BIDS welcomes Urban Displacement Project's conference on "Predicting Neighborhood Change Using Big Data and Machine Learning"

February 26, 2020

BIDS recently welcomed Karen Chapple and her colleagues as part of the Urban Displacement Project for their conference on Predicting neighborhood change using big data and machine learning. The conference brought together urban researchers from universities across the United States as well as Spain, Australia, England, Argentina, Germany, and Singapore, who engaged in a lively conversation with Bay Area planners and housing advocates. The research projects discussed use machine learning and big data in innovative ways to explore, map and quantify urban change in some of the world’s most dynamic cities.

The Urban Displacement Project (UDP) is a research initiative at UC Berkeley that conducts community-centered, data-driven research to examine the nature of gentrification and displacement in an effort to improve equity and inclusivity in urban areas. Their work enables advocates and policymakers to reframe conversations about how policy interventions and investment can respond to and support more equitable urban development.

This is Data Science from UC Berkeley Data Science on Vimeo.


Karen Chapple, PhD, is Professor and Chair of City & Regional Planning at UC Berkeley, where she holds the Carmel P. Friesen Chair in Urban Studies. Her studies focus on inequalities in the planning, development, and governance of regions in the US and Latin America, with a focus on economic development and housing. In Fall 2015, she co-founded the Urban Displacement Project, a research portal examining patterns of residential, commercial, and industrial displacement, as well as policy and planning solutions.

Read more about the January 2020 conference at Berkeley:
Predicting Neighborhood Change Using Big Data and Machine Learning: Potential and Pitfalls
February 28, 2020 | Karen Chapple | BIDS Blog: Data Science Insights

The UDP's next conference is in Sydney, Australia, in August 2020. Abstracts are now due on March 9, 2020.