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 a 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. In addition, our model provides more accurate out-of-sample predictions than standard neighborhood measures.
翻译:格细的地理数据为我们了解社区如何形成以及如何影响政治提供了新机遇。同时,社区的固有主观性在测量和建模社区时带来了方法学上的挑战。我们开发了一种调查工具,允许受访者在地图上绘制自己的社区。我们还提出了一个统计模型来分析受访者的特征和当地区域如何影响主观社区的形成。我们进行了两项调查:从迈阿密、纽约市和凤凰城的选民那里收集了主观社区数据,以及要求纽约市居民在其市议会选区内绘制感兴趣的社区。我们的分析表明,其他因素保持不变的情况下,白人受访者更有可能将有更多白人居民的人口普查区包含在其社区中。同样,民主党和共和党更有可能包含同党派的区域。此外,我们的模型提供了比标准社区度量更准确的样本外预测。