This virtual symposium will convene international urban researchers with deep interests in data science and neighborhood change. The first symposium in this series, Predicting neighborhood change using big data and machine learning: Implications for theory, methods, and practice, was held at UC Berkeley on January 9-10, 2020. This second event will be held virtually (originally scheduled at the University of Sydney) in late-August 2020 with a program consisting of panel discussions culminating with a workshop that introduces practitioners to data science techniques for housing.
Program information coming soon.
Session 1 - Registration
August 10: 4:00 – 7:00 PM Pacific Time (GMT -7)
August 11, 9:00 AM – 12:00 PM Australian EST (GMT +10)
Session 2 - Registration
August 18, 8:00 – 11:00 AM BST (GMT +1) and
August 18, 5:00 – 8:00 PM Australian EST (GMT +10)
Session 3 - Registration
August 24, 4:00 – 7:00 PM Pacific Time (GMT -7) and
August 25, 9:00 AM - 12:00 PM Australian EST (GMT +10)
CALL FOR ABSTRACTS
Submissions were due by March 27, 2020.
If you would like to participate in the August 2020 event, please submit an abstract of no more than 500 words by March 27, 2020, to firstname.lastname@example.org. 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.
Building on the Berkeley event, the organizers seek papers about neighborhood change that innovate by using user-generated geographic information, social media data, machine learning, image processing, or the like. The organizers are particularly interested in theoretically-informed and transdisciplinary studies that adopt a comparative lens or mixed methods.
In addition to our general call for the August/Sydney event, the organizers 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?
The organizers 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.
Please contact Karen Chapple (email@example.com) for more information about this initiative and/or to request to be added to the mailing list.
- Nicole Gurran, Professor and Chair, Urban and Regional Planning and Policy, University of Sydney
- Somwrita Sarkar, Senior Lecturer, Design, University of Sydney
- Karen Chapple, Professor and Chair, City and Regional Planning, University of California, Berkeley
This conference is made possible with support from the Urban Studies Foundation, the University of California-Berkeley, and the University of Sydney. At Berkeley, the conference was held at the Berkeley Institute of Data Science, which co-sponsored the Berkeley event. The Urban Displacement Project at UC Berkeley aims to understand the nature of gentrification and displacement in American cities, focusing on creating tools to help communities identify the pressures surrounding them and take more effective action.