New information for new journalists pt2: data

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Presentation to ESCACC, Barcelona, 2010

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New information for new journalists pt2: data

  1. 1. Introduction Paul Bradshaw Data journalism
  2. 2. Ivy Lee
  3. 3. “Each weekday, my computer program goes to the Chicago Police Department's website and gathers all crimes reported in Chicago.” Adrian Holovaty
  4. 4. Great stories Engagement Targeting/relevance Why?
  5. 5. “The Tribune’s biggest magnet by far has been its more than three dozen interactive databases, which collectively have drawn three times as many page views as the site’s stories.” http://bit.ly/dj2dmz
  6. 6. Times film genres
  7. 7. Data Journalism Continuum
  8. 8. 1. Finding data
  9. 9. What is data?
  10. 10. Numbers Text Connections Live data Behavioural data Images, audio, video Anything that a computer can work with
  11. 11. Start with the data and look for the stories? (MPs’ expenses) Or start with a lead and look for the data? Passive vs active data journalism
  12. 12. Data.gov.uk What Do They Know Openlylocal, Scraperwiki Disclosure logs RSS feeds, XML, structured data Some UK projects
  13. 13. Delicious.com/paulb/car CAR
  14. 14. Advanced search by file type “Performance figures” Filetype: pdf Filetype: xls Filetype: doc Filetype: ppt Filetype: rdf OR xml
  15. 15. Advanced search by domain “Disclosure logs” site: .gov.es Database site: .org.cat OR .org +Tables –chairs site: Health, police, military domains
  16. 16. Use overseas sources • US medicine databases • EU subsidy databases • Swedish people data • International police agency correspondence
  17. 17. Scraping Scraping can automate & schedule the gathering process if there are multiple sources Tools: OutWit Hub plugin, Yahoo! Pipes, Scraperwiki, Google Spreadsheets formulae
  18. 18. Interrogating data
  19. 19. Humans collect data Humans enter data Human error Time spent now...
  20. 20. Different words for the same thing Double spaces, punctuation Wrong data type Mistyped Duplicate entries Default entries (1/1/00) ...Saves time later
  21. 21. "Because we take the time to clean the data, we are able to do lobbying stories no other news organisation can do." David Donald, Center for Public Integrity
  22. 22. Group by term then sort to see duplications Find & replace double spaces, etc. Select column/row & check data type Sort to find unusually large/small, and neighbouring misspellings Cleaning methods
  23. 23. Never publish a name from data without running a background check Check.
  24. 24. Other tools Freebase Gridworks: see http://vimeo.com/10081183
  25. 25. Visualising data
  26. 26. or http://chartchooser.juiceanalytics.com/
  27. 27. (trends, dips, correlations)
  28. 28. (comparison, themes)
  29. 29. (proportions, comparison)
  30. 30. Mashing data
  31. 31. Geocoded data with map - Live data (e.g. Twitter API) - Static data (e.g. Google Docs) - Dynamic data (e.g. Google Form) 2 spreadsheets with common data - Tools: MySQL, Access, etc. Combining data sources
  32. 32. Twittermap Wikipedia map NYT Property Guardian vs Nature BBC Most Read BBC Olympic Village Combining data sources
  33. 33. Big events (protests, Olympics, inauguration) Comparisons Geocoded data Connections What mashes well?
  34. 34. Aggregates Maps Filters Counts Cleans or reformats (regex) Yahoo! Pipes
  35. 35. Scraperwiki – mapping library Maptube – combine maps Google Docs – publish in different formats +++ Other tools
  36. 36. Computer-readable data Paris – France, Texas, or Hilton? Unique identifiers – usually URI RDF, RDFa, XML, etc. Semantic web & linked data
  37. 37. Application Programming Interface Build on top of data Google Maps, Twitter, Facebook, Digg, Guardian, NYT, NPR, They Work For You, etc. API
  38. 38. Slideshare.net/onlinejournalist Twitter.com/paulbradshaw Q&A
  39. 39. Delicious.com/paulb/datajournalism Delicious.com/paulb/visualisation Delicious.com/paulb/statistics Bookmarks

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