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Data journalism at Techraking 6


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Data journalism at Techraking 6

  1. 1. Data Journalism at CIR (and beyond) Jennifer LaFleur Michael Corey
  2. 2. Data Journalism at CIR ⦿Apps developers ⦿Data journalists attached to topic teams ⦿Researcher ⦿Reporters who also do data analysis ⦿CIR provides data or helps other news organizations
  3. 3. Painkiller prescriptions to veterans
  4. 4. Now
  5. 5. Drug seizures along U.S.-Mexican border
  6. 6. Lessons learned along the way ⦿ Data is not always rows and columns ⦿ Most government data is bad ⦿ Data doesn’t always have to come from FOIA ⦿ Even the most savvy data analyst needs to wash, rinse, repeat ⦿ Transparent analyses make the best analyses
  7. 7. Data Challenges: Federal ⦿Many of these goals have not been met ⦿Outdated technology that makes providing data difficult ⦿High charges for records ⦿Privatized data systems ⦿Use agreements ⦿Other statutes that protect information
  8. 8. Data Challenges: State/Local ⦿Inconsistent laws state by state ⦿California • Lacks administrative appeal process • High-cost charges for public records • Agencies refuse, or are unable, to provide data • Many records still in paper (yes, in the land of technology)
  9. 9. Our goal: to put online child care inspection and complaint records... There are roughly 11,000 day care facilities and preschools in the region. We started with a small county, Napa, to test the process. Through a public records request, CIR and NBC Bay Area got access to thousands of paper files for the county. We’ve scanned them and plan to sort and publish the most relevant documents online, including any findings from inspection reports and complaint files.
  10. 10. Questions?