With the availability of granular geographical data, social scientists are increasingly interested in examining how residential neighborhoods are formed and how they influence attitudes and behavior. To facilitate such studies, we develop an easy-to-use online survey instrument that allows respondents to draw their neighborhoods on a map. We then propose a statistical model to analyze how the characteristics of respondents, relevant local areas, and their interactions shape subjective neighborhoods. The model also generates out-of-sample predictions of one's neighborhood given these observed characteristics. We illustrate the proposed methodology by conducting a survey among registered voters in Miami, New York City, and Phoenix. We find that across these cities voters are more likely to include same-race and co-partisan census blocks in their neighborhoods. Net of other factors, White respondents are 6.1 to 16.9 percentage points more likely to include in their neighborhoods a census block composed entirely of White residents compared to one with no White residents. Similarly, Democratic and Republican respondents are 8.6 to 19.2 percentage points more likely to include an entirely co-partisan census block compared to one consisting entirely of out-partisans. Co-partisanship exhibits a similar, independent, influence. We also show that our model provides more accurate out-of-sample predictions than the standard distance-based measures of neighborhoods. Open-source software is available for implementing the proposed methodology.

翻译：由于有颗粒地理数据,社会科学家越来越有兴趣研究居住区是如何形成的,以及它们如何影响态度和行为。为了便利这些研究,我们开发了一个方便使用的在线调查工具,使答卷人能够将自己的社区绘制在地图上。然后我们提出一个统计模型,分析答卷人的特点、有关地方及其相互作用如何塑造主观社区。这个模型还得出了基于这些观察到的特征对邻居的无抽样预测。我们通过对迈阿密、纽约市和凤凰城的登记选民进行调查来说明拟议的方法。我们发现,在这些城市中,选民更有可能在他们的社区中包括同种和同党的人口普查区块。除其他因素外,白人答卷人中有6.1至16.9个百分点可能在其社区中包括完全由白人居民组成的人口普查区块,而没有白人居民的人口普查区块。同样,民主党和共和共和党的答卷人有8.2至19.2个百分点,更可能包括完全由外部党派组成的选民块块。我们发现,这些城市中的选民更可能在其社区中包括类似的、独立的和共同党派的人口普查区块。我们所展示了类似、独立的、独立的、有影响的区段影响。我们所选择的区距的模型提供了更准确的区域的模型。我们可以用来预测。我们还显示的区域的区距的模型提供了更精确的模型。