Search for: All records

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. 

Community science has increased in popularity in communities where residents hope to investigate the relationship between environmental issues and personal health. This study partnered with neighborhoods in the most polluted residential zip code in the US to conduct community science air quality monitoring. We conducted 60 semi-structured interviews after two monitoring deployments to understand participants’ subjective experiences of pollution exposure, their engagement with low-cost air quality monitors, and their data interpretation. We utilize the environmental health concept ‘exposure experience’ to analyze how participants use personal monitors, understand their data, and reinterpret their pollution exposure as a result. We further explore how participants’ understandings are circumscribed by the technological features of low-cost monitors. We find that participants adopt both protective and mitigating behavioral changes based on information gained from personal experiments and hypothesis testing while using the monitors. Of their own accord, 40% of participants in this study adopted mitigation behaviors after identifying sources that impacted their personal air quality. Our analysis reveals that real-time data accessibility through low-cost monitors builds exposure awareness and enables residents of environmental justice communities to test, validate, or invalidate sensory experiences and challenge existing assumptions. These findings point to specific pathways for using low-cost monitors to support individual decision-making and contribute to behavioral change. Findings also identify some limitations of low-cost monitors; designers of low-cost monitors should consider how composite Air Quality Scores may encourage community scientists to equally value scientifically-established pollutants (e.g., PM) with less scientifically-established pollutants (e.g., TVOCs), without additional scientific training and health-related information. 

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.