The spatial and social correlates of neighborhood morphology
Principal Investigator(s): View help for Principal Investigator(s) Noah Durst; Esther Sullivan; Warren Jochem
Version: View help for Version V1
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Project Citation:
Durst, Noah, Sullivan, Esther, and Jochem, Warren. The spatial and social correlates of neighborhood morphology. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-01-23. https://doi.org/10.3886/E197829V1
Project Description
Summary:
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Using building footprints in five of the ten largest
U.S. metropolitan areas (Atlanta, Boston, Chicago, Houston, and Los Angeles)
and the open-source R package, foot, we examine how neighborhood
morphology differs across U.S. metropolitan areas and across the urban-exurban
landscape. Principal components analysis,
unsupervised classification (K-means), and Ordinary Least Squares regression analysis
are used to develop a morphological typology of neighborhoods and to examine
its association with the spatial, socioeconomic, and demographic
characteristics of census tracts. Our findings illustrate substantial variation
in the morphology of neighborhoods, both across the five metropolitan areas as
well as between central cities, suburbs, and the urban fringe within each
metropolitan area. We identify five different types of neighborhoods indicative
of different stages of development and distributed unevenly across the urban
landscape: these include low-density neighborhoods on the urban fringe; mixed
use and high-density residential areas in central cities; and uniform
residential neighborhoods in suburban cities. Results from regression analysis
illustrate that the prevalence of each of these forms is closely associated
with variation in socioeconomic and demographic characteristics such as
population density, the prevalence of multifamily housing, and income,
race/ethnicity, homeownership, and commuting by car. The accompanying datasets include the block- and tract-level data used to conduct the analysis. R and Python scripts for calculating morphometrics, conducting unsupervised classification, and conducting the descriptive statistics and regression analysis at the census block and census tract levels are also included.
Funding Sources:
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National Science Foundation (2048562)
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