Identifying Communities of Interest Using Contact Graphs

Abstract

A community of interest is generally defined as a territorially contiguous community of people with distinct, shared interests worthy of representation in a legislature. Preserving communities within voting districts gives communities the power to form voting coalitions, self-determine needs, and effectively organize and engage with their political representative. However, map drawers, community advocates and NGOs, and researchers face extensive problems identifying these communities, hindering community representation and academic research. I introduce a systematic method for identifying communities of interest using contact graphs and community detection algorithms and evaluate this method using three datasets: (1) 2020 Census population counts, (2) Census population estimates from the 2016-2020 5-year American Community Survey, and (3) cellular device geolocation data from four separate months across nearly 2 years. I explore and discuss validation, calibration, and use cases for these graphical communities before discussing policy implications, recommendations for use, and avenues for future research. i Contents List of Figures v List of Tables ixFinally, I want to thank my family and friends for all of their love and support. To my parents, Jonathan and Deborah, thank you for always for pushing me, for supporting me, and of course, for raising me. I learned-and continue to learn-so much from you both. Hannah and Sydney and Arthur and Kyra, thank you for always being there for me, letting me be myself, pushing me through this, and keeping me in check. To my grandfather, grandmother, and Cathy, I wish you could have been here to see this finished after having gotten me through the process. Matt, Jacob, Victoria, thank you for keeping my head in the game and always being dependable, supportive, and there (Benny, thanks for keeping your parents in check). Shane and Jeremy-thanks for letting me abscond from fantasy football to finish this thing and for keeping me sane mid-pandemic. Max, Edwin, Glen, and Marcelo, thank you for never letting me forget that a drum machine is cheaper and easier to deal with than a drummer. PhuongGiang, I would not be here if it weren't for you putting up with me and the late-night work marathons and stress. You helped shepherd me through a pandemic and this little side quest. Thank you for boba, travel, tacos, bún, chile, dumplings, salsa, art, music, movies, conversation, and everything in between. xi Abbreviations