Congressional Redistricting: Keeping Communities Together?

Rossiter, Kalyn M.; Wong, David W. S.; Delamater, Paul L.

doi:10.1080/00330124.2018.1443477

Cited by 8 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As work continues quantifying the degree to which communities are divided by district lines (e.g., Rossiter, Wong, and Delamater 2018), we offer the unnecessary overlapping of congressional and state House districts as a gerrymandering measure that is intuitive, easily accessed, widely applicable, durable, and complementary to well‐established measures of gerrymandering‐like compactness but with a distinct capacity to indicate the imposition of needless complication.…”

Section: Discussionmentioning

confidence: 99%

The boundaries of confusion: Gerrymandering and racial disparities in state House and congressional district line congruity

Niven

Combs

Norwood

et al. 2022

Social Science Quarterly

View full text Add to dashboard Cite

Objectives: We introduce a new, stable, and reliable measure of gerrymandering based on the number of unnecessary overlapping state House districts within U.S. House districts. We then test for the presence of racial disparities by considering the relative number of unnecessary overlapping districts in majority-white and majority-minority areas. Methods: We utilize a data set encompassing maps in place in the 2012-2022 period from all 35 states with at least four congressional districts. Collectively this includes 386 U.S. House districts and more than 3600 state House districts. Results: We find widespread unnecessary district overlaps. In 34 of the 35 states examined, congressional districts overlap with more state House districts than is mathematically required. Moreover, unnecessary district overlaps are not imposed uniformly. Regression results suggest that districts with the largest white population experience roughly half the unnecessary overlaps imposed on districts with the smallest white population. Conclusion:We argue the implications of this racial disparity are significant given that previous research finds that confusing district boundaries have a deleterious effect on the basic pillars of representation. This effort represents a new and straightforward gerrymandering metric with implications for the study of representation and the pursuit of fair districts in the courts.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

show abstract

Section: Discussionmentioning

confidence: 99%

The boundaries of confusion: Gerrymandering and racial disparities in state House and congressional district line congruity

Niven

Combs

Norwood

et al. 2022

Social Science Quarterly

View full text Add to dashboard Cite

show abstract

“…Other quantitative methods for identifying communities of interest have been proposed in the literature. Most notably, Rossiter et al [91] generated communities of interest using Thessian polygons and, somewhat similarly to the approach discussed herein, geospatial clustering of block groups based on the minimum spanning tree method of R. M. AssunC ¸ão et al [11]. However, the approach discussed below takes a more graph-centric approach, allowing for characterizations of interest using novel data and scalability on tessellations, while also clustering based on graphical community structure.…”

Section: A Practical Motivation For Systematic Evidence Of Coismentioning

confidence: 99%

Identifying Communities of Interest Using Contact Graphs

2024

View full text Add to dashboard Cite

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

show abstract

A multistart and recombination algorithm for finding many unique solutions to spatial aggregation problems

Xiao,

Kim,

Lin

2024

Geoinformatica

View full text Add to dashboard Cite

Spatial aggregation is essential for applications where data at low level spatial units such as census blocks are grouped into larger regions. This type of problem can be formulated as spatial optimization problems where the goal is to minimize the difference between the grouped regions. These problems are difficult to solve because of their computational intensity. In addition, these problems often have multiple, instead of singular, optimal solutions that have the same or similar objective function values but exhibit different spatial configurations. Existing solution methods often aim to find single solutions to these problems. In this paper, we discuss a new heuristic method that can be used to find a set of unique optimal or near-optimal solutions to spatial aggregation problems. The algorithm consists of two phases. A multistart phase first generates a pool of random solutions to a problem. The size of the pool is specified by the user and contains the number of solutions desired to be found. Each random solution is then improved using an efficient algorithm called give-and-take. The second phase uses a recombination algorithm to create new solutions based on solutions randomly selected from the pool. The worst solution in the pool will be replaced by the new solution if the latter is better and does not exist in the pool. We test this multistart and recombination algorithm (MSRA) using a variety of problems with different sizes and the results suggest the effectiveness of the algorithm in finding multiple unique optimal or near-optimal solutions.

show abstract

Congressional Redistricting: Keeping Communities Together?

Cited by 8 publications

References 18 publications

The boundaries of confusion: Gerrymandering and racial disparities in state House and congressional district line congruity

The boundaries of confusion: Gerrymandering and racial disparities in state House and congressional district line congruity

Identifying Communities of Interest Using Contact Graphs

A multistart and recombination algorithm for finding many unique solutions to spatial aggregation problems

Contact Info

Product

Resources

About