Exploring new understandings of the cost of living at a basic needs level using the Self-Sufficiency Standard database


Outlines of a house, short, umbrella, bike, car, etc.Project leads: Annie Kuclick, Research Coordinator, and Lisa Manzer, Director, Center for Women’s Welfare, University of Washington School of Social Work

Data scientist: Bryna Hazelton

DSSG fellows: Azizakhon Mirsaidova, Priyana Patel, Cheng Ren, Hector Sosa

Participant bios available here.

Project Summary: The best-known measure of income adequacy, the Official Poverty Measure, (OPM) is too low with flawed and problematic methodology. Since its original development in the 1960s, societal changes have not been reflected in the measure’s calculation. The OPM does not vary geographically or by age of children, meaning someone is considered “poor” at the same threshold in Sioux City, Iowa and South Manhattan, New York. As the OPM is used to set eligibility for critical benefits (e.g. food assistance, child care subsidies, or housing vouchers), many families unable to afford their basic needs are not considered “in need” by the OPM and cannot access these supports.

The Self-Sufficiency Standard provides an alternative to the OPM by defining the income working families need to meet their basic necessities without public or private assistance. The Standard is widely used to understand issues of income adequacy, create and analyze policy, and help individuals striving to meet their basic needs. The Standard’s aim to be a suitable replacement to the OPM cannot be realized without the ability to host each state’s data in a single database. Having this national resource for the first time would allow community partners to access historical trends, research regional comparisons, and provide a framework for analyses—something frequently requested.

The main intellectual challenge of this project will be to design a database that will house the Self-Sufficiency Standard for all 42 states and all the years in which it has been calculated. The second component will be connecting other administrative databases to provide comparison, such as HUD’s median income levels or food insecurity data by county. Once the database is designed, possible investigations are endless. Students and staff will consult with community partners using the data to design additional queries of the data.

Learn more on the project blog and project website. Watch the presentation here, or the full recording of all four team presentations here.

Heating Loads in Alaska and Beyond


house surrounded by treesProject leads: Erin Trochim, Research Assistant Professor, Alaska Center for Energy and Power, University of Alaska Fairbanks

Data scientist: Nicholas Bolten

DSSG fellows: Vidisha Chowdhury, Maddie Gaumer, Philippe Schicker, Shamsi Soltani

Participant bios available here.

Project Summary: Decarbonization is a critical global issue where planning and executing implementation strategies is currently regionally underway. In the Arctic, there is extra urgency and complexity as warming is occurring twice as fast as the rest of the planet. Transitioning power and heat from primarily carbon intensive to net-zero sources is needed to meet decarbonization targets. About 75% of the energy requirements in the Arctic region are thermal (heating). Developing better estimates of heating needs is important for navigating decarbonization pathways, including weighing the role of building energy efficiency and centralized vs decentralized approaches.

The goal of this project is to create an improved method for estimating thermal energy use in Alaska Railbelt and Arctic regions. Building outlines are now available throughout the region. We will match these other data sources to estimate the height and age of the building, then overlay climate and downscaled scenarios data. Machine learning approaches will be used to more rapidly generalize between the datasets and final heating estimates. This work will examine current and future heating loads to provide critical information for decarbonization planning.

Learn more on the project blog and project website. Watch the team presentation here, or the full recording of all four team presentations here.


Satellite Streaks in Astronomical Images


satellite image

The background image shows the double star Albireo in Cygnus and was taken on 26 December 2019. Two out of ten 2.5-minute exposures recorded Starlink satellites moving across the field. Credit:Rafael Schmall

Project leads: Dino Bektešević, Graduate Student, Department of Astronomy; and Meredith Rawls, Research Scientist, Department of Astronomy, and Data-intensive Research And Cosmology (DiRAC) Fellow, University of Washington

Data scientist: Vaughn Iverson

DSSG fellows: Abhilash Biswas, Kilando Chambers, Ashley Santos

Participant bios available here.

Project Summary:

We are on the cusp of fundamentally altering the night sky, which has been a source of wonder, storytelling, and discovery since humanity’s earliest ancestors. Space is becoming increasingly industrialized, and large numbers of bright low-Earth-orbit satellites are beginning to leave bright streaks visible to the unaided eye and to telescopes. These impact our ability to observe and analyze astrophysical phenomena from Earth as well as traditional and cultural practices centered on the night sky. The pace of satellite launches is increasing, meanwhile, the scope of the impacts are not well constrained.

Our project addresses this problem by quantifying that change and enabling astronomers and more to do something about it. Students will work with astronomical images containing satellite streaks to develop methods to consistently measure streak brightness. Once we can measure how bright satellites appear in different situations, we can study how the streaks are changing over time and better inform stakeholders about mitigation options. Measured streak parameters will be publicly released alongside the images so astronomers worldwide can use this data to assess how satellites impact science and the sky. This will contribute directly to the international effort to mitigate impacts of satellite constellations on all sky observers. Well-informed science-driven policies and regulations are urgently needed, and our project’s streak brightness measurement techniques will enable these.

Learn more on the project blog and project website. Watch the presentation here, or the full recording of all four team presentations here.

Tracking family and intergenerational poverty using administrative data


sidewalk and street

Photo by Seattle Department of Transportation, used under Creative Commons license

Project leads: Jennie Romich, Professor of Social Welfare at the UW School of Social Work and Faculty Director of the West Coast Poverty Center

Data scientist: Jessica Godwin

DSSG fellows: Zhaowen Guo, Ihsan Kahveci, Betelhem Aklilu Muno, Eliot Stanton

Participant bios available here.

Project Summary:

This project will examine whether the Seattle $15 minimum wage policy reduced poverty or affected intergenerational economic mobility. Research to date on the impacts of the Seattle law have largely focused on individual workers’ experiences.  This project will create the data needed to examine impacts of the policy on poverty.

Why do we need new data? Poverty is a household-level measure and cannot be known from the individual records used in pervious work.  A single worker earning $25,000 per year is above the poverty line; that same income would render a sole earning parent with a spouse and two children “in poverty.” On the other hand, this worker could be a secondary earner in a wealthy household.  Hence to measure poverty, we need to know how individual earners are grouped into households or families.

This project will use data science methods to create households from a unique set of merged public records, the Washington Merged Longitudinal Administrative Data (WMLAD). WMLAD contains records on over 10 million individuals from seven Washington state agencies linked using a single unique person identifier. The data’s size (monthly records of 10 million persons over 7 years) requires efficient code and careful sequencing. The data are stored in a secure limited-access enclave, so the team will need creative and sophisticated workflow planning to protect the highly sensitive records while accomplishing project goals.

Learn more on the project blog and project website. Watch the presentation here, or the full recording of all four team presentations here.