Journal article
Machine learning to model gentrification: A synthesis of emerging forms
Computers, environment and urban systems, v 111, 102119
01 Jul 2024
Featured in Collection : UN Sustainable Development Goals @ Drexel
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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Details
- Title
- Machine learning to model gentrification: A synthesis of emerging forms
- Creators
- Mueller Maya - Drexel UniversityHoque Simi - Drexel University, Civil, Architectural, and Environmental EngineeringHamil Pearsall - Temple University
- Publication Details
- Computers, environment and urban systems, v 111, 102119
- Publisher
- Elsevier
- Number of pages
- 15
- Grant note
- 2035176 / National Science Foundation; National Science Foundation (NSF)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Web of Science ID
- WOS:001239083100001
- Scopus ID
- 2-s2.0-85192220677
- Other Identifier
- 991022202121604721
UN Sustainable Development Goals (SDGs)
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Source: SDGs in the Output
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Engineering, Environmental
- Environmental Studies
- Geography
- Operations Research & Management Science
- Regional & Urban Planning