Harnessing the power of data to support community‐based research. (17th January 2018)

• Record Type: Journal Article
• Authors: Keller, Sallie; Shipp, Stephanie; Korkmaz, Gizem; Molfino, Emily; Goldstein, Joshua; Lancaster, Vicki; Pires, Bianica; Higdon, David; Chen, Daniel; Schroeder, Aaron
• Is Part Of: Wiley interdisciplinary reviews. Volume 10:Number 3(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-01-17
• Subjects: Mathematical statistics -- Data processing -- Periodicals; Science -- Data processing -- Periodicals; Social sciences -- Data processing -- Periodicals; Mathematical statistics -- Periodicals; 519.50285
• DOI: 10.1002/wics.1426
• Languages: English
• ISSNs: 1939-5108

Abstract

Abstract : Volumes of data are generated at every moment as we go through the paces of our daily lives. Many of these data flows are routinely captured through administrative records, social media, and surveys. Historically, agencies at different levels of government have been responsible for curating and reporting statistics about our social, economic, and health conditions associated with these data flows. Recently, the U.S. government has proposed the use of data derived from administrative records at the federal level to support social policy and program evaluation. Why not consider parallel activities at state and local levels? Harnessing local data sources and integrating them with state and federal sources will provide timelier and more geographically specific analyses to support local insights and policy development. Leveraging community‐based participatory research, researchers and civic leaders can work together to identify the questions and execute rigorous, yet flexible, processes for building local sustainable community learning cultures based on data‐driven discovery. In the process of conducting research with local civic leaders, we have observed that issues can be classified into 3 categories: locating and describing a population within a community; estimating a statistic and a measure of variability; and evaluating a program, policy, or procedure. Through a series of case studies, this paper demonstrates the unexpected value in liberating and repurposing local data. This article is categorized under: Applications of Computational Statistics > Organizations and Publications Abstract : Locations of fire stations in Arlington, Virginia and locations of 911 Fire/EMS calls during 2010–2015. Black points are the 10 fire stations. The colored points are the locations of the calls where points of same color are calls dispatched from the same fire station.