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1. Replication Data for: Mapping super-gentrification in large US cities, 1990 to 2020

   https://doi.org/10.7910/dvn/bsaf99  

   Lauermann, John; Wang, Ziqi; Feldman, Anna; Wu, Yuanhao; Smash, Nathan; Lauermann, John (January 2025, Harvard Dataverse)

   This paper analyzes the geography of super-gentrification in US cities, the further intensification of class upgrading after a neighborhood has already gentrified. Building a national longitudinal tract database of gentrification intensity indicators, we analyze where this process has occurred across the 45 most populous metropolitan regions. We develop a method for quantifying metro-specific gentrification indices, then compare the class and racial demographics of super-gentrified tracts against other kinds of affluent places. We also interpret these national patterns with a case study of gentrification’s broader geographies in greater New York City. While super-gentrification is most commonly researched in global mega-cities, we found a wider geography including substantial suburban and smaller city patterns. We also found that super-gentrified neighborhoods are less racially diverse than other gentrified neighborhoods, and are more demographically similar to historically affluent (but not recently gentrified) neighborhoods. The study contributes a national comparative analysis of gentrification intensity patterns, and a longitudinal analysis of what happens after a neighborhood has already gentrified. 

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2. Triple Disadvantage: Neighborhood Networks of Everyday Urban Mobility and Violence in U.S. Cities

   https://doi.org/10.1177/0003122420972323  

   Levy, Brian L.; Phillips, Nolan E.; Sampson, Robert J. (December 2020, American Sociological Review)

   null (Ed.)

   This article develops and assesses the concept of triple neighborhood disadvantage. We argue that a neighborhood’s well-being depends not only on its own socioeconomic conditions but also on the conditions of neighborhoods its residents visit and are visited by, connections that form through networks of everyday urban mobility. We construct measures of mobility-based disadvantage using geocoded patterns of movement estimated from hundreds of millions of tweets sent by nearly 400,000 Twitter users over 18 months. Analyzing nearly 32,000 neighborhoods and 9,700 homicides in 37 of the largest U.S. cities, we show that neighborhood triple disadvantage independently predicts homicides, adjusting for traditional neighborhood correlates of violence, spatial proximity to disadvantage, prior homicides, and city fixed effects. Not only is triple disadvantage a stronger predictor than traditional measures, it accounts for a sizable portion of the association between residential neighborhood disadvantage and homicides. In turn, potential mechanisms such as neighborhood drug activity, interpersonal friction, and gun crime prevalence account for much of the association between triple disadvantage and homicides. These findings implicate structural mobility patterns as an important source of triple (dis)advantage for neighborhoods and have implications for a broad range of phenomena beyond crime, including community capacity, gentrification, transmission in a pandemic, and racial inequality. 

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3. The spatial and social correlates of neighborhood morphology: Evidence from building footprints in five U.S. metropolitan areas

   https://doi.org/10.1371/journal.pone.0299713  

   Durst, Noah J; Sullivan, Esther; Jochem, Warren C (April 2024, PLOS ONE)

   Xu, Gang (Ed.)

   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. 

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4. The Social Integration of American Cities: Network Measures of Connectedness Based on Everyday Mobility Across Neighborhoods

   https://doi.org/10.1177/0049124119852386  

   Phillips, Nolan E.; Levy, Brian L.; Sampson, Robert J.; Small, Mario L.; Wang, Ryan Q. (July 2019, Sociological Methods & Research)

   The social integration of a city depends on the extent to which people from different neighborhoods have the opportunity to interact with one another, but most prior work has not developed formal ways of conceptualizing and measuring this kind of connectedness. In this article, we develop original, network-based measures of what we call “structural connectedness” based on the everyday travel of people across neighborhoods. Our principal index captures the extent to which residents in each neighborhood of a city travel to all other neighborhoods in equal proportion. Our secondary index captures the extent to which travels within a city are concentrated in a handful of receiving neighborhoods. We illustrate the value of our indices for the 50 largest American cities based on hundreds of millions of geotagged tweets over 18 months. We uncover important features of major American cities, including the extent to which their connectedness depends on a few neighborhood hubs, and the fact that in several cities, contact between some neighborhoods is all but nonexistent. We also show that cities with greater population densities, more cosmopolitanism, and less racial segregation have higher levels of structural connectedness. Our indices can be applied to data at any spatial scale, and our measures pave the way for more powerful and precise analyses of structural connectedness and its effects across a broad array of social phenomena. 

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5. Urban Mobility and Neighborhood Isolation in America's 50 Largest Cities

   https://doi.org/https://doi.org/10.1073/pnas.1802537115  

   Wang, Ryan Q.; Phillips, Nolan E.; Small, Mario L.; Sampson, Robert J. (July 2018, Proceedings of the National Academy of Sciences of the United States of America)

   Influential research on the negative effects of living in a disadvantaged neighborhood assumes that its residents are socially isolated from nonpoor or “mainstream” neighborhoods, but the extent and nature of such isolation remain in question. We develop a test of neighborhood isolation that improves on static measures derived from commonly used census reports by leveraging fine-grained dynamic data on the everyday movement of residents in America’s 50 largest cities. We analyze 650 million geocoded Twitter messages to estimate the home locations and travel patterns of almost 400,000 residents over 18 mo. We find surprisingly high consistency across neighborhoods of different race and income characteristics in the average travel distance (radius) and number of neighborhoods traveled to (spread) in the metropolitan region; however, we uncover notable differences in the composition of the neighborhoods visited. Residents of primarily black and Hispanic neighborhoods—whether poor or not—are far less exposed to either nonpoor or white middle-class neighborhoods than residents of primarily white neighborhoods. These large racial differences are notable given recent declines in segregation and the increasing diversity of American cities. We also find that white poor neighborhoods are substantially isolated from nonpoor white neighborhoods. The results suggest that even though residents of disadvantaged neighborhoods travel far and wide, their relative isolation and segregation persist. 

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