Aggregating community maps

Chambers, Erin Wolf; Duchin, Moon; Edmonds, Ranthony A. C.; Edwards, Parker B.; Matthews, J N; Pizzimenti, Anthony E.; Richardson, Chanel; Rule, Parker; Stern, Ari

doi:10.1145/3557915.3560961

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…Preservation of these communities within single districts may reduce inequities or imbalances in the redistricting process (Barabas and Jerit 2004). Some states and cities have collected citizen-drawn maps of COI, and recent work has collected these data and introduced methods for classifying them (Chambers et al 2022). Our methodology can be applied to understand COI, quantifying which factors influence residents to define their political communities and demonstrating whether existing districts reflect these communities.…”

Section: Empirical Applicationsmentioning

confidence: 99%

“…Research on housing and NIMBYism requires accurate measurement of the areas where residents might be opposed to new development (Hankinson 2018). Lastly, one common requirement of legislative redistricting is to keep communities of interest (COI) intact when drawing district boundaries, but these communities are subjectively defined (Chambers et al 2022).…”

Section: Introductionmentioning

confidence: 99%

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Measuring and Modeling Neighborhoods

McCARTAN,

BROWN,

IMAI

2024

Am Polit Sci Rev

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Granular geographic data present new opportunities to understand how neighborhoods are formed, and how they influence politics. At the same time, the inherent subjectivity of neighborhoods creates methodological challenges in measuring and modeling them. We develop an open-source survey instrument that allows respondents to draw their neighborhoods on a map. We also propose a statistical model to analyze how the characteristics of respondents and local areas determine subjective neighborhoods. We conduct two surveys: collecting subjective neighborhoods from voters in Miami, New York City, and Phoenix, and asking New York City residents to draw a community of interest for inclusion in their city council district. Our analysis shows that, holding other factors constant, white respondents include census blocks with more white residents in their neighborhoods. Similarly, Democrats and Republicans are more likely to include co-partisan areas. Furthermore, our model provides more accurate out-of-sample predictions than standard neighborhood measures.

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Section: Empirical Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%