Please join us for a UW Data Science Seminar event on Tuesday, October 8th from 4:30 to 5:20 p.m. PDT. The seminar will feature two projects from this past summer’s UW Data Science for Social Good program.
The seminar will be held in the Physics/Astronomy Auditorium (PAA), Room A118 – campus map.
“Geo-indexing Water Reuse Potential…and beyond”
Team Project Leads: Carolyn Hayek, Researcher and Teaching Fellow, Columbia University, and Miriam Hacker, Research Program Manager, Water Research Foundation
Data Science Mentor: Curtis Atkisson, eScience Institute, University of Washington
Student Fellows: Daniel Vogler, Jihyeon Bae, Mbye Sallah, Nora Povejsil
Abstract:
Communities around the United States are thinking of alternative water systems to address local water challenges. One example of this is water reuse, which is defined by the Environmental Protection Agency (EPA) as “the practice of reclaiming water from a variety of sources, treating it, and reusing it for beneficial purposes.” The current social problem is that communities only see water reuse as an opportunity for areas that are experiencing water scarcity, rather than realizing its full potential to address a wide range of water challenges, like lowering flood risk, reducing combined sewer overflows, and minimizing the nutrients that are discharged to the environment.
Our project aims to address this social problem by developing a framework for quantifying a community’s potential for water reuse based on various motivators—or drivers—to identify whether water reuse could be a local solution that merits further investigation. Using publicly available data across the US, our project looks at the correlation between drivers (both presence and intensity) and characterizes the benefits communities might find by exploring water reuse. Combining complex data into an informative index and displaying the results in a clear, digestible format will help assess water challenges and needs across the country, as well as support local decision-making.
Beyond the scope of water reuse specifically, we also allow index-creators across disciplines to seamlessly create their own indices and web pages using the same generalized functions that we wrote and used in our process. You can think of our Interactive Water Reuse Website as a case study of an end-to-end index and website creation tool. The outputs of the project are an interactive web tool that allows users to map the severity of water reuse drivers across the United States, a geospatial data processing and conversion pipeline in the R programming language, and a general website creation (html file) template and functions.
“Measuring Fairness and Equity in Crowd-Flow Generation Models”
Team Project Lead: Afra Mashhadi, Assistant Professor, Computer Software and Systems, University of Washington, Bothell, and Ekin Ugurel, Ph.D. Candidate, College of Engineering, University of Washington
Data Science Mentor: Bernease Herman, eScience Institute, University of Washington
Student Fellows: Apoorva Sheera, Jiaqi He, Manurag Khullar, Sakshi Charvan
Abstract:
Generative crowd-flow models, which simulate city population movements, have advanced from physics-based to neural network-based models, improving performance by incorporating city-specific features. However, concerns about the equity of these models and the potential social biases brought by the models remain largely unaddressed, which is critical for government planning, pandemic prevention, and etc. This project aims to develop and implement new fairness metrics for CF models to ensure equitable representation of each groups’ travel demands. The project involves exploring current equity disparities among different demographic groups, reviewing fairness literature, engaging with stakeholders, and creating a Python package to test CF models.