Keller, Sallie A.Lancaster, V. A.Shipp, S. S.2017-05-232017-05-232017-03http://hdl.handle.net/10919/77698Existing 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.In CopyrightStatistical LearningGeospatial LearningAdministrative DataEvaluationVulnerable PopulationsArticle - Refereed