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New information for new journalists pt2: data
This document discusses data journalism and provides tips for finding, cleaning, visualizing, and combining data sources to tell stories. It begins with an introduction to data journalism and provides examples of successful data-driven journalism projects. It then offers guidance on finding both public and private data sources, cleaning data to reduce errors, visualizing data to identify patterns and stories, mashing or combining different data sets, and making data computer-readable to build applications. The overall message is that data journalism involves using data to drive the discovery of new stories and insights.
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New information for new journalists pt2: data
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- 3. “Each weekday, mycomputer program goes to the Chicago Police Department's website and gathers all crimes reported in Chicago.” Adrian Holovaty
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- 11. “The Tribune’s biggestmagnet 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
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- 19. Numbers Text Connections Live data Behavioural data Images,audio, video Anything that a computer can work with
- 21. Start with thedata and look for the stories? (MPs’ expenses) Or start with a lead and look for the data? Passive vs active data journalism
- 22. Data.gov.uk What Do TheyKnow Openlylocal, Scraperwiki Disclosure logs RSS feeds, XML, structured data Some UK projects
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- 24. Advanced search byfile type “Performance figures” Filetype: pdf Filetype: xls Filetype: doc Filetype: ppt Filetype: rdf OR xml
- 25. Advanced search bydomain “Disclosure logs” site: .gov.es Database site: .org.cat OR .org +Tables –chairs site: Health, police, military domains
- 26. Use overseas sources •US medicine databases • EU subsidy databases • Swedish people data • International police agency correspondence
- 27. Scraping Scraping can automate& schedule the gathering process if there are multiple sources Tools: OutWit Hub plugin, Yahoo! Pipes, Scraperwiki, Google Spreadsheets formulae
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- 29. Humans collect data Humansenter data Human error Time spent now...
- 30. Different words forthe same thing Double spaces, punctuation Wrong data type Mistyped Duplicate entries Default entries (1/1/00) ...Saves time later
- 31. "Because we takethe time to clean the data, we are able to do lobbying stories no other news organisation can do." David Donald, Center for Public Integrity
- 32. Group by termthen 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
- 33. Never publish aname from data without running a background check Check.
- 34. Other tools Freebase Gridworks: seehttp://vimeo.com/10081183
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- 44. Geocoded data withmap - 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
- 52. Twittermap Wikipedia map NYT Property Guardianvs Nature BBC Most Read BBC Olympic Village Combining data sources
- 53. Big events (protests,Olympics, inauguration) Comparisons Geocoded data Connections What mashes well?
- 54. Aggregates Maps Filters Counts Cleans or reformats(regex) Yahoo! Pipes
- 55. Scraperwiki – mappinglibrary Maptube – combine maps Google Docs – publish in different formats +++ Other tools
- 56. Computer-readable data Paris –France, Texas, or Hilton? Unique identifiers – usually URI RDF, RDFa, XML, etc. Semantic web & linked data
- 57. Application Programming Interface Buildon top of data Google Maps, Twitter, Facebook, Digg, Guardian, NYT, NPR, They Work For You, etc. API
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