Category: Machine Learning

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Displacement Map SF and Bay Area

Predicting neighborhood change using big data and machine learning: Implications for theory, methods, and practice

Despite decades of research on neighborhood change, there has been little corresponding methodological development: studies still tend to either rely primarily on demographic data aggregated at the neighborhood level (which masks complex and micro-scale causal dynamics), or on in-depth case studies (which present challenges for generalization). Advances in data science, particularly if informed by critical urban theory, offer the potential to remedy some of these methodological shortcomings. To the extent that these and other approaches support an early warning system designed to be readily understood by stakeholders, they have the ability to empower communities, at a minimum, and potentially to transform policy as well. 

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Understanding Gentrification through ML

Although it has taken rather a long time to see the light of day, our just-published paper is one of the reasons I love my job: drawing on a mix of data science and deep geographical knowledge, we look at the role that new Machine Learning (ML) techniques – normally seen as just a ‘black box’ for making predictions – can play in helping us to develop a deeper understanding of gentrification and neighbourhood change. For those of a ‘TL;DR’ nature (or without the privilege of an institutional subscription!), we wanted to share some of our key ideas in a more accessible format.