Electoral Redistricting with moment of Inertia and Diminishing Halves Models Andrew Spann, Dan Gulotta, Daniel Kane Presented July 9, 2008
Electoral Redistricting with Moment of Inertia and Diminishing Halves Models Andrew Spann, Dan Gulotta, Daniel Kane Presented July 9, 2008 1
Outline 1. Introduction 2. Motivation 3. Simplifying Assumptions 4. Moment of Inertia Method 5. Diminishing Halves Methods 6. Quantitative Compactness Analysis 7. Conclusion
Outline 1. Introduction 2. Motivation 3. Simplifying Assumptions 4. Moment of Inertia Method 5. Diminishing Halves Methods 6. Quantitative Compactness Analysis 7. Conclusion 2
Problem Statement: Congressional Apportionment We wish to draw congressional districts for a state Goal: Algorithm that avoids gerrymandering · Want to create“ simplest” shapes. · Definition of“ simple” left to problem solvers. e Only rule is that districts have equal population
Problem Statement: Congressional Apportionment • We wish to draw congressional districts for a state. • Goal: Algorithm that avoids Gerrymandering. • Want to create “simplest” shapes. • Definition of “simple” left to problem solvers. • Only rule is that districts have equal population. 3
Gerrymandering Examples Mohme Canton Lake Havasu City Phot Illinois Arizona Adapted from National Atlas of the United States
Gerrymandering Examples Adapted from National Atlas of the United States. 4
Motivation Many possible criteria suggested in literature Equality of district size e Compactness · Contiguity Similarity to existing borders Targeted homogeneity/heterogeneity Instead of specifying many properties, we wish to explicitly specify as few as possible. Additional properties become an emergent behavior 5
Motivation Many possible criteria suggested in literature. • Equality of district size • Compactness • Contiguity • Similarity to existing borders • Targeted homogeneity/heterogeneity Instead of specifying many properties, we wish to explicitly specify as few as possible. Additional properties become an emergent behavior. 5