race
Party
8
9
8
8
2010 US Senate
Race
Party
11
8
Black Voting Age Population
15
4
7
8
6
6
9
6
Black Registered Voters
20
7
7
6
6
4
9
4
Source: District_1 DWP Edit.xlsx
studies, there is more support for the Race Hypothesis than for the Political Hypothesis, and in
each of these eight, the imbalance is more pronounced than in either of the two studies favoring
the Political Hypothesis.
16. While the classification of a segment as Type R or Type P depends on just how one
characterizes its precincts’ racial and political populations, there are just two segments which are
unequivocal across all twelve studies – one of these is invariably of Type R, the other of Type P.
17. The studies above may be compared with a similar study undertaken of North
Carolina’s 12th Congressional District in the wake of the 1990 census and the ensuing litigation
cited in Footnote 1 above. In that case, the dozen studies analogous to those depicted in Table
P5.1 resulted in seven instances favoring the Political Hypothesis, three favoring the Race
Hypothesis, and two ties. Thus, while this earlier study on balance favored the Political
Hypothesis, the results in Table P5.1, in contrast, favor the Race Hypothesis.
Conclusions
18. I reviewed the steps undertaken in the Segment Analysis and determined that the
calculations were correctly done. The analysis indicates that racial considerations better account
for the boundary definition of the 1st NC Congressional Voting District than do party affiliation
considerations. There is no indication that party affiliation dominated racial considerations.
6 Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 27 of 99
State of NORTH CAROLINA
County of DURHAM
I certify that the above person personally appeared before me this day, acknowledging to
me that he voluntarily signed the foregoing document for the purpose stated therein and in the
capacity indicated:
Date:
Mf1. t
~
Official Signature ofN
otary\---I-"-~--""-'-='k-I--=---\l-_-'----f----='--_o1ftl)_
Notary's Printed or Typed Name:
~~----=-_f'--'-_____....I..-.1H--1~d_c:--=s,,--., Notary Public
~. . . . ,. .-" "'~"-"'-~=-+- '- -=-. <. . :='- -
My Commission Expires:ba,A_....
(Official Seal)
carolyn V Rhodes
NOTARY PUBUC
Durham County, NC
7
Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 28 of 99
Figure P5.1. Segment Analysis Results From Table P5.1.
Black Pop. v. Reg. Dem.
Black Pop. v. 2008 Gov.
Black Pop. v. 2010 US Sen.
Black Pop. v. 2008 Pres.
Party
25%
Party
53%
Race
47%
Party
50%
Party
42%
Race
50%
Race
58%
Race
75%
Black VA Pop. v. Reg. Dem.
Black VA Pop. v. 2008 Gov.
Black VA Pop. v. 2008 Pres.
Black VA Pop. v. 2010 US Sen.
Party
21%
Party
53%
Race
47%
Party
50%
Race
50%
Party
40%
Race
60%
Race
79%
Black Reg. v. Reg. Dem.
Black Reg. v. 2008 Gov.
Party
26%
Party
46%
Race
74%
Black Reg. v. 2008 Pres.
Party
31%
Party
40%
Race
54%
Black Reg. v. 2010 US Sen.
Race
60%
Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 29 of 99
Race
69%
1st District Boundary Precincts
Appendix A
Seq
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
InsidePrecinct
37013WASH1
37013WASH1
37013WASH1
37013WASH1
37013PSJW3
37013PSJW3
37013WASH2
37013WASH2
37015C1
37015C1
37015MH
37015W1
37015WH
370414
370414
370414
370412
370411
3704909
3704909
37049N4
37049N4
3704907
3704907
3704907
3704907
3704907
3704907
3704907
37049N2
37049N2
37049N2
3704906
3704906
3704906
3704906
3704908
3704908
3704908
37049N1
37049N1
37049N1
37049N1
37049N1
37049N5
37049N5
3706347
district_1 DWP Edit.xlsx
OutsidePrecinct
37013CHOCO
37013WASH4
37013BEADM
37013WASHP
37013OLDF
37013WASH4
37013TCRK
37013CHOCO
370415
370413
370416
37117W
370413
370415
370413
37143BELVID
370413
370416
3704910
37103P01
37049N3
37049N6
3704910
3704915
3704913
3704914
3704904
37103P04
37103P05
3704903
37049N3
37049N6
3704913
3704904
3704911
37049N6
3704910
37103P01
37103P05
3704921
3704903
3704923
3704911
3704916
37049N3
3704911
3706333
Inside Precinct
BPOP
BVAP
0.47945
0.44852
0.47945
0.44852
0.47945
0.44852
0.47945
0.44852
0.65289
0.61181
0.65289
0.61181
0.52730
0.49626
0.52730
0.49626
0.49959
0.47769
0.49959
0.47769
0.58266
0.57213
0.66110
0.65281
0.40669
0.39168
0.42802
0.43561
0.42802
0.43561
0.42802
0.43561
0.41670
0.38784
0.55364
0.52483
0.45141
0.42902
0.45141
0.42902
0.32484
0.30660
0.32484
0.30660
0.33569
0.30748
0.33569
0.30748
0.33569
0.30748
0.33569
0.30748
0.33569
0.30748
0.33569
0.30748
0.33569
0.30748
0.66749
0.64397
0.66749
0.64397
0.66749
0.64397
0.39270
0.35771
0.39270
0.35771
0.39270
0.35771
0.39270
0.35771
0.33413
0.33469
0.33413
0.33469
0.33413
0.33469
0.47571
0.44071
0.47571
0.44071
0.47571
0.44071
0.47571
0.44071
0.47571
0.44071
0.47932
0.45387
0.47932
0.45387
0.81074
0.83658
BREG
0.43724
0.43724
0.43724
0.43724
0.67746
0.67746
0.49579
0.49579
0.45051
0.45051
0.57722
0.61230
0.42115
0.42449
0.42449
0.42449
0.38307
0.52184
0.44881
0.44881
0.35562
0.35562
0.34636
0.34636
0.34636
0.34636
0.34636
0.34636
0.34636
0.63282
0.63282
0.63282
0.38328
0.38328
0.38328
0.38328
0.40239
0.40239
0.40239
0.41254
0.41254
0.41254
0.41254
0.41254
0.50541
0.50541
0.92662
DREG
0.59823
0.59823
0.59823
0.59823
0.75873
0.75873
0.61763
0.61763
0.74630
0.74630
0.77595
0.78819
0.71827
0.64531
0.64531
0.64531
0.58558
0.65646
0.62799
0.62799
0.50069
0.50069
0.54304
0.54304
0.54304
0.54304
0.54304
0.54304
0.54304
0.69323
0.69323
0.69323
0.57872
0.57872
0.57872
0.57872
0.58765
0.58765
0.58765
0.56043
0.56043
0.56043
0.56043
0.56043
0.61712
0.61712
0.86713
GOV08
0.72400
0.72400
0.72400
0.72400
0.82759
0.82759
0.70109
0.70109
0.66388
0.66388
0.73309
0.76536
0.62703
0.63373
0.63373
0.63373
0.64645
0.72550
0.71363
0.71363
0.69173
0.69173
0.63194
0.63194
0.63194
0.63194
0.63194
0.63194
0.63194
0.80241
0.80241
0.80241
0.68669
0.68669
0.68669
0.68669
0.66399
0.66399
0.66399
0.71674
0.71674
0.71674
0.71674
0.71674
0.76720
0.76720
0.94625
PRES08
0.64358
0.64358
0.64358
0.64358
0.77360
0.77360
0.66502
0.66502
0.49076
0.49076
0.60469
0.68018
0.46900
0.51895
0.51895
0.51895
0.54260
0.67853
0.54378
0.54378
0.63151
0.63151
0.43691
0.43691
0.43691
0.43691
0.43691
0.43691
0.43691
0.78195
0.78195
0.78195
0.55145
0.55145
0.55145
0.55145
0.51406
0.51406
0.51406
0.63891
0.63891
0.63891
0.63891
0.63891
0.69643
0.69643
0.96710
SEN10
0.54651
0.54651
0.54651
0.54651
0.71746
0.71746
0.58333
0.58333
0.41728
0.41728
0.61836
0.61624
0.43352
0.45305
0.45305
0.45305
0.43853
0.58900
0.48505
0.48505
0.51763
0.51763
0.38154
0.38154
0.38154
0.38154
0.38154
0.38154
0.38154
0.73126
0.73126
0.73126
0.48333
0.48333
0.48333
0.48333
0.46731
0.46731
0.46731
0.47505
0.47505
0.47505
0.47505
0.47505
0.59943
0.59943
0.97121
Outside Precinct
BPOP
BVAP
0.23361
0.22347
0.25569
0.24387
0.06343
0.05580
0.20911
0.20061
0.29968
0.29400
0.25569
0.24387
0.15297
0.15132
0.23361
0.22347
0.18380
0.19373
0.04277
0.04237
0.23933
0.23955
0.23567
0.25956
0.04277
0.04237
0.18380
0.19373
0.04277
0.04237
0.18571
0.19843
0.04277
0.04237
0.23933
0.23955
0.27126
0.28142
0.33706
0.30414
0.22152
0.20671
0.16952
0.14727
0.27126
0.28142
0.26348
0.25849
0.11463
0.10811
0.33295
0.33512
0.28431
0.30259
0.32734
0.32255
0.47451
0.49350
0.01925
0.01637
0.22152
0.20671
0.16952
0.14727
0.11463
0.10811
0.28431
0.30259
0.08246
0.07560
0.16952
0.14727
0.27126
0.28142
0.33706
0.30414
0.47451
0.49350
0.08503
0.08705
0.01925
0.01637
0.12520
0.12769
0.08246
0.07560
0.10571
0.10707
0.22152
0.20671
0.08246
0.07560
0.40585
0.37937
BREG
0.22149
0.21882
0.05671
0.19339
0.34957
0.21882
0.15120
0.22149
0.18191
0.02957
0.23376
0.29129
0.02957
0.18191
0.02957
0.23907
0.02957
0.23376
0.30230
0.34362
0.22035
0.14365
0.30230
0.29332
0.10829
0.30455
0.36316
0.34255
0.52035
0.01401
0.22035
0.14365
0.10829
0.36316
0.07254
0.14365
0.30230
0.34362
0.52035
0.09348
0.01401
0.13815
0.07254
0.10496
0.22035
0.07254
0.41220
DREG
0.47142
0.50167
0.36050
0.49752
0.58680
0.50167
0.43504
0.47142
0.52050
0.44885
0.47359
0.65960
0.44885
0.52050
0.44885
0.57580
0.44885
0.47359
0.55439
0.60449
0.49084
0.37326
0.55439
0.51114
0.44251
0.54494
0.46842
0.59146
0.73388
0.35807
0.49084
0.37326
0.44251
0.46842
0.36129
0.37326
0.55439
0.60449
0.73388
0.30043
0.35807
0.31156
0.36129
0.35392
0.49084
0.36129
0.56420
1 of 6
Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 30 of 99
GOV08
0.52176
0.54210
0.39964
0.54439
0.59141
0.54210
0.48219
0.52176
0.52181
0.42778
0.55796
0.66019
0.42778
0.52181
0.42778
0.55508
0.42778
0.55796
0.61300
0.66164
0.61152
0.54249
0.61300
0.58712
0.50954
0.63653
0.62018
0.61867
0.73499
0.51781
0.61152
0.54249
0.50954
0.62018
0.49299
0.54249
0.61300
0.66164
0.73499
0.48282
0.51781
0.49143
0.49299
0.48052
0.61152
0.49299
0.72029
PRES08
0.36252
0.43023
0.16376
0.40211
0.46943
0.43023
0.31043
0.36252
0.31455
0.26180
0.44435
0.37202
0.26180
0.31455
0.26180
0.37370
0.26180
0.44435
0.37672
0.41432
0.47411
0.43277
0.37672
0.41636
0.24432
0.44828
0.51994
0.46403
0.61747
0.25360
0.47411
0.43277
0.24432
0.51994
0.26630
0.43277
0.37672
0.41432
0.61747
0.30992
0.25360
0.37189
0.26630
0.30159
0.47411
0.26630
0.77873
SEN10
0.32498
0.36521
0.19636
0.33754
0.45758
0.36521
0.26637
0.32498
0.28918
0.23012
0.37176
0.43478
0.23012
0.28918
0.23012
0.37413
0.23012
0.37176
0.38462
0.44625
0.39043
0.34519
0.38462
0.40227
0.24496
0.41128
0.43520
0.40697
0.58050
0.19943
0.39043
0.34519
0.24496
0.43520
0.19604
0.34519
0.38462
0.44625
0.58050
0.24143
0.19943
0.27474
0.19604
0.25622
0.39043
0.19604
0.73241
5/8/2012 3:32 PM
1st District Boundary Precincts
Appendix A
Seq
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
InsidePrecinct
3706302
3706305
3706305
3706329
3706329
3706329
3706329
3706329
3706330 2
3706323
3706323
3706306
3706324
3706324
3706324
3706334
3706334
3706309
3706309
3706303
3706341
3706341
3706354
3706354
3706354
3706354
3706340
3706340
3706331
3706331
3706331
3706330 1
370650104
370650301
370650102
370650102
370650201
370650201
3706911
3706911
3706915
3706915
3706915
3706915
3706902
3706902
3706902
district_1 DWP Edit.xlsx
OutsidePrecinct
3706304
3706350
3706304
3706332
3706345
3706328
37077CRDM
3718314 01
3706332
3706345
3706337
3706343
3706337
3706350
3706304
3706333
3706351
3706348
3706336
3706304
3706348
3706351
3706333
3706335
3706316
3706351
3706343
3706336
3706333
3706332
3718305 05
3706332
370650103
371470401
370650801
370650103
370650801
371470401
3706912
3706909
3706912
3706914
3706918
37181KITT
3706912
3706905
3706917
Inside Precinct
BPOP
BVAP
0.25096
0.23016
0.26281
0.24020
0.26281
0.24020
0.37494
0.35470
0.37494
0.35470
0.37494
0.35470
0.37494
0.35470
0.37494
0.35470
0.64276
0.61592
0.66241
0.64150
0.66241
0.64150
0.19970
0.21030
0.27983
0.25341
0.27983
0.25341
0.27983
0.25341
0.56526
0.56850
0.56526
0.56850
0.36210
0.34976
0.36210
0.34976
0.07034
0.06995
0.91133
0.92111
0.91133
0.92111
0.40159
0.38879
0.40159
0.38879
0.40159
0.38879
0.40159
0.38879
0.34313
0.32887
0.34313
0.32887
0.36690
0.34742
0.36690
0.34742
0.36690
0.34742
0.39312
0.37814
0.46380
0.41108
0.40570
0.42408
0.48412
0.45324
0.48412
0.45324
0.47219
0.44894
0.47219
0.44894
0.41317
0.41974
0.41317
0.41974
0.50638
0.51322
0.50638
0.51322
0.50638
0.51322
0.50638
0.51322
0.44662
0.46081
0.44662
0.46081
0.44662
0.46081
BREG
0.25258
0.28756
0.28756
0.39806
0.39806
0.39806
0.39806
0.39806
0.63912
0.68246
0.68246
0.26249
0.24610
0.24610
0.24610
0.61932
0.61932
0.35215
0.35215
0.06977
0.94596
0.94596
0.39845
0.39845
0.39845
0.39845
0.39208
0.39208
0.34051
0.34051
0.34051
0.41714
0.39070
0.45553
0.41791
0.41791
0.47298
0.47298
0.47642
0.47642
0.57899
0.57899
0.57899
0.57899
0.47274
0.47274
0.47274
DREG
0.56142
0.51956
0.51956
0.59030
0.59030
0.59030
0.59030
0.59030
0.69142
0.73695
0.73695
0.69338
0.56905
0.56905
0.56905
0.70581
0.70581
0.67839
0.67839
0.58088
0.89193
0.89193
0.58654
0.58654
0.58654
0.58654
0.66447
0.66447
0.55120
0.55120
0.55120
0.58243
0.72836
0.72885
0.73655
0.73655
0.72319
0.72319
0.71096
0.71096
0.68794
0.68794
0.68794
0.68794
0.67862
0.67862
0.67862
GOV08
0.82117
0.70373
0.70373
0.59411
0.59411
0.59411
0.59411
0.59411
0.78031
0.83346
0.83346
0.75865
0.57676
0.57676
0.57676
0.83319
0.83319
0.73928
0.73928
0.78307
0.95889
0.95889
0.74814
0.74814
0.74814
0.74814
0.78657
0.78657
0.60177
0.60177
0.60177
0.65510
0.63529
0.67712
0.65211
0.65211
0.63573
0.63573
0.71222
0.71222
0.73143
0.73143
0.73143
0.73143
0.64054
0.64054
0.64054
PRES08
0.86832
0.83925
0.83925
0.57189
0.57189
0.57189
0.57189
0.57189
0.82288
0.85576
0.85576
0.86180
0.60486
0.60486
0.60486
0.88720
0.88720
0.81580
0.81580
0.86401
0.97998
0.97998
0.80981
0.80981
0.80981
0.80981
0.87118
0.87118
0.61909
0.61909
0.61909
0.67398
0.51997
0.58879
0.56307
0.56307
0.54263
0.54263
0.63529
0.63529
0.74344
0.74344
0.74344
0.74344
0.59560
0.59560
0.59560
SEN10
0.88279
0.84533
0.84533
0.57364
0.57364
0.57364
0.57364
0.57364
0.82961
0.84404
0.84404
0.82949
0.55461
0.55461
0.55461
0.87675
0.87675
0.77716
0.77716
0.84923
0.97388
0.97388
0.78699
0.78699
0.78699
0.78699
0.85565
0.85565
0.58447
0.58447
0.58447
0.66519
0.50555
0.56061
0.52769
0.52769
0.53974
0.53974
0.64507
0.64507
0.69316
0.69316
0.69316
0.69316
0.59097
0.59097
0.59097
Outside Precinct
BPOP
BVAP
0.06693
0.06105
0.22096
0.20987
0.06693
0.06105
0.10458
0.09394
0.21021
0.20299
0.14269
0.14142
0.29591
0.28619
0.05545
0.06266
0.10458
0.09394
0.21021
0.20299
0.14201
0.14196
0.07257
0.07221
0.14201
0.14196
0.22096
0.20987
0.06693
0.06105
0.40585
0.37937
0.17955
0.17057
0.29402
0.29115
0.28469
0.28208
0.06693
0.06105
0.29402
0.29115
0.17955
0.17057
0.40585
0.37937
0.28168
0.27216
0.27172
0.28311
0.17955
0.17057
0.07257
0.07221
0.28469
0.28208
0.40585
0.37937
0.10458
0.09394
0.21020
0.19577
0.10458
0.09394
0.40221
0.38068
0.55425
0.54106
0.30619
0.30156
0.40221
0.38068
0.30619
0.30156
0.55425
0.54106
0.34700
0.34122
0.21679
0.21529
0.34700
0.34122
0.12970
0.12351
0.12157
0.12086
0.35555
0.34918
0.34700
0.34122
0.19670
0.19039
0.13023
0.13007
BREG
0.05412
0.18873
0.05412
0.09099
0.19415
0.14494
0.30711
0.07099
0.09099
0.19415
0.10611
0.06959
0.10611
0.18873
0.05412
0.41220
0.16738
0.30226
0.32765
0.05412
0.30226
0.16738
0.41220
0.28423
0.25265
0.16738
0.06959
0.32765
0.41220
0.09099
0.16837
0.09099
0.32515
0.57555
0.32428
0.32515
0.32428
0.57555
0.36124
0.23897
0.36124
0.12152
0.12000
0.37132
0.36124
0.23182
0.12338
DREG
0.63284
0.52356
0.63284
0.39340
0.50786
0.39887
0.53278
0.35957
0.39340
0.50786
0.48556
0.52278
0.48556
0.52356
0.63284
0.56420
0.51824
0.57088
0.60600
0.63284
0.57088
0.51824
0.56420
0.53126
0.53748
0.51824
0.52278
0.60600
0.56420
0.39340
0.36393
0.39340
0.68475
0.74929
0.59736
0.68475
0.59736
0.74929
0.58915
0.59606
0.58915
0.35409
0.33636
0.62627
0.58915
0.48687
0.36227
2 of 6
Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 31 of 99
GOV08
0.72080
0.63774
0.72080
0.44991
0.52017
0.41889
0.53394
0.34969
0.44991
0.52017
0.48944
0.61683
0.48944
0.63774
0.72080
0.72029
0.67748
0.68857
0.73643
0.72080
0.68857
0.67748
0.72029
0.65408
0.68065
0.67748
0.61683
0.73643
0.72029
0.44991
0.51122
0.44991
0.57482
0.71373
0.54069
0.57482
0.54069
0.71373
0.60328
0.52145
0.60328
0.40458
0.38462
0.56424
0.60328
0.45173
0.41378
PRES08
0.79147
0.70230
0.79147
0.47589
0.54034
0.43356
0.51958
0.31504
0.47589
0.54034
0.50936
0.69832
0.50936
0.70230
0.79147
0.77873
0.75622
0.76874
0.80000
0.79147
0.76874
0.75622
0.77873
0.72454
0.75998
0.75622
0.69832
0.80000
0.77873
0.47589
0.59326
0.47589
0.43594
0.62665
0.42222
0.43594
0.42222
0.62665
0.51974
0.40190
0.51974
0.39909
0.38879
0.48820
0.51974
0.41876
0.40337
SEN10
0.78547
0.64779
0.78547
0.31705
0.52251
0.39114
0.47375
0.26648
0.31705
0.52251
0.47142
0.66356
0.47142
0.64779
0.78547
0.73241
0.73181
0.70670
0.75443
0.78547
0.70670
0.73181
0.73241
0.69862
0.71853
0.73181
0.66356
0.75443
0.73241
0.31705
0.47818
0.31705
0.43336
0.58154
0.43594
0.43336
0.43594
0.58154
0.51351
0.43697
0.51351
0.35092
0.33083
0.47350
0.51351
0.38356
0.33308
5/8/2012 3:32 PM
1st District Boundary Precincts
Appendix A
Seq
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
InsidePrecinct
3706910
3706916
3706916
370731
370731
370731
370731
370731
370734S
370734S
37077ANTI
37077SALM
37077SALM
37077TYHO
37077TYHO
37077TYHO
37077TYHO
37077TYHO
37077WOEL
37077WOEL
37077CORI
37077CORI
37077CORI
37077BTNR
37077BTNR
37079BULL
37079BULL
37079BULL
37079SHIN
37079SHIN
37079SH1
37079SH1
37079SUGG
37079SUGG
37079ARBA
37079ARBA
37091CM
37091CM
37091CO
37091WN
37091HV
37091HV
37107SH
37107SH
37107SH
37107SH
37107MH
district_1 DWP Edit.xlsx
OutsidePrecinct
3706909
3706909
3706908
370415
370735
370733
370732
370734N
370735
370734N
37181WATK
37077SASS
37181WATK
3706328
37077BERE
37077WILT
37077CRDM
37145MTTZ
37077BERE
37077SASS
37077WILT
37181WATK
37181KITT
3706328
37077CRDM
37079BEAR
3719102
37195PRST
37079BEAR
3719115
37079BEAR
37079MAUR
37079MAUR
37079HOOK
37079BEAR
37107I
370733
370732
370732
370732
370415
370732
3704910
37103P01
37107SW
37107C
37107I
Inside Precinct
BPOP
BVAP
0.27301
0.27500
0.45091
0.44911
0.45091
0.44911
0.49578
0.50326
0.49578
0.50326
0.49578
0.50326
0.49578
0.50326
0.49578
0.50326
0.46687
0.46711
0.46687
0.46711
0.56519
0.59430
0.31231
0.32106
0.31231
0.32106
0.15271
0.15383
0.15271
0.15383
0.15271
0.15383
0.15271
0.15383
0.15271
0.15383
0.52507
0.51913
0.52507
0.51913
0.29947
0.30472
0.29947
0.30472
0.29947
0.30472
0.41329
0.43042
0.41329
0.43042
0.33756
0.34187
0.33756
0.34187
0.33756
0.34187
0.28298
0.27149
0.28298
0.27149
0.41909
0.40010
0.41909
0.40010
0.39445
0.38219
0.39445
0.38219
0.46440
0.43889
0.46440
0.43889
0.37690
0.38298
0.37690
0.38298
0.70737
0.68642
0.66143
0.64378
0.54046
0.52413
0.54046
0.52413
0.11863
0.12593
0.11863
0.12593
0.11863
0.12593
0.11863
0.12593
0.40700
0.40516
BREG
0.28355
0.47037
0.47037
0.46879
0.46879
0.46879
0.46879
0.46879
0.47448
0.47448
0.66445
0.32173
0.32173
0.16267
0.16267
0.16267
0.16267
0.16267
0.53067
0.53067
0.36428
0.36428
0.36428
0.36082
0.36082
0.40499
0.40499
0.40499
0.30801
0.30801
0.43020
0.43020
0.40105
0.40105
0.46593
0.46593
0.40839
0.40839
0.72100
0.70215
0.53974
0.53974
0.13828
0.13828
0.13828
0.13828
0.46013
DREG
0.59357
0.69533
0.69533
0.68327
0.68327
0.68327
0.68327
0.68327
0.68998
0.68998
0.76967
0.64530
0.64530
0.46645
0.46645
0.46645
0.46645
0.46645
0.72579
0.72579
0.66819
0.66819
0.66819
0.60457
0.60457
0.67577
0.67577
0.67577
0.64669
0.64669
0.73165
0.73165
0.66178
0.66178
0.68901
0.68901
0.67550
0.67550
0.79937
0.84158
0.77461
0.77461
0.44687
0.44687
0.44687
0.44687
0.64773
GOV08
0.54632
0.66263
0.66263
0.71277
0.71277
0.71277
0.71277
0.71277
0.68984
0.68984
0.77919
0.52686
0.52686
0.42815
0.42815
0.42815
0.42815
0.42815
0.72338
0.72338
0.56793
0.56793
0.56793
0.58897
0.58897
0.64522
0.64522
0.64522
0.55762
0.55762
0.68455
0.68455
0.61696
0.61696
0.67618
0.67618
0.64396
0.64396
0.85430
0.83559
0.69231
0.69231
0.46311
0.46311
0.46311
0.46311
0.64486
PRES08
0.41622
0.61712
0.61712
0.63352
0.63352
0.63352
0.63352
0.63352
0.63158
0.63158
0.77612
0.48142
0.48142
0.36490
0.36490
0.36490
0.36490
0.36490
0.68799
0.68799
0.50136
0.50136
0.50136
0.55988
0.55988
0.53217
0.53217
0.53217
0.39074
0.39074
0.54880
0.54880
0.46712
0.46712
0.54429
0.54429
0.56156
0.56156
0.81004
0.80577
0.60672
0.60672
0.25470
0.25470
0.25470
0.25470
0.53213
SEN10
0.46228
0.63836
0.63836
0.59709
0.59709
0.59709
0.59709
0.59709
0.60944
0.60944
0.74221
0.44016
0.44016
0.32849
0.32849
0.32849
0.32849
0.32849
0.66894
0.66894
0.48249
0.48249
0.48249
0.52568
0.52568
0.50660
0.50660
0.50660
0.39060
0.39060
0.55556
0.55556
0.46813
0.46813
0.50389
0.50389
0.59903
0.59903
0.84266
0.76102
0.56069
0.56069
0.22823
0.22823
0.22823
0.22823
0.50860
Outside Precinct
BPOP
BVAP
0.21679
0.21529
0.21679
0.21529
0.22170
0.20782
0.18380
0.19373
0.24740
0.24668
0.38980
0.39497
0.13379
0.12839
0.21026
0.21120
0.24740
0.24668
0.21026
0.21120
0.03281
0.03571
0.30025
0.30153
0.03281
0.03571
0.14269
0.14142
0.33165
0.33773
0.09644
0.09990
0.29591
0.28619
0.13144
0.13483
0.33165
0.33773
0.30025
0.30153
0.09644
0.09990
0.03281
0.03571
0.35555
0.34918
0.14269
0.14142
0.29591
0.28619
0.30465
0.27925
0.17142
0.16965
0.37043
0.36310
0.30465
0.27925
0.15549
0.15816
0.30465
0.27925
0.47383
0.48173
0.47383
0.48173
0.29838
0.29296
0.30465
0.27925
0.23554
0.20969
0.38980
0.39497
0.13379
0.12839
0.13379
0.12839
0.13379
0.12839
0.18380
0.19373
0.13379
0.12839
0.27126
0.28142
0.33706
0.30414
0.08711
0.09290
0.41151
0.39391
0.23554
0.20969
BREG
0.23897
0.23897
0.22010
0.18191
0.26234
0.41984
0.15392
0.21415
0.26234
0.21415
0.03622
0.35589
0.03622
0.14494
0.35681
0.10629
0.30711
0.12740
0.35681
0.35589
0.10629
0.03622
0.37132
0.14494
0.30711
0.32019
0.17177
0.41103
0.32019
0.17730
0.32019
0.41221
0.41221
0.31277
0.32019
0.22972
0.41984
0.15392
0.15392
0.15392
0.18191
0.15392
0.30230
0.34362
0.09100
0.41926
0.22972
DREG
0.59606
0.59606
0.40555
0.52050
0.65631
0.61358
0.63883
0.44776
0.65631
0.44776
0.53521
0.64016
0.53521
0.39887
0.63313
0.32854
0.53278
0.44247
0.63313
0.64016
0.32854
0.53521
0.62627
0.39887
0.53278
0.70251
0.38472
0.59632
0.70251
0.39765
0.70251
0.74261
0.74261
0.61406
0.70251
0.45295
0.61358
0.63883
0.63883
0.63883
0.52050
0.63883
0.55439
0.60449
0.42085
0.60121
0.45295
3 of 6
Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 32 of 99
GOV08
0.52145
0.52145
0.48265
0.52181
0.58605
0.64022
0.45271
0.48144
0.58605
0.48144
0.34048
0.60375
0.34048
0.41889
0.56529
0.37206
0.53394
0.42757
0.56529
0.60375
0.37206
0.34048
0.56424
0.41889
0.53394
0.61310
0.40206
0.62581
0.61310
0.41586
0.61310
0.63127
0.63127
0.56203
0.61310
0.48751
0.64022
0.45271
0.45271
0.45271
0.52181
0.45271
0.61300
0.66164
0.48309
0.64237
0.48751
PRES08
0.40190
0.40190
0.46522
0.31455
0.43169
0.58550
0.32932
0.42974
0.43169
0.42974
0.24178
0.52776
0.24178
0.43356
0.50919
0.37056
0.51958
0.35120
0.50919
0.52776
0.37056
0.24178
0.48820
0.43356
0.51958
0.39225
0.28405
0.52929
0.39225
0.27959
0.39225
0.48588
0.48588
0.40525
0.39225
0.32903
0.58550
0.32932
0.32932
0.32932
0.31455
0.32932
0.37672
0.41432
0.21534
0.49496
0.32903
SEN10
0.43697
0.43697
0.40515
0.28918
0.38173
0.55720
0.30070
0.32759
0.38173
0.32759
0.30797
0.52369
0.30797
0.39114
0.45624
0.30831
0.47375
0.34649
0.45624
0.52369
0.30831
0.30797
0.47350
0.39114
0.47375
0.45843
0.25392
0.54294
0.45843
0.23498
0.45843
0.47208
0.47208
0.39433
0.45843
0.26501
0.55720
0.30070
0.30070
0.30070
0.28918
0.30070
0.38462
0.44625
0.22145
0.44802
0.26501
5/8/2012 3:32 PM
1st District Boundary Precincts
Appendix A
Seq
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
InsidePrecinct
37107MH
37107MH
37107MH
37107K7
37107K7
37107K9
37107K9
37107K1
37107K1
37107K6
37107K8
37107K3
37107K3
37107K3
37107K5
37117HM
37117W2
37117W2
37117R2
37117R2
37117R2
37117W1
37117W1
37117W1
37117W1
37117R1
37117R1
371270007
371270007
371270022
371270003
371270003
371270003
371270038
371270038
371270031
371270040
371270032
371270034
371270034
371270011
371270011
371270011
371270011
371270011
371270011
371270002
district_1 DWP Edit.xlsx
OutsidePrecinct
37107FC
37107T2
3719115
37107SW
37107C
37107FC
37107K4
37107SW
37107N
37107C
37107SW
37107N
37107FC
37107K4
37107K4
37117PP
37117PP
37117CR
37117PP
37117CR
371470401
37117W
37117GR
37117CR
37117BG
37117CR
371470401
371270026
371270015
371270026
3706909
3706908
371270015
371270041
371270036
371270036
371270041
371270036
371270036
371270035
3706908
371270015
371270012
371270004
371270008
371270006
371270041
Inside Precinct
BPOP
BVAP
0.40700
0.40516
0.40700
0.40516
0.40700
0.40516
0.80886
0.79256
0.80886
0.79256
0.48844
0.46597
0.48844
0.46597
0.96298
0.96779
0.96298
0.96779
0.85644
0.83463
0.98276
0.98390
0.61090
0.57300
0.61090
0.57300
0.61090
0.57300
0.60108
0.57028
0.58963
0.56728
0.53602
0.50372
0.53602
0.50372
0.64910
0.63346
0.64910
0.63346
0.64910
0.63346
0.50487
0.47481
0.50487
0.47481
0.50487
0.47481
0.50487
0.47481
0.60818
0.59030
0.60818
0.59030
0.56194
0.56422
0.56194
0.56422
0.50946
0.50518
0.31626
0.31623
0.31626
0.31623
0.31626
0.31623
0.48938
0.46660
0.48938
0.46660
0.64367
0.63204
0.46256
0.44411
0.56112
0.54789
0.75304
0.72179
0.75304
0.72179
0.39119
0.39269
0.39119
0.39269
0.39119
0.39269
0.39119
0.39269
0.39119
0.39269
0.39119
0.39269
0.47607
0.48299
BREG
0.46013
0.46013
0.46013
0.80761
0.80761
0.44915
0.44915
0.96559
0.96559
0.85060
0.98182
0.60671
0.60671
0.60671
0.54803
0.57684
0.52728
0.52728
0.64997
0.64997
0.64997
0.48627
0.48627
0.48627
0.48627
0.63163
0.63163
0.61224
0.61224
0.50249
0.36222
0.36222
0.36222
0.42898
0.42898
0.62378
0.41311
0.52041
0.72951
0.72951
0.43105
0.43105
0.43105
0.43105
0.43105
0.43105
0.47239
DREG
0.64773
0.64773
0.64773
0.78579
0.78579
0.66737
0.66737
0.84562
0.84562
0.83819
0.91082
0.69736
0.69736
0.69736
0.73048
0.74134
0.71313
0.71313
0.77076
0.77076
0.77076
0.71009
0.71009
0.71009
0.71009
0.74034
0.74034
0.69388
0.69388
0.60518
0.54190
0.54190
0.54190
0.60314
0.60314
0.68512
0.54101
0.66257
0.75281
0.75281
0.63432
0.63432
0.63432
0.63432
0.63432
0.63432
0.60911
GOV08
0.64486
0.64486
0.64486
0.87275
0.87275
0.69421
0.69421
0.97735
0.97735
0.90353
0.98788
0.78322
0.78322
0.78322
0.77811
0.77196
0.75786
0.75786
0.77539
0.77539
0.77539
0.71528
0.71528
0.71528
0.71528
0.76291
0.76291
0.75350
0.75350
0.66896
0.58108
0.58108
0.58108
0.63402
0.63402
0.77640
0.62964
0.67408
0.82367
0.82367
0.63304
0.63304
0.63304
0.63304
0.63304
0.63304
0.61998
PRES08
0.53213
0.53213
0.53213
0.84321
0.84321
0.55734
0.55734
0.98833
0.98833
0.88153
0.99174
0.71059
0.71059
0.71059
0.66897
0.64590
0.62797
0.62797
0.68695
0.68695
0.68695
0.55750
0.55750
0.55750
0.55750
0.66579
0.66579
0.73047
0.73047
0.62259
0.49844
0.49844
0.49844
0.61082
0.61082
0.75448
0.60444
0.65512
0.81097
0.81097
0.58575
0.58575
0.58575
0.58575
0.58575
0.58575
0.56189
SEN10
0.50860
0.50860
0.50860
0.82378
0.82378
0.57561
0.57561
0.95918
0.95918
0.84615
0.98399
0.69732
0.69732
0.69732
0.67544
0.62529
0.61354
0.61354
0.63748
0.63748
0.63748
0.53472
0.53472
0.53472
0.53472
0.64229
0.64229
0.71930
0.71930
0.62261
0.51635
0.51635
0.51635
0.60757
0.60757
0.71642
0.58044
0.56221
0.77391
0.77391
0.59259
0.59259
0.59259
0.59259
0.59259
0.59259
0.53938
Outside Precinct
BPOP
BVAP
0.18447
0.17170
0.05893
0.05607
0.15549
0.15816
0.08711
0.09290
0.41151
0.39391
0.18447
0.17170
0.28342
0.27450
0.08711
0.09290
0.24761
0.22986
0.41151
0.39391
0.08711
0.09290
0.24761
0.22986
0.18447
0.17170
0.28342
0.27450
0.28342
0.27450
0.30137
0.28682
0.30137
0.28682
0.32079
0.31520
0.30137
0.28682
0.32079
0.31520
0.55425
0.54106
0.23567
0.25956
0.16719
0.17729
0.32079
0.31520
0.13220
0.13317
0.32079
0.31520
0.55425
0.54106
0.09536
0.09210
0.37996
0.37739
0.09536
0.09210
0.21679
0.21529
0.22170
0.20782
0.37996
0.37739
0.22667
0.21863
0.33506
0.31554
0.33506
0.31554
0.22667
0.21863
0.33506
0.31554
0.33506
0.31554
0.24327
0.21633
0.22170
0.20782
0.37996
0.37739
0.19220
0.18623
0.05959
0.05978
0.15877
0.16833
0.18024
0.16985
0.22667
0.21863
BREG
0.15250
0.06908
0.17730
0.09100
0.41926
0.15250
0.21097
0.09100
0.25033
0.41926
0.09100
0.25033
0.15250
0.21097
0.21097
0.29936
0.29936
0.34872
0.29936
0.34872
0.57555
0.29129
0.18615
0.34872
0.13842
0.34872
0.57555
0.08556
0.35419
0.08556
0.23897
0.22010
0.35419
0.20914
0.28449
0.28449
0.20914
0.28449
0.28449
0.20625
0.22010
0.35419
0.20091
0.05751
0.22954
0.18635
0.20914
DREG
0.43374
0.43941
0.39765
0.42085
0.60121
0.43374
0.56595
0.42085
0.54450
0.60121
0.42085
0.54450
0.43374
0.56595
0.56595
0.61146
0.61146
0.60511
0.61146
0.60511
0.74929
0.65960
0.65476
0.60511
0.52587
0.60511
0.74929
0.35810
0.55565
0.35810
0.59606
0.40555
0.55565
0.42680
0.51969
0.51969
0.42680
0.51969
0.51969
0.41997
0.40555
0.55565
0.54110
0.33842
0.51692
0.46560
0.42680
4 of 6
Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 33 of 99
GOV08
0.47678
0.49664
0.41586
0.48309
0.64237
0.47678
0.59521
0.48309
0.56061
0.64237
0.48309
0.56061
0.47678
0.59521
0.59521
0.62802
0.62802
0.66189
0.62802
0.66189
0.71373
0.66019
0.59067
0.66189
0.50734
0.66189
0.71373
0.33255
0.55070
0.33255
0.52145
0.48265
0.55070
0.40854
0.51471
0.51471
0.40854
0.51471
0.51471
0.41698
0.48265
0.55070
0.47049
0.28238
0.45505
0.42318
0.40854
PRES08
0.27176
0.23205
0.27959
0.21534
0.49496
0.27176
0.38649
0.21534
0.33430
0.49496
0.21534
0.33430
0.27176
0.38649
0.38649
0.38679
0.38679
0.44699
0.38679
0.44699
0.62665
0.37202
0.29815
0.44699
0.25501
0.44699
0.62665
0.21737
0.47588
0.21737
0.40190
0.46522
0.47588
0.30929
0.44470
0.44470
0.30929
0.44470
0.44470
0.33347
0.46522
0.47588
0.31438
0.17989
0.36774
0.34433
0.30929
SEN10
0.23338
0.18993
0.23498
0.22145
0.44802
0.23338
0.38677
0.22145
0.30958
0.44802
0.22145
0.30958
0.23338
0.38677
0.38677
0.41045
0.41045
0.46288
0.41045
0.46288
0.58154
0.43478
0.31801
0.46288
0.30013
0.46288
0.58154
0.20982
0.47676
0.20982
0.43697
0.40515
0.47676
0.32612
0.41300
0.41300
0.32612
0.41300
0.41300
0.28571
0.40515
0.47676
0.35358
0.17447
0.35791
0.33667
0.32612
5/8/2012 3:32 PM
1st District Boundary Precincts
Appendix A
Seq
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
InsidePrecinct
371270002
37139MH
37139MH
37139MH
371393 A
371391 A
37143PARKVI
37143PARKVI
37143PARKVI
37143PARKVI
37143EAST H
37143EAST H
37143WEST H
371470301
371471504
371471501
371471101
371471101
371471101
371470901
37181WH2
37181TWNS
37181MIDD
37181DABN
37181WMSB
37181NH2
37181SH2
37181SH2
37181HTOP
37181HTOP
37181EH2
371856
371856
37187LM
37187LM
37187LM
37187LM
3719117
3719117
3719117
3719117
3719126
3719126
3719127
3719127
3719111
3719111
district_1 DWP Edit.xlsx
OutsidePrecinct
371270026
37139NIX
37143NICANO
37143NEW HO
37139NIX
37029CH
37143BELVID
37143NICANO
37143BETHEL
37143NEW HO
370416
37143BETHEL
37143BELVID
371470401
371470101
371471508B
37013CHOCO
371470601
371471102B
370650801
37181WATK
37077SASS
37181SCRK
37181WATK
37077SASS
37181SCRK
37181SCRK
37181KITT
37181WATK
37181KITT
37181SCRK
3706912
37181SCRK
37013PANTE
370416
37095BM
37187SK
3719123
3719128
3719109
3719116
3719128
3719116
3719128
3719116
3719105
3719106
Inside Precinct
BPOP
BVAP
0.47607
0.48299
0.27564
0.27241
0.27564
0.27241
0.27564
0.27241
0.49706
0.47580
0.43541
0.42458
0.33074
0.32313
0.33074
0.32313
0.33074
0.32313
0.33074
0.32313
0.53689
0.49869
0.53689
0.49869
0.26985
0.25325
0.48477
0.48381
0.61913
0.57753
0.75236
0.76761
0.34403
0.33245
0.34403
0.33245
0.34403
0.33245
0.32670
0.31909
0.52235
0.48525
0.50410
0.49311
0.47980
0.46817
0.34564
0.34359
0.52101
0.50908
0.50085
0.47162
0.39385
0.37879
0.39385
0.37879
0.57929
0.57311
0.57929
0.57311
0.53988
0.51012
0.60665
0.58085
0.60665
0.58085
0.57430
0.55391
0.57430
0.55391
0.57430
0.55391
0.57430
0.55391
0.74654
0.68279
0.74654
0.68279
0.74654
0.68279
0.74654
0.68279
0.37738
0.38603
0.37738
0.38603
0.54569
0.55123
0.54569
0.55123
0.46032
0.41910
0.46032
0.41910
BREG
0.47239
0.30070
0.30070
0.30070
0.49041
0.39174
0.33389
0.33389
0.33389
0.33389
0.46786
0.46786
0.23516
0.59058
0.59717
0.87282
0.36240
0.36240
0.36240
0.32976
0.46000
0.52604
0.47635
0.34178
0.55139
0.46267
0.49467
0.49467
0.58435
0.58435
0.58952
0.66897
0.66897
0.58574
0.58574
0.58574
0.58574
0.84024
0.84024
0.84024
0.84024
0.47893
0.47893
0.66459
0.66459
0.41675
0.41675
DREG
0.60911
0.49350
0.49350
0.49350
0.63053
0.56838
0.58292
0.58292
0.58292
0.58292
0.68715
0.68715
0.58906
0.69876
0.66535
0.82294
0.57748
0.57748
0.57748
0.57738
0.75222
0.70244
0.69162
0.64077
0.71173
0.73867
0.72899
0.72899
0.76284
0.76284
0.75983
0.81331
0.81331
0.78456
0.78456
0.78456
0.78456
0.78178
0.78178
0.78178
0.78178
0.58407
0.58407
0.67817
0.67817
0.58088
0.58088
GOV08
0.61998
0.59651
0.59651
0.59651
0.73090
0.68706
0.61675
0.61675
0.61675
0.61675
0.70255
0.70255
0.64505
0.74817
0.78946
0.92387
0.60854
0.60854
0.60854
0.56645
0.62500
0.64571
0.64884
0.57863
0.69341
0.66969
0.73905
0.73905
0.73684
0.73684
0.76376
0.80075
0.80075
0.77952
0.77952
0.77952
0.77952
0.92235
0.92235
0.92235
0.92235
0.64156
0.64156
0.77778
0.77778
0.57697
0.57697
PRES08
0.56189
0.48642
0.48642
0.48642
0.66703
0.63710
0.48870
0.48870
0.48870
0.48870
0.61486
0.61486
0.46167
0.68736
0.80108
0.93569
0.49592
0.49592
0.49592
0.45719
0.58104
0.62890
0.61187
0.50152
0.67580
0.60503
0.68124
0.68124
0.69586
0.69586
0.70159
0.76989
0.76989
0.66849
0.66849
0.66849
0.66849
0.94977
0.94977
0.94977
0.94977
0.60767
0.60767
0.75873
0.75873
0.54534
0.54534
SEN10
0.53938
0.46818
0.46818
0.46818
0.61588
0.58643
0.45455
0.45455
0.45455
0.45455
0.52670
0.52670
0.40432
0.65302
0.79564
0.91614
0.43381
0.43381
0.43381
0.42857
0.53070
0.58351
0.57844
0.48654
0.61873
0.58777
0.62667
0.62667
0.67991
0.67991
0.69737
0.78218
0.78218
0.62587
0.62587
0.62587
0.62587
0.92217
0.92217
0.92217
0.92217
0.52031
0.52031
0.75279
0.75279
0.46542
0.46542
Outside Precinct
BPOP
BVAP
0.09536
0.09210
0.17568
0.17565
0.17391
0.18657
0.17138
0.17792
0.17568
0.17565
0.14338
0.14773
0.18571
0.19843
0.17391
0.18657
0.14339
0.12285
0.17138
0.17792
0.23933
0.23955
0.14339
0.12285
0.18571
0.19843
0.55425
0.54106
0.41756
0.39830
0.10813
0.09088
0.23361
0.22347
0.11565
0.11159
0.14983
0.15673
0.30619
0.30156
0.03281
0.03571
0.30025
0.30153
0.40530
0.38786
0.03281
0.03571
0.30025
0.30153
0.40530
0.38786
0.40530
0.38786
0.35555
0.34918
0.03281
0.03571
0.35555
0.34918
0.40530
0.38786
0.34700
0.34122
0.40530
0.38786
0.31975
0.33693
0.23933
0.23955
0.04890
0.04751
0.33438
0.32560
0.24480
0.23143
0.23463
0.22948
0.23763
0.25870
0.28254
0.25594
0.23463
0.22948
0.28254
0.25594
0.23463
0.22948
0.28254
0.25594
0.13691
0.13647
0.28703
0.28170
BREG
0.08556
0.18656
0.25000
0.18038
0.18656
0.18358
0.23907
0.25000
0.12119
0.18038
0.23376
0.12119
0.23907
0.57555
0.39112
0.09742
0.22149
0.10717
0.16876
0.32428
0.03622
0.35589
0.43219
0.03622
0.35589
0.43219
0.43219
0.37132
0.03622
0.37132
0.43219
0.36124
0.43219
0.34039
0.23376
0.02685
0.37722
0.26976
0.29123
0.14162
0.26635
0.29123
0.26635
0.29123
0.26635
0.13264
0.30227
DREG
0.35810
0.42128
0.73276
0.43707
0.42128
0.44503
0.57580
0.73276
0.35696
0.43707
0.47359
0.35696
0.57580
0.74929
0.55814
0.37836
0.47142
0.39995
0.39634
0.59736
0.53521
0.64016
0.67232
0.53521
0.64016
0.67232
0.67232
0.62627
0.53521
0.62627
0.67232
0.58915
0.67232
0.57498
0.47359
0.44295
0.66785
0.41156
0.45260
0.37446
0.48387
0.45260
0.48387
0.45260
0.48387
0.32120
0.47731
5 of 6
Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 34 of 99
GOV08
0.33255
0.48502
0.63158
0.48539
0.48502
0.48871
0.55508
0.63158
0.43424
0.48539
0.55796
0.43424
0.55508
0.71373
0.61202
0.55820
0.52176
0.39086
0.46005
0.54069
0.34048
0.60375
0.64626
0.34048
0.60375
0.64626
0.64626
0.56424
0.34048
0.56424
0.64626
0.60328
0.64626
0.52768
0.55796
0.32673
0.66777
0.51658
0.51885
0.37227
0.48939
0.51885
0.48939
0.51885
0.48939
0.33631
0.50459
PRES08
0.21737
0.36287
0.38919
0.36708
0.36287
0.34395
0.37370
0.38919
0.36064
0.36708
0.44435
0.36064
0.37370
0.62665
0.53478
0.57724
0.36252
0.24038
0.34862
0.42222
0.24178
0.52776
0.56093
0.24178
0.52776
0.56093
0.56093
0.48820
0.24178
0.48820
0.56093
0.51974
0.56093
0.37546
0.44435
0.14851
0.50906
0.46753
0.44096
0.27651
0.39964
0.44096
0.39964
0.44096
0.39964
0.27050
0.42083
SEN10
0.20982
0.35260
0.40336
0.26278
0.35260
0.33731
0.37413
0.40336
0.29624
0.26278
0.37176
0.29624
0.37413
0.58154
0.49723
0.49111
0.32498
0.19613
0.32410
0.43594
0.30797
0.52369
0.54514
0.30797
0.52369
0.54514
0.54514
0.47350
0.30797
0.47350
0.54514
0.51351
0.54514
0.34219
0.37176
0.19868
0.46620
0.38277
0.36123
0.22272
0.32318
0.36123
0.32318
0.36123
0.32318
0.23002
0.37739
5/8/2012 3:32 PM
1st District Boundary Precincts
Appendix A
Seq
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
InsidePrecinct
3719110
3719110
3719119
3719107
3719107
3719107
3719122
3719121
3719112
3719113
3719113
37195PRWC
37195PRWE
37195PRWN
37195PRWH
37195PRWH
37195PRWI
37195PRWR
district_1 DWP Edit.xlsx
OutsidePrecinct
3719109
3719105
3719123
3719115
3719102
3719106
3719123
3719123
3719106
3719123
3719114
37195PRWK
37195PRTO
37195PRST
37195PRBL
37195PRST
37195PRBL
37195PRTO
Inside Precinct
BPOP
BVAP
0.74354
0.71095
0.74354
0.71095
0.66680
0.72304
0.21594
0.21927
0.21594
0.21927
0.21594
0.21927
0.34151
0.30729
0.55685
0.52717
0.36341
0.33697
0.46420
0.47842
0.46420
0.47842
0.72220
0.72408
0.58120
0.56553
0.83682
0.85178
0.78490
0.79903
0.78490
0.79903
0.53782
0.51473
0.64443
0.66299
BREG
0.70472
0.70472
0.84347
0.22991
0.22991
0.22991
0.29114
0.51018
0.34776
0.53916
0.53916
0.80197
0.60922
0.91952
0.93657
0.93657
0.56969
0.84594
DREG
0.71734
0.71734
0.80918
0.41991
0.41991
0.41991
0.54661
0.64310
0.50390
0.58817
0.58817
0.81308
0.70013
0.87192
0.86323
0.86323
0.69483
0.81927
GOV08
0.76167
0.76167
0.91262
0.44991
0.44991
0.44991
0.53375
0.65011
0.55475
0.68198
0.68198
0.88930
0.73954
0.94448
0.96237
0.96237
0.69666
0.90843
PRES08
0.77617
0.77617
0.93950
0.36293
0.36293
0.36293
0.47619
0.64377
0.49976
0.67033
0.67033
0.88929
0.71190
0.95460
0.95799
0.95799
0.67734
0.92874
SEN10
0.67651
0.67651
0.92507
0.32447
0.32447
0.32447
0.40362
0.59043
0.46119
0.66521
0.66521
0.87838
0.69333
0.94251
0.96507
0.96507
0.67542
0.92119
Outside Precinct
BPOP
BVAP
0.23763
0.25870
0.13691
0.13647
0.24480
0.23143
0.15549
0.15816
0.17142
0.16965
0.28703
0.28170
0.24480
0.23143
0.24480
0.23143
0.28703
0.28170
0.24480
0.23143
0.14154
0.13996
0.16341
0.15679
0.39253
0.37450
0.37043
0.36310
0.13310
0.12703
0.37043
0.36310
0.13310
0.12703
0.39253
0.37450
BREG
0.14162
0.13264
0.26976
0.17730
0.17177
0.30227
0.26976
0.26976
0.30227
0.26976
0.11634
0.14799
0.41223
0.41103
0.14082
0.41103
0.14082
0.41223
DREG
0.37446
0.32120
0.41156
0.39765
0.38472
0.47731
0.41156
0.41156
0.47731
0.41156
0.36433
0.50836
0.58035
0.59632
0.43709
0.59632
0.43709
0.58035
6 of 6
Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 35 of 99
GOV08
0.37227
0.33631
0.51658
0.41586
0.40206
0.50459
0.51658
0.51658
0.50459
0.51658
0.33782
0.40278
0.56588
0.62581
0.37648
0.62581
0.37648
0.56588
PRES08
0.27651
0.27050
0.46753
0.27959
0.28405
0.42083
0.46753
0.46753
0.42083
0.46753
0.25770
0.35104
0.51829
0.52929
0.27787
0.52929
0.27787
0.51829
SEN10
0.22272
0.23002
0.38277
0.23498
0.25392
0.37739
0.38277
0.38277
0.37739
0.38277
0.19351
0.34457
0.47936
0.54294
0.29570
0.54294
0.29570
0.47936
5/8/2012 3:32 PM
P-17
P-17
Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 36 of 99
EXPERT REPORT OF STEPHEN ANSOLABEHERE
I. Background and Qualifications
1. I am a professor of Government in the Department of Government at Harvard
University in Cambridge, MA. Formerly, I was an Assistant Professor at the
University of California, Los Angeles, and I was Professor of Political Science at the
Massachusetts Institute of Technology, where I held the Elting R. Morison Chair and
served as Associate Head of the Department of Political Science. I directed the
Caltech/MIT Voting Technology Project from its inception in 2000 through 2004, am
the Principal Investigator of the Cooperative Congressional Election Study, a survey
research consortium of over 250 faculty and student researchers at more than 50
universities, and serve on the Board of Overseers of the American National Election
Study. I am a consultant to CBS News’ Election Night Decision Desk. I am a member
of the American Academy of Arts and Sciences (inducted in 2007).
2. I have worked as a consultant to the Brennan Center in the case of McConnell v.
FEC, 540 US 93 (2003). I have testified before the U.S. Senate Committee on Rules,
the U.S. Senate Committee on Commerce, the U.S. House Committee on Science,
Space, and Technology, the U.S. House Committee on House Administration, and the
Congressional Black Caucus on matters of election administration in the United
States. I filed an amicus brief with Professors Nathaniel Persily and Charles
Stewart on behalf of neither party to the U.S. Supreme Court in the case of Northwest
1
Exhibit 1 - Expert Report of Stephen Ansolabehere
Case
Case1:13-cv-00949-WO-JEP
1:13-cv-00949-WO-JEP Document
Document109-1
18-1 Filed 12/24/13
09/21/15 Page 1
37ofof41
99
Austin Municipal Utility District Number One v. Holder, 557 US 193 (2009). I am
consultant for the Rodriguez plaintiffs in Perez v. Perry, currently before the District
Court in the Western District of Texas (No. 5:11-cv-00360 W. D. Tex), and the
Gonzales intervenors in State of Texas v. United States before the District Court in the
District of Columbia (No. 1:11-cv-01303); I consulted for the Department of Justice
in State of Texas v. Holder, before the District Court in the District of Columbia (No.
1:12-cv-00128); I consulted for the Guy plaintiffs in Guy v. Miller in Nevada District
Court (No. 11-OC-00042-1B, Nev. Dist. Ct., Carson City); I consulted for the Florida
Democratic Party in In re Senate Joint Resolution of Legislative Apportionment (Nos.
2012-CA-412, 2012-CA-490); I am consultant for the Romo plaintiffs in Romo v.
Detzner in the Circuit Court of the Second Judicial Circuit in Florida (No. 2012 CA
412); I am consultant for the San Antonio Water District in League of United Latin
American Citizens v. Edwards Aquifer Authority (No. 5:12cv620-OLG, U. S. District
Court for the Western District of Texas, San Antonio Division) .
3. My areas of expertise include American government, with particular expertise in
electoral politics, representation, and public opinion, as well as statistical methods
in social sciences. I am author of numerous scholarly works on voting behavior and
elections, the application of statistical methods in social sciences, legislative politics
and representation, and distributive politics. This scholarship includes articles in
such academic journals as the Journal of the Royal Statistical Society, the American
Political Science Review, the American Economic Review, the American Journal of
Political Science, Legislative Studies Quarterly, the Quarterly Journal of Political
2
Exhibit 1 - Expert Report of Stephen Ansolabehere
Case
Case1:13-cv-00949-WO-JEP
1:13-cv-00949-WO-JEP Document
Document109-1
18-1 Filed 12/24/13
09/21/15 Page 2
38ofof41
99
Science, Electoral Studies, and Political Analysis. I have published articles on issues
of election law in the Harvard Law Review, Texas Law Review, Columbia Law
Review, New York University Annual Survey of Law, and the Election Law Journal,
for which I am a member of the editorial board. I have coauthored three scholarly
books on electoral politics in the United States, The End of Inequality: Baker v. Carr
and the Transformation of American Politics, Going Negative: How Political
Advertising Shrinks and Polarizes the Electorate, and The Media Game: American
Politics in the Media Age. I am coauthor with Ted Lowi, Ben Ginsberg, and Ken
Shepsle of American Government: Power and Purpose. My curriculum vita with
publications list is attached to this report.
4. I have been hired by the Harris Plaintiffs in this case. I have been asked to
assess whether race is a predominant factor in the configuration of Congressional
District 1 (CD 1) and Congressional District 12 (CD 12) in the North Carolina
Congressional District Map. I am retained for a rate of $400 per hour, which is my
standard consulting rate.
II. Sources
5. I relied on data and tables available through the North Carolina General Assembly
website: http://www.ncleg.net/representation/redistricting.aspx.
III. Findings
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6. This report examines the geographic characteristics and racial composition of CD
1 and CD 12 in the Congressional District map passed by the North Carolina General
Assembly in 2011, referred to as the Rucho-Lewis Map, and in the Congressional
District map passed by the North Carolina General Assembly in 2001, referred to as
the 2001-2011 Map.
7. I conclude that CDs 1 and 12 are substantially less compact under the Rucho-
Lewis map than under the 2001-2011 Map, and the version of these districts in the
Rucho-Lewis map crosses a large number of county, city, and town boundaries. The
shift in district boundaries from the previous decade’s map to the current decade’s
map had the effect of increasing the percentage Black population, Black voting age
population, and Black registration in CDs 1 and 12. Examination of registration
patterns in the counties surrounding the districts and in the VTDs moved into and
out of the districts reveals that race was the predominant factor in configuring these
districts, and party played only a small part.
A. Geographic Characteristics
8. The Rucho-Lewis map reduced substantially the compactness of CDs 1 and 12.
These districts’ boundaries also affect the compactness of neighboring districts, and
the boundaries of these districts cut a large number of county and municipal
boundaries.
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9. Table 1 presents two different compactness measures for the districts in the
2001-2011 Map and the Rucho-Lewis Map. One indicator is the Reock score. This is
a commonly used measure of compactness that is calculated as the ratio of the area
of a district to the area of the smallest inscribing circle of a district. 1 A second
compactness measure is the ratio of the area of district to the perimeter of the
district. This measure indicates districts that have particularly complicated
boundaries. Consider two districts: a circle and a circular shape whose boundary is
not smooth but jogs in and out around the arc of a circle. These might have similar
Reock scores, but, because of its intricate boundary, the second district would have a
much lower ratio of area to perimeter. Hence, the ratio of Area to Perimeter
provides a different indication of non-compactness in the shape of a district.
[Table 1 here]
10. CD 1 is noticeably less compact in the Rucho-Lewis Map, by either measure,
than is the version of this district in the 2001-2011 Map.
11. Neighboring CDs 4 and 7 are also less compact in the Rucho-Lewis Map. Both
CD 4 and CD 7 have much lower Reock scores and much lower ratios of area to
perimeter than the versions of these CDs in the 2001-2011 Map. Other neighboring
The circle is the most compact geometric shape. As a reference consider a district
that is a perfect square. Its Reock Score would be the ratio of the area of a square to
the area of its inscribing circle, or .637.
1
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CDs (3, 13, and 6) showed little change in the compactness measures from the 20012011 Map to the Rucho-Lewis Map.
12. CD 1 in the 2001-2011 Map split 9 counties. These are: Granville, Vance, Wilson,
Wayne, Lenoir, Jones, Craven, Pitt, and Beaufort counties.
13. CD 1 in the Rucho-Lews Map splits 18 counties. The Rucho-Lewis map splits 8
of the 9 counties that were split in the previous version of the CD; CD 1 no longer
contains any part of Jones County. However, the Rucho-Lewis version of CD 1
crosses the boundaries of 10 other counties. These are: Durham County, Franklin
County, Nash County, Edgecombe County, Martin County, Washington County, Gates
County, Chowan County, Perquimans County, and Pasquotank County. Of these, the
following counties were whole in the 2001-2011 Map (with previous CD in
parentheses): Chowan County (CD 1), Durham County (CD 4), Edgecombe County
(CD 1), Franklin County (CD 2), Gates County (CD 1), Martin County (CD 1),
Pasquotank (CD 1), Perquimans (CD 1), and Washington County (CD 1).
14. CD 1 splits 22 cities or towns. Specifically, it splits Butner (Granville County)
into CDs 1 and 13; Dortches (Nash) into 1 and 13; Durham (Durham) into 1, 4, 6, and
13; Edenton (Chowan) into 1 and 3; Elizabeth City (Pasquotank) into 1 and 3;
Goldsboro (Wayne) into 1 and 13; Greenville (Pitt) into 1 and 3; Grimesland (Pitt)
into 1 and 3; Hertford (Perquimans) into 1 and 3; Kingston (Lenoir) into 1 and 7;
Mount Olive (Wayne) into 1 and 13; New Bern (Craven) into 1 and 3; Plymouth
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(Washington) into 1 and 3; Red Oak (Nash) into 1 and 13; Rocky Mount
(Edgecombe) into 1 and 13; Rocky Mount (Nash) into 1 and 13; Snow Hill (Greene)
into 1 and 3; Tarboro (Edgecombe) into 1 and 13; Walstonburg (Greene) into 1 and
3; Washington (Beaufort) into 1 and 3; Wilson (Wilson) into 1 and 13; and
Winterville (Pitt) into 1 and 3.
15. CD 12 is highly non-compact. It is the least compact district in the map, by
either measure. The Rucho-Lewis map makes it much less compact, reducing the
Reock from .116 to .071. This is an extremely low Reock score. The typical district
in the state has a Reock score of .377 (median score), making CD 12 five times less
compact than the typical district in the state. Moreover, the lack of compactness is
not due to geographic or cartographic features such as shoreline or state
boundaries.
16. The reconfiguration of CD 12 also reduced the compactness of CD 9. The
compactness of the other surrounding districts (2, 5, 6, and 8) is not altered much.
17. CD 12 splits 13 cities or towns. These are Charlotte (Mecklenburg) into 8, 9, and
12; Concord (Cabarrus) into 8 and 12; East Spencer (Rowan) into 8 and 12;
Greensboro (Gulford) into 6 and 12; High Point (Davidson, Forsyth, Guilford, and
Randolph) into 2, 5, 6, and 12; Jamestown (Guilford) into 6 and 12; Kannapolis
(Cabarrus) into 8 and 12; Landis (Rowan) into 8 and 12; Lexington (Davidson) into
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8 and 12; Salisbury (Rowan) into 5, 8 and 12; Thomasville (Davidson) into 8 and 12;
Wallburg (Davidson) into 5 and 12; and Winston-Salem (Forsyth) into 5 and 12.
B. Racial Composition of Districts
18. There were no majority Black Congressional Districts under the 2001-2011 Map
at the time of the 2010 United States Census. According to data provided on the
website of the North Carolina General Assembly, 48.6% of the Voting Age Population
(VAP) was Black in CD 1, and 43.8% of the VAP was Black in CD 12. Of Registered
Voters, 50.7% were Black in CD 1 and 48.6% were Black under the 2001-2011 Map
in 2010. 2
19. There are two majority Black Congressional Districts in the Rucho-Lewis Map.
In CD 1, 52.7% of the VAP is Black, and 54.5% of Registered Voters are Black. In CD
12, 50.7% of the VAP is Black, and 57.0% of Registered Voters are Black. Table 2
presents the Racial Composition of the Population, Voting Age Population, and
Registered Voters in each Congressional District in the Rucho-Lewis Map.
[Table 2 here]
C. Race as a Factor in the Composition of the Districts
Figures come from tables at the NC General Assembly Redistricting website, under
the tag Archived files, Congress Zero Deviation Plan, 2011 data:
http://www.ncleg.net/representation/Content/Plans/PlanPage_DB_2011.asp?Plan
=Congress_ZeroDeviation&Body=Congress
2
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20. This section presents two types of analyses to gauge the importance of race in
the construction of CDs 1 and 12 in the Rucho-Lewis Map. The first type of analysis
examines the envelope of counties in which a given CD is situated; that is, the set of
counties that are partly or wholly in the CD. These counties are the approximate
region or area in which each CD is drawn, and they contain the population from
which each CD could be drawn without crossing county boundaries or completely
reconfiguring the CD. Taking this as a potential population for a district, the analysis
then computes the likelihood that a Registered Voter of a given race from this
population was included in the given CD. If the lines were drawn without respect to
race, one would expect that White and Black Registered Voters would have
approximately the same likelihood of inclusion in a given CD.
21. The second type of analysis examines all Voting Tabulation Districts (VTDs) that
were in a given CD (1 or 12) in either the 2001-2011 Map or the Rucho-Lewis Map.
The analysis examines the composition of the VTDs that remained in the CD across
two cycles of districting (called the CORE of the district), the VTDs moved OUT of a
District, and the VTDs moved INTO a district. If changes in district lines are
unrelated to race, we expect the composition of the VTDs moved INTO a district to
be similar to the composition of the VTDs moved OUT of a district, on average.
C.1. CD 1
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22. Analysis of the population in the Envelope of CD 1 – the first type of analysis
described above – shows that registered Black voters were twice as likely to be in
CD 1 as were registered White Voters, even though Whites comprise nearly 60% of
the Registered Voters in the area.
23. CD 1 is contained as part or whole of the following counties: Beaufort, Bertie,
Chowan, Craven, Durham, Edgecombe, Franklin, Gates, Granville, Greene, Halifax,
Hertford, Lenoir, Martin, Nash, Northampton, Pasquotank, Perquimans, Pitt, Vance,
Warren, Washington, Wayne, and Wilson. I call these counties the Envelope of CD 1.
24. Table 3 presents the total number of Registered Voters, the number of White
Registered Voters and the number of Black Registered Voters in the envelope of CD
1 and in CD 1 itself. The envelope of CD 1 has 926,105 Registered Voters. Of these
532,188 (57.5%) are White, and 354,151 (38.2%) are Black. CD 1 itself has 465,154
Registered Voters, which is 50.2% of the Registered Voters in the envelope of the
district. That is, CD 1 contains roughly half of the Registered Voters in the counties
in which it is situated.
[Table 3 here]
25. Of the 532,188 registered Whites in the Envelope of counties of CD 1, 190,011
(35.7%) are in CD 1 in the Rucho-Lewis Map. That figure is significantly lower than
57.5% white for the envelope of CD 1 as a whole.
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26. Of the 354,151 registered Blacks in the envelope of counties of CD 1, 253,661
(71.6%) are in CD 1 in the Rucho-Lewis Map. That is, Black Registered Voters in the
envelope of counties in which CD 1 is situated are twice as likely to be incorporated
in CD 1 as White Registered Voters in the same area.
27. Analysis of VTDs in CD 1 – the second type of analysis discussed above – shows
that in the construction of CD 1 in the Rucho-Lewis Map, Blacks were a higher
percentage of Registered Voters in VTDs moved into the district than in VTDs
moved out of the district. The construction of the district also treated as the core of
the district VTDs with relatively high concentrations of Black Registered Voters.
28. Table 4 presents the percent Black and percent White of Registered Voters in the
VTDs in the Core of CD 1 (i.e., in the 2001-2011 Map and the Rucho-Lewis Map), in
the VTDs moved INTO CD 1 (i.e., in the Rucho-Lewis Map but not in the 2001-2011
Map), and in the VTDs move OUT of CD 1 (i.e., in the 2001-2011 Map but not in the
Rucho-Lewis Map).
[Table 4 here]
29. The VTDs kept in CD 1 (the Core) are 56.4% Black registration and 37.4% White
registration. The VTDs moved out of CD 1 are 27.4% Black registration and 66.7%
White registration. The VTDs moved into CD 1 are 48.1% Black registration and
37.7% White registration. The net difference in% Black registration between VTDs
moved into CD 1 and VTDs moved out of CD 1 is 20.7%. Similar patterns hold if
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population or voting age population is the metric of racial composition of the
electorate.
30. Maps 1 and 2 provide an example of changes in the boundary of CD 1. Map 1
shows the northeastern portion of CD 1 under the 2001-2011 Map. District
boundaries are shown in green; the black lines are the county boundaries. VTDs are
shown as shaded polygons, and the darker shading along the gray scale corresponds
to higher percent Black among Registered Voters. As shown in the map the
boundary of CD 1 follows the boundaries of Gates, Pasquotank, Perquimans,
Washington, Martin, and Pitt Counties, and Chowan is in the interior of the district.
[Maps 1 and 2 here]
31. Map 2 provides a close-up picture of the same area under the Rucho-Lewis plan.
CD 3 crosses all of these county borders and encroaches into the area once covered
by CD 1. Within each county, the boundary takes VTDs with lower black
populations and puts them in CD 3 and leaves VTDs with higher black populations in
CD 1. In Gates County, for example, there are 6 VTDs. The county is split in the
Rucho-Lewis map in a way that leaves the two VTDs with the highest percent Black
in CD 1. CD 3 now reaches into Chowan County (which was previously in the
interior of CD 1), and grabs the three VTDs with the highest White percent, leaving
the three VTD with the highest Black percent. The same pattern occurs in
Perquimans, Pasquotank, Washington, and Martin counties, as shown in the map.
The protrusion of CD 1 that cuts Chowan, Perquimans, and Pasquotank counties
divides Elizabeth City, keeping the Black population in the central city in CD 1.
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32. The cumulative consequence of such changes, as borne out in the statistical
analysis, was to increase the concentration of Black Registered Voters in CD 1. Areas
with high concentrations of Blacks were kept in CD 1. Areas with low
concentrations of Blacks were removed, and they were replaced with areas that
have substantially higher percentages of Black Registered Voters.
C.2. CD 12
33. Analysis of the population in the Envelope of CD 12 shows that registered Black
voters were four times as likely to be in CD 12 as were registered White voters.
34. CD 12 is contained as part or whole of the following counties: Cabarrus,
Davidson, Forsyth, Guilford, Mecklenburg, and Rowan. These counties comprise the
Envelope of CD 12. Table 3 (above) presents the total number of Registered Voters,
the number of White Registered Voters and the number of Black Registered Voters
in the envelope of CD 12 and in the district itself. The Envelope of CD 12 has
1,473,318 Registered Voters. Of these, 993,642 (67.4%) are White, and 396,078
(26.9%) are Black. CD 12 contains 445,685 Registered Voters, which is 30.3% of the
Registered Voters in the envelope of the district. That is, CD 12 contains roughly a
third of the Registered Voters in the counties in which it is situated.
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35. Of the 993,642 registered Whites in the Envelope of counties of CD 12, 158,959
(16.0%) are in CD 12 in the Rucho-Lewis Map. That figure is significantly lower
than 67.4% White of the counties comprising the envelope of CD 12.
36. Of the 396,078 registered Blacks in the Envelope of CD 12, 254,199 (64.2%) are
in CD 12 in the Rucho-Lewis Map. That is, Black Registered Voters in the envelope
of counties in which CD 12 is situated are four times as likely to be incorporated in
CD 12 as White Registered Voters in the same area.
37. Analysis of Voting Tabulation Districts shows a pattern similar to that in CD 1.
Table 4, again, presents the relevant figures for CD 12.
38. The VTDs kept in CD 12 (the Core) are 54.0% Black registration and 31.9%
White registration. The VTDs moved out of CD 12 are 23.2% Black registration and
64.0% White registration. The VTDs moved into CD 12 are 44.0% Black registration
and 37.1% White registration. The net difference in% Black registration between
VTDs moved into CD 12 and VTDs moved out of CD 12 is 20.9% (44.0 minus 23.2).
Similar patterns hold if population or voting age population is the metric of racial
composition of the electorate.
39. Maps 3 and 4, 5 and 6, and 7 and 8 provide examples of the way VTDs are
shifted between the 2001-2011 Map and the Rucho-Lewis Map in CD 12. Maps 3
and 4 present the changes in District lines in Mecklenburg County; Maps 5 and 6
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show Forsyth County, and Maps 7 and 8 show the changes in Guilford County. In all
three counties VTDs with relatively high White populations were drawn out of CD
12. CD 9, for instance, wraps further around CD 12 to capture VTDs with relatively
high White population in the Southern section of Charlotte. In Forsyth County, the
footprint of CD 12 is shrunk from the 2001-2011 Map, leaving in the district the
VTDs with the highest percentage Black registration. In Guilford County, CD 12 now
incorporates VTDs that were previously in CD 12 and had relatively high Black
percentages. These VTDs are on the north and eastern parts of the Greensboro area.
Relatively White areas in the western part of Greensboro are taken out of the old
version of CD 12 and put into new CD 6.
[Maps 3, 4, 5, 6, 7 and 8 here]
D. Race and Party
40. One possible explanation is that CDs 1 and 12 were drawn for partisan reasons,
and that race was not the dominant factor. Registration data make it possible to
examine whether race or party was a dominant factor in composing CD 1 or CD 12.
Specifically, within each category of partisan registration (Republican, Democrat,
and Undeclared), it is possible to calculate the percent of people who identify as
Black or White. With that information, it is possible to calculate the percent of
Blacks and of Whites within each partisan group who are included in CD 1 or in CD
12, similar to the two analyses performed above. Likewise, it is possible to calculate
the percent of Republicans, Democrats and Undeclared within each racial group
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who are included in CD 1 or CD 12. If race is not a predominant factor then the
percent of Whites and Blacks included in a district should be similar within each
partisan group, and within each racial group a high percentage of Registered Voters
included in CDs 1 and 12 should be Democrats.
D.1. Analysis of the Envelope of Counties Containing CD 1 or CD 12
41. Within each partisan category, Blacks are disproportionately more likely than
Whites to be included in CD 1 or CD 12. Table 5 presents the percentages of Blacks
and Whites in the Envelope of counties containing CD 1 that are included in CD 1 for
each of the three party registration categories. Consider the first two rows,
corresponding Democrats. Under the Rucho-Lewis Map, 72.1% of Black Democrats
are included in CD 1, compared with 41.5% of White Democrats – a 30.6 point
difference. Among Republicans, a similarly large racial gap exists. Under the Rucho-
Lewis Map, 69.2% of Black Republicans are included in CD 1, compared with 29.9%
of White Republicans. And, 68.2% of Black Undeclared Registered Voters are in CD
1, compared with 34.7% of White Undeclared Registered Voters.
[Table 5 here]
42. These figures represent a significant increase in the likelihood that a Black voter
is included in CD 1 within each partisan group from the 2001-2011 Map. Table 6
presents a similar analysis to Table 5, but for the past decade’s districts. Under the
2001-2011 Map, 58.3% of Black Democrats in the Envelope of the district were
included in CD 1 compared with 39.6% of White Democrats – a gap of 18.7 points
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(versus 30.6 points under Rucho-Lewis). Under the 2001-2011 Map, 60.5% of Black
Republicans were in CD 1, as opposed to 31.0% of White Republicans. And, 51.4%
of Black Undeclared Registered Voters were in CD 1, compared with 33.2% of White
Undeclared Registered Voters.
[Table 6 here]
43. Within all three party categories, the percent of Blacks in the Envelope who
were included in CD 1 increased substantially. The percent of Whites in the
Envelope included in CD 1 decreased slightly within each of the Party categories.
44. A similar pattern holds for CD 12. Table 7 presents the percentages of
registered Black and White voters within each party category who were included in
CD 12 under the Rucho-Lewis Map. In this map, 65.0% of Black Democrats, 59.9%
of Black Republicans, and 59.7% of Black Undeclared Registered Voters in the
Envelope of counties around CD 12 are in fact in that district. By comparison, 18.3%
of White Democrats, 13.8% of White Republicans, and 17.4% White Undeclared
Registered Voters are in CD 12. Within each of the three party groups there is a very
large difference in the likelihood that a Black Registered Voter is included in CD 12
and the likelihood that a White Registered Voter is included in CD 12.
[Table 7 and 8 here]
45. Those differences are much larger under the Rucho-Lewis Map than they were
in the 2001-2011 Map. In that map, 57.2% of Black Democrats, 52.5% of Black
Republicans, and 50.4% of Black Undeclared Registered Voters in the Envelope of
counties around CD 12 were in fact in that district. By comparison, 40.4% of White
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Democrats, 19.8% of White Republicans, and 21.2% White Undeclared Registered
Voters were in CD 12.
46. Table 9 summarizes the results of the analyses shown in Tables 5 to 8. Within
every partisan group there are very large differences between the percent of Blacks
and the percent of Whites who were included in CDs 1 and 12 from the counties that
comprise the Envelope of these districts. Also, within each partisan category the
difference between the racial groups grew noticeably.
[Table 9 here]
D.2. Analysis of VTDs in the Core, Moved Into, or Moved Out of CDs 1 or 12
47. Parallel to the analysis above of VTDs, it is possible to control for partisanship
when calculating the racial disparities in the populations moved into and out of CDs
1 and 12. For example, among all Democrats, one may calculate the Black percent
of all Registered Voters in VTDs moved into a given district, of all Registered Voters
in VTDs moved out of a given district, and of all Registered Voters kept in a given
district. Table 10 presents these calculations for CDs 1 and 12 for each of the
partisan groups.
[Table 10 here]
48. Consider, first, CD 1. Among Democrats in VTDs that remained in CD 1, 70.6%
are Black Registered Voters and 26.5% are White Registered Voters, with the
remainder being other races or undetermined. Among Democrats in VTDs that
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were moved into CD 1, 66.4% are Black and 28.6% are White. Among Democrats in
VTDs that were moved out of CD 1, 48.6% are Black and 49.4% are White. In other
words, in the VTDs moved into or kept in CD 1 the Democrats were predominately
Black. And in the VTDs moved out, the Democrats were plurality White. The
difference in the percent Black between those in VTDs in the Core and those in VTDs
moved Out is very large – 22 percentage points, as shown in the row at the bottom
of the panel for CD 1. There are similarly large differences (19 percentage points)
among Undeclared Registered Voters. The differences among Republicans are
about seven points.
49. CD 12 shows the same pattern. Among each of the partisan groups, the
percentage Black in the Core of the district and in the VTDs moved into the district
far exceeded the percentage Black in the VTDs moved out of the district. The
difference in percentage Black between those kept in the district and those moved
out is 34 points among Democrats, 8 points among Republicans, and 24 points
among Undeclared Registered Voters.
50. Party, by comparison, has little or no effect on the likelihood of being included in
CDs 1 or 12. Table 11 constructs a statistical analysis analogous to that in Table 10,
but this time the comparison is of the percentages Democrat, Republican, or
Undeclared within racial groups.
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51. The differences in partisan composition across the Core VTDs, the VTDs moved
Into, and the VTDs moved Out, are trivially small, especially compared with the
racial effects in Table 10. Consider CD 1. Among Whites, 47.3% of those in the Core
VTDs are Democrats, 44.6% of those in VTDs moved into the district are Democrats,
and 40.9% of those in VTDs moved Out of the district are Democrats. Among
Whites, then, the difference in percentage Democrat between the Core and those in
VTDs moved out is only 6% (compared to a difference of 33 point in percent Black
across these VTDs among Democrats).
52. Examining the other columns in Table 11, it is evident that the differences in
partisanship are very small across the VTDs kept in the districts, moved into the
districts, or moved out of the districts. The differences are in the single digits, and
the largest observed difference is in the wrong direction. The Democratic
registration rate among Blacks was higher in VTDs moved out of CD 12 than it was
in VTDs kept in or moved into the district.
53. Ultimately, then, race, and not party, had a disproportionate effect on the
configuration of CDs 1 and 12. Party has a small and somewhat uneven effect in
explaining whether a VTD was moved into or out of CDs 1 and 12. Race, alone or
controlling for party, has a very large effect in explaining whether a VTD or part of a
county was included in CDs 1 or 12. Viewed in terms of the composition of the
districts and the effects of race and party on the likelihood that an area was included
20
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in these districts, I conclude that race was the dominant factor in constructing CDs 1
and 12 in the Rucho-Lewis Map.
21
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Table 1. Measures of Compactness of Districts,
2001-2011 Map and Rucho-Lewis Map
Compactness Measure
Reock:
Ratio of Area of District to
Ratio of Area to Perimeter of
Smallest Inscribing Circle
District
District
2001-2011
Rucho-Lewis
2001-2011
Rucho-Lewis
1
.390
.294
11098
6896
2
.303
.426
7644
8579
3
.409
.368
11727
16067
4
.480
.173
7795
3265
5
.399
.397
14434
10853
6
.377
.241
7237
9763
7
.614
.408
16437
13097
8
.341
.353
12022
14651
9
.339
.169
4986
3969
10
.410
.340
11233
11146
11
.344
.264
17748
17551
12
.116
.071
2404
1839
13
.237
.382
6217
5377
22
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Table 2. Racial Composition of Districts in the Rucho-Lewis Map
Population
Voting Age Population
%
%
%
%
White*
Black**
White
Black
1
35.2
54.4
40.5
52.7
2
66.8
17.6
74.0
16.5
3
71.4
18.2
76.4
18.4
4
48.6
31.4
56.6
31.7
5
76.4
12.0
81.9
12.2
6
76.0
14.6
80.1
14.8
7
69.4
17.2
74.8
17.4
8
63.3
18.2
69.0
18.3
9
74.3
13.2
80.0
12.4
10
79.7
11.1
84.4
11.2
11
87.7
3.2
91.5
3.2
12
29.1
50.2
36.8
50.7
13
70.9
16.8
76.1
17.0
*Single Race White, Non-Hispanic
**Any Part Black, Not Native American
District
Registration
Percent
%
White
Black
40.8
54.5
76.5
17.0
76.7
19.4
57.6
33.1
84.8
12.0
82.2
14.4
78.2
17.5
72.2
19.1
82.5
11.4
85.6
11.1
94.2
2.6
35.7
57.0
78.6
16.4
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Table 3. Race and the Composition of CDs 1 and 12 in the Rucho-Lewis Map
Number and Percent of a Registered Voters of a Given Race who are in CD 1 or CD 12
Analysis of the Envelope of Counties Containing CD 1 or 12
AREA
CD 1
AREA
CD 12
Registered
Voters of
Group
In Envelope
Registered
Voters of
Group in CD 1
% of Group
That is in
CD 1
926,105
465,154
50.2%
White
532,188
190,011
35.7%
Black
354,151
253,661
71.6%
Group
Registered
Voters
In Envelope
% of Group
That is in
CD 12
Total
1,473,318
Registered
Voters of
Group in CD
12
445,685
30.3%
White
993,642
158,959
16.0%
Black
396,078
254,119
64.2%
Group
Total
24
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Table 4: Analysis of the Racial Composition of
VTDs In the Core of, Moved Into, and Moved Out
of CD 1 and CD 12
Racial Registration
Percent
Percent
CD 1
Black
White
Core
56.4
37.4
Into District
48.1
37.7
Out of District
27.4
66.7
CD 12
Core
Into District
Out of District
54.0
44.0
23.2
31.9
37.1
64.0
25
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Map 1. CD 1 Boundaries in Gates, Pasquotank, Perquimans, Washington, Martin, and
Pitt Counties under the 2001-2011 Map
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Map 2. CD 1 Boundaries in Gates, Pasquotank, Perquimans, Washington, Martin, and
Pitt Counties under the Rucho-Lewis Map
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Map 3. CD 12 Boundaries in Mecklenburg County in the 2001-2011 Map
can.
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Map 4. CD 12 Boundaries in Mecklenburg County in the Rucho-Lewis Map
ican. Darker shaded ara8S ara higher percent Afncan Amencan.
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Map 5. CD 12 Boundaries in Forsythe County in the 2001-2011 Map
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Map 6. CD 12 Boundaries in Forsythe County in the Rucho-Lewis Map
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Map 7: CD 12 Boundaries in Guilford County in the 2001-2011 Map
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Map 8: CD 12 Boundaries in Guilford County in the Rucho-Lewis Map
age African American. Darker shaded areas are higher percent Afr1can Amer1can.
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Table 5. Race and Party in the Rucho-Lewis Map
Number and Percent of Registered Voters of a Given Race and Party who are in CD 1
Analysis of the Envelope of Counties Containing CD 1
Party of
Registration
Democrat
Republican
Undeclared
Registered
Voters of
Group
In Envelope
Registered
Voters of
Group That is
in CD 1
% of Group
That is in CD 1
White
212,500
88,173
41.5%
Black
312,190
224,950
72.1%
White
192,278
57,553
29.9%
Black
9,373
6,486
69.2%
White
126,562
43,962
34.7%
Black
32,464
22,136
68.2%
Group
34
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Table 6. Race and Party in the 2001-2011 Map
Number and Percent of Registered Voters of a Given Race and Party who are in CD 1
Analysis of the Envelope of Counties Containing CD 1
Party of
Registration
Democrat
Republican
Undeclared
Registered
Voters of
Group
In Envelope
Registered
Voters of
Group in
CD 1
% of
Group That is
in CD 1
White
212,500
84,064
39.6%
Black
312,190
182,111
58.3%
White
192,278
59,531
31.0%
Black
9,373
5,674
60.5%
White
126,562
41,965
33.2%
Black
32,464
16,692
51.4%
Group
35
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Table 7. Race and Party in the Rucho-Lewis Map
Number and Percent of Registered Voters of a Given Race and Party who are in CD 12
Analysis of the Envelope of Counties Containing CD 12
Party of
Registration
Democrat
Republican
Undeclared
Registered
Voters
Of Group in
Envelope
Registered
Voters in
CD 12
% of a given
Group That is in
CD 12
White
280,915
51,367
18.3%
Black
334,427
217,266
65.0%
White
448,914
61,740
13.8%
Black
10,341
6,199
59.9%
White
262,024
45,496
17.4%
Black
51,061
30,505
59.7%
Group
36
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Table 8. Race and Party in the 2001-2010 Map
Number and Percent of Registered Voters of a Given Race and Party who are in CD 12
Analysis of the Envelope of Counties Containing CD 12
Party of
Registration
Democrat
Republican
Undeclared
Number
Registered
Voters
In Envelope
Number
Registered
Voters in
CD 12
% of a given
Group That is in
CD 12
White
280,915
113,593
40.4%
Black
334,427
191,184
57.2%
White
448,914
88,803
19.8%
Black
10,341
5,432
52.5%
White
262,024
55,532
21.2%
Black
51,061
25,733
50.4%
Group
37
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Table 9. Summary Comparison of Race and Party in the 2001-2011 and Rucho-Lewis Maps
Party
Comparison of the Likelihood that a Registered Voter of a Given Race and Party
in the Envelope of Counties Containing CDs 1 or 12
is in either CD1 or CD 12
Race
Democrat
Republican
Undeclared
White
2001-2011
39.6%
CD 1
CD 12
Rucho-Lewis
2001-2011
Rucho-Lews
41.5%
40.4%
18.3%
Black
58.3%
72.1%
57.2%
65.0%
White
31.0%
29.9%
19.8%
13.8%
Black
60.5%
69.2%
52.5%
59.9%
White
33.2%
34.7%
21.2%
17.4%
Black
51.4%
68.2%
50.4%
59.7%
38
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Table 10. Racial Composition Within Partisan Groups of Populations of VTDs Kept
in (Core), Moved Into and Moved Out of CDs 1 and 12.
Among Democrats
CD 1
Core
Into CD
Out of CD
Effects:
Core v. Out
In v. Out
CD 12
Core
Into CD
Out of CD
Effects:
Core v. Out
In v. Out
%B
%W
70.6
48.6
66.4
Among Republicans
%B
%W
26.5
10.9
49.4
3.6
28.6
7.5
Among Undeclared
%B
%W
86.2
32.0
60.8
94.1
13.0
82.1
88.6
26.8
57.5
+22.0
+17.8
-22.9
-20.8
+7.3
+3.9
-7.9
-5.5
+19.0
+13.8
-21.3
-24.6
79.5
15.3
9.6
85.7
37.0
49.3
45.8
48.8
1.7
95.6
13.0
78.4
68.1
+33.7
+22.3
24.8
-33.5
-24.0
6.7
+7.9
+5.0
87.0
-9.9
-8.6
29.8
+24.0
+16.8
55.2
-29.1
-23.2
39
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Table 11. Partisan Composition Within Racial Groups of Populations of VTDs Kept
in (Core), Moved Into and Moved Out of CDs 1 and 12.
CD 1
Core
Into CD
Out of CD
Effects:
Core v. Out
In v. Out
CD 12
Core
Into CD
Out of CD
Effects:
Core v. Out
In v. Out
%D
Among Whites
%R
%U
%D
47.3
30.7
21.8
40.9
34.7
24.3
44.6
29.4
25.8
Among Blacks
%R
%U
89.1
2.7
8.1
88.6
2.9
87.7
2.1
10.2
8.5
+6.4
+3.7
-4.0
-5.3
-2.5
+1.5
+0.5
-0.9
-0.2
-0.8
-0.4
+1.7
31.1
40.4
28.3
85.7
2.4
11.3
29.3
45.1
25.4
95.6
2.5
12.9
34.3
+1.8
+5.0
36.2
-4.7
-8.9
29.2
+2.9
+3.8
87.0
-9.9
-8.6
2.5
-0.1
0.0
14.0
-1.6
+1.1
40
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\
a
n **'1""aT4-r/du
Stephen Ansolabehere
Stephen
Ansolabeherre
December 23,
2013
23,2013
Newton,, Massachusetts
Newton
Massachusetts
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P-18
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Case 1:13-cv-00949-WO-JEP Document 109-1 Filed 09/21/15 Page 78 of 99
REPORT OF STEPHEN ANSOLABEHERE IN RESPONSE TO THOMAS B. HOFELLER
I. Statement of Inquiry
1. I have been asked to evaluate the rebuttal report issued by Dr. Thomas B.
Hofeller in this case.
II. Background and Qualifications
2. My background and qualifications are discussed in my initial report, signed
December 23, 2013.
III. Sources
3. I relied on data and tables available through the North Carolina General Assembly
website: http://www.ncleg.net/representation/redistricting.aspx.
IV. Findings
A. Geographic Characteristics of CDs 1 and 12
4. Dr. Hofeller states that the reduction in compactness in CDs 1 and 12 is not
substantial. Specifically, he states that a reduction of the Reock score from .116 to
1
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.071 in CD 12 and from .394 to .296 in CD 1 is small compared with the difference
between those districts’ scores and the Reock score of the ideal square district
(.637) or ideal circular district (1.00). (Paragraphs 35-38 of his report.)
5. This observation underscores the fact that CD 12 was highly non-compact in the
2001-2011 map, and the new map, rather than improving on the district’s
compactness, only made it worse. Dr. Hofeller concedes that “both versions of the
12th District have miserable scores.” (Paragraph 37) A Reock score can never go
above 1 or below 0. It is the ratio of the area of a given district to the area of the
most compact district of the same length (i.e., the inscribing circle). The lower the
Reock score the smaller the area covered by the district relative to the most
compact district of the same length. The smaller area actually covered for a given
length, the less compact the district is. Or to put it another way, comparing two
districts of the same area, the district that has a longer perimeter to encompass that
area is less compact. Previous CD 12, the least compact district in the 2001 to 2011
map, had a very low Reock score of .116. The Reock of new CD 12 is even closer to
the lower bound of 0.
6. New CD 1 has a Reock compactness score that is 37 percent lower than the Reock
score of previous CD 1. That is, the new version of CD 1 reduced by 37 percent the
area covered by CD 1 relative to the smallest inscribing circle around the district.
New CD 12 has a Reock compactness score that is 25 percent lower than that of
2
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Previous CD 12. These are noticeable reductions in the area of these districts,
relative to the ideal district of the same length (i.e., the smallest inscribing circle).
7. Dr. Hofeller does not discuss the alternative measure of compactness offered in
my report, which is the ratio of the area of a district to its perimeter. See Table 1 in
my original report. The Area to Perimeter measure offers a somewhat different
score of compactness. Comparing two districts of the same area, the district that has
a longer perimeter to encompass that area is less compact. Or alternatively, the
district that covers less area per mile of perimeter is less compact.
8. The new version of CD 1 has a ratio of Area to Perimeter of 6896, compared with
11098 in the previous version. In other words, each mile of perimeter in new CD 1
incorporates or encompasses 6,896 miles of land area. By comparison, each mile of
perimeter in Previous CD 1 incorporates or encompasses 11,098 miles of land area.
That is a 38% reduction in the compactness as measured by the Area to Perimeter
metric (i.e., (11098-6898)/11098). This is the second largest reduction in the ratio
of area to perimeter in the map.
9. The new version of CD 12 has a ratio of Area to Perimeter of 1839, compared
with 2404 in the old version. In other words, each mile of perimeter in new CD 12
incorporates or encompasses 1,839 miles of land area. By comparison, each mile of
perimeter in old CD 12 incorporates or encompasses 2,404 miles of land area. That
is a 24% reduction in the compactness as measured by the Area to Perimeter metric
3
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(i.e., (2404-1839)/2404). CD 12 already had, by far, the lowest ratio of Area to
Perimeter, and it has the fifth largest reduction in this compactness measure in the
map.
10. Dr. Hofeller points out that four other districts are highly non-compact
according to the Reock measure. These are new CDs 4, 6, 9, and 11. (Paragraph 38)
New CDs 4, 6, and 9 border new CDs 1 and 12 and are likely affected by the noncompact configurations of CDs 1 and 12. New CD 11 covers most of western North
Carolina, and much of its shape is defined (and constrained) by the border of the
state.
11. Both Reock and Area to Perimeter scores reveal that the map reduced the
compactness of CDs 1 and 12 substantially, and CD 12 was already highly noncompact. Compactness is a traditional redistricting principle. Neither new CD 1 nor
new CD 12 were constructed to improve their compactness.
B. Obama Vote, Black Registration, and Black VAP
12. Dr. Hoffeler states that the vote for Obama in 2008 was used as the main
indicator in drawing district boundaries. He suggests that the Obama vote in 2008
was used to achieve partisan purposes, but offers no supporting evidence that these
data had primarily partisan and not racial effects. (Paragraphs 36-39, 59-64)
4
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13. What was the effect of using the Obama 2008 vote as an indicator to draw CD 1
and CD 12 in North Carolina? It is problematic and unusual to choose a single
election with a Black candidate as an indicator of partisan performance. The reason
that one wants to avoid using only one election in which one of the candidates is
Black candidate in order to determine the partisanship of the vote is that it is
difficult to infer whether the vote for that candidate was based on race or party. The
relevant question is what is the effect of one of these factors (race or party)
controlling for the other on the likelihood that an individual voter was included in
either CD 1 or CD 12.
14. My initial report revealed that the effect of race controlling for party was
substantial and much larger than the effect of party given race on the likelihood that
an individual was included in CDs 1 or 12.
15. Further analysis of VTD level data reveals that the Obama vote is very highly
correlated with Black Registration, and that analysis of Census data would have
masked that association. Dr. Hofeller notes that the increase in TBVAP in CD 12 is
nearly identical to the increase in 2008 vote for Obama (see his paragraph 63).
16. Table 1 presents the correlations between percent Black VAP or percent White
VAP and percent vote for Obama in 2008. Statewide, the correlation between Black
VAP and Obama vote is .60. Obama vote is correlated with Census racial data, but
the correlation is not very high.
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17. Table 1 also presents the correlations between percent Black Registration or
percent White Registration and percent vote for Obama in 2008. Statewide, the
correlation between Black VAP and Obama vote is very high, .80.
18. In this particular circumstance, then, registration data reflecting race are a
stronger correlate with Obama 2008 Vote than are Census data. Specifically, Black
and White percent of voting age population in Census data are more weakly
correlated with Obama vote than are Black and White Percent of Registered Voters
in the State of North Carolina. If one were to look only at the association between
Census data and Obama vote in North Carolina, then the effect on Black registered
voters of using the Obama vote as an indicator in districting would be obscured.
19. The correlations between Black (or White) Registration and the Obama vote are
particularly high in CDs 1 and 12. I further divided the VTDs in the state into those
VTDs that are or were in new or previous CD 1, new or previous CD 12, and all other
VTDs. The correlations between Black (or White) Registration and the Obama vote
are .82 (or -.87 for Whites) in CD 1 and .92 (or -.93 for Whites) in CD 12. These are
extremely high correlations. In the VTDs in CD 12, the correlation between the
Obama vote and Black Registration is approaching 1. The Obama vote, then, is an
extremely strong positive indicator of the location of Black registered voters in the
areas around CDs 1 and 12. It is extremely strong negative indicator of the location
of White registered voters in the areas around CDs 1 and 12.
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20. Thus, Dr. Hofeller’s statement that he only used the Obama vote to draw
congressional districts does not undermine the conclusion that race predominated
over party as a factor in drawing CDs 1 and 12. Whatever indicator or indicators
were used by the map drawers, the measures had the effect of making Black
registered voters of each partisan group much more likely to be included in CDs 1
and 12 than White registered voters of the same partisan group. And those
indicators had relatively little effect on making Democratic registered voters of each
racial group more likely to be included in a district than Republican registered
voters of that same racial group.
C. Methodology for assessing racial and partisan patterns
21. A central question is whether race or party was the predominant factor in
explaining or predicting which voters were included in CDs 1 or 12. In order to
make this determination, one wants to gauge the effect of race controlling for party
and party controlling for race. Otherwise, the vote for a candidate who is, say, a
Democrat and Black may be interpreted as either an indicator of Democratic vote or
of Black vote.
22. The methodology that I employed addresses that question in three steps. First, I
ascertained the extent to which race of the registrant predicts the likelihood of
being included in CD 1 or CD 12, holding constant the party of the registrant. The
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effect of race given party equals the difference between the likelihood that a Black
voter of a given party is included in a district and the likelihood that a White voter of
that same party is included in that district. Second, I ascertained the extent to which
party of the registrant predicts the likelihood of being included in CD 1 or CD 12,
holding constant the race of the registrant. The effect of party given race equals the
difference between the likelihood that a Democrat of a given race is included in a
district and the likelihood that a Republican of that same race is included in that
district. Third, I compared the effect of race given party with the effect of party
given race. I examined the relationship between race and party on the likelihood
that different types of registered voters are included in CD 1 or CD 12. This
approach is suggested elsewhere in the literature on racial voting, such as Gary King,
A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior
from Aggregate Data, Princeton University Press: Princeton, NJ, 1997, pages 12-14
generally and Chapter 10, on registration specifically. Dr. Hofeller is critical of this
approach (e.g., paragraphs 27, 33, and 52).
1. General Criticisms
23. Dr. Hofeller states (paragraph 27) that the analysis offered shows nothing more
than that there is higher Black Voting Age Population in the areas moved into CDs 1
and 12 than then areas moved out. In fact, my analysis goes further than that, as it
estimates the effect of race controlling for party and party controlling for race. The
simple Census data only state whether there are more Blacks or more Whites in CD1
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or CD12, not whether the increase in Black population was due to an increase in
Democrats who happened to be Black or an increase in Blacks across all party
groups. My analysis showed that it was the latter.
24. Dr. Hofeller suggests that a better approach would be to look at the relationship
between Census demographic data and the vote for Obama in 2008. (Paragraph 55)
He never states what sort of analysis exactly is to be performed or how those data
could be used to separate the effects of race and party in estimating the likelihood of
inclusion in CD 1 or CD 12.
25. He offers no such analysis of the VTD-level data. He offers no assessment of the
likelihood that a Black or White voter of a given party was included in CDs 1 or 12.
He offers no assessment of the likelihood that a Democrat or Republican of a given
race was included in CDs 1 or 12.
2. Use of Registration Data
26. Dr. Hofeller questions the use of registration data to perform this analysis,
rather than election results. (paragraphs 33 and 55)
27. The unique advantage of registration data in this particular circumstance is that
it allows us to measure separately the effect of party given race and the effect of race
given party on the likelihood that an individual is included in CD 1 or CD 12. North
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Carolina is one of a handful of states that lists race and party on the voter
registration lists. By counting the numbers of White and Black Democrats, White
and Black Republicans, and White and Black Unaffiliated registrations we can
estimate the effect of race given party and party given race. Those estimates were
offered in my initial report. (See Tables 9 and 10 in that report.)
28. The individual level data allow analysis of the question at hand without
resorting to ecological regression, ecological inference or other more complicated
methods.1 My original analysis estimated the effect of race controlling for party and
of party controlling for race using data on individual registered voters in the State of
North Carolina and in the areas of the districts in question.
29. Dr. Hofeller offers no alternative methodology or analyses using aggregate
Census and election data, such as at the level of the VTD, to address this matter.
30. Dr. Hofeller suggests, but offers no evidence, that registration is not tightly
related to election results. (Hofeller, paragraph 33)
31. Registration is highly correlated with actual election results in the State of North
Carolina. The correlation between Democratic share of party Registration and the
Obama Vote is .78. The correlations were even higher with respect to the other
See Gary King, A Solution to the Ecological Inference Problem: Reconstructing
Individual Behavior from Aggregate Data, Princeton University Press: Princeton, NJ,
1997, especially Chapters 1 and 10.
1
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statewide elections in 2008. The correlation between Democratic share of party
Registration and the Democratic share of vote for Governor in 2008 is .90, and the
correlation Democratic share of party Registration and the Democratic share of vote
for United States Senate in 2008 is .83. 2
These correlations reveal that
registration is in fact a strong predictor of electorate choice in the State of North
Carolina. It is further worth noting that the Obama vote in 2008 has the weakest
correlation with party registration of the three statewide elections that year,
suggesting that it may have been the least useful of the three elections to use as a
pure indicator of party. And, the correlation between Black registration and the
Obama vote is slightly stronger (.80) than the correlation between Democratic
registration and Obama vote (.78).
32. Party registration is itself an electoral choice in the State of North Carolina.
Party registration in the State of North Carolina restricts in which party’s primary a
person can vote.
33. As demonstrated in Table 1, Black Registration, not Black VAP, is a much
stronger correlate of the Obama vote. Given Dr. Hofeller’s claim that the Obama vote
is the relevant indicator, the strength of correlation of Black Registration suggests
that the analysis of registration is highly informative of voting behavior, and more
indicative of the electoral effects on Black voters than would be an analysis of the
association of Obama vote with Black VAP. Hence, I conclude that analysis of
Correlations are weighted correlations, and VTDs are weighted by the total
number of presidential ballots in 2008.
2
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registration data is highly relevant to understanding the effects of the new CDs 1
and 12 on Black voters. And, the analysis in my original report revealed that race
controlling for party is a much stronger indicator of inclusion in those CDs than is
party controlling for race.
3. Areas of Analysis
34. I offer two separate analyses of the target areas or populations for the location
of CDs 1 and 12. One such analysis examines all registered voters in the counties in
which the CDs are located – called the envelope of counties. The other analysis
examines sets of VTDs that were in either new CD 1 or previous CD 1 (or new CD 12
or previous CD 12).
35. Dr. Hofeller questions my use of the counties in which CDs are located and
states that such a choice is highly unusual. (paragraph 52)
36. Analysis of racial voting patterns at the county level and of the counties in which
a district is situated is quite common in voting rights cases. The wide use of
counties in performing ecological regressions informed my decision to use counties
as a target area. Other expert reports in cases concerning the 2011 North Carolina
redistricting, including a report filed by Defendants’ expert in the state court case,
also examine county-level racial voting data to assess the likely effects of the
districts. See the reports of Thomas Brunell, “Report on Racially Polarized Voting in
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North Carolina,” June 14, 2011. 11 CVS 16896, 11 CVS 16940, and Ray Block, Jr.,
“Polarized Voting in 2006, 2008, and 2010 in North Carolina State Legislative
Contests,” Case 1:13-cv-00949-WO-JEP. Filed 1/17/14.
37. Crossing of county lines is sometimes used as an indicator of respect for natural
or other political geographies in the process of drawing lines. Hence, counties are
sometimes treated as a relevant unit of analysis in understanding the locus of
districts.
38. Other researchers have used VTDs in and neighboring a district as the target
areas for the analysis of the racial effects of a districting plan. See the report of Gary
King and Benjamin Schneer to the Arizona Independent Redistricting Commission;
Gary King and Benjamin Schneer, “Analysis of the Arizona Independent Redistricting
Commission Congressional Map” http://gking-projects.iq.harvard.edu/AZDOJ/az_report_cd.pdf.
D. District Population Growth
39. Dr. Hofeller states that, in addition to partisanship and preclearance, an
important policy goal of the legislature was guarding against the underpopulation of
CD 1 in the future. (Paragraph 71.)
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40. Dr. Hofeller states that CD 1 was moved away from slow growing rural counties
and into urban areas in order to create a district that would likely retain its
population over the coming decade. He discusses the rural counties in his rebuttal
report. (Paragraphs 20 and 51) However, he does not state what data or
population forecasts were used for the district in 2020. He does not state the
process for deciding which urban areas (especially which VTDs) to include in CD 1.
41. To assess the claim that the areas added to CD 1 were included primarily to
counteract population declines, and were not racially motivated, I examined the
changes to CD 1 in the City of Durham and County of Durham. CD 1 in the 20012011 map did not include any part of the City or County of Durham. New CD 1
includes 159,691 persons from this county, which accounts for 21.8% of the
population of the New CD 1.
42. Population growth data for the VTDs are not available in the North Carolina
State Legislature’s Redistricting website. Population and registration counts by
race, however, are available at that website. I analyzed those data to see if the
Voting Age Population and registered voters in the portions of the City and County
of Durham that were included in CD 1 were disproportionately Black.
43. Table 2 presents the racial composition of the City of Durham and the County of
Durham and the racial composition of the portions of these jurisdictions that were
added to CD 1. Each cell presents the number of persons in that category. In the
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City of Durham, for example, 69,454 persons of Voting Age are Black alone, and the
VTDs in the City of Durham that were included in CD 1 contain 55,265 persons who
are Black alone.
44. Comparison of the percent of Blacks and percent of Whites who were included
in CD 1 from the City of Durham and the County of Durham reveals that Blacks in
these jurisdictions were disproportionately likely to be added to CD 1. The majority
of Whites in these jurisdictions were included in other CDs.
45. Using the data in Table 2, one can calculate the percent of a given group in the
City or County of Durham that was included in CD 1. For example 79.6 percent
(55,265/69,454) of all people in the City of Durham who considered themselves to
be Black (and no other race) were included in CD 1. Similar calculations can be
made for each racial group and for Voting Age Population and Registered Voters.
46. In the City of Durham, 79.6 percent of the Black Voting Age Population was
included in CD 1. 48.4 percent of the White Voting Age Population was included in
CD 1.
47. In the City of Durham, 80.5 percent of Black Registered Voters were included in
CD 1. 44.2 percent of White Registered Voters were included in CD 1.
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48. In the County of Durham, 77.6 percent of the Black Voting Age Population was
included in CD 1. 43.3 percent of the White Voting Age Population was included in
CD 1.
49. In the County of Durham, 78.5 percent of Black Registered Voters were included
in CD 1. 38.9 percent of White Registered Voters that were included in CD 1.
50. The boundary of CD 1 in the City and County of Durham was disproportionately
more likely to incorporate Blacks than Whites. Black registered voters in the County
of Durham were twice as likely as Whites to be included in CD 1.
E. Population Equality
51. Dr. Hofeller states that equalizing population was one of the four major policy
objectives of the State Legislature in the construction of new CDs 1 and 12. This is a
legal requirement under the United States Constitution as interpreted by the
Supreme Court of the United States.
52. In the case of CD 12, this requirement did not appear to exert much of a
constraint on the extent to which the State Legislature shifted populations among
districts in the process of drawing the Rucho-Lewis map.
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53. Previous CD 12 needed few changes in order to equalize population. According
to the 2010 Census enumeration statistics provided by the North Carolina
Legislative Redistricting site, the district had only 2,847 more persons than the ideal
district of 733,500. In other words, prior to redistricting, CD 12 needed to have only
2,847 people removed from the district in order to achieve the equal population
objective.
54. The Rucho-Lews map, however, added 239,064 people to CD 12 and removed
241,911 people from CD 12. And, the populations added to new CD 12 were
disproportionately Black compared with the areas removed from previous CD 12.
Of the 239,064 people added to CD 12, 105,132 people (44%) were Black. Of the
241,911 people removed from CD 12, 56,046 people (23%) were Black. In other
words, the changes in CD 12 from the 2001-2011 map to the Rucho-Lewis map
increased the number of Black persons in CD 12 by 49,086 (i.e., 105,132 minus
56,046). The change in the Black population far surpasses the changes in the
district needed to maintain equal population.
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Table 1. Correlation Between Obama Share of Two Party Vote and Racial
Composition of VAP and Registered Voters in VTDs
Entire State
CD 1
CD12
Districts Other
than CD 1 and
CD 12
Black VAP
+.60
+.47
+.65
+.46
White VAP
-.64
-.54
-.69
-.50
Black R.V.
White R.V.
+.80
-.81
+.82
-.87
+.92
-.93
+.69
-.69
Table 2. Racial Composition of Areas in City and County of Durham
City of Durham
Black Alone
White Alone
Total
Voting Age Population
All Areas
In CD 1
69,454
55,265
80,598
39,010
176,435
111,769
Registered Voters
All Areas
In CD 1
62,768
50,570
75,664
33,442
152,297
92,492
County of Durham
Black Alone
White Alone
Total
Voting Age Population
All Areas
In CD 1
75,440
58,560
103,053
44,624
207,266
121,895
Registered Voters
All Areas
In CD 1
69,542
54,610
94,725
36,867
179,309
100,189
18
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Stephen Ansolabehere
January 29,2014
Cambridge, Massachusetts
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P-50
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