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1. What Are the Relationships between Public Transit and Gentrification Progress? An Empirical Study in the New York–Northern New Jersey–Long Island Areas

   https://doi.org/10.3390/land12020358  

   Chen, Yefu; Xi, Hao; Jiao, Junfeng (February 2023, Land)

   Transit-oriented development has been a widely accepted tool among transportation planning practitioners; however, there are concerns about the risk of increasing residential property values leading to gentrification or displacements. Therefore, it is critical to provide precise investigations of the relationships between public transit and gentrification. Although numerous studies have explored this topic, few have discussed these relationships based on detailed measurements of gentrification from a regional perspective. This study aims to fill the research gap by measuring the gentrification subcategories through a hierarchical definition based on data in the New York–Northern New Jersey–Long Island areas and applying the transit desert concept as the measurement of transit services. Through multinomial logistic regression and machine-learning approaches, findings indicate that the rate of transit deserts in economically disadvantaged neighborhoods is higher than the others. In addition, the impacts of transit services are significant in gentrification but insignificant in super-gentrification. These findings can advance the knowledge of the role of the transit service in different gentrification progresses. Based on these findings, policymakers need to be careful when allocating public transit budgets and note the effects of these investments on neighborhoods with different socioeconomic statuses. 

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2. Looking at Our STEM Teacher Workforce: How to Model Self-Efficacy

   https://doi.org/10.1177/0891242420973758  

   Ofem, Brandon; Polizzi, Samuel_J; Rushton, Gregory_T; Beeth, Michael; Couch, Brock; Doering, Jessica; Konz, Rebecca; Mohr-Schroeder, Margaret; Roehrig, Gillian; Sheppard, Keith (November 2020, Economic Development Quarterly)

   There is currently a severe shortage of teachers in the U.S. workforce. The problem is especially acute among science, technology, engineering, and mathematics (STEM) teachers and exacerbated by high turnover among new teachers—those with less than 5 years of teaching experience. In this article, the authors investigate one piece of the puzzle. The authors model a social cognitive approach to understanding self-efficacy, a key precursor to job performance and retention. Their interactionist approach accounts for both demographic (i.e., gender and age) and relational variables (i.e., social networks). The authors test their ideas on a sample of 159 STEM teachers across five geographic regions in the United States. Their analysis reveals patterned differences in self-efficacy across gender that are contingent on the communities of practice in which the teachers are embedded. Together, their theory and findings highlight the value of taking a holistic, interactionist view in explaining STEM teacher self-efficacy. 

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3. New rail transit stations and the out-migration of low-income residents

   https://doi.org/10.1177/0042098019836631  

   Delmelle, Elizabeth; Nilsson, Isabelle (May 2019, Urban Studies)

   This article tests the hypothesis that low-income residents disproportionately move out of neighbourhoods in close proximity to new rail transit stations. This transit-induced gentrification scenario posits that the development of rail transit will place an upward pressure on land and housing values and that higher-income residents will outbid low-income residents for this new amenity. The most transit-dependent population may therefore be displaced from the most accessible locations, forming a paradox in the investment in new transit systems. We test this hypothesis using the Panel Study on Income Dynamics (PSID) dataset to trace the out-migration of residents across the United States from census tracts within five years of the opening of a new station, between 1970 and 2014. We find that low-income individuals are more likely to move, regardless of their neighbourhood. However, we do not find significant evidence that low-income individuals are more likely to move out of transit neighbourhoods, after controlling for both individual and other neighbourhood characteristics. The odds of moving out of a transit neighbourhood for low-income residents is statistically insignificant. In other words, they do not have a heightened probability of leaving new transit neighbourhoods compared with other residents. Our results are robust across decades, when examining renters alone, for different time spans and for varying definitions of transit neighbourhoods. We further find that those living in transit neighbourhoods are not more likely to live in a crowded dwelling. Our results therefore suggest that, on average, across the nation, low-income residents do not disproportionately exit new transit neighbourhoods. 

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4. How electricity utility practitioners in the United States approach power system resilience

   https://doi.org/10.55670/fpll.fuen.4.1.3  

   Tiwari, Shardul; Chakrabarty, Aritra; Schelly, Chelsea; Sahraei-Ardakani, Mostafa; Ou, Gaby; Chen, Jianli (February 2025, Future Energy)

   This study explores the understanding and practice of resilience among electrical utilities in the United States, focusing on how practitioners in the utility sector conceptualize and apply resilience in their work. As electricity becomes increasingly central to modern life, powering critical infrastructure and essential services, the resilience of power systems has gained prominence in energy policy and planning. However, there is a lack of standardized definitions and approaches to resilience in both academia and practice, particularly from an energy service perspective. The research employs a qualitative approach, utilizing semi-structured interviews with experts (practitioners) from transmission and distribution utilities in the United States to examine their definitions, understanding, and applications of resilience. By adopting a grounded approach, the study aims to identify key themes and concepts that practitioners associate with power system resilience. The findings outline that there is no clear definition of resilience amongst utility practitioners, and resilience and reliability are often used interchangeably/synonymously as there are no fixed indicators for resilience amongst practitioners. At present, unlike reliability, utilities are not including resilience as a term in their long-term resource planning, and neither are reporting resilience-based indicators to any of the government agencies. The findings contribute to the ongoing dialogue on energy resilience and offer a foundation for developing more comprehensive and context-specific approaches to building resilient energy systems that prioritize critical services and vulnerable populations. 

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5. 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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