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1. Mapping super-gentrification in large US cities, 1990–2020

   https://doi.org/10.1080/02723638.2025.2528418  

   Lauermann, John; Alexander, Zoe; Wang, Ziqi (July 2025, Urban Geography)

   This paper analyzes the geography of super-gentrification in US cities, the further intensification of class upgrading after a neighborhood has already been 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 the New York City metropolitan region. 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 supergentrified 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 to a national comparative analysis of gentrification intensity patterns, and a longitudinal analysis of what happens after a neighborhood has already been 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 (November 2020, American Sociological Review)

   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 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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4. Ridesourcing and urban inequality in Chicago: Connecting mobility disparities to unequal development, gentrification, and displacement

   https://doi.org/10.1177/0308518X211047872  

   McKane, Rachel G; Hess, David J (May 2022, Environment and Planning A: Economy and Space)

   Ridesourcing advocates and companies promise many benefits to cities, such as increased accessibility, a solution to the last-mile transit problem, and even reduced need for automobiles. However, an important body of research has indicated that ridesourcing is more heavily used by more privileged consumers and in more affluent and whiter neighborhoods. Questions have also emerged about the effects of ridesourcing on public transportation. This study builds on a mobility disparities perspective by analyzing ridesourcing in the context of urban inequality, including gentrification and displacement. Using a large data set from the Chicago area, this study shows that ridesourcing is associated with areas that have seen rising rents and have become whiter and more educated. The results also show that ridesourcing is more prevalent in areas that are accessible by public transportation. Although the causal relationship between ridesourcing and gentrification is complex, the study suggests a new direction in the literature that embeds the analysis of ridesourcing in the broader frameworks of unequal urban development and neoliberalization. The study also suggests policy approaches that could help to reduce some of the connections between ridesourcing and urban inequity. 

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5. Discovering Underground Maps from Fashion

   https://doi.org/10.1109/WACV51458.2022.00057  

   Mall, Utkarsh; Bala, Kavita; Berg, Tamara; Grauman, Kristen (January 2022, 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV))

   The fashion sense -- meaning the clothing styles people wear -- in a geographical region can reveal information about that region. For example, it can reflect the kind of activities people do there, or the type of crowds that frequently visit the region (e.g., tourist hot spot, student neighborhood, business center). We propose a method to automatically create underground neighborhood maps of cities by analyzing how people dress. Using publicly available images from across a city, our method finds neighborhoods with a similar fashion sense and segments the map without supervision. For 37 cities worldwide, we show promising results in creating good underground maps, as evaluated using experiments with human judges and underground map benchmarks derived from non-image data. Our approach further allows detecting distinct neighborhoods (what is the most unique region of LA?) and answering analogy questions between cities (what is the "Downtown LA" of Bogota?). 

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