Suchergebnisse

Research on how neighborhood racial composition affects where gentrification unfolds yields mixed conclusions, but these studies either capture broad national trends or highly segregated cities. Drawing on the case of Seattle—a majority–White city with low segregation levels and growing ethnoracial diversity—this study uncovers an underexplored mechanism shaping patterns of uneven development and residential selection in the contemporary city: immigrant replenishment. The share of all minorities is negatively associated with gentrification during the 1970s and 1980s, and, in contrast to expectations, shares of Blacks positively predict recent gentrification while shares of Asians negatively predict it. Increased concentrations of recent immigrants in neighborhoods with greater shares of Asians explain these relationships. These findings suggest that where arriving immigrants move limits residential selection in gentrification and shifts pressures to low–cost Black neighborhoods. This study highlights how immigration and points of entry are important factors for understanding uneven development in the contemporary city and has implications for the future of racial stratification as cities transform.

Following the Great Recession, homeownership rates declined precipitously, raising concerns for the stability and well–being of neighborhoods. While many studies document shifts in household constraints, this article draws from foreclosure records from 2006 to 2011, subsequent transactions, tax exemption filings, and maintenance data in Boston, Massachusetts to show how the foreclosure crisis altered the landscape of ownership and unfolded differentially across hard–hit neighborhoods. Results from logistic regression analyses show that corporate investors were more likely to purchase foreclosures in predominantly black hard–hit neighborhoods, while owner–occupants were more likely to purchase foreclosures in hard–hit mixed–ethnoracial neighborhoods with substantial shares of non–Hispanic/Latinx whites. Relative to other foreclosure buyers, corporations were more likely to resell previously foreclosed properties to other investors and have reported maintenance issues against them. The findings have implications for further disadvantages for hard–hit black neighborhoods and highlight how the housing crisis exacerbated neighborhood inequality by race and ethnicity.

This article examines how the rise of immigration and its associated racial and ethnic changes relate to gentrification. In the decades following the 1965 Hart-Celler Act, gentrification has occurred more in cities with high levels of immigration and in neighborhoods with higher levels of immigrants. These relationships, however, vary by the ways in which a city is racially segregated and by the extent to which its immigrant population has been incorporated. Using crime data, surveys, and new gentrification measures, this article compares Chicago, a highly segregated city and predominantly Hispanic immigrant destination, with Seattle, a predominantly white city with high levels of Asian immigration. The findings show that immigration and its correlates have distinct and evolving relationships with neighborhood changes that are embedded in the racial and immigrant histories of each city, and that gentrification perpetuates racial and ethnic inequality in both cities.

This study draws upon cognitive maps and interviews with 56 residents living in a gentrifying area to examine how residents socially construct neighborhoods. Most minority respondents, regardless of socioeconomic status and years of residency, defined their neighborhood as a large and inclusive spatial area, using a single name and conventional boundaries, invoking the area's Black cultural history, and often directly responding to the alternative way residents defined their neighborhoods. Both long-term and newer White respondents defined their neighborhood as smaller spatial areas and used a variety of names and unconventional boundaries that excluded areas that they perceived to have lower socioeconomic status and more crime. The large and inclusive socially constructed neighborhood was eventually displaced. These findings shed light on how the internal narratives of neighborhood identity and boundaries are meaningfully tied to a broader structure of inequality and shape how neighborhood identities and boundaries change or remain.

Property owners play pivotal roles in the trajectories of neighborhoods with discretion over upkeep, residential turnover, and affordability. Yet, little is known about how and why the racial composition of ownership changes over time relative to residents within a neighborhood and, in turn, how this relates to the neighborhood's change and stability. With a self-constructed dataset of all residential transactions in San Francisco from 1990 to 2017, we consider how the ethnoracial composition of ownership differs from that of residents and how this difference relates to neighborhood change. We find that neighborhoods with more non-White residents have greater differences between the ethnoracial compositions of owners and residents, with the largest differences in neighborhoods with more Black residents. An increase in the divergence between these distributions is related to future increases in White and Asian residents and higher socioeconomic status residents and decreases in Black and Hispanic residents, illustrating that neighborhoods where owners are more ethnoracially distinct from the residents are more prone to neighborhood change and residential turnover. Our findings contribute to understandings of inequalities in property ownership and illuminate the role of ownership in neighborhood change in the contemporary city.

Analysis of neighborhood environments is important for understanding inequality. Few studies, however, use direct measures of the visible characteristics of neighborhood conditions, despite their theorized importance in shaping individual and community well-being, because collecting data on the physical conditions of places across neighborhoods and cities and over time has required extensive time and labor. The authors introduce systematic social observation at scale (SSO@S), a pipeline for using visual data, crowdsourcing, and computer vision to identify visible characteristics of neighborhoods at a large scale. The authors implement SSO@S on millions of street-level images across three physically distinct cities—Boston, Detroit, and Los Angeles—from 2007 to 2020 to identify trash across space and over time. The authors evaluate the extent to which this approach can be used to assist with systematic coding of street-level imagery through cross-validation and out-of-sample validation, class-activation mapping, and comparisons with other sources of observed neighborhood characteristics. The SSO@S approach produces estimates with high reliability that correlate with some expected demographic characteristics but not others, depending on the city. The authors conclude with an assessment of this approach for measuring visible characteristics of neighborhoods and the implications for methods and research.