St. Louis’s “urban prairie”: Vacant land and the potential for revitalization

ABSTRACT

As part of a larger project to understand the relative health and disorder of St. Louis's neighborhoods, this article presents estimates of the number of vacant parcels in the city. These estimates, which are considerably higher than previously published ones, are heavily concentrated in the city’s disinvested and segregated north side. We term this heavy concentration of vacancy urban prairie. After accounting for other factors as well as possible sources of statistical error, we identify both long-term population loss since 1970 and the proportion of African American residents as significant covariates associated with the amount of urban prairie land per neighborhood. These high levels of concentrated vacancy lead us to critique the city’s existing approaches as being too limited in scope and to suggest a range of possibilities for revitalizing portions of northern St. Louis while allowing prairie land to continue to exist in others.

Notes

1. These codes included Debris-Vacant Bldg, Missed Cut-VacantBldg, Unsatisfy Cut-VBldg, VACANT BLDG INITIV, VACANT BLDG INITIV, Vacant Unit Appeal, Vacant Bldg Unsecured, Weeds-Vacant Bldg, WTR-Vacant-BLDG, Building Collapse, Debris-Vacant Lot, Missed Cut-V Lot, Unsatisfy Cut-VLot, Weeds-Vacant Lot.

2. Data and other materials related to this project can be found at https://chris-prener.github.io/vacancy.

3. Other data, including on median house value, median rent, and median income, were also obtained and tested in initial analyses. However, the home value data were missing from a number of tracts for both the 2015 ACS and the 2010 Decennial Census and could therefore not be used. Including median income and median rent in our analyses introduced a high degree of multicollinearity with both the number of African American residents and the number of individuals living below the poverty line. Median income and median rent were therefore not included in our final models. Additional historical variables, including the racial composition and median income of neighborhoods in 1970, did not provide any improvements in model fit and were not included.

4. A predictable number of neighbors is important for spatial weighting, which is a key aspect of calculating spatial statistics. Spatial weights are typically based on either distance or, in our case, the number of shared features between two polygons. Because each polygon is a square, we can rely on it having three or four neighbors with shared sides.

5. A key aspect of GMM regression models is that they relax one of the key assumptions of OLS regression: that errors are equally variable throughout the model. In many spatial regression applications, heteroskedasticity is introduced when data are aggregated from specific points at a given latitude and longitude to a polygon that summarizes points across a given space. GMM models are therefore useful not only because they can compensate for spatial autocorrelation but also because they can be used on data where heteroskedasticity is a concern.

Additional information

Notes on contributors

Christopher G. Prener

Christopher G. Prener is an Assistant Professor in the Department of Sociology and Anthropology at Saint Louis University. He is a graduate of St. Lawrence University with a PhD from Northeastern University in Boston. Chris is an urban and medical sociologist with an interest in mixed methods research designs that incorporate spatial data. He is currently developing a book manuscript entitled Medicine at the Margins that explores the ways in which urban emergency medical services work is impacted by and affects the neighborhoods where it occurs. He is also leading research efforts to understand sociospatial patterns of crime, neighborhood disorder, and inequality in St. Louis.

Taylor Harris Braswell

Taylor Harris Braswell is an environmental sociology PhD student at Northeastern University. His primary interest is in using geographic information science tools to study the political economy of urbanization and natural resource extraction. He is particularly focused on the linkages between urbanization and energy infrastructures, as well as how urbanization processes create conflictual land uses on urban peripheries. Before joining Northeastern, Taylor earned an MA in sociology at Saint Louis University, where he researched local demographic trends and land use practices, and a BA in economics at Georgia State University in Atlanta.

Daniel J. Monti

Daniel J. Monti is Professor of Sociology and the doctoral program in Public and Social Policy at Saint Louis University. He is a graduate of Oberlin College and the University of North Carolina at Chapel Hill. A former Woodrow Wilson Fellow and member of the Missouri State Advisory Committee to the U.S. Commission on Civil Rights, he is the author of over 50 scholarly articles and the author or editor of eight books on subjects ranging from educational reform and youth gangs to urban redevelopment and American urban history. His most recent books include Engaging Strangers (2013), which deals with civic life in contemporary Boston, and Urban People and Places (2014), a survey of cities and urban life in more and less developed societies. He is editor of Polis, a book and monograph series on urban affairs that is published by Fordham University Press.