Gi forum Raper Lessons of Open data from London

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  • 1. Realising the benefits of open geodata: lessons from London's experience Jonathan Raper Placr Ltd. giCentre, City University London Mayor’s Digital Advisory Board
  • 2. Revolution in geodata User pays Physical distribution Web distribution Marginal cost/ free 2000 2010
  • 3. Open geodata technologies
  • 4. Open geodata apps
  • 5. Open geodata content
  • 6. Open geodata = open data
    • The problem of “closed data”
    • How [we opened | are opening] the data
    • What “open” means for data
    • Why open data depends on politics
    • How open data can be sustained
    • Examples of open transport data from London
    • The implications of open data
  • 7. Problems of closed-ness
  • 8. The road to #opendata
    • ‘ The reason that we are now seeing major shifts in policy is that government has been hit by a perfect storm, with waves of change coming from different directions, which together are compelling change’
    • Digital Geographer January 2010
  • 9. 1 st wave: hyperconnected mobile society
    • ‘ the huge success of smartphones and app stores means people now have the platform in their hand for the delivery of real-time information services’
    • ‘ There’s an app for that’: expectations have been raised that government delivers its services digitally
    • Many new mashups for mobile that put the government to shame e.g. Cyclestreets
  • 10. 2 nd wave: the digital economy
    • ‘ Digital goods have different economic properties, notably, they have low marginal costs of distribution after the first copy’
    • Companies (try to) protect digital goods and spread the cost over all purchasers
    • When governments collect data to fulfill a public duty eg postcodes to deliver mail, any further releases can be at marginal distribution cost…
    • Guardian ‘Free our data’ campaign
  • 11. 3 rd wave: open source philosophies
    • ‘ the open source movement (has) beliefs (that) derive ultimately from principles of freedom, self help and an opposition to monopoly’
    • OpenStreetMap is the classic open source response to government using crowd sourcing to create open alternative to traded map data
    • Has ultimately led to Ordnance Survey free map data releases
  • 12. 4 th wave: growth of the open society
    • ‘ a new generation has developed a paradigm of open social life that has confounded the “broad-casting” model of the 20 th century’
    • The rise of Facebook, Twitter and Foursquare create collaborative models of publishing and communication never previously envisaged
    • Social media are in the process of re-shaping open societies eg social transport models
  • 13. Pre-history of open data 1700 Copyright 1800 Moral rights 1900 Patents Free trade Public libraries Privacy 1950 1960 Free markets Civil liberties 1970 Intellectual property Efficient markets FOI 1980 Standards GATT Online communities 1990 Data protection WIPO Public sector info 2000 Public domain Monopoly Open government Open society 2002 Digital Rights Man’t Walled gardens New Public Man’t Broadcasting 2004 P2P sharing Open source Blogging 2006 Creative Commons eCommerce Gov 2.0 Web 2.0 2008 Wikipedia Micropayments Gov as a platform 2010 Open licences “ Free” models Communicative gov Social media 2012
  • 14. How open data was released in UK
    • PSI re-use requests
    • Freedom of info requests
    • Civil disobedience
    • Press campaigns
    • Political persuasion
    • Economic self-interest
  • 15. Open data- what do we mean?
    • Released with an open licence
    • Updated at intervals, not single data dumps
    • Free of explicit and implicit costs
    • Based on publicly available specifications
    • No proprietary dependencies
    • Scalable distribution, not 1 st come 1 st served
    • Release is in the public interest
  • 16. What data should be opened?
    • Any dataset with
      • a necessary public sector first purchaser
      • low marginal costs of distribution
      • no personal data content
      • no security implication
    • Presumption of release... typical objections
      • What if we are blamed?
      • What if they use the data wrongly
      • What happens if our decisions are challenged
    • Open data means “openness”
    • Open data means ending monopolies on usage
  • 17. London DataStore
  • 18. Open government reality Web 2.0 Gov 1.1
  • 19. Engagement with TfL, Met, LDA
    • Requests for data were met with incredulity
    • Followed by organisational denial at the top
    • Then middle management fight-back eg MyTfL
    • http://placr.co.uk/blog/2010/10/journeyplanner_api/
    • The press got involved
    • Re-assertion of political authority
    • Pragmatic negotiations begin
    • First wave of data releases
    • Now TfL are an open data reference
    • Initiatives in PTEs+ Scotland/Wales
  • 20. London DataStore
  • 21. DataGM
  • 22. Plan for open data releases…
    • ‘ Let not the best be the enemy of the good’
    • Start with actionable information: allow access that can make something useful to most people
    • Follow on with datasets that drive transparency: build public support for the programme
    • Then open the public sector “too difficult” box: let someone else try to develop data
    • Publish the best: London DS inspirational uses
  • 23. Mashups
  • 24. Interactive guides
  • 25. Public analytics
  • 26. Feedback
  • 27. Placr data
  • 28. Open transport data in UK
    • Transport data is available to users
      • Live train & metro departure boards
      • National Traveline bus timetables
      • Traffic and accident data
      • Journey planners e.g. TfL
      • Reference data eg NaPTAN
    • Some of this data not reusable
      • Developers stillscraping datasets
      • Publicly funded data mixed with private IPR e.g. NR
    • New opportunities for transport
  • 29. New services built on open data
    • Independent performance statistics
    • Fare splitting to reduce prices
    • New visualisations of service opportunities
    • Commentary on ‘official’ versions of events
      • Re-ordering of journey planner options
      • Advice when service breaks down
    • New service compositions
      • Driving, tolls, parking
      • Bus, train, taxi
  • 30. Open data business models
    • Input costs low (open data is free!)
    • Processing costs high (need skilled labour)
    • Delivery costs low (cloud computing, app stores)
    • Sales revenues are low without v. high volumes
    • Closing the gap
      • Freemium app offers
      • Ad-funded communities around services
      • Data Store sales
  • 31. Performance indicators
  • 32. Implications
    • Open data unleashes huge innovation
    • Challenges state monopolies to be efficient
    • Creates new opportunities for businesses
    • BUT
    • Huge amount of work to clean up the data
    • Needs platforms for neutral data distribution
    • Needs engaging new business models
    • Needs ongoing political support
  • 33. Contact
    • Placr Ltd ( http://www.placr.co.uk/
    • @MadProf on Twitter
    • Journal of Location Based Services http://www.informaworld.com/jlbs