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：粒度地理数据为了解邻里如何形成及其对政治的影响提供了新机遇。与此同时，邻里固有的主观性在测量与建模过程中带来了方法论挑战。我们开发了一种调查工具，使受访者能够在地图上绘制自己的邻里范围；同时提出一个统计模型，用于分析受访者特征与当地环境如何共同决定主观邻里认知。我们开展了两项调查：分别收集迈阿密、纽约市和凤凰城选民的主观邻里信息，并邀请纽约市居民绘制其认为应纳入市议会选区的利益社区。分析表明，在其他因素不变的情况下，白人受访者更倾向于将白人居民比例更高的统计区块划入自己的邻里；同样，民主党人与共和党人也更可能纳入本党支持者集中的区域。此外，我们的模型在样本外预测准确性上优于标准邻里测量方法。