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1. Hygienisation, Gentrification, and Urban Displacement in Brazil

   https://doi.org/10.1111/anti.12584  

   Garmany, Jeff; Richmond, Matthew A. (October 2019, Antipode)

   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. 

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2. Machine learning to model gentrification: A synthesis of emerging forms

   Mueller, Maya; Hoque, Simi; Pearsall, Hamil (May 2024, Computers environment and urban systems)

   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. 

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3. 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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4. Power Resilience Assessment from Physical and Socio-Demographic Perspectives

   https://doi.org/10.1109/ICSRS48664.2019.8987673  

   Ulak, Mehmet Baran; Yazici, Anil; Ozguven, Eren Erman; Vanli, Arda; Arghandeh, Reza (November 2019, 4th International Conference on System Reliability and Safety (ICSRS))

   Urban resilience is a multifaceted concept including the recovery of the physical infrastructure and various urban activities that depend on that physical infrastructure. It is relatively straightforward to quantify infrastructure resilience by tracking the recovered facilities in time and marking the time that the infrastructure is fully functioning again. However, the physical infrastructure recovery does not necessarily indicate that the urban activities bounce back to the predisaster conditions. The restoration of urban activities depends on the areas that a particular infrastructure serves (e.g., residential, commercial) and the connections with other critical facilities (e.g., health, education). It is important to investigate the infrastructure recovery and “resilience divide” with respect to the enabled services and affected populations in order to achieve all-inclusive resilience. For this purpose, we examined the resilience of different physical elements such as power feeders (i.e., underground or overhead lines), critical facilities (e.g., fire and rescue services, hospitals) and different socio-demographic segments of the population (i.e., different age groups, ethnicities, and income levels) which constitute an urban environment. The analyses were conducted using the power outages experienced after Hurricane Hermine in Tallahassee, as a case study. The findings show that overall resilience performance can be distinct and/or not homogeneous for the resilience of different physical elements, urban services, and population groups. 

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5. Environmental justice implications of siting criteria in urban green infrastructure planning

   https://doi.org/10.1080/1523908X.2021.1945916  

   Hoover, Fushcia-Ann; Meerow, Sara; Grabowski, Zbigniew J.; McPhearson, Timon (June 2021, Journal of Environmental Policy & Planning)

   null (Ed.)

   Green infrastructure (GI) has become a panacea for cities working to enhance sustainability and resilience. While the rationale for GI primarily focuses on its multifunctionality (e.g. delivering multiple ecosystem services to local communities), uncertainties remain around how, for whom, and to what extent GI delivers these services. Additionally, many scholars increasingly recognize potential disservices of GI, including gentrification associated with new GI developments. Building on a novel dataset of 119 planning documents from 19 U.S. cities, we utilize insights from literature on justice in urban planning to examine the justice implications of criteria used in the siting of GI projects. We analyze the GI siting criteria described in city plans and how they explicitly or implicitly engage environmental justice. We find that justice is rarely explicitly discussed, yet the dominant technical siting criteria that focus on stormwater and economic considerations have justice implications. We conclude with recommendations for centering justice in GI spatial planning. 

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