redistricting: when participative geography meets politics

Redistricting: When Participative Geography meets Politics

Micah Altman <[email protected]> Director of Research,MIT Libraries

Non-Resident Senior Fellow, Brookings Institution

Prepared for Spatial Analysis Seminar

Institutue for Social Research, University of MichiganJune 2012

micah

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This Talk

Why is redistricting a difficult problem? How is technology changing redistricting?

Application: DistrictBuilder & PublicMapping What kind of inferences can we draw from

maps? Application: BARD


Collaborators*


Michael P. McDonaldAssociate ProfessorDepartment of Public and International AffairsGeorge Mason UniversityWeb: http://elections.gmu.edu[ A Principle Investigator on the Public Mapping project, regular co-author since 1999]

Karin Mac DonaldDirector Statewide DatabaseU.C. Berkeley[ Co-author on studies of computer use in redistricting1980-2000 ]

Research Support

Thanks to the Sloan Foundation, the Joyce Foundation, Amazon, Inc.

* And co-conspirators

Related Work


Reprints available from: micahaltman.com

Micah Altman, 1997. "Is Automation the Answer? The Computational Complexity of Automated Redistricting", Rutgers Computer and Technology Law Journal 23 (1), 81-142 Altman, M. (1998b). Districting principles and democratic representation.

California Institute of Technology. Ph.D. Thesis., Altman, Micah (1999). "Modeling the Effect of Mandatory District Compactness on Partisan Gerrymanders", Political Geography 17 (8): 989-1012.

Altman , Micah . 1998. "Traditional districting principles - Judicial myths vs. reality Social Science History22 (2): 159-200

Altman, Micah 1998. "Modeling the Effect of Mandatory District Compactness on Partisan Gerrymanders", Political Geography 17 (8): 989-1012.

Altman, Micah, 2002. "A Bayesian Approach to Detecting Electoral Manipulation" Political Geography 22(1):39-48

Micah Altman, Karin Mac Donald, and Michael P. McDonald, 2005. "From Crayons to Computers: The Evolution of Computer Use in Redistricting" Social Science Computer Review 23(3).

Micah Altman, Karin Mac Donald, and Michael P. McDonald, 2005. "Pushbutton Gerrymanders", in Party Lines: Competition, Partisanship, and Congressional Redistricting Thomas E. Mann and Bruce E. Cain (eds), Brookings Press.

McDonald, M.P. (2007) “Regulating Redistricting”, PS: Political Science & Politics, 40 : pp 675-679

Levitt, J. & M.P. McDonald (2007), ‘Taking the “Re” out of Redistricting’, Georgetown Law Journal 95(4) 1247-1285.

Altman, M. and M.P. McDonald. (2010) “The Promises and Perils of Computer Use in Redistricting”, Duke Constitutional Law and Policy Journal.

Altman, M. and M.P. McDonald. (2011). "BARD: Better Automated Redistricting." Journal of Statistical Software..

Altman, M., & McDonald, M. P. (2012). Technology for Public Participation in Redistricting. In G. Moncrief (Ed.), Redistricting and Reapportionment in the West . Lexington Books

Altman, M., and M.P. McDonald (Forthcoming), Redistricting Principles for the 21rst Century, Case-Western Law Review.

Warning: this presentation is for educational purposes only and may contain oversimplifications, errors, and/or preliminary conclusions. Caveat Lector.

For citation and reference please use the related published work below.

Why is redistricting a difficult problem?


Definitions?Redistricting. The aim of redistricting is to assign voters to equipopulous geographical districts from which they will elect representatives, in order to reflect communities of interest and to improve representation.

Gerrymandering. Gerrymandering is a form of political boundary delimitation, or redistricting, in which the boundaries are selected to produce an outcome that is improperly favorable to some group. The name “gerrymander” was first used by the Boston Gazette in 1812 to describe the shape of Massachusetts Governor Elbridge Ger- ry’s redistricting plan, in which one district was said to have resembled a salamander.


Redistricting Often Fails to Capture the Public Imagination


Simple solution – Version 1“First principles”


Choose the redistricting plan that provides the “best” representation for the state.

Choose district plan X > plan Y, iff. Representativeness(X)> Representativeness(Y)

Problem with Version 1… There is a story about a very senior political scientist and a world- renowned scholar in the field of representation who traveled to Russia shortly after the fall of communism to lecture to the newly formed Duma.

After speaking, a newly-minted member of the Duma approached him and asked him a question with great earnestness.

“I have been elected as a representative,” the Duma member asked, “so when I vote, should I vote the way I think the electors want me to, or should I vote the way I think is right?”

“That’s a good question… Scholars have been studying this for two thousand years. And, let me just say, there are many opinions.”


Simple solution – Version 2 “Let’s Randomize”

Pure random redistricting equivalent to at-large elections[Grofman 1982]

Compact districts on randomly clustered population disadvantage parties with geographically clustered support [Altman 1999, Jerit and Barabas 2004; Rodden and Chen 2010]


In Vieth vs. Jubelirer 2004 , Justice Kennedy Agreed:

Second, even those criteria that might seem promising at the outset (e.g., contiguity and compactness) are not altogether sound as independent judicial standards for measuring a burden on representational rights. They cannot promise political neutrality when used as the basis for relief. Instead, it seems, a decision under these standards would unavoidably have significant political effect, whether intended or not. For example, if we were to demand that congressional districts take a particular shape, we could not assure the parties that this criterion, neutral enough on its face, would not in fact benefit one political party over another.

Simple Solution Version 3“Neutral Criteria”

Eliminating judgment leads to calcification:

Electoral District-based systems are unique in incorporating expert judgment into this process converting voter preferences to candidate selection

Weak empirical links between process and outcomes Little empirical support for

restrictions other than population Population restriction, etc. has not

prevented gerrymanders Unintended consequences

Baker & Karcher lead to widescale abandonment of other traditional principles (Altman 1998a)

Intended (second order) consequences Choice of combination of neutral

rules to disadvantage minorities (Parker 1990

Compactness rules have partisan consequences (Altman 1999; Barabas 2005; Rodden & Chen 2010)Redistricting: When Participative

Geography meets Politics

(Parker 1990)

Solution Version 4“Consensus Criteria”


Scholarly criteria

Neutrality’ (unbiasedness) [Niemi & Deegan 1978] symmetry of seats-votes curve‘Range of responsiveness’ [Niemi & Deegan 1978]range of vote shares across which electoral results changeConstant Swing [Niemi & Deegan 1978]increase of seat share is constant in increase in seat share‘Competitiveness’ [Niemi & Deegan 1978]maximize number of districts with competitive marginsCompactness – perception of district appearance [see Altman 1998b]Minimize voting for a loser (anticompetitiveness) [Brunell 2008]‘Cognizability’ [Grofman 1985]‘Communities of Interest’ [See Forrest 2004]Clustering [Fryer & Holden 2007]Conformance with natural/administrative boundariesMedia market preservationModerate majoritarianism Continuity of representative relationship (incumbency protection) [ see Persily 2003]Graphical symmetry around expected partisan vote share [Kousser 1996]

U.S. State Criteria

Coincidence with “major roads, streams, or other natural boundaries”.Coincidence with census tract boundaries.Being “square, rectangular or hexagonal in shape to the extent permitted by natural or political boundaries.”Being “easily identifiable and understandable by voters”.Facilitating “communication between a representative and his constituents”. Preserving “media markets”.Enhancing “opportunity for voters to know their representative and the other voters he represents.”Aligning with “prior legislative boundaries”.Consistency with “political subdivisions”.Utilizing “vernacularly insular regions so as to allow for the representation of common interest”.

Problems with Version 4 - Satisfiability Logically exclusive:

Competitiveness and anticompetitiveness

Mathematically bounded: Can’t maximize competitiveness & guarantee

constant swing [Niemi & Deegan 1978] Can’t maximize competitiveness & symmetry

[Niemi & Deegan 1978]

Empirically bounded: Compactness, communities of interest,

competitiveness, symmetry, etc.


Solution Version 5“Let a Computer Do it”

First suggested: 1961. [Vickrey]

Regularly proposed Advantages:

Could increase transparency

Could reveal range of alternatives not otherwise generated by political process


Results from “redistricter” software. [Olson 2008]

Problems with Solution 5


Too many solutions to enumerate:

Even redistricting using common criteria is NP-complete [Altman 1997]

Optimal partisan gerrymander and optimal unbiased districts also NP-complete [Puppe & Tasnadi 2008,2009]

Not mathematically possible to find optimal solutions to general redistricting criteria!

Are Redistricting Criteria more Transparent than Plans? Compactness

30+ different base measures to choose from, e.g.Then variations … Map orientation can change results for Length-Width/Bounding box measures Map measurement scale can matter for perimeter based measures Map projection can matter for any measure Treatment of water?

Ignore it Assign to closest land area Include it Transform it away

Treatment of bounding region? Ideal area – all area in bounding region Practical area – all area available for redistricting

Treatment of population Ignore it Drop zero population blocks Weight it Type of population: any, voting age, citizen, eligible voter Transform map

Even ‘contiguity’ involves many operational decisions: theoretical contiguity vs. census block contiguity vs. transportation feasibility single point vs. multi-point vs. line segment crossover permissibility nesting permissibility


C= .194C = .158

A: A Square is morecompact than a circle?

B: Rotating a district makes itless compact?

No unbiased algorithm


Dozens of ad-hoc heuristics for particular criteria Lots of general heuristics applied:

tabu-search, hill-climbing, evolutionary optimization, GRASP, TSP , recursive partitioning, k-means [See Altman 1997, de Cortona et al 1999, Duque 2007]

No heuristic does best on all problems – “no free lunch” [Wolpert & Macready 1997]

General heuristics still require adaptation/tuning to particular problem in practice

Thus potential for interaction between geography, criteria and algorithms (“third order” bias)

Not possible to completely disconnect algorithm, criteria, and political geography

Solution Version 5“Institutional Design”

Redistricting/boundary “commissions” appear to be reasonably well-insulated from partisan politics in most other developed democracies using geographical districts: United Kingdom, Canada, Australia, New Zealand [See Altman-McDonald 2012 for a summary]

Redistricting commissions in many states


The Problem with Institutions –Participation Theater?

Many U.S. independent non-partisan redistricting commissions aren’t Ex-ante bi-partisanship incumbent gerrymanders Ex-ante power to veto/modify by legislature Ex-post partisan litigation against commission?

Many public hearings on redistricting are theater Do not change the plans actually adopted Do not produce evidence that can be used by the

courts later Much public information isn’t

Much electoral data is not shared Proposed plans not available for analysis Closed meetings and back-room deals are common


How is technology changing redistricting?


Media Coverage is Oversimplified

“In summary, elimination of gerrymandering would seem to require the establishment of an automatic and impersonal procedure for carrying out a redistricting. It appears to be not at all difficult to devise rules for doing this which will produce results not markedly inferior to those which would be arrived at by a genuinely disinterested commission.” -- [Vickrey 1961]

“The purpose of this Article is … to describe a simple and politically feasible computer program which can reapportion a legislature or other body of people who represent geographical districts. …The redistricting program proposed is designed to implement the value judgments of those responsible for reapportionment”– [Nagel 1965]

“There is only one way to do reapportionment — feed into the computer all the factors except political registration.” - Ronald Reagan [Goff 1973]

“The rapid advances in computer technology and education during the last two decades make it relatively simple to draw contiguous districts of equal population [and] at the same time to further whatever secondary goals the State has.” - Justice Brennan, in Karcher v. Daggett (1983)

“Let a computer do it”-Washington Post, 2003 ( And many, many blogs)

“Until recently only political parties had the manpower and the tools to redraw boundaries while keeping districts equal in population. Now anybody can play this game, at least as a kibitzer. For as little as $3,500 the geographic analysis firm Caliper Corp. will let you have the software and census data you need to try out novel geometries on a PC screen. Harvard researcher Micah Altman and others have put together a program that draws compact districts. His software is free.

Democratic redistricting could work like this. After a census, a commission in each state entertains proposals from the political parties and any do-gooder group or individual willing to compete. The commission picks the most compact solution, according to some simple criterion. (Say, add up the miles of boundary lines, giving any segments that track municipal borders a 50% discount, and go for the shortest total.) The mathematical challenge might inspire some gifted amateurs to weigh in.” – William Baldwin, Forbes 2008

How can computers improve redistricting?

When computers became ubiquitous

Computing systems used in only a handful of states in the 1980’s

In 1990 redistrictings computer use was nearly universal

In 2000, computer use was universal, data use had increased


What could redistricting system do in the last round?All systems used 2000 Congressional

redistricting could perform: Thematic mapping

Most could perform: Tabulations

demographic/voting variables Geographic reports

(compactness, holes, plan comparisons, contiguity)

Only one could perform practical automated redistricting.

With this exception, the available software tools were qualitatively the same, although much cheaper and faster, as in the previous 1990’s round of redistricting


Software Package

% of States

Thematic Mapping

Numeric Tabulation

& Geographic Reporting

Automated Redistricting

Autobound (Digital Engineering Corp.)

45% Yes. Yes Yes. .

Maptitude (Caliper Corp.)

14% Yes. No. No.

Maptitude for Redistricting (Caliper Corp.)

12% Yes. Yes No.

Plan 2000 (Public Systems Associates)

5% Yes. Yes No.

Custom systems

14% Yes. Yes. No. (Except Texas)

GIS – Ubiquitous

Goal: Aid in the efficient creation of maps associated with data.

First invented: 1962. [Tomlinson] Maturity: The 2000 round of redistricting.

[Altman, Mac Donald, McDonald 2005]


Redistricting: When Participative Geography

meets Politics

Computer use and compactness, compared

Measured Plan Compactness ‘Reock’ and Perimeter-Area

Compactness scores congressional districting

plans (>3 districts) No significant difference by

software capability or voting data use

Districts using block data are slightly more compact

Voting Data

PA

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0.11

0 1

USE_VOTING_DATA

Automated Redistricting

PA

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0.11

0 1

soft_automated_redis Block Data

PA

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0.11

0 1

USE_BLOCK_DATA

Tabulations and Geographical Reports

PA

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0.11

0 1

soft_tabulation

Expert Support Systems Unjustly Feared

FearsMappers were able to specify a desired outcome or outcomes — the number of people in a district, say, or the percentage of Democrats in it — and have the program design a potential new district instantly. These systems allow redistricters to create hundreds of rough drafts easily and quickly, and to choose from among them maps that are both politically and aesthetically appealing. [Peck and Caitlin, 2003]

Evidence Widespread adoption of computers in the 1990’s post-dates

precipitous changes in district shape and composition Redistricting software prices dropped in 2000, but features remained

essentially the same. Competitiveness declined in 2000, after computers and election

data already ubiquitous. No statistical correlation between computer use/data and bad

outcomes


Optimal Automated Redistricting – State of the Art Enumeration

Explicit enumeration intractable even for small #’s of units

Early work with implicit enumeration (branch and cut) yielded solutions for 30-50 units [E.g. Mehohtra, et. al 1998]

Practice: Integer Programming Similar, but not equivalent, school districting problem

solved for < 500 units.[Caro et. al 2004]

Related multi-site land-use allocation problem solved for 900 units [Aerts, et al 2003]

Integer programming applied to < 400 units, but used early termination, rendering solution non-exact. [Shirabe 2009]


Heuristic Automated RedistrictingGeneral Limitations

Limited to population, compactness, contiguity Ad-hoc definition of compactness Often implicitly include a specific geographic model for districts

Recent Work “Redistricter” [Olson 2008]

Not peer reviewed, but open source uses kmeans with ad-hoc refinements (including annealing) to solve Using 500000 census blocks can find solutions within 1% of population

Weighted Voronoi Diagrams [Ricca, et. al 2008] Applied to up to <1300 population units Yielded large population variances

Q State Pott’s Model [Chou and Li 2007] Applied to <450 population units

Shortest Split-line [Kai et al 2007] Population variance of 5% Appears to require continuous data, results of discretizing solution not clear

Ad Hoc Greedy Heuristics [Sakguchi and Wado 2008] <1000 units Yielded large population variance


Metaheuristics Approaches Genetic Algorithms [Xiao 2003]

<500 Units (?) Population variance< 1%

Genetic Algorithm w/TSP Encoding [Forman and Yu 2003] <400 Units Some post-processing Population variance< 1%

Annealing [Andrade & Garcia 2009] <400 Units

Tabu Seach [Bozkaya et. al 2003] <850 units Population variance <25%

General Metaheuristics [Altman & McDonald 2010] Framework for multiple metaheuristics & criteria Preliminary results on <1000 units


State of the Practice


No unbiased algorithm


Dozens of ad-hoc heuristics for particular criteria Lots of general heuristics applied:

tabu-search, hill-climbing, evolutionary optimization, GRASP, TSP , recursive partitioning, k-means [See Altman 1997, de Cortona et al 1999, Duque 2007]

No heuristic does best on all problems – “no free lunch” [Wolpert & Macready 1997]

General heuristics still require adaptation/tuning to particular problem in practice

Thus potential for interaction between geography, criteria and algorithms (“third order” bias)

Not possible to completely disconnect algorithm, criteria, and political geography

Application: DistrictBuilder & PublicMapping


The Next Wave – Open Access Redistricting

In last round of redistricting much more data was available publicly, but public participation lagged.

Generally, only well-organized political interests – political parties, incumbents, and minority voting rights groups – have had the capacity to draw redistricting plans.

This can change… “We have the technology.”

Ohio 2009 Competition

Demonstration project by Ohio Secretary of State

Based on 2000 round data

Complex user interface.

Spearheaded by Mark Salling, for state

100+ participants, 11 final plans

Major Components

Identify barriers and principles Software Data Education Dissemination

Challenges to Transparency

Participation matters Systems that help people identify their neighborhoods Systems that can create plans meeting all measurable legal

criteria Systems that people can use

Code matters Open Source for verification, replication, and correction Documented algorithms for measuring and adjusting plans

Data matters Open data Complete information Accessible formats Known provenance

Online systems do not guarantee transparency Are algorithms, code and data used open and transparent? Is sponsorship of the system transparent? Can data and plans be transferred in and out of the system freely

Principles for Transparency

All redistricting plans should include sufficient information such that the public can verify, reproduce, and evaluate a plan Proposed redistricting plans should be publicly available in non-proprietary formats. Public redistricting services should provide the public with the ability to make

available all published redistricting plans and community boundaries in non-proprietary formats.

Public redistricting services must provide documentation of any organizations providing significant contributions to their operation.

All demographic, electoral and geographic data necessary to create legal redistricting plans and define community boundaries should be publicly available, under a license allowing reuse of these data for non-commercial purposes.

The criteria used to evaluate plans and districts should be documented. Software used to automatically create or improve redistricting plans should be either open-source or

provide documentation sufficient for the public to replicate the results using independent software. Software used to generate reports that analyze redistricting plans should be accompanied by

documentation of data, methods, and procedures sufficient for the reports to be verified by the public. Software necessary to replicate the creation or analysis of redistricting plans and community

boundaries produced by the service must be publicly available.

Endorsements for Principles of Transparency

• Americans for Redistricting Reform• Brennan Center for Justice• Campaign Legal Center• Center for Governmental Studies• Center for Voting and Democracy• Common Cause• Demos• League of Women Voters of the United States.

Project Advisory Board

- Nancy Bekavac, Director, Scientists and Engineers for America- Derek Cressman, Western Regional Director of State Operations, Common Cause- Anthony Fairfax, President, Census Channel- Representative Mike Fortner (R), Illinois General Assembly- Karin Mac Donald, Director, Statewide Database, U. C. Berkeley- Thomas E. Mann*, Brookings Institution- Norman J. Ornstein*, American Enterprise Institute.- Leah Rush, Executive Director, Midwest Democracy Network- Mary Wilson, President, League of Women Voters

* Co-Principle Investigators and Editors

Bi-Partisan Endorsements for Principles

Public Mapping Software – Features

Create Create districts and plans Identify communities*

Evaluate Visualize Summarize

Population balance Geographic compactness Completeness and contiguity

Report in depth Share

Import & export plans Publish a plan Run a contest

Powered by Open Source

* Coming soon Redistricting: When Participative


The Public Mapping Project

Michael McDonaldGeorge Mason UniversityBrookings Institution

Michael McDonaldGeorge Mason UniversityBrookings Institution

Micah AltmanHarvard UniversityBrookings Institution

Micah AltmanHarvard UniversityBrookings Institution

Robert CheethamAzaveaRobert CheethamAzavea

Supported byThe Sloan FoundationJoyce FoundationAmazon CorporationJudy Ford Wason Center at Christopher

Newport Univ.

Supported byThe Sloan FoundationJoyce FoundationAmazon CorporationJudy Ford Wason Center at Christopher

Newport Univ.

The DistrictBuilder software was developed by the Public Mapping Project with software engineering and implementation services provided by Azavea

The DistrictBuilder software was developed by the Public Mapping Project with software engineering and implementation services provided by Azavea


Mike Fortner Illinois state Representative, 95th District

Carling Dinkler John Tanner's office, Tennessee 8th Congressional District

Mary Wilson Past President, League of Women Voters

Derek Cressman Western Regional Director of State Operations, Common Cause

Anthony Fairfax President, Census Channel

Kimball Brace President, Election Data Services

Gerry HebertExecutive Director, Campaign Legal Center and Americans for Redistricting Reform

Leah Rush Executive Director, Midwest Democracy Network

Nancy Bekavac Director, Scientists and Engineers for America

Karin Mac Donald

Director, Statewide Database, Institute for Government Studies, UC Berkeley

Thomas E. Mann Senior Fellow, The Brookings Institution

Norman J. Ornstein

Senior Fellow, The American Enterprise Institute

Advisory Board


Map a State -- Change the Nation

Identify communitiesExplore the alternativesUnderstand political consequences Establish transparencyCatalyze participationCreate alternatives to politics-as-usual


Launch Your Own Competitionwww.districtbuilder.org

More Information atwww.publicmapping.org

Now…Public Access Redistricting

Open Data Open Access Open SourceRedistricting: When Participative Geography meets Politics

Choose Your Legislature


Look Around – Get the Picture


Drill Down – Get The Facts


Make A Plan


Get the Details


Run The Numbers


Is it legal? How Well Are You Doing?


Spread the Word

Share your plans with othersHave a contestMash up and plug in


Early Results

More plans produced in the Virginia Competition than in any previous public effort

Suggests that public plans can be truly different

Illuminates tradeoffs among redistricting goals


Completed Redistricting Competitions


Eight competitions in different states

Hundreds of legal plans

Thousands of active participants

Millions of viewers

External Impact?


Politico “best policy innovation” of 2011

APSA ITP AwardDozens of local and national media articles

What didn’t we do?


SurveysRandomized interventions – group level

Pre-post evaluationRandomized intervention – individual level

Deploy community mapping effectively

Increasing Public Participation


Get the data

Evaluate maps

Draw the Lines

"For the first time in U.S. history, a court has allowed the public to submit their own redistricting maps for consideration."

Watch theNews

Where Do We Go From Here?


What kind of inferences can we draw from maps?


Redistricting as Graph Partitioning


Forecasting


Forecasting, Inference & Optimization in redistricting Forecasting election results does not require combinatoric

optimization, may be convincing Observational/correlational evidence may be compelling

when analyzing change of institutions Revealed preference sometimes revealing Statistical causal inference (e.g. that district plan intended

as a gerrymander) often rests on hidden & unreliable computational assumptions

Using WARP in Redistricting

1. Divide the relevant characteristics of a plan into three categories. Criteria that describe the feasible set of districts, K, such as the maximum population deviation

between districts, contiguity, etc. A characteristic, I, that best proxies the intent you wish to test. Characteristics, R, representing any other politically relevant criteria.

2. Enter the current plan, and any alternative plan that is part of the public record, into a GIS system, along with the data necessary to evaluate K, I, and R.

3. Use these plans as starting points for metaheuristic optimization (simulated annealing, genetic algorithms, or GRASP)

4. Use the optimization algorithm to search for a plan p* that such that:

If a feasible p* exists, then the motive proxied by I can not have been overriding (lexically preferred).

5. To further explore the trade-off among criteria. Use the optimization algorithm to search for a plan p** that such that:And then maximize I, subject to holding all but one (j) of the other relevant criteria constant

Use optimization algorithms to find alternatives to given plan Attempt to find alternatives that differ in one dimension Probe the trade-offs among maj-minority seats, partisan

seats, etc.


Sampling Problem - General


Sampling Bias in “Random Districting” Analytic Approach

5*

2

1 5*

4

3

3

1

5*

6

1

2

3

4

5*

2

4 5

3

2

1

6 5*

2

6 5

Event-trees showing the generation of the district plans in six block case. Each sub-tree is equally likely (P=1/6), and the probability of following any branch at each node is equal to 1/(number of branchs). Starred nodes indicate illegal plans, which cause the algorithm to restart with subtree selection

1 2 3

4 5 6

1 2 3

4 5 6

1 2 3

4 5 6

2-District Plans using 6 counties


Legal Standards for Proving Discriminatory Intent

“Ely”: no other rational basis can explain state action “Arlington Heights”: action would not have occurred

but for illicit motives “Feeney”: action was caused, in part, because of illicit

motives “Miller”: race is the predominant motive of

legislature


Intent: Definitional ProblemsCollective Intent Hypotheticals

Predominant Intent Hypotheticals

1. Four legislators split on plan for non-racial reasons. Fifth legislator has only racial motivations votes for legislation solely on racial grounds.

1. Legislature wants to create maj-min. districts, but would trade two maj-min. seats in to gain an additional democratic seat overall.

2. Like 1 above. But 5th legislator has lexicographic preferences, race is 10th in the list of her preferences, but plans are identical on first 9 criteria.

2. Control of legislature is threshold criteria (lexically preferred). Once this threshold is reached, legislature prefers to create majority minority seats.

3. Only one legislator in a (non-minimal) majority has any racial motive. That legislator’s sole motive is racial.

3. As above, but legislature will only values creating a maximum of K maj-min seats.

4. Legislature assigns simple linear weights of {3,3,4} to protecting (non-racial) communities of interests, protecting incumbents, and creating majority-minority seats

Collective Intent. collective choice

fundamentally differs from individual intent (Arrow)

Scalia:with respect to 99.99 percent of the issues of construction reaching the courts, there is no legislative intent, so that any clues provided by the legislative history are bound to be false.” – Antonin Scalia

Predominant Intent Must be defined precisely

before a mechanical test can be applied


Intent: Statistical ProblemsThe planner chooses some plan p* from the set of all possible plans conditioned on their intent y.

The scholar must supply plausible hypotheses about the value that y could take on. E.g.:

(0) intent only to create legal districts

(1) mixed -- intent to maximize probability of controlling the legislature, protect incumbents, protect communities, and create k maj-min seats

(2) predominant intent to create a racial gerrymander.

We observe characteristics of the plan C, and of elections EV(E)

When is it likely that the observed characteristics indicate a gerrymander? Using Bayes’ rule, this occurs iff. (Altman 2002):

1)2(2|)

~(,

})01{(}01{|)~

(,

)~

(,|2

)~

(,|}01{

yprobyEVprob

yprobyEVprob

EVyprob

EVyprob

EC

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EC

* If you prove the existence of a deity…Redistricting: When Participative Geography meets Politics

Current Statistical Methods for Assessing Intent Ignore Counterfactuals*

CDR Methodology: if “random” plans differ from actual plan racial motivation predominates

Typical Use of Bias/Responsiveness: if bias is high assume partisan motivation

Flaws: At most, we can reject ‘null’ hypothesis that

No basis for evaluating likelihood of competing hypotheses regarding intent

* Will the real null hypothesis please stand up…

)~

(,|0 EC EVyprob

Redistricting: When Participative Geography

meets Politics

Application: Better Automated ReDistricting (BARD)


BARD – Open Source for Experts

Create plans Fix/Refine plans

automatically Generate reports Compare plans Analyze

… Released in 2006

BARD


What is BARD A tool for modifying and analyzing redistricting

plans An open model for implementation of redistricting

criteria A framework for experimentation with redistricting An R module

What BARD is not A GIS system “the redistricting game” A comprehensive battery of tests A pushbutton gerrymander A solution to optimal redistricting

What can you do with BARD?

Analyze district plan characteristics Create districting plans Explore trade-offs among different

redistricting goals


Modular, Object-Oriented Configuration

Data Goals

Plan generation Randomly generate Use existing plan Create plan interactively

Refinement Refine starting plan to fit

goals Create sequence of plan with

reweighted pairs of goals Analysis

Analyze single plan Compare pairs of plans “Comparative statics” for

pair of goals


Plan Input and Manipulation

Load a plan from a GIS shape file

Generate plans kmeans greedy random assignment

Create or edit a plan interactively


Generating Plans


Plan Comparison Generate reports

contiguity holes compactness population equality competitiveness partisanship communities of

interest completely extensible


Plan Comparison (Visual)


Metaheuristic Refinement

Greedy Local Search:

Metaheuristics combine some form of greedy local search with a mechanism for making non-improving selections – needed to

escape local optima a mechanism for increasing search effort in a selected solution

space, based on history of plans evaluated As much “art” as science

Examples: Simulated annealing Tabu Search Genetic optimization GRASP Redistricting: When Participative


Arbitrary Plan Sampling


Some Informal Observations

Field experiments are hard … Kranzberg’s law

– technology is neither good, nor bad – neither is it neutral

Technology matters in politics Transparency, data and information technology are

interconnected Data transparency can enable participation

Transparency & Data Involves IP law Electronic Access / formats Timeliness Completeness


Additional References J. Aerts, C.J.H,. Erwin Eisinger,Gerard B.M. Heuvelink and Theodor J. Stewart, 2003. “Using Linear Integer Programming for Multi-Site

Land-Use Allocation”, Geographical Analysis 35(2) 148-69. M. Andrade and E. Garcia 2009, “Redistricting by Square Cells”, A. Hernández Aguirre et al. (Eds.): MICAI 2009, LNAI 5845, pp. 669–

679, 2009. J. Barabas & J. Jerit, 2004. "Redistricting Principles and Racial Representation," State and Politics Quarterly¸4 (4): 415-435. B. Bozkaya, E. Erkut and G. Laporte 2003, A Tabu Search Heuristic and Adaptive Memory Procedure for Political Districting. European

Journal of Operational Re- search 144(1) 12-26. F. Caro et al . , School redistricting: embedding GIS tools with integer programming Journal of the Operational Research Society

(2004) 55, 836–849 PG di Cortona, Manzi C, Pennisi A, Ricca F, Simeone B (1999). Evaluation and Optimization of Electoral Systems. SIAM Pres,

Philadelphia. J.C. Duque, 2007. "Supervised Regionalization Methods: A Survey" International Regional Science Review, Vol. 30, No. 3, 195-220 S Forman & Y. Yue 2003, Congressional Districting Using a TSP-Based Genetic Algorithm P. Kai, Tan Yue, Jiang Sheng, 2007, “The study of a new gerrymandering methodology”, Manuscript. http://arxiv.org/abs/0708.2266 J. Kalcsics, S. Nickel, M. Schroeder, 2009. A Geometric Approach to Territory Design and Districting, Fraunhofer Insititut techno und

Wirtshaftsmethematik. Dissertation. A. Mehrotra, E.L. Johnson, G.L. Nemhauser (1998), An optimization based heuristic for political districting, Management Science 44,

1100-1114. Grofman, B. 1982, "For single Member Districts Random is Not Equal", In Representation and Redistricting Issues, ed. B. Grofman, A.

Lijphart, R. McKay, H. Scarrow. Lexington, MA: Lexington Books. B. Olson, 2008 Redistricter. Software Package. URL: http://code.google.com/p/redistricter/ C. Puppe,, Attlia Tasnadi, 2009. "Optimal redistricting under geographical constraints: Why “pack and crack” does not work",

Economics Letter 105:93-96 C. Puppe,, Attlia Tasnadi, 2008. "A computational approach to unbiased districting", Mathematical and Computer Modeling 48(9-10),

November 2008, Pages 1455-1460 F. Ricca, A. Scozzari and B. Simeone, Weighted Voronoi Region Algorithms for Political Districting. Mathematical Computer Modelling

forthcoming (2008). F. Ricci, C, Bruno Simeone, 2008, "Local search algorithms for political districting", European Journal of Operational Research189,

Issue 3, 16 September 2008, Pages 1409-1426 T. Shirabe, 2009. District modeling with exact contiguity constraints, Environment and Planning B (35) 1-14 S. ,Toshihiro and Junichiro Wado. 2008, "Automating the Districting Process: An Experiment Using a Japanese Case Study" in Lisa

Handley and Bernard Grofman (ed.) Redistricting in Comparative Perspective, Oxford University Press D.H. Wolpert, Macready, W.G. (1997), "No Free Lunch Theorems for Optimization," IEEE Transactions on Evolutionary Computation 1,

67 N. Xiao, 2003. Geographical Optimization using Evolutionay Alogroithms, University of Iowa. Dissertation


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