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. 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. (Last Revised, October,
2021)