Data Science for the Public Good Symposium 2022: Keynote Speaker Joseph Salvo
Thursday, August 4, 2022
12:00pm-3:30pm Eastern Time (ET)
Missed the event? Joseph Salvo's slides are available here and you can watch the recorded keynote speech here.
12-1:30 p.m. | Keynote Speech and Student Speed Presentations | Registration Link Here
1:30-3:30 p.m. | Student Project Sessions | Zoom Link Here
Keynote Speaker: Joseph Salvo, former Chief Demographer at the New York City Department of City Planning
Talk Title: Better Data and Measurement for Local Public Policy Decisions
Abstract: Local officials make decisions daily on policies, strategies and programs that affect the quality of life of their residents. We live in a data driven world where “open data” has become an institutionalized mantra, especially in government at all levels. Yet, the increased availability of these data sets does not presage their usefulness for application to the practical problems that the locals face. Different concepts, methods of collection, units of analysis, time periods, disclosure avoidance methods, and level of accessibility affect the ability of local decision makers to put the data to work. After all, the power of these data sets lies not in their singular use, but in their integration in the interest of problem solving. What is needed is a strategy for curation to successfully integrate data to form products that can inform local problems – the 21st Century Curated Data Enterprise (CDE).
The CDE concept directly addresses changing demographic, economic and social conditions through exploiting multiple data sources across sample surveys, censuses, and administrative and private-sector data, to produce more robust, granular, timelier, and comprehensive data products. This will allow addressing time-sensitive questions and require the curation of associated data and processes. For example, managing the impact of economic shifts and crises due to pandemics, wildfires, hurricanes, on vulnerable populations requires real-time and geographically detailed data to address stakeholder questions. This presentation uses selected Use Cases to explain how the CDE framework can be used to discover and integrate data in new ways. While the CDE is being adopted by the U.S. Census Bureau, this framework has worldwide applicability and, indeed, is part-and-parcel of efforts internationally, coordinated by the United Nations, to develop measures to track Sustainable Development Goals (SDGs).
Project Descriptions | UVA Students and Mentors
Coastal Futures: Building Capacity for Data-driven Adaptation in Rural Coastal Communities
In collaboration with UVA’s Environmental Resilience Institute, the UVA BII Social and Decision Analytics Division is studying climate impact on the rural Eastern Shore of Virginia. Our goal is to develop tools and build the capacity of local communities to deal with these impacts. We are developing a representative synthetic population of these communities down to the level of individual households and farms by combining information from administrative and survey data. This data will be input into agent-based models and hydrological models measuring flood hazard, water supply, and groundwater salinization to inform stakeholder decision making.
Research Team: Kishore Sundaram, Jillian Eberhart, Joshua Goldstein, and Aritra Halder
Impacts of Broadband Development on Rural Property Values
The UVA BII Social and Decision Analytics Division USDA Broadband Subsidy project evaluated the economic impact of various broadband initiatives in rural communities. This project implemented spatial regression discontinuity designs of residential property values and broadband download speeds from areas both inside and surrounding the program regions. Additionally, we evaluated speed test data from Ookla and illustrated the relationship between project funding and tangible improvements in internet quality.
Research Team: Donovan Cates, Kristian Olsson, Joshua Goldstein, and Aritra Halder
Leveraging Existing DoD Data Towards Optimized Individual and Team Performance in the Army
The UVA BII Social and Decision Analytics Division Army Research Institute Qualitative Analysis project utilized document analysis to answer the research question: What are qualities of an individual soldier that contribute to unit performance? Through qualitative analysis, we interpreted documents to give meaning to our phenomena of interest and triangulated our interpretations for credibility. This project will inform and contextualize future quantitative modeling of Soldier and Unit performance in the U.S. Army.
Research Team: Skylar Haskiell, Jillian Eberhart, Joanna Schroeder, and Joel Thurston
Mastercard Center for Inclusive Growth and Virginia Department of Health Social Impact Data Commons
Sponsored by the Mastercard Center for Inclusive Growth and Virginia Department of Health, the UVA BII Social and Decision Analytics Division Data Commons is building an open knowledge repository that compiles data from trusted open access sources, curates data insights, and provides tools designed to track issues over time and geography. Our toolkit and methodologies allow governments and community stakeholders to access timely data and make informed policy decisions. Several data commons have been deployed focusing on crucial social equity issues across the National Capital Region, as well as between urban and rural Virginia communities.
Research Team: Alan Wang, Kishore Sundaram, Steve Zhou, Donovan Cates, Aaron Schroeder, and Joel Thurston
Use of Statistical and Survey Methodology Research to Improve or Redesign Surveys: Product Innovation
The UVA BII Social and Decision Analytics Division Product Innovation project built a proof-of-concept toolkit that enables the use of North American Industry Classification System (NAICS) to track innovation activities sustainably using opportunity data. The toolkit accelerates Really Simple Syndication (RSS) queries and news source text extraction using open-source modules and browser automation. The collected texts are then piped to natural language processing (NLP) modules that detect business, product, and innovation status.
Research Team: Alan Wang, Steve Zhou, Neil Kattampallil
Emerging Digitalization Trends
The UVA BII Social and Decision Analytics Division NCSES Research and Development project conducted a thorough literature review of the emerging concept of “digitalization” and explored natural language processing techniques to use in identifying grant abstracts about this theme. We utilized a variety of techniques on a test corpus to discover the most accurate method, which we then applied to the Federal RePORTER database to discover research trends related to the area of digitalization.
Research Team: Skylar Haskiell, Kristian Olsson, and Kathryn Linehan
Project Descriptions | Virginia Tech Students and Mentors
Agricultural Land Use Change in Powhatan and Goochland County
Goochland and Powhatan County would like to understand land-use conversion from agriculture. This project uses publicly available geospatial data and administrative parcel records to construct a profile of land parcels over time and inform Goochland and Powhatan counties about land conversion/agriculture loss. For each land parcel, we build a data frame that includes whether it parcellates, the type of crop grown if applicable, soil type, travel time to Richmond, provision of utilities, and existing land use. We then use these data to conduct geospatial and statistical analysis to understand the factors most likely associated with land-use change.
Research Team: Rachel Inman, John Malla, Christopher Vest, Nazmul Huda, Samantha Rippley, Yuanyuan Wen, and Susan Chen
Using Remote Sensed Data for Social and Economic Decision Making in Zimbabwe
The Zimbabwean government has recently approved an agricultural policy framework based on climate-smart principles. Still, it contains little geographic specificity in an incredibly diverse agricultural economy. This project uses remotely sensed weather-related data to construct a spatial profile of agricultural conditions in Zimbabwe. Using geospatial analysis and statistical modeling, we assess the utility of using remotely sensed data to understand district-level poverty and its components. Our analysis provides a spatially disaggregated look at whether climate data can be used to identify at-risk regions for potential policy intervention.
Research Team: Frankie Fan, Ari Liverpool, Josue Navarrete, Leonard-Allen Quaye, Poonam Tajanpure, Naveen Abedin, Brianna Posadas, and Susan Chen
Sensing Drought in the Sahel for Household Climate Resilience
Frequent weather shocks impede poverty alleviation efforts in areas dependent on rainfed agriculture, such as the drought-prone Sahel. To help break the link between drought and distress, our DSPG team is creating a reproducible analysis pipeline to examine how historical drought indicators extracted from remotely-sensed data are most closely linked with food insecurity. This analysis pipeline, in turn, can help our partners at the World Bank quickly issue relief to the most vulnerable when poor weather strikes.
Research Team: Catherine Back, Milind Gupta, Riley Rudd, Poonam Tajanpure, Armine Poghosyan, and Elinor Benami
Illustrating Potential Opportunities for Community Schools in Loudoun County
This project examines the resources and services available for elementary schools in Sterling, Loudoun County, involved in the Community Schools Initiative. We analyze services in four key areas - Basic Needs, Emotional and Mental Health, Student Engagement and Motivation, and Family Engagement and determine potential opportunities for improvement to meet the needs of students, families, and the community.
Research Team: Amanda Ljuba, Jontayvion Osborne, Abdullah Rizwan, Nandini Das, and Chanit'a Holmes
Assessing Livelihood Diversification in Sundarbans, India using High Frequency Data
This project aims to evaluate livelihood-diversification strategies for approximately 300 households in the Sundarbans region in India using weekly financial data. We provide insights into the effects of climate change in this area by describing and visualizing households' income, expenditure, and consumption patterns.
Research Team: Siddarth Ravikanti, Taj Cole, Samantha Rippley, Nandini Das, and Chanit'a Holmes