An official website of the United States government
This article examines a case of urban displacement currently underway in central Rio de Janeiro, Brazil. In some respects, this case represents a classic example of what researchers call ‘downward raiding’: a type of urban displacement whereby low-income housing is exploited by higher-income groups. Yet, in other respects, it also raises important questions about the ways urban displacement happens in public housing, as well as how downward raiding operates on the ground in cities. By exploring these questions, this article aims to accomplish two goals: first, to investigate an overlooked and often hidden form of urban displacement that, in this case, coincides with a large-scale, public–private housing initiative; and, second, to critically interrogate the concept of downward raiding in order to better understand and define the process. It is argued that by placing greater emphasis on how, empirically speaking, urban displacement happens, researchers may gain new insight into diverse forms of urban displacement in cities around the world.
more »
« less
Hygienisation, Gentrification, and Urban Displacement in Brazil
Abstract This article engages recent debates over gentrification and urban displacement in the global South. While researchers increasingly suggest that gentrification is becoming widespread in “Southern” cities, others argue that such analyses overlook important differences in empirical context and privilege EuroAmerican theoretical frameworks. To respond to this debate, in this article, we outline the concept ofhigienização(hygienisation), arguing that it captures important contextual factors missed by gentrification. Hygienisation is a Brazilian term that describes a particular form of urban displacement, and is directly informed by legacies of colonialism, racial and class stigma, informality, and state violence. Our objective is to show how “Southern” concepts like hygienisation help urban researchers gain better insight into processes of urban displacement, while also responding to recent calls to decentre and provincialise urban theory.
more »
« less
- Award ID(s):
- 1632145
- PAR ID:
- 10122115
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Antipode
- Volume:
- 52
- Issue:
- 1
- ISSN:
- 0066-4812
- Page Range / eLocation ID:
- p. 124-144
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Gentrification is a complex and context-specific process that involves changes in the built environment and social fabric of neighborhoods, often resulting in the displacement of vulnerable communities. Machine Learning (ML) has emerged as a powerful predictive tool that is capable of circumventing the methodological challenges that historically held back researchers from producing reliable forecasts of gentrification. Additionally, computer vision ML algorithms for landscape character assessment, or deep mapping, can now capture a wider range of built metrics related to gentrification-induced redevelopment. These novel ML applications promise to rapidly progress our understandings of gentrification and our capacity to translate academic findings into more productive direction for communities and stakeholders, but with this sudden development comes a steep learning curve. The current paper aims to bridge this divide by providing an overview of recent progress and an actionable template of use that is accessible for researchers across a wide array of academic fields. As a secondary point of emphasis, the review goes over Explainable Artificial Intelligence (XAI) tools for gentrification models and opens up discussion on the nuanced challenges that arise when applying black-box models to human systems. Abstract: Gentrification is a complex and context-specific process that involves changes in the built environment and social fabric of neighborhoods, often resulting in the displacement of vulnerable communities. Machine Learning (ML) has emerged as a powerful predictive tool that is capable of circumventing the methodological challenges that historically held back researchers from producing reliable forecasts of gentrification. Additionally, computer vision ML algorithms for landscape character assessment, or deep mapping, can now capture a wider range of built metrics related to gentrification-induced redevelopment. These novel ML applications promise to rapidly progress our understandings of gentrification and our capacity to translate academic findings into more productive direction for communities and stakeholders, but with this sudden development comes a steep learning curve. The current paper aims to bridge this divide by providing an overview of recent progress and an actionable template of use that is accessible for researchers across a wide array of academic fields. As a secondary point of emphasis, the review goes over Explainable Artificial Intelligence (XAI) tools for gentrification models and opens up discussion on the nuanced challenges that arise when applying black-box models to human systems.more » « less
-
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.more » « less
-
The effects of gentrification are well studied, with varied findings. Studies debating and nuancing gentrification’s effects have subsequently entailed variation on how the phenomena should be defined. The variance in definitions can create different calculations and potentially muddy findings on its effects. Having a well-defined methodology for calculating gentrification is essential to ensuring a deeper understanding of the phenomena and its effects. This article seeks to establish such a methodology that relies exclusively on publicly available data. This article overviews the definitions used in several peerreviewed articles to identify 12 different methods for calculating gentrification. The authors created an interactive tool that classifies census tracts as gentrifying (https://ogilani.shinyapps.io/Gentrification/), nongentrifying, and nongentrifiable in metropolitan areas in the United States. Through a case study of Pittsburgh, the authors offer insights into which definition of gentrification best fits a qualitative understanding of the city. This article leaves readers with a methodology and tool for defining and mapping gentrification across the United States, making it easy to compare the results across different definitions. This tool and application offer a way for researchers, activists, and policymakers to compare various definitions in a particular geography to ensure consistent findings in studies across the United States.more » « less
-
When policymakers invest in urban infrastructure, there are concerns that poor residents living near the infrastructure will be displaced. This paper investigates mechanisms that may lead to such infrastructure-induced gentrification using a general equilibrium urban commuting model. Our goal is to elucidate the channels through which infrastructure-induced gentrification occurs and understand how policy choices mitigate or accentuate gentrification.more » « less
