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 involving two events; one of which was held at the University of California, Berkeley (January 9-10, 2020) and the second to be held at the University of Sydney (August 10-11, 2020). The seminar series consists 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. Building on the Berkeley event, we seek 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 and transdisciplinary studies that adopt a comparative lens or mixed methods. In addition to our general call out, for the Sydney event we particularly welcome papers which shed light on emerging critical debates about the implications of new housing supply through urban redevelopment, renewal, and ‘upzoning’ as either a remedy for, or a precursor to, displacement associated with neighbourhood change. How might big data and/or machine learning methods offer new insights into the implications of these processes, and the extent to which regulatory or market factors shape housing supply, affordability, and access at neighbourhood and city scales? We also welcome papers which use big data and/or machine learning to provide insights on urban processes associated with the removal or under-utilisation of existing housing units from permanent rental or owner occupation – for instance, the rise of short term rental platforms, or speculative property investment.
Abstract Submission If you would like to participate in the Sydney event, please submit an abstract of no more than 500 words by March 27, 2020 to email@example.com. Unfortunately we cannot offer any funding to support travel; but remote participation options for selected papers is available. Authors of the selected abstracts will be notified by April 3 and be expected to submit their completed papers by one week before the conference. Conference Organizers Project Leads Karen Chapple Professor and Chair, City and Regional Planning University of California, Berkeley; Nicole Gurran Professor and Chair, Urbanism; University of Sydney; Somwrita Sarkar Senior Lecturer, 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.