GEOPOLITICAL
GEOMETRY THROUGH SAMPLING
REVEALING THE
JONATHAN MATTINGLY (+ THE TEAM) DUKE MATH
R EVEALING THE G EOPOLITICAL GEOMETRY THROUGH SAMPLING J ONATHAN M - - PowerPoint PPT Presentation
R EVEALING THE G EOPOLITICAL GEOMETRY THROUGH SAMPLING J ONATHAN M ATTINGLY (+ THE TEAM ) D UKE M ATH gerrymander manipulate the boundaries of an electoral constituency to favor one party or class. achieve (a result) by manipulating the
GEOMETRY THROUGH SAMPLING
JONATHAN MATTINGLY (+ THE TEAM) DUKE MATH
gerrymander
electoral constituency to favor one party
boundaries of an electoral constituency. "a total freedom to gerrymander the results they want" racial vs partisan gerrymander
North Carolina 13 Congressional Representatives
NC has around 10.2 million people
Every decade, required to redo the (13) congressional districts
Charlotte Area: Charlotte-Gastonia-Salisbury- population 2,402,623 The Triangle: Raleigh-Durham-Cary-Chapel Hill- population 1,749,525 The Piedmont Triad: Greensboro—Winston-Salem—High Point- population 1,589.200
Population Density Presidential Election 2016
U.S. House congressional districts for 2012 election
The will of the people is expressed
every person can
every vote is
(once)
–Justice Ruth Bader Ginsburg Evenwel v. Abbott, April 2016
“By ensuring that each representative is subject to requests and suggestions from the same number of constituents, total population apportionment promotes equitable and effective representation.”
2012 North Carolina Elections for U.S. House
VOTES PERCENTAGE SEATS Democratic 2,218,357 50.65% 4 Republican 2,137,167 48.80% 9 Libertarian 24,142 0.55%
Are these results due to political gerrymandering ?
Are these results natural outcomes of NC's geopolitical structure of the spatial distribution of partisan votes ?
40% Blue 60% Red Red wins 3 Blue wins 2
from Wikipedia after an image by Steven Nass
Red wins 2 Blue wins 3 Red wins 5 Blue wins 0
2012 2016 Judges’
How to quantify how gerrymandered
How does one find the true message in an election ?
What if we drew the districts randomly ?
with no regard for party registration or most demographics
reveal the geopolitical structure encoded in the votes
Many Groups using algorithmic generated maps to benchmark
Not all the same. Not all Random. Some generating alternative maps. Some Sampling a defined distribution. some using actual surrogate districts. Focus on our group at Duke
Common Cause v. Rucho (N.C. Congressional):
Closing arguments on October 16
Gill v. Whitford (WI State Assembly) :
North Carolina v. Covington (N.C. State Assembly):
SCOTUS in June.
13 U.S. house districts.
votes in each of the new districts using the actual 2012 votes.
redistricting.
Use Markov Chain Monte Carlo to sample a distribution on redistrictings
districts to affect the winner.
Use Markov Chain Monte Carlo to sample from redistricting with good scores.
Sample: (density) ∝ e−β(score of redistricting)
Not just generating a large number of alternatives. Know what distribution we are sampling from.
counties
counties
P(ξ) = 1 Z e−βJ(ξ) J(ξ) = wpJpop(ξ) + wIJcompact(ξ) + wcJcounty(ξ) + wmJmino(ξ) ξ : {Precincts} 7! {1, . . . , 13} (a 13 color Potts Model with an unusual energy)
Sum of square deviation from ideal district population
13
X
n=1
h Ideal − (Pop in district n) i2 Ideal = Population of N.C. 13 ≈ 733, 499
Minimized for a circle (Perimeter)2 Area ≥ 4π ≈ 12.5 Also considered the ratio of district’s area to the smallest circumscribing rectangle
Also include score terms for Voting Rights Act and Preserving County Boundaries
Soft penalization :
minimal voting age black population.
Judges NC2016 NC2012 Fraction of result 0.1 0.2 0.3 0.4 Number of Democrats Elected (2012 votes) 3 4 5 6 7 8 9 10 Judges NC2016 NC2012 Fraction of result 0.1 0.2 0.3 0.4 0.5 0.6 Number of Democrats Elected (2016 votes) 2 3 4 5 6 7 8
Election Results for Ensemble of Redistricting Plans
Overall Fraction of Republican Vote 0.44 0.46 0.48 0.50 0.52 0.54 0.56 Elected Republicans (2012 Votes) 5 10 0.44 0.46 0.48 0.50 0.52 0.54 0.56 Elected Republicans (2016 Votes) 5 10
PRE12 GOV12 USH12 USH16 PRE16 GOV16 NCSS16
Republican Vote Fraction 0.47 0.48 0.49 0.50 0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 Elected Republicans 4 6 8 10 12
Historical and Shifted Elections
2012 2016 Judges
Wisconsin General Assembly
WI 50 55 60 65 70 75 WSA12 Fraction of result 0.1 0.2 WI WSA14 Fraction of result 0.1 0.2 WI (int) WI (act) WSA16 Fraction of result 0.1 0.2 Elected Republicans 50 55 60 65 70 75
PRE12 USS12 SOS14 USH12 WSA12 PRE16 WSA14 USH14 GOV14 GOV12 WSA16
Fraction of Republican vote 0.46 0.48 0.50 0.52 0.54 Number of Republican seats 40 50 60 70
Wisconsin historical elections
WSA16 Majority Super Majority WSA14 Majority Super Majority
Republicans elected Fraction of Republican vote 0.44 0.46 0.48 0.50 0.52 0.54 0.56 40 60 80 40 60 80
Shift the global percentages
WSA12 Super Majority Majority Expected seats WI (contested) Standard Deviation 90% of ensemble Bound
Number of Republican seats 10 20 30 40 50 60 70 80 90 % Vote to the Republicans 45 50 55 60
WSA14 Super Majority Majority Expected seats WI (contested) Standard Deviation 90% of ensemble Bound
Number of Republican seats 20 30 40 50 60 70 80 90 % Vote to the Republicans 45 50 55 60
WSA16 Super Majority Majority Expected seats WI (contested) Standard Deviation 90% of ensemble Bound
Number of Republican seats 30 40 50 60 70 80 90 % Vote to the Republicans 45 50 55 60
WI Probability 20 40 60 80 100 Republican vote needed for parity in election (2012) 0.46 0.48 0.50 WI Probability 50 100 Republican vote needed for parity in election (2014) 0.44 0.46 0.48 0.50 WI Probability 20 40 60 80 Republican vote needed for parity in election (2016) 0.44 0.46 0.48
WI % of maps 5 10 15 20 WSA12 Interpolated Votes (shifted to parity) 40 45 50 55 WI % of maps 5 10 15 20 WSA14 Interpolated Votes (shifted to parity) 40 45 50 55 WI % of maps 5 10 15 20 WSA16 Interpolated Votes (shifted to parity) 40 45 50 55
Red wins 2 Blue wins 3 Red wins 2 districts by 8 votes each Blue wins 3 districts by 2 votes each
Percentage of Democrats from lowest to highest
⇥ 10% 10% 60% 60% 60% ⇤
1 2 3 4 5 6 7 8 9 10 11 12 13
Most Republican to Most Democratic Districts
0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8
Democratic Winning Percentages Democratic Winning Percentages (House 2012)
1 2 3 4 5 6 7 8 9 10 11 12 13
Most Republican to Most Democratic Districts
0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8
Democratic Winning Percentages Democratic Winning Percentages (House 2012)
1 2 3 4 5 6 7 8 9 10 11 12 13
Most Republican to Most Democratic Districts
0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8
Democratic Winning Percentages Democratic Winning Percentages (House 2012)
1 2 3 4 5 6 7 8 9 10 11 12 13
Most Republican to Most Democratic Districts
0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8
Democratic Winning Percentages Democratic Winning Percentages (House 2012)
NC2012
1 2 3 4 5 6 7 8 9 10 11 12 13
Most Republican to Most Democratic Districts
0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8
Democratic Winning Percentages Democratic Winning Percentages (House 2012)
Judges NC2012 NC2016
NC2012 NC2016 Judges Democratic vote fraction 0.3 0.4 0.5 0.6 0.7 0.8 Most Republican To Most Democratic Districs (2012 votes) 2 4 6 8 10 12
NC2012 NC2016 Judges
Most Republican To Most Democratic Districts (2016 votes) (2012 votes) Democratic vote fraction
0.2 0.3 0.4 0.5 0.6 0.7 0.8 2 4 6 8 10 12
.
2 4 6 8 10 12
NC Congressional Delegation
Identify Cracked and Packed districts
NC Congressional Delegation
WSA14 Fraction of Democratic vote 0.2 0.4 0.6 0.8 1.0 District from most to least Republican 10 20 30 40 50 60 70 80 90 100
0.40 0.45 0.50 0.55 40 50 60
Wisconsin General Assembly
Gerrymander Index
Measure deviation for expected district structure
NC2012 NC2016 Judges NC2012 NC2016 Judges
Gerrymandering index (2016 votes) (2012 votes) Fraction w/ worse index
0.2 0.4 0.6 0.8 1.0 0.1 0.2 0.30 0.1 0.2 NC2012 NC2016 Judges NC2012 NC2016 Judges
Gerrymandering index (2016 votes) (2012 votes) Probability density
5 10 15 20 25 30 0.1 0.2 0.30 0.1 0.2
Frac Republican Vote 0.42 0.44 0.46 0.48 0.50 0.52 0.54 0.56 0.58 0.60 0.62 0.64 0.66 Republicans Elected 40 60 80
WI (int) WI (act) WSA16 Fraction of result 0.1 0.2 Elected Republicans 50 55 60 65 70 75
`(map) = − log Prob(outcome map produces)
Average `(map) over shift
Measuring Representativeness
WI WSA12 Probability 0.5 1.0 1.5 2.0 H 1 2 3 4 5 6 7
WI WSA14 Probability 0.5 1.0 1.5 2.0 H 1 2 3 4 5 6 7
WI WSA16 Probability 0.5 1.0 1.5 2.0 1 2 3 4 5 6 7
The Wisconsin plans are clearly an outlier for the average log likelihood over shifts 45%-55% Measuring Representativeness
NC2012 NC2016 Judges Fraction w/ worse index 0.2 0.4 0.6 0.8 1.0 Efficiency gap (2012 votes) 0.1 0.2 0.3 0.4 0.5
NC2012 NC2016 Judges Fraction w/ worse index 0.2 0.4 0.6 0.8 1.0 Efficiency gap (2016 votes) 0.1 0.2 0.3 0.4 0.5 0.6 0.7
Efficiency Gap
Situate EG inside the ensemble
results should be stable under small changes to districts sample near by districts and observe changes
NC 2012 NC 2016 Judge’s districts resemble near by districts NC 2012 and NC 2016 do not
Judges NC2016 NC2012
Gerrymandering index (2012 votes) Fraction w/ worse index
0.2 0.4 0.6 0.8 1.0 0.20 0.22 0.24 0.26 0.28 0.12 0.13 0.14 0.15 0.16 0.04 0.05 0.06 0.07
Gerrymander Index
Local Perturbations
model a random distribution of political parties Assume the population is uniform Q: Find null distribution of order statistics for district make up
Q: Give some form of stability of plots
which have certain marginal statistics.
No change β=0.8 β=1.2 wI=2 wI=3 wm=700 wm=900 wp=2500 wp=3000 Democratic vote fraction 0.3 0.4 0.5 0.6 0.7 0.8 Most Republican To Most Democratic Districs (2012 votes) 1 2 3 4 5 6 7 8 9 10 11 12 13
Q: Characterize the structure of the energy landscape
Even with just population and compactness
Accelerate the sampling
Christy Graves Sachet Bangia Sophie Guo Bridget Dou
Robert Ravier Justin Luo Hansung Kang Greg Herschlag
Michael Bell
arXiv:1709.01596 arXiv:1704.03360
Jonathan Mattingly
arXiv:1410.8796
Identify the background NULL hypothesis Identify the background geopolitical structure of state. Identify the outliers, the unreasonable maps. Benchmark different proposed metrics Interaction of geopolitical structure and metrics Considered other states and effect of VRA Lots of interesting math questions