Noah Durst, assistant professor in the Urban and Regional Planning program, receives NSF grant to map informal and alternative housing in the U.S.
SPDC faculty proposal “Mapping Informal and Alternative Housing in the United States: A Big Data Approach for Examining Spatial Inequality” was selected to receive a NSF grant of $359,208
Michigan State University’s proposal “Mapping Informal and Alternative Housing in the United States: A Big Data Approach for Examining Spatial Inequality” was funded by the Human-Environment and Geographical Sciences Program at the National Science Foundation for a total of $359,208. Noah Durst, from Urban and Regional Planning will serve as the principal investigator; Esther Sullivan University of Colorado-Denver will serve as co-PI; and Yue Cui and Holly Madill are senior personnel on the study.
"This project will lead to the development of a national database documenting the location of and conditions in tens of thousands of the most vulnerable neighborhoods in the nation. It will allow scholars and policymakers to better understand the factors that contribute to and policy solutions needed to address patterns of residential segregation and exposure to concentrated environmental risk," said Durst.
Millions of Americans currently live in two distinct community forms:
1) Informal subdivisions (ISs), where residents use incremental self-building for housing development that does not adhere to formal land planning and housing construction practices; and
2) Manufactured home communities (MHCs), where the dominant housing model is manufactured housing, low-cost factory-built housing that provides the single largest source of unsubsidized affordable housing in the U.S.
These communities provide a major source of affordable housing and low-income home ownership, but case study research suggests that they are spatially marginalized and exposed to concentrated forms of economic, social, and environmental vulnerability. Due to the difficulty of identifying their location across a broader geography, there is currently no systematic data on their total number or location, nor are there national-level analyses of the spatial inequalities they face.
This project uses big data and machine learning to produce more robust and refined measurements of the characteristics of all U.S. neighborhoods (formally planned suburbs, ISs, and MHCs). This will allow the research team to document the location of ISs and MHCs nationwide and model the policy and market factors that may explain patterns of uneven development, segregation, and environmental inequalities across neighborhood types. The databases and publications created by this research will enable a broader range of future geospatial and housing scholarship as well as more equitable housing policy. The project’s findings will be broadly disseminated to the public, local planners, and other stakeholders and policymakers through local community engagement workshops, a series of regional webinars, and an easy-to-use, and publicly available, data mapping and visualization dashboard.
The study builds on geographic theories of socio-spatial peripheralization and uneven development by examining the nature, causes, and consequences of the proliferation of ISs and MHCs and their relationship with the uneven spatial distribution of poverty and vulnerability in the U.S. This project will use Python programming language, a national dataset of building footprints, and supervised and unsupervised machine learning methods to identify the distinct dimensions of neighborhood morphology (the size, shape, orientation, and other arrangements of buildings) in ISs, MHCs, and formally planned suburbs across the country. In doing so, it will produce more robust and refined measurements of the characteristics of all U.S. neighborhoods, as well as a first-time national level database of ISs and MHCs.
Using this dataset, the research team will examine the relationship between segregation by neighborhood type and spatial inequalities, including residential segregation by race, income, and tenure and exposure to various types of environmental risk.
Project findings will contribute to methodological advancements in the spatial study of neighborhood morphologies, theoretical advancements in scholarship on peripheralization, uneven development, and the suburbanization of poverty and empirical advancements in the documentation and analysis of informal housing relative to social vulnerabilities and environmental hazards in the U.S.
Project investigators will then develop and implement community workshops and a series of regional webinars to train planners, policymakers, researchers and the public on a website that will allow users to analyze neighborhood morphologies, examine social, economic, and environmental impacts of uneven community development, and identify policies that can ameliorate these impacts.
This project is funded by the Human-Environment and Geographical Sciences Program at the National Science Foundation. For more information about this study, please contact Noah Durst at email@example.com.