You Can Do It Too!

Learn how to replicate our fair redistricting analysis in your own community

Our methodology is open source and can be applied to any jurisdiction. Whether you're a data scientist, activist, or concerned citizen, you can use these tools to ensure fair representation in your community.

Open Source Project

All our code, methodology, and tools are freely available on GitHub. You can download, modify, and use them for your own redistricting analysis.

redistricting_analysis.py
# Fair Maps Redistricting Analysis
import gerrychain
import geopandas as gpd

# Load your jurisdiction's data
districts = gpd.read_file('your_county.geojson')

# Generate fair redistricting plans
plans = generate_ensemble(districts, 
                         num_plans=10000,
                         top_plans=25)

# Analyze and rank plans
results = analyze_fairness(plans)
                            

How to Replicate Our Analysis

1

Get Your Data

Download census blocks data from the Census Bureau or from our GitHub repository.

What You Need:

  • Census block boundaries (GeoJSON format, available on our GitHub)
  • Population data by block (available on our GitHub)
  • Demographic data (race, ethnicity) (available on our GitHub)
  • The code to run the analysis (available on our GitHub)
2

Set Up Your Environment

Install the required Python packages and set up your analysis environment.

Required Packages:

  • gerrychain (for redistricting algorithms)
  • geopandas (for geographic data)
  • pandas & numpy (for data analysis)
  • shapely (for geometric operations)
pip install gerrychain geopandas pandas numpy shapely
3

Configure Your Analysis

Set the parameters for your specific jurisdiction and analysis goals.

Key Parameters:

  • Number of districts
  • Population tolerance (we use 1%)
  • Ensemble size (we use 10,000)
  • Top plans to select (we use 25)
  • Fairness metrics weights (we have a default set of weights)
4

Run the Analysis

Execute the Markov Chain Monte Carlo algorithm to generate thousands of possible redistricting plans.

What Happens:

  • Computer generates 10,000+ possible maps
  • Each plan is scored for fairness
  • Plans are ranked by overall score
  • Top 25 plans are selected
5

Analyze Results

Review the top plans and their fairness metrics to understand the range of possible fair outcomes.

Key Metrics:

  • Polsby-Popper and Reockcompactness
  • Demographic segregation indices
  • Note that our code directly provides an overall fairness score for you
6

Present to Your Community

Share your findings with local officials, community groups, and the public to advocate for fair redistricting.

Next Steps:

  • Create visualizations of top plans
  • Write a report explaining findings
  • Present to local government
  • Engage with community organizations

Technical Requirements

Computing Power

Generating 10,000 redistricting plans requires significant computational resources. We recommend:

  • Multi-core processor (8+ cores ideal)
  • 16GB+ RAM
  • SSD storage for faster I/O
  • Cloud computing options available
  • Any computer with a modern processor and 8GB+ of RAM will be sufficient

Data Sources

You'll need to obtain and prepare several types of data:

  • Census Bureau TIGER/Line files (available on our GitHub)
  • American Community Survey data (available on our GitHub)

Community Support

Technical analysis is just the beginning. You'll also need:

  • Local government contacts
  • Community organization partnerships
  • Public engagement strategy

Success Stories

Santa Clara County

Our flagship analysis generated 1,000+ plans and identified 25 optimal redistricting options for Silicon Valley's largest county.

Read the full analysis →

Academic Collaborations

Contact us for collaboration →

Community Groups

Local advocacy organizations are adapting our tools for their own redistricting campaigns.

Join our network →

Need Help Getting Started?

We're here to help you succeed! Whether you need technical support, guidance on data collection, or advice on community engagement, our team is ready to assist.

We are happy to help you get started and answer any questions you have.