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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. 

Abstract Community Geography offers researchers, community groups, and students opportunities to engage in action oriented applied geographical research. Creating and sustaining these research programs can be challenging, programs can involve many partners from both academic and the community, have different goals and purposes, and utilize a variety of methods to perform research. In this paper we offer a framework of three primary overarching principles for implementing CG projects; (1) Who, (2) Why, and (3) How. (1) “Who” describes who is involved in CG, including researchers, community partners, academic institutions, (2) “Why” describes the justifications and benefits of taking this approach. (3) “How” explains how CG borrows methodologies from many disciplines within geography and beyond. Our examples are not exhaustive; rather, they serve as starting points to inspire researchers interested in CG. 

Community geography is a growing subfield that provides a framework for relevant and engaged scholarship. In this paper, we define community geography as a form of research praxis, one that involves academic and public scholars with the goal of co-produced and mutually-beneficial knowledge. Community geography draws from a pragmatist model of inquiry, one that views communities as emergent through a recursive process of problem definition and social action. We situate the growth of community geography programs as rooted in two overlapping but distinct traditions: disciplinary development of participatory methodologies and institutional traditions of community engagement in American higher education. We then trace the historical development of these programs, identifying common themes and outlining several challenges that community geographers should prioritize as this subfield continues to grow.