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.
UC-Berkeley Symposium, January 9-10, 2020 / University of Sydney Symposium, Summer 2020
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. For instance, real-time data on activity patterns, such as geotagged tweets, can help overturn traditional conceptions of residential segregation (Shelton, Poorthuis, and Zook 2015), and bridge time lags in census data (Hristova et al., 2016). Using machine learning techniques, we can also analyze existing patterns of neighborhood ascent and decline in order to predict future change (Reades, de Souza, and Hubbard, 2019). 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 (Chapple and Zuk 2016).
We are convening an international group of urban researchers with deep interests in data science and neighbourhood change in a seminar series to be held at the University of California, Berkeley (January 9-10, 2020) and the University of Sydney (June 1-2, 2020). The seminar series will consist of two full days in each venue, with a mix of keynote speakers, panels, and workshops with data science researchers and government officials. We expect to publish the results of our work in a special issue of a peer-reviewed journal, to be determined.
We are seeking papers about neighborhood change that innovate by using user-generated geographic information, social media data, machine learning, image processing, or the like. We are particularly interested in theoretically informed studies that adopt a comparative lens or mixed methods.
Abstract Submission
If you would like to present a paper at one of the symposia, please submit an abstract of no more than 500 words by September 13, 2019 to neighborhoodbigdata@gmail.com. Please specify which conference you would like to attend: Berkeley, Sydney, or both. Unfortunately we cannot offer any funding to support travel. Authors of the selected abstracts will be notified by early October and be expected to submit their completed papers by one week before each conference.
Conference Organizers
Project Leads
Karen Chapple
Professor and Chair, City and Regional Planning
University of California, Berkeley
Nicole Gurran
Professor and Chair, Urban and Regional Planning and Policy
University of Sydney
Somwrita Sarkar
Senior Lecturer, Design
University of Sydney
Project Team
Cynthia Goytia
Professor and Director, Urban Economics
Universidad Torcuato di Tella
Ate Poorthuis
Assistant Professor, Geography
Singapore University of Technology and Design
Jon Reades
Senior Lecturer, Quantitative Human Geography
King’s College, London
Matthew Zook
Professor and Interim Chair, Geography
University of Kentucky
King's Geocomputation & Data Science 