Predicting neighborhood change using big data and machine learning: Implications for theory, methods, and practice - Part 2

Urban Displacement Project

Conference

August 10, 2020 to August 11, 2020
9:00am to 5:00pm
Sydney, Australia

Predicting neighborhood change using big data and machine learning: Implications for theory, methods, and practice - University of Sydney Symposium
August 10-11, 2020
University of Sydney, Australia

This symposium will convene an international urban researchers with deep interests in data science and neighborhood change. The first symposium in this series was held at UC Berkeley on January 9-10, 2020, and this second event will be held at the University of Sydney on August 10-11, 2020, with each two-day program consisting of a mix of keynote speakers, panels, and workshops with data science researchers and government officials. The organizers expect to publish the results in a special issue of a peer-reviewed journal, to be determined.

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).

REGISTRATION
Please contact Karen Chapple (chapple@berkeley.edu) for more information about registration, this initiative and/or to request to be added to the mailing list for the Sydney meeting.

CALL FOR ABSTRACTS: Submissions are due by March 27, 2020.

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 neighborhoodbigdata@gmail.com. Unfortunately, the organizers cannot offer any funding to support travel. 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. Remote participation options may be offered for selected papers.

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 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.

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

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

 

Speaker(s)

Karen Chapple

Professor and Chair, City & Regional Planning, UC Berkeley