Emily Keller-O'Donnell, Data Journalist

2022 DSSG Teams Present Their Final Project Results

Student fellows at the University of Washington Data Science for Social Good (DSSG) program presented the results of their 10-week summer projects on August 17th. The fellows conducted their work on four interdisciplinary teams, working with project leads and data scientists. 

Read the full article at eScience News here.

Interdisciplinary Team Creates Tool to Detect and Quantify Satellite Streaks

The recent growth of low-Earth-orbit satellites is posing a threat to a range of groups who rely on the sky for science, spirituality and tradition, by leaving a plethora of bright streaks across the night sky. A project team at the University of Washington Data Science for Social Good (DSSG) program is working this summer to create a satellite streak detection algorithm that will serve as a tool for quantifying changes in the sky. This tool is aimed at supporting people in astronomy and other fields to take steps to mitigate the prevalence of streaks and their negative effects.

Read the full article at eScience News here.

Research Team Examines the Impacts of Seattle’s Minimum Wage Ordinance on Household-Level Poverty

Research about the relationship between poverty and the minimum wage in Seattle has often focused on examining the economic circumstances of individual workers. One of the projects of the Data Science for Social Good (DSSG) summer program at the University of Washington is expanding this lens to create data that views workers as household members, to provide greater context about the role of individual wages in reducing poverty in the region.

Read the full article at eScience News here.

Interdisciplinary Team Designs Database to Advance ‘Self-Sufficiency Standard’ Research

Determining the income level that working families need to meet their basic needs requires a dynamic approach. An interdisciplinary team at the University of Washington (UW) Data Science for Social Good (DSSG) program is laying the groundwork for the integration of multi-sourced data from across the U.S. to provide researchers and policy makers with an expanded breadth of opportunities for analyzing complex information in new ways.

The project is centered on designing and testing a database for the Self-Sufficiency Standard, a project of the Center for Women’s Welfare at the UW School of Social Work that determines the income level required for working families to meet their minimum basic needs, based on family composition, children’s ages, and geographic differences in the cost of living. The Standard provides an alternative to the Official Poverty Measure that is currently used to calculate eligibility for benefits such as housing subsidies and food assistance across the U.S.

Read the full article at eScience News here.

Applying ‘Geospatial First’ Approach to Estimating Heating Loads in Alaska

Decarbonization efforts to mitigate the impacts of climate change face a unique challenge in Alaska. While about 70 percent of energy use in the state goes to heating, there is a lack of reliable broad-scale data on the portion of energy that is used for heating in buildings. An interdisciplinary team at the University of Washington Data Science for Social Good (DSSG) program is working to create an improved method for estimating thermal energy use in buildings in the Alaska Railbelt and Arctic regions, to support these efforts.

Read the full article at eScience News here.

Data Science for Social Good Teams Present Their Project Results

On August 18th, student fellows at the eScience Institute’s Data Science for Social Good (DSSG) program presented the results of their 10-week summer projects on the topics of fairness in computational redistricting, and the impacts of Seattle’s minimum wage ordinance in relation to residential displacement and commuting patterns. The student fellows conducted their work on two interdisciplinary teams, working with project leads and data scientists. 

Read the full article at eScience News here.

Data Science for Social Good Team Builds Tools to Support Fairness in Computational Redistricting

This fall, Congressional leaders will begin the state redistricting process that takes place every ten years to reflect population changes captured by the U.S. Census. As the process gets underway, a team in the  University of Washington Data Science for Social Good (DSSG) program is creating a resource guide to help nonpartisan groups who want to evaluate the fairness of proposed maps, or draw their own, using a computational redistricting tool called GerryChain.

Read the full article at eScience News here.

Migrating the DSSG Summer Program to a Remote Setting

After five years of running the Data Science for Social Good (DSSG) summer program in the eScience Institute’s open studio space – where project leads spend at least two days per week with full-time student fellows and in-house data scientists for ten weeks – the program has now completed its first successful summer remotely.

Read the full article at eScience News here.

Data Science for Social Good Fellows Present Their Project Results

This year, two interdisciplinary teams at the eScience Institute’s Data Science for Social Good (DSSG) program tackled timely issues, conducting projects to identify disinformation articles about the coronavirus and detect minority vote dilution resulting from geographic boundary setting in state, city, county and school board districts.

On August 19th, the DSSG student fellows presented the results of their projects, conducted with project leads and data scientists, to more than 130 people via zoom.  The ten-week summer program joins student fellows from universities around the country with data and domain researchers, and real-world stakeholders, to work on collaborative projects for societal benefit. This year the program took place remotely for the first time due to the coronavirus pandemic.

Read the full article at eScience News here.

DSSG Project Supports Equity in Voting District Boundaries

As states, counties, cities and school boards across the country prepare to re-draw their voting districts in 2021, a software package that is being updated through the Data Science for Social Good (DSSG) program will help to ensure equal representation among constituents.

Voting district boundaries are re-drawn after each Census to account for changes in population, ensuring that districts have an equal number of voters and equal representation by racial and ethnic group, in accordance with Section 2 of the federal Voting Rights Act of 1965. When voting patterns show minority and majority racial or ethnic groups are primarily voting in opposite directions, known as “racially polarized voting,” district boundaries may be challenged in court.

Read the full article at eScience News here.