Browsing by Author "Keller, Sallie A."
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- Building Capacity for Data Driven Governance - Creating a New Foundation for DemocracyKeller, Sallie A.; Lancaster, V. A.; Shipp, S. S. (Francis & Taylor, 2017-03)Existing data flows at the local level, public and administrative records, geospatial data, social media, surveys, as well as other federal, state, and local databases, are ubiquitous in our everyday life. These data, when integrated, can tell the story of a community. The Community Learning Data Driven Discovery (CLD3) process liberates, integrates and makes these data available to government leaders and researchers to tell their community’s story and to use these stories to build an equitable and sustainable social transformation within and across communities to address their most pressing needs. The CLD3 process starts with asking local leaders what their questions are but cannot currently answer; identifying data sources that can provide insights; wrangling the data (profiling, cleaning, transforming, linking); using statistical and geospatial learning along with the communities’ collective knowledge to inform policy decisions; and developing, deploying, and evaluating intervention strategies based on scientifically based principles. CLD3 is a continuous, sustainable and controlled feedback loop. CLD3 is described conceptually and through examples as a process that builds capacity for data driven governance at the local level.
- Can Administrative Housing Data Replace Survey Data?Molfino, Emily; Korkmaz, Gizem; Keller, Sallie A.; Schroeder, Aaron; Shipp, Stephanie; Weinberg, Daniel H. (HUD, 2017)This article examines the feasibility of using local administrative data sources for enhancing and supplementing federally collected survey data to describe housing in Arlington County, Virginia. Using real estate assessment data and the American Community Survey (ACS) from 2009 to 2013, we compare housing estimates for six characteristics: number of housing units, type of housing unit, year built, number of bedrooms, housing value, and real estate taxes paid. The findings show that housing administrative data can be repurposed to enhance and supplement the ACS, but limitations exist. We then discuss the challenges of repurposing housing administrative data for research.
- STRATEGY FOR A NATIONAL COMMUNITY LEARNING NETWORK: COMMUNITY LEARNING DATA DRIVEN DISCOVERY - Building Capacity for Evidence-Based Decision MakingKeller, Sallie A. (2016-12-19)The Social and Decision Analytics Laboratory (SDAL) of the Biocomplexity Institute of Virginia Tech received a planning grant from the Laura and John Arnold Foundation (LJAF) to develop a strategy for a national community learning network. Through the development of this strategy Virginia Tech formed a partnership with Iowa State University, placing Virginia and Iowa at the center of initiating a national movement that would empower local governments to embrace data-driven governance. The first steps of this national strategy involve massive deployment of community learning across Virginia and Iowa, using these states as exemplars for igniting a full national movement. Based on the research completed in this grant, Virginia Tech and Iowa State University are developing a proposal with LJAF to realize the first steps of this national strategy - a Land Grant Partnership for Data-Driven Governance.
- Towards an in silico Experimental Platform for Air Quality: Houston, TX as a Case StudyPires, Bianica; Korkmaz, Gizem; Ensor, Katherine; Higdon, David; Keller, Sallie A.; Lewis, Bryan L.; Schroeder, Aaron (CSSSA, 2015)In this paper we couple a spatiotemporal air quality model of ozone concentration levels with the synthetic information model of the Houston Metropolitan Area. While traditional approaches often aggregate the population, activities, or concentration levels of the pollutant across space and/or time, we utilize high performance computing and statistical learning tools to maintain the granularity of the data, allowing us to attach specific exposure levels to the synthetic individuals based on the exact time of day and geolocation of the activity. We demonstrate that maintaining the granularity of the data is critical to more accurately reflect the heterogeneous exposure levels of the population across time within the greater Houston area. We nd that individuals in the same zip code, neighborhood, block, and even household have varying levels of exposure depending on their activity patterns throughout the day.