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New information for new journalists pt2: data



Presentation to ESCACC, Barcelona, 2010

Presentation to ESCACC, Barcelona, 2010



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New information for new journalists pt2: data New information for new journalists pt2: data Presentation Transcript

  • Introduction Paul Bradshaw Data journalism
  • Ivy Lee
  • “ Each weekday, my computer program goes to the Chicago Police Department's website and gathers all crimes reported in Chicago.” Adrian Holovaty
  • Great stories Engagement Targeting/relevance Why ?
  • “ 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
  • Times film genres
  • Data Journalism Continuum
  • 1. Finding data
  • What is data?
  • Numbers Text Connections Live data Behavioural data Images, audio, video Anything that a computer can work with
  • 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
  • Data.gov.uk What Do They Know Openlylocal, Scraperwiki Disclosure logs RSS feeds, XML, structured data Some UK projects
  • Delicious.com/paulb/car CAR
  • Advanced search by file type “ Performance figures” Filetype: pdf Filetype: xls Filetype: doc Filetype: ppt Filetype: rdf OR xml
  • Advanced search by domain “ Disclosure logs” site: .gov.es Database site: .org.cat OR .org +Tables –chairs site: Health, police, military domains
  • Use overseas sources
      • US medicine databases
      • EU subsidy databases
      • Swedish people data
      • International police agency correspondence
  • Scraping Scraping can automate & schedule the gathering process if there are multiple sources Tools: OutWit Hub plugin, Yahoo! Pipes, Scraperwiki, Google Spreadsheets formulae
  • Interrogating data
  • Humans collect data Humans enter data Human error Time spent now...
  • Different words for the same thing Double spaces, punctuation Wrong data type Mistyped Duplicate entries Default entries (1/1/00) ...Saves time later
  • "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
  • 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
  • Never publish a name from data without  running a background check Check.
  • Other tools Freebase Gridworks: see http://vimeo.com/10081183
  • Visualising data
  • or http://chartchooser.juiceanalytics.com/
  • (trends, dips, correlations)
  • (comparison, themes)
  • (proportions, comparison)
  • Mashing data
  • 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
  • Twittermap Wikipedia map NYT Property Guardian vs Nature BBC Most Read BBC Olympic Village Combining data sources
  • Big events (protests, Olympics, inauguration) Comparisons Geocoded data Connections What mashes well?
  • Aggregates Maps Filters Counts Cleans or reformats (regex) Yahoo! Pipes
  • Scraperwiki – mapping library Maptube – combine maps Google Docs – publish in different formats +++ Other tools
  • Computer-readable data Paris – France, Texas, or Hilton? Unique identifiers – usually URI RDF, RDFa, XML, etc. Semantic web & linked data
  • Application Programming Interface Build on top of data Google Maps, Twitter, Facebook, Digg, Guardian, NYT, NPR, They Work For You, etc. API
  • Slideshare.net/onlinejournalist Twitter.com/paulbradshaw Q&A
  • Delicious.com/paulb/datajournalism Delicious.com/paulb/visualisation Delicious.com/paulb/statistics Bookmarks