IN THE UNITED STATES DISTRICT COURT

FOR THE SOUTHERN DISTRICT OF FLORIDA

GRACE, INC., et al.,

Plaintiffs,

v. Case No. 1:22-cv-24066-KMM

CITY OF MIAMI,

Defendant.

EXPERT REPORT

Cory McCartan, Ph.D.

July 1, 2023

I. INTRODUCTION AND SCOPE OF WORK

1. My name is Cory McCartan, and I am a Data Science Assistant Professor / Faculty

Fellow at the Center for Data Science at New York University. I specialize in the development and

application of statistical methodology in the social sciences.

2. I have been retained by counsel representing the Plaintiffs to provide an analysis of

population and compactness of Miami City Commission redistricting plans, including the Enjoined

plan, the City’s proposed remedial plan, the Plaintiffs’ proposed remedial plan, and other alternative

plans.

II. QUALIFICATIONS AND EXPERIENCE

3. I have a B.A. in mathematics from Grinnell College (2019) and an M.A. (2021) and

Ph.D. (2023) from Harvard University in statistics. My research focuses on developing and applying

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statistical methodology to problems in the social sciences. Specifically, I have extensively studied

redistricting in the United States, publishing six peer-reviewed journal articles and working papers

on redistricting in the last two years.

4. As part of my redistricting research agenda, I have developed a simulation algorithm

that generates redistricting plans (McCartan and Imai, 2023). Part of this algorithm involves

computing demographic, population, and compactness statistics for tens of thousands of randomlydrawn districts. The algorithm can be used to measure and evaluate existing redistricting plans

along a variety of dimensions.

5. At Harvard, I also helped to start the Algorithm-Assisted Redistricting Methodology

(ALARM) Project, which applies computational tools to study and evaluate redistricting plans

and processes in the U.S. and around the globe.1 One effort that I led as part of the ALARM

Project involved collecting every congressional district drawn in the 2021-22 redistricting cycle,

and generating over 200,000 algorithmic redistricting plans which complied with all relevant state

laws and constitutions (McCartan et al., 2022). For each of the real-world and algorithmic plans,

we calculated a battery of population, demographic, compactness, and partisan measures for each

district, and released all of the plans and statistics to the public. These statistics included multiple

different compactness metrics and the population overlap between each algorithmic district and the

respective enacted district.

6. In 2021, I was hired to assist with algorithmic redistricting simulations for two court

cases in the state of Ohio (Ohio Supreme Court Cases 2021–1193 and 2021–1449; both titled League

of Women Voters of Ohio v. Ohio Redistricting Commission). As part of this work I computed

measures of compactness, county splits, and demographics for various enacted, proposed, and

1Project website: https://alarm-redist.org/

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remedial redistricting plans, along with 5,000 algorithmically simulated plans. These calculations

and the accompanying algorithmic simulations provided evidence that the enacted plans in each

case were statistical outliers as regards both partisan outcomes and traditional redistricting criteria

such as compactness.

7. I have also developed and continue to maintain a variety of open-source software

packages for using census data and studying redistricting plans. These tools can be installed

for free on any personal computer and operating system. The packages include redist (Kenny

et al., 2020), which implements several cutting-edge redistricting simulation algorithms, and an

accompanying package redistmetrics (Kenny et al., 2021), which lets users calculate dozens

of compactness, partisan, and demographic measures for redistricting plans. They also include

easycensus (McCartan, 2023), PL94171 (McCartan and Kenny, 2022), alarmdata (McCartan

et al., 2023), and tinytiger (Kenny and McCartan, 2023), which provide access to Census

data and geography shapefiles related to redistricting. Together, these software packages have

been downloaded tens of thousands of times, and are widely used in academic research and by

redistricting practitioners.

8. A copy of my curriculum vitae is attached as Exhibit A.

III. DATA, SOFTWARE, AND METHODOLOGY

9. I calculated compactness and population statistics for 8 plans, which are hereinafter

abbreviated as follows:

• 2013: the City’s 2013 enacted plan;

• Enjoined: the City’s 2022 enacted plan, enjoined by the Court;

• City: the City’s final proposed remedial plan, adopted June 14, 2023 (counsel provided two

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files for this plan: “City Enacted” and “City Opposing Counsel,” as discussed in Part IV

below);

• P4: the Plaintiffs’ proposed remedial plan; and

• P1–P3: prior plans proposed by Plaintiffs.

10. Counsel representing the Plaintiffs provided me the geographic boundaries for all 8

plans as Block Assignment Files (BAFs) in comma-delimited format (.csv). I verified that these

files were properly formatted and translated to geographically contiguous city council districts.

11. I downloaded 2020 decennial census total population counts for every census block

in the City of Miami from the U.S. Census Bureau’s software interface. These total population

counts are the same that are mandated by P.L. 94–171 and are used in congressional apportionment.

I also downloaded geographic shapefile information for the blocks.

12. I calculated four compactness measures for each plan: the Polsby-Popper score

(Polsby and Popper, 1991), the Reock score (Reock, 1961), the Convex Hull score (ratio of the

area of each district to the area of the district’s convex hull), and the Edge-Cut score (see Dube

and Clark, 2016, and McCartan and Imai (2023)). This latter measure is graph-theory based and

is much less sensitive than other measures to particularities of local geography such as irregular

coastlines and city boundaries. As such, it has recently gained traction among algorithmic and

computational redistricting researchers. The calculations were carried out with the aforementioned

redistmetrics software, which has been numerically validated and extensively tested.

13. I also calculated the degree to which each plan’s districts overlap with the districts

in the Enjoined and 2013 plans. To do so, I used population overlap routines implemented in my

redist software, using the 2020 decennial census population data described above.

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IV. ACCURACY OF BLOCK ASSIGNMENT FILES

14. Counsel representing Plaintiffs also provided me a slideshow presentation containing

maps of various plans considered by the Miami City Commisison and asked me to identify any

differences between the “D3 alt map v3” map on slide 6 and the final plan provided by the opposing

counsel (“City Opposing Counsel”). I generated my own district map of the BAF provided by

opposing counsel and compared it to the “D3 alt map v3” map.

15. I identified a discrepancy between the BAF provided by the opposing counsel and the

“D3 alt map v3” map on the slide on the boundary between District 3 and District 2. Specifically, the

“D3 alt map v3” map assigns census blocks 120860067201002 and 120860066051000 to District

3, while the BAF provided by opposing counsel assigns those blocks to District 2. In contrast,

the “City Enacted” plan assigns these blocks to District 3, matching the “D3 alt map v3” in the

slideshow.

16. The two blocks are not populated, and thus the discrepancy does not impact the

population overlap analysis in Part VI.

V. COMPACTNESS OF REDISTRICTING PLANS

17. The Polsby-Popper scores are reported in Table 1 for each district of each plan. All

values were multiplied by 100, so they lie on a 0–100 scale, for interpretability. Higher values

indicate more compact districts.

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Table 1: Polsby-Popper compactness scores.

Plan District 1 District 2 District 3 District 4 District 5

2013 25 28 62 24 29

Enjoined 21 26 41 22 30

City Enacted 19 26 34 22 28

City Opposing Counsel 19 26 37 22 28

P1 24 39 77 34 55

P2 41 34 55 39 51

P3 35 31 54 39 43

P4 32 31 57 38 40

18. The Reock scores are reported in Table 2. All values were multiplied by 100, so they

lie on a 0–100 scale. Higher values indicate more compact districts.

Table 2: Reock compactness scores.

Plan District 1 District 2 District 3 District 4 District 5

2013 20 32 60 23 33

Enjoined 21 30 47 24 34

City Enacted 21 30 43 24 33

City Opposing Counsel 21 30 43 24 33

P1 18 36 66 33 61

P2 34 37 35 29 54

P3 30 34 35 29 47

P4 31 34 37 28 47

19. The convex hull scores are reported in Table 3. All values were multiplied by 100,

so they lie on a 0–100 scale. Higher values indicate more compact districts.

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Table 3: Convex hull compactness scores.

Plan District 1 District 2 District 3 District 4 District 5

2013 67 65 84 61 61

Enjoined 58 63 71 53 65

City Enacted 58 63 67 54 62

City Opposing Counsel 58 63 67 54 62

P1 64 80 94 71 85

P2 72 71 90 87 82

P3 67 66 89 87 75

P4 66 66 92 86 75

20. The Edge-Cut measure is reported in Table 4 for each plan. This measure is planwide and not district-specific. It counts the number of pairs of neighboring census blocks which

are separated by a district line. In contrast with the above measures, lower values indicate more

compact districts.

Table 4: Edge-Cut measure by plan. Lower values indicate more compact plans.

Plan Edges cut by district boundaries

2013 342

Enjoined 420

City Enacted 400

City Opposing Counsel 398

P1 168

P2 184

P3 248

P4 237

VI. POPULATION OVERLAP BETWEEN REDISTRICTING PLANS

21. I first calculated the overlap between districts in the Enjoined plan to corresponding

districts in the City and P1–P4 plans. These calculations are summarized in Tables 5 (percentage

overlap) and 6 (raw population counts). Because the two blocks that differ between both provided

versions of the City plan (“City Enacted” and “City Opposing Council”) are not populated, these

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two versions have identical overlap calculations; they are reported together as “City” below.

Table 5: District population overlap between Enjoined and various other plans, expressed as a

percentage of the population of each plan’s corresponding district.

Overlap with...

Enjoined plan Enjoined City P1 P2 P3 P4

District 1 100 98.2 61.1 56.7 54.8 53.3

District 2 100 92.2 63.3 89.5 96.7 96.7

District 3 100 90.6 83.5 45.4 46.0 47.1

District 4 100 94.8 56.9 42.7 42.7 44.0

District 5 100 94.7 84.1 85.8 94.0 92.5

Districts 1, 3, and 4 100 97.8 77.0 48.7 48.8 49.3

Overall 100 94.1 69.8 64.3 66.9 67.0

Table 6: District population overlap between Enjoined and various other plans.

Overlap with...

Enjoined plan Enjoined City P1 P2 P3 P4

District 1 88,108 85,892 52,916 49,042 48,043 46,690

District 2 93,300 82,563 56,388 80,476 86,533 86,533

District 3 87,658 80,842 73,237 38,662 39,527 41,383

District 4 86,597 84,861 50,699 38,554 38,554 38,554

District 5 86,578 81,843 75,561 77,483 83,418 82,981

Districts 1, 3, and 4 262,363 251,595 176,852 126,258 126,124 126,627

Overall 442,241 416,001 308,801 284,217 296,075 296,141

22. I then calculated the overlap between districts in the 2013 plan to corresponding

districts in the Enjoined, City, and P1–P4 plans. These calculations are summarized in Tables 7

(percentage overlap) and 8 (raw population counts). Because the two blocks that differ between

both provided versions of the City plan are not populated, these two versions have identical overlap

calculations; they are reported together as “City” below.

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Table 7: District population overlap between 2013 and various other plans, expressed as a percentage

of the population of each plan’s corresponding district.

Overlap with...

2013 plan Enjoined City P1 P2 P3 P4

District 1 92.1 92.1 53.4 49.0 48.4 48.4

District 2 100.0 98.1 78.4 100.0 100.0 100.0

District 3 91.5 83.8 83.5 45.4 46.0 45.5

District 4 86.0 84.6 63.8 49.4 49.4 49.4

District 5 87.5 88.4 79.5 81.3 85.9 86.0

Districts 1, 3, and 4 97.5 96.3 79.0 51.9 52.3 51.9

Overall 91.5 89.4 71.8 65.4 66.1 66.1

Table 8: District population overlap between 2013 and various other plans.

Overlap with...

2013 plan Enjoined City P1 P2 P3 P4

District 1 81,120 80,553 46,257 42,383 42,383 42,383

District 2 93,300 87,891 69,873 89,897 89,522 89,522

District 3 80,169 74,737 73,237 38,662 39,527 39,999

District 4 74,504 75,749 56,796 44,651 44,651 43,267

District 5 75,753 76,398 71,427 73,419 76,232 77,156

Districts 1, 3, and 4 235,793 231,039 176,290 125,696 126,561 125,649

Overall 404,846 395,328 317,590 289,012 292,315 292,327

23. At the request of counsel for the Plaintiffs I also calculated the overlap for the

grouped set of Districts 1, 3 and 4. These are reported as a separate summary line in Tables 5–8.

Cory McCartan, Ph.D.

July 1, 2023

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REFERENCES

Dube, M. P. and Clark, J. T. (2016). Beyond the circle: Measuring district compactness using graph

theory. In Annual Meeting of the Northeastern Political Science Association.

Kenny, C. T. and McCartan, C. (2023). tinytiger: Lightweight Interface to TIGER/Line Shapefiles.

https://cran.r-project.org/package=tinytiger.

Kenny, C. T., McCartan, C., Fifield, B., and Imai, K. (2020). redist: Computational Algorithms for

Redistricting Simulation. https://CRAN.R-project.org/package=redist.

Kenny, C. T., McCartan, C., Fifield, B., and Imai, K. (2021). redistmetrics: Redistricting Metrics.

https://CRAN.R-project.org/package=redistmetrics.

McCartan, C. (2023). easycensus: Quickly Find, Extract, and Marginalize U.S. Census Tables.

https://cran.r-project.org/package=easycensus.

McCartan, C. and Imai, K. (2023). Sequential Monte Carlo for sampling balanced and compact

redistricting plans. Annals of Applied Statistics, Accepted.

McCartan, C. and Kenny, C. T. (2022). PL94171: Tabulate P.L. 94-171 Redistricting Data Summary

Files. https://cran.r-project.org/package=PL94171.

McCartan, C., Kenny, C. T., Simko, T., Garcia III, G., Wang, K., Wu, M., Kuriwaki, S., and Imai, K.

(2022). Simulated redistricting plans for the analysis and evaluation of redistricting in the united

states. Scientific Data, 9(1):689.

McCartan, C., Kenny, C. T., Simko, T., Zhao, M., and Imai, K. (2023). alarmdata: Download,

Merge, and Process Redistricting Data. https://github.com/alarm-redist/alarmdata/.

Polsby, D. D. and Popper, R. D. (1991). The third criterion: Compactness as a procedural safeguard

against partisan gerrymandering. Yale Law & Policy Review, 9(2):301–353.

Reock, E. C. (1961). A note: Measuring compactness as a requirement of legislative apportionment.

Midwest Journal of Political Science, 5(1):70–74.

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EXHIBIT A

Curriculum Vitae

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Cory McCartan

Curriculum Vitae

June 2023

Contact

Information

(425) 770-9244 corymccartan.com

corymccartan@nyu.edu

Employment The Pennsylvania State University Expected 2024

Assistant Professor of Statistics

New York University 2023 – 2024

Center for Data Science

Data Science Assistant Professor / Faculty Fellow

Education Harvard University 2019 – 2023

Ph.D., Statistics, 2023. Advisor: Kosuke Imai.

A.M., Statistics, 2021.

Grinnell College 2015 – 2019

B.A., Mathematics, with honors.

Publications “Sequential Monte Carlo for Sampling Balanced and Compact Redistricting Plans,” with Kosuke Imai. Annals of Applied Statistics, Forthcoming.

Covered by the Washington Post, Quanta Magazine.

“Widespread Partisan Gerrymandering Mostly Cancels Nationally, but Reduces Electoral Competition,” with Christopher Kenny, Tyler Simko, Shiro Kuriwaki, and Kosuke Imai. Proceedings of the National Academy of Sciences 120:25 (2023).

“Recalibration Of Predicted Probabilities Using the ‘Logit Shift’: Why does it work, and when

can it be expected to work well?” with Evan T. R. Rosenman and Santiago Olivella. Political Analysis 1-11 (2023).

“Comment: The Essential Role of Policy Evaluation for the 2020 Census Disclosure Avoidance

System” with Christopher T. Kenny, Shiro Kuriwaki, Tyler Simko, Evan T. R. Rosenman,

and Kosuke Imai. Harvard Data Science Review, Special Issue 2 (2023).

“Simulated Redistricting Plans for the Analysis and Evaluation of Redistricting Plans in the

United States,” with Christopher Kenny, Tyler Simko, Shiro Kuriwaki, George Garcia III,

Kevin Wang, Melissa Wu, and Kosuke Imai. Scientific Data 9:689 (2022).

“The Use of Differential Privacy for Census Data and its Impact on Redistricting: The Case of

the 2020 U.S. Census,” with Christopher T. Kenny, Shiro Kuriwaki, Tyler Simko, Evan T.

R. Rosenman, and Kosuke Imai. Science Advances 7:41 (2021).

Covered by the Associated Press, the Washington Post, the San Francisco Chronicle,

and others.

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“Geodesic Interpolation on Sierpinski Gaskets,” with Caitlin M. Davis, Laura A. LeGare, and

Luke G. Rogers. Journal of Fractal Geometry 8:2 (2021).

Working Papers “Estimating Racial Disparities When Race is Not Observed,” with Jacob Goldin, Daniel E. Ho,

and Kosuke Imai.

“Individual and Differential Harm in Redistricting,” with Christopher T. Kenny.

“Measuring and Modeling Neighborhoods,” with Jacob R. Brown and Kosuke Imai. Under

Review.

“Evaluating Bias and Noise Induced by the U.S. Census Bureau’s Privacy Protection Methods,”

with Christopher T. Kenny, Shiro Kuriwaki, Tyler Simko, and Kosuke Imai.

“Making Differential Privacy Work for Census Data Users," with Tyler Simko and Kosuke Imai.

“Finding Pareto Efficient Redistricting Plans with Short Bursts.”

Works in Progress “Studying Officeholders’ Perceived Geographic Constituencies,” with Jacob R. Brown and Hunter

E. Rendleman.

“Regression of the Conditional Median,” with Xiao-Li Meng.

“Algorithm-Assisted Redistricting Methodology” (book), with Kosuke Imai, Christopher Kenny,

and Tyler Simko.

Other Writing “Researchers need better access to US Census data,” with Tyler Simko and Kosuke Imai. Science, 380:6648 (2023).

“Candy cane shortages and the importance of variation.” International Statistical Institute:

Statisticians React to the News (December 21, 2021).

“Where will the rocket land?” International Statistical Institute: Statisticians React to the News

(May 12, 2021).

“Who’s the most electable Democrat? It might be Warren or Buttigieg, not Biden.” The Washington Post (October 23, 2019).

“I-405 Express Toll Lanes: Usage, benefits, and equity,” with Shirley Leung, C.J. Robinson,

Kiana Roshan Zamir, Vaughn Iverson, and Mark Hallenbeck. Technical report for the

Washington State Department of Transportation (2019).

Software redist: Simulation Methods for Legislative Redistricting

redistmetrics: Redistricting Metrics

easycensus: Quickly Find, Extract, and Marginalize U.S. Census Tables

birdie: Bayesian Instrumental Regression for Disparity Estimation

causaltbl: Tidy Causal Data Frames and Tools

PL94171: Tabulate P.L. 94-171 Redistricting Data Summary Files

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adjustr: Stan Model Adjustments and Sensitivity Analyses using Importance Sampling

conformalbayes: Jackknife(+) Predictive Intervals for Bayesian Models

alarmdata: Download, Merge, and Process Redistricting Data

blockpop: Estimate Census Block Populations for 2020

ggredist: Scales, Palettes, and Extensions of ggplot2 for Redistricting

tinytiger: Lightweight Interface to TIGER/Line Shapefiles

wacolors: Colorblind-friendly Palettes from Washington State

Presentations Joint Statistical Meetings, Invited Paper Panel: 2022, 2021.

Society for Political Methodology, Annual Meeting, Paper: 2022; Poster: 2022, 2021.

American Association for Public Opinion Research, Annual Meeting, Poster: 2022.

Institute for Quantitative Social Science, Harvard University, Applied Statistics Workshop, Paper: 2023, 2022, 2021, 2020.

Teaching Harvard University

STAT 117: Introduction to Biostatistics Spring 2021

Awarded a Certificate of Distinction in Teaching

STAT 221: Monte Carlo Methods & Other Computational Fall 2020

Tools for Statistical Learning

Grinnell College

MAT 215: Linear Algebra Fall 2017 and Spring 2019

MAT 310: Statistical Modeling Fall 2018

Grinnell College Math Lab 2018 – 2019

Honors and

Awards

Best Statistical Software Award, for developing statistical software that makes a significant research contribution, awarded to the redist software package by the Society for Political

Methodology, 2022.

Service Harvard Statistics Graduate Council 2020 – 2023

Organized Ph.D. student retreat and research “lightning talks,” 2020 and 2021.

First-year Ph.D. Student Mentor 2020 – 2023

Harvard Graduate Students Union – UAW Local 5118 2019 – 2021

Elected member, Bargaining Committee, 2020–2021 and 2021–2024 contracts.

Interim chair, Finance and Benefits Committee, 2020.

Reviewer: Election Law Journal, Sloan Foundation.

Membership American Statistical Association, Society for Political Methodology, American Political Science Association.

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Other Experience Data for Progress 2022

Consultant, Midterm election modeling

American Civil Liberties Union of Ohio 2021 – 2022

Consultant (with Prof. Kosuke Imai), League of Women Voters of Ohio v.

Ohio Redistricting Commission (Ohio Supreme Court Cases 2021–1193 and

2021–1449).

University of Washington eScience Institute Summer 2019

Data Science for Social Good Fellow

Union of Grinnell Student Dining Workers 2016 – 2019

Founder, President (2016–17), and Advisor to the Executive Board (2018–19)

University of Connecticut Summer 2018

REU Participant, Department of Mathematics

Fred Hutchinson Cancer Research Center Summer 2017

Lead Intern, Department of Biostatistics

Grinnell College Department of Mathematics 2017

Course Grader

Cray, Inc. Summer 2015

Intern, Chapel language testing

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