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<h1>Developing methodology and tools to analyze legislative redistricting.</h1>
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<div class="package-box" style="background: #9ACABA">
<a href="/fifty-states/">
<h2>50-State Redistricting Simulations</h2>
</a>
<div style="display:flex">
<img src="/assets/fifty_states.svg" height=180>
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<p>
Comprehensive project to simulate alternative congressional redistricting plans for all fifty states in the 2022 redistricting cycle.
</p>
<p>
<a href="/fifty-states/">
<b>Explore the project and data &raquo;</b>
</a>
</p>
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The Algorithm-Assisted Redistricting Methodology (ALARM) Project is a research group directed by Co-PIs [Kosuke Imai](https://imai.fas.harvard.edu/), [Christopher T. Kenny](https://christophertkenny.com/), [Cory McCartan](https://corymccartan.com/), and [Tyler Simko](https://tylersimko.com/).

ALARM conducts research on political geography and spatial inequality. We actively work in areas like legislative redistricting, segregation, Census data, and school rezoning. We also develop open-source software to enable this research, like the R package [redist](https://alarm-redist.org/redist/).

<div class="package-box">
<a href="https://alarm-redist.github.io/redist/">
<h2><code>redist</code>: Simulation Methods for Legislative Redistricting</h2>
</a>
<p>
Enables researchers to sample redistricting plans from a pre-specified target
distribution using state-of-the-art algorithms. Implements a wide variety
constraints in the redistricting process, such as geographic compactness and
population parity requirements. Tools for analysis such as computation of
various summary statistics and plotting functionality are also included.
</p>
<p>
<a href="https://alarm-redist.github.io/redist/">
Go to package home page &raquo;
</a>
</p>
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<div class="package-box" style="background: #8f9fbc">
<a href="/posts/2021-08-10-census-2020/">
<h2>2020 Redistricting Data Files</h2>
</a>
<p>
Precinct-level demographic and election data from the
<a href="https://www.census.gov/2020census" target="_blank">2020 decennial census</a>
and the
<a href="https://dataverse.harvard.edu/dataverse/electionscience">Voting and Election Science Team</a>
which have been tidied and joined together using 2020 precinct boundaries.
</p>
<p>
<a href="/posts/2021-08-10-census-2020/">
Access the data &raquo;
</a>
</p>
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