Climate Cabinet Campaign Finance Tracker

Process

1. Collect: Gather key states' political campaign finance report data which should include recipient information, donor information, and transaction information.
2. Transform: Define database schema for storing transaction and entity information and write code to transform and validate raw data to fit appropriate schema.
3. Clean: Perform record linkage and fix likely data entry errors.
4. Classify: Label all entities as fossil fuel, clean energy, or other
5. Graph: Construct a network graph of campaign finance contributions
6. Analyze: Perform analysis on network data and join with other relevant dataset

Setup

Data Collection and Standardization Pipeline

1. Collect the data through one of the steps below a. Collect state's finance campaign data either from web scraping (AZ, MI, PA) or direct download (MN) OR b. Go to the Project's Google Drive to download each state's data to their local repo following this format: repo_root / "data" / "raw" / state acronym / "file"
2. Run pip install -r requirements.txt and pip install -e . if not in Docker (not recommended for development)

Usage

The main components of the package are broken up into subpackages which can be imported and used in external code. To run pipelines directly you can use the scripts in the scripts directory. These scripts have been dockerized already and can be run simply using make commands.

• make run-transform-pipeline: This runs the pipeline to transform raw data from each state into the appropriate schema.
  • Expects there to be a folder for each state in a data/raw folder. Follow setup instructions to get data.
• make run-clean-classify-graph-pipeline: This runs the pipeline to clean, classify, and graph data that is already in the correct schema.
  • Expects there to be an inds_mini.csv, orgs_mini.csv, and trans_mini.csv in a data/transformed directory (should be in git by default)

For developing, please use either a Docker dev container or slurm computer cluster. See more details in CONTRIBUTING.md

Repository Structure

utils

Project python code

notebooks

Contains short, clean notebooks to demonstrate analysis.

data

Contains details of acquiring all raw data used in repository. If data is small (<50MB) then it is okay to save it to the repo, making sure to clearly document how to the data is obtained.

If the data is larger than 50MB than you should not add it to the repo and instead document how to get the data in the README.md file in the data directory.

This README.md file should be kept up to date.

output

This folder is empty by default. The final outputs of make commands will be placed here by default.

Team Member

Student Name: Nicolas Posner Student Email: nrposner@uchicago.edu

Student Name: Alan Kagiri Student Email: alankagiri@uchicago.edu.

Student Name: Adil Kassim Student Email: adilk@uchicago.edu

Student Name: Nayna Pashilkar Student Email: npashilkar@uchicago.edu

Student Name: Yangge Xu Student Email: yanggexu@uchicago.edu

Student Name: Bhavya Pandey
Student Email: bhavyapandey@uchicago.edu

Student Name: Kaya Lee Student Email: klee2024@uchicago.edu