During the 2022 redistricting process, New Yorkers submitted over 13K oral, electronic, and hard-copy testimony to the New York City Districting Commission (NYCDC), who made these files publicly available as PDFs and audio transcripts on its website. This corpus of public testimony serves as the raw data we use to answer the following research questions: Which areas of New York City had the highest levels of participation in the redistricting process? What does this distribution say about the concentration of political organizing power in New York City? (See map to learn more)
To answer these questions, we used Named Entity Recognition, a computational text analysis method in Python that identifies important keywords within a text corpus related to location, people, identities, laws, events, organizations, and other important entities within a text. We saved the results in tabular format (i.e., a spreadsheet) and followed a rigorous data cleaning process to save just the information related to location, events, laws, and identities (including nationality, language, religious affiliations, ethnicities, party affiliations, sexual orientation, and other personal identity characteristics). For each NYC neighborhood, we also added shape file data to then map the distribution of submitted testimony across space. Finally, we attached links to the original public testimony files. Follow the link to view and download the database here.
As public policy scholars, we believe making data easily accessible to the public is essential to better understanding power dynamics in a democracy. This becomes even more essential when a large amount of text data is stored in formats like PDF that make it hard for the public to draw broad conclusions without having to read through a huge corpus. We hope this database will be useful to scholars who study New York City politics, redistricting processes, and community engagement in political organizing as well as to New York City political organizers. This project will fill knowledge gaps by: 1) supporting new research on public testimony through the transformation of the testimonial text into tabular data; 2) making this data accessible while it is most timely; and 3) adding to knowledge of the concentration of political organizing power in New York City through geospatial visualization.
Our team
Rebecca Krisel: principal investigator and doctoral candidate in political science at the CUNY Graduate Center.
Akela Lacy: doctoral student in political science at the CUNY Graduate Center.
Lukas Louwagie: M.A. candidate in political science at the CUNY Graduate Center.
Mary Madsen: doctoral student in political science at the CUNY Graduate Center.
Nicholas Reyes: M.A. candidate in political science at the CUNY Graduate Center.
This project was made possible by the 2023 Data for Public Good Fellowship, which is generously supported by the GC Digital Initiatives and the Mina Rees Library.
