Authors: Rachel Berney*, Assistant Professor, Dept. of Urban Design & Planning, University of Washington, Seattle, Gundula Proksch, Associate Professor, Dept. of Architecture, University of Washington, Seattle, Bernease Herman, Data Scientist, eScience Institute, University of Washington, Seattle
Topics: Spatial Analysis & Modeling, Quantitative Methods, Urban Geography
Keywords: urban equity, civic technology, technology adoption, indicator evaluation, opportunity mapping
Session Type: Paper
Start / End Time: 1:20 PM / 3:00 PM
Room: Studio 5, Marriott, 2nd Floor
Presentation File: No File Uploaded
There is a growing community of data and social scientists working together to help their cities solve urban problems using advanced technical approaches. These collaborations are challenged to ensure that the analyses and tools they create can be well understood and smoothly adopted by multiple stakeholders. Opportunity mapping is frequently used in urban planning to evaluate inequitable conditions across cities physically and socially. This approach is primed to benefit from techniques in the software and data science community. An injection of statistical sophistication into this process could reduce bias, and increase transparency and replicability, enabling city planners and governments to better understand, predict, and respond to residents’ inequitable access to city resources and to gentrification across cities. We have built a web-based visualization and equity analysis tool underpinned by a structural equation model to enable prediction and increase understanding of relationships between the underlying mechanisms of urban inequality. This is in contrast to the procedure in practice today in which hand-picked variables are weighted based on educated guesses and displayed as an aggregate. We discuss the challenges of adopting advanced software products by non-technical government teams in the case of tools like ours. We analyze features of civic technology projects that have failed to gain traction, along with features that have aided others in the adoption process. Finally, we conclude with proposed next steps for our tool.