# Clustering and Expected Seat-Share for District Maps

## TL;DR

This paper introduces a framework to measure party clustering in district maps and analyzes how such clustering influences expected election results, with a focus on Pennsylvania's political geography.

## Contribution

It develops a new measurement of party clustering and applies it to assess its impact on election outcomes in redistricting.

## Key findings

- Clustering affects expected seat-share outcomes.
- The framework applies to real-world political geography.
- Results highlight the importance of geographic distribution in elections.

## Abstract

In the context of modern sampling methods for redistricting, we define a natural measurement of the clustering of a political party, and we study how clustering affects the expected election outcome. We first prove general results and then apply this framework to understand how the political geography in Pennsylvania affects the expected outcome of congressional elections.