Browsing by Author "Schroeder, Aaron"

Now showing 1 - 3 of 3
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    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.
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    Report to the Virginia Department of Veterans Services Virginia Wounded Warrior Program: Assessing the Experiences, Supportive Service Needs and Service Gaps of Veterans in the Commonwealth of Virginia Final Report
    Still, George; Dickerson, Thomas; White, Nancy; Sforza, Peter M.; Schroeder, Aaron; Willis-Walton, Susan M. (Virginia Tech Institute for Policy & Governance, 2010-08-05)
    The Commonwealth of Virginia is the home to over 800,000 veterans who have served in conflicts ranging from World War II to the current engagements in the gulf region, Operation Iraqi Freedom (OIF/Iraq) and Operation Enduring Freedom (OEF/Afghanistan). The Virginia Wounded Warrior Program has been charged with coordinating and facilitating the services that are needed by Virginia’s veterans who have served in the United States military. In order to evaluate how to best serve and facilitate services for these veterans, the VWWP has commissioned a needs assessment of Virginia’s veterans that is summarized in this report.
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    Towards an in silico Experimental Platform for Air Quality: Houston, TX as a Case Study
    Pires, 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.