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This study uses building footprints from Microsoft and OpenStreetMap and the Python package momepy to measure the shape, size, and placement of buildings and their 5, 10, and 20 nearest neighbors across the continental United States. Using building and neighborhood morphology and machine learning estimates, we predict whether each building is a singlewide manufactured home and whether it is in a manufactured home park, informal or manufactured home subdivision, or another setting. We describe the methods used to create these predictions and discuss issues of model performance and their implications for future research, compare our estimates with the locations of manufactured homes documented in the American Community Survey and with government and private registries of these communities, illustrate their distribution nationwide, and present descriptive statistics on their demographic and socioeconomic characteristics. Our findings illustrate that manufactured home parks are more common in Midwestern and Northeastern states, whereas informal or manufactured home subdivisions are more common in Southern and Western states. We find that both neighborhoods are demographically diverse but economically disadvantaged. We conclude by briefly discussing the implications of our research for state and federal housing policy. 

In recent years, states and municipalities have taken steps to reform land use and zoning regulations. While prior research documents that density zoning contributes to residential segregation on the basis of income and race, the mechanisms remain largely unexplored. In this paper, we examine the relationship between density zoning, neighborhood type, and residential segregation. To do so, we use a national dataset of building footprints and machine learning to develop a neighborhood typology based on building characteristics. We then use land cover data to examine changes in building development in these neighborhoods between 2001 and 2019. Finally, we pair these data with demographics at the municipality level to examine changes in income and race between 2000 and 2020. In cross-sectional analyses, we find that density zoning is strongly associated with building characteristics and the presence of different neighborhood types. Although we find that density zoning is also associated with income and race, the effects are attenuated when accounting for neighborhood types. Our results provide new evidence into the ``chain of exclusion" between density zoning and residential segregation, as we find that density zoning is primarily associated with reductions in the supply of single-family housing along the urban fringe. Lastly, we find that maximum density restrictions and changes in maximum density cannot explain the changes in demographics that we observe during this time period. We do, however, find some evidence of a relationship between changes in building development and changes in demographics. These results demonstrate the potential effects of upzoning policies. 

Recent advances in quantitative tools for examining urban morphology enable the development of morphometrics that can characterize the size, shape, and placement of buildings; the relationships between them; and their association with broader patterns of development. Although these methods have the potential to provide substantial insight into the ways in which neighborhood morphology shapes the socioeconomic and demographic characteristics of neighborhoods and communities, this question is largely unexplored. 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. We conclude by discussing the implications of our findings and suggesting avenues for future research on neighborhood morphology, including ways that it might provide insight into issues such as zoning and land use, housing policy, and residential segregation.