Community Learning Through Data-Driven Discovery (CLD3)

From public transportation to emergency first responders, our communities rely on public services to keep us healthy, mobile, and secure. Data analytics can provide new levels of insight into how these services function: who they reach, where they’re most needed, and how they can be made even more efficient. SDAL researchers work directly with community leaders to help diagnose their most pressing problems. Click here to learn more.

  • To ensure their resources will reach the populations that need them most, Fairfax County Health & Human Services partnered with our lab to develop data-driven approaches for developing effective outreach programs.

  • By linking a population’s activity patterns to time- and location-specific air quality data, our research team is able to develop computational models which can be used to study differing rates of ozone exposure.

  • Using data from the Washington Metropolitan Area Transit Authority, students in SDAL’s Data Science for the Public Good Program diagnosed some of the determinants that contribute to bus fare evasion, an issue that represents up to $20 million in lost revenue each year.

  • Using 911 data from Arlington County Fire Department, SDAL's data scientists have identified factors that affect response time to structure fires—insights that can be used to improve the general safety of Arlington County residents and make the allocation of emergency resources more efficient.

  • Using data from Arlington County's Operation Firesafe program, SDAL's data scientists have developed a model to predict which homes are least likely to contain a functioning smoke alarm—insights that can be used to target public safety initiatives toward the regions where they're most needed.

  • By integrating diverse data streams, our research team is able to provide policymakers with a detailed view of how emergency services resources can be deployed to maximize safety and efficiency.

  • In collaboration with the State Council of Higher Education for Virginia, our lab developed new methods policymakers can use to identify factors that are likely to have the greatest impact on boosting higher education attainment rates in their region.

  • School assignment policies can play a critical role in making our schools more diverse. Our researchers performed a close examination of district data to assess their unique strengths and limitations.

  • SDAL consulted with the Kentucky Center for Education and Workforce Statistics to identify trends among graduating high school students. The results reveal new insights about state policies used to promote college readiness.

  • In collaboration with Arlington County, our laboratory conducted a pilot study to identify messaging patterns that keep users engaged with their regional emergency alert systems, allowing officials to focus outreach efforts toward populations with the lowest levels of enrollment.

  • Collaborating with the US Army Research Institute for Social and Behavioral Research, we are working to answer a key question: Do soldiers leave the Army because of better opportunities in civilian life? Our data-driven approach characterizes the social and economic conditions surrounding Army installations.

  • Our educational program trains aspiring scholars how to sift through vast amounts of information related to public safety, employment, and the provision of services to discover how communities can become more efficient and sustainable.

Science of All Data

Quality data sources are a cornerstone of effective policymaking and timely research. As information is captured in ever-increasing quantities and varieties, it’s essential that our data repositories evolve to keep pace. SDAL research is helping to guide the growth of these key resources, developing new methods to help them scale without sacrificing accuracy or security. Click here to learn more.

  • Our researchers are testing the use of local property data to develop more robust, up-to-date measures of housing value by employing information sources outside of the traditional federal survey system.

  • Working with the Department of Housing and Urban Development, our research team conducted a series of experiments to assess whether current reporting practices pose a significant risk to respondent confidentiality in the American Housing Survey.

  • Our researchers are developing new methods to capture the diversity of local communities in precise geographical detail through the use of local real estate assessment data.

  • Among regional governments, funding for services such as public transportation often relies on accurate estimates of local employment.To capture a clearer picture of its working population, Arlington County commissioned our researchers to develop new methods for filling in the gaps left by federal employment data.

  • A new framework encompassing the methods needed to capture, repurpose, and integrate external data sources will help the Federal Census Bureau meet the challenges of declining response rates and increasing costs.

Information Diffusion Analytics

Community information-sharing is a complex process, involving diverse populations, interconnected infrastructure, and a steady stream of daily interactions. Our data science research can help decision-makers develop a deeper understanding of how their communities spread new knowledge, providing the resources they need to make sure their messaging reaches its intended audience.

  • Knowledge-sharing is critical to the efficient functioning of any large organization—through research funded by the Army Research Institute, our lab has developed a simulation system to help leaders forecast how their communication processes will be affected by factors such as attrition, structural changes, and individual attitudes.

  • A collaborative effort between physicians, researchers, and government representatives is establishing a new set of guidelines to assist organizations in sharing health information responsibly and effectively.

  • SDAL researchers are developing new tools to model the dynamics of belief on a massive network scale—resources that could help policymakers predict how the spread of information through digital media will affect public health behaviors.

Back to top