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Urbanopoly @ SoHuman - SocialCom 2012


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Urbanopoly: a Social and Location-based Game with a Purpose to Crowdsource your Urban Data

presentation given in Amsterdam on 2012/09/03 during SocialCom 2012

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Urbanopoly @ SoHuman - SocialCom 2012

  1. 1. Urbanopoly – a Social andLocation-based Game with a Purpose to Crowdsource your Urban Data Irene Celino, Dario Cerizza, Simone Contessa, Marta Corubolo, Daniele Dell’Aglio, Emanuele Della Valle and Stefano Fumeo SoHuman Workshop @ SocialCom 2012 - 2012/09/03
  2. 2. Agenda Context Gamification Human Computation & Games with a Purpose Smart Cities & Mobile Users Citizen Computation Games Motivation Approach Urbanopoly Purpose & high-level view Storyboard and gameplay Data Collection, Data Consolidation & Data Publishing Evaluation Conclusions SoHuman Workshop @ SocialCom 2012 - 2012/09/03 2 Urbanopoly
  3. 3. Context: Gamification Definition: “The integration of the mechanics that make gamesfun and absorbing into non-game platforms and experiences in order to improve engagement and participation” TNS Global “50% of companies will “Over 70% of Global 2000embrace gamification by 2015” organizations will have at least one Gartner Research gamified application by 2015” Gartner Research “Gamification projects will grow from $100 million in 2011 to $1.6 billion by 2015” “Gamification Market to Reach M2 Research $2.8 Billion in US by 2016” M2 Research SoHuman Workshop @ SocialCom 2012 - 2012/09/03 3 Urbanopoly
  4. 4. Context: is Gamification a fad? In some ways it is a fad – adding points and badges in tacky ways, looking at "gamification" as an easy way to make boring things seem interesting – that is a fad. However, the idea of designing business processes so that those who engage in them find them more intrinsically rewarding – that is a long term trend. Schell Games In three years, we will talk about what is at the core of it – design for motivation – not about the one strategy to get there: getting inspiration from games. Coding Conduct SoHuman Workshop @ SocialCom 2012 - 2012/09/03 4 Urbanopoly
  5. 5. Context: Human Computation Definition: "A paradigm for utilizing human processing power to solve problems that computers cannot yet solve (e.g. image recognition that is trivial for humans, but challenges the most sophisticated computer programs)" Luis von Ahn (CMU) Famous examples: CAPTCHA Google Image Labeller (and all "Games with a Purpose") Freebase data cleansing campaign Amazon Mechanical Turk CrowdDB SoHuman Workshop @ SocialCom 2012 - 2012/09/03 5 Urbanopoly
  6. 6. Context: Smart Cities and Mobile Users Success of Smart Phones and portable devices Success of location-based services (e.g. Siri) Popularity of "light-weight" social networks (e.g. foursquare) Large availability of data about the physical world (e.g. VGI) Users in Mobility are a natural target of (casual) games Urban Spaces of Smart Cities (and thus urban data) are interesting for a large number of "Citizens" Inhabitants Commuters Tourists ... SoHuman Workshop @ SocialCom 2012 - 2012/09/03 6 Urbanopoly
  7. 7. Motivation: Citizen Computation Games citizens as sensors, Urban Computing and check-in logging, Location-based Services mobile apps Citizen Computation Games Linked Data and Games with a Semantic Web Purpose and Crowdsourcing open/gov data, structured data, collecting data, cleaning data, social networks, engaging the user, supporting tourism data and the user with entertainment recommendations SoHuman Workshop @ SocialCom 2012 - 2012/09/03 7 Urbanopoly
  8. 8. Citizen Computation approach Citizen Computation Games: to consume, create and assess the quality of Smart Cities-related (Linked) Data via a Human Computation approach for users in mobility with smart phone devices Traditional Human Computation approaches are based on users domain/background knowledge… …while Citizen Computation is also based on and aims at exploiting "on site" users experience knowledge SoHuman Workshop @ SocialCom 2012 - 2012/09/03 8 Urbanopoly
  9. 9. Urbanopoly purpose Casual Mobile Location-based Game with a Purpose in urban environments aimed to: consume “clean” urban Linked Data assess and improve “doubtful” urban Linked Data contribute “new” urban Linked Data Why Casual? To involve the largest possible user base (i.e. no entry barrier) Why Mobile? To address users in mobility and exploit mobile devices capabilities (e.g. GPS location) Why GWAP? To engage the user base with entertainment SoHuman Workshop @ SocialCom 2012 - 2012/09/03 9 Urbanopoly
  10. 10. Urbanopoly – high-level view game to buy / sell 2 LinkedGeoData venues with missions players (OpenStreetMap) data about 1 venues as missions bootstrap of "venues" data GWAP approach to consolidate data 3 verified / improved data + new data 4 Game purpose: check and correct pre-existing data from LinkedGeoData (OpenStreetMap) + collect missing data SoHuman Workshop @ SocialCom 2012 - 2012/09/03 10 Urbanopoly
  11. 11. Urbanopoly Input Data OpenStreetMap (OSM) data as key-value pairs + pre-defined tags LinkedGeoData (LGD) data as RDF triples (linked data), described by an ontology Urbanopoly data bootstrap: venues are "instances" of selected LGD "classes" with their OSM tags as features Urbanopoly data are RDF statements of the form: <venue> <feature> <value> E.g.: <Rijksmuseum> rdf:type uo:Museum . <Rijksmuseum> uo:museum_type "art museum" . SoHuman Workshop @ SocialCom 2012 - 2012/09/03 11 Urbanopoly
  12. 12. Urbanopoly "game mood" Collect information about your city by playing with the neighborhood around you. Create your venues portfolio and become the greatest landlord ever! SoHuman Workshop @ SocialCom 2012 - 2012/09/03 12 Urbanopoly
  13. 13. Urbanopoly gameplaythe map with the the player’s venue the “wheel of the leaderboardclose-by venues portfolio fortune” when with the best to be visited visiting an players occupied venue SoHuman Workshop @ SocialCom 2012 - 2012/09/03 13 Urbanopoly
  14. 14. Urbanopoly mini-games for Data Collection data acquisition challenges as data validation challenges to checkcontributions to an advertising campaign pre-existing data or other players’ – left: inserting a value, contribution – left: answering a quiz, right: taking a picture right: rating a poster SoHuman Workshop @ SocialCom 2012 - 2012/09/03 14 Urbanopoly
  15. 15. Urbanopoly Data Consolidation Each statement has a confidence score: { <venue> <feature> <value> . } <confidence> which indicates the probability of the statement to be true Each player action (inserting a value, choosing an option, verifying a piece of data) is taken as an evidence of the associated knowledge evidences from players Each evidence alters the confidence score alter confidence score of the related statement(s) by an increment true or a decrement when > upper threshold When a statements confidence score overcomes a threshold, after the evaluation false of 2+ distinct players actions, the statement when < lower becomes either true or false threshold SoHuman Workshop @ SocialCom 2012 - 2012/09/03 15 Urbanopoly
  16. 16. Urbanopoly Data Publication True statements published as linked data If a statements confidence becomes greater that the upper threshold, the statement is asserted: <venue> <feature> <value> This is the approach adopted by OpenStreetMap/LinkedGeoData But theres more interesting information to publish! False statements, statements confidence, provenance info, etc. We decided to publish this evidenceFrom provo:Entity further knowledge as annotations Input usedEvidence to the statements (reification) by extending the W3C PROV-O consolidatedFrom Aggregated Output to create a GWAP ontology Player contributedBy ( generatedBy playedBy Game provo:Agent provo:Activity Cf. SoHuman Workshop @ SocialCom 2012 - 2012/09/03 16 Urbanopoly
  17. 17. Urbanopoly Evaluation (1/2) "Enjoyability" of the game (engagement potential): Average life play: ALP = Played Time / Active Players ~ 80 minutes very good result ☺ "Effectiveness" of the GWAP mechanism: Throughput = Solved Problems / Played Time ~ 5 consolidated statements / hour can be improved "Precision" of the results (measured on results subset) Accuracy = ( (P – FP) + (N – FN) ) / (P + N) ~ 92 % very good result ☺ SoHuman Workshop @ SocialCom 2012 - 2012/09/03 17 Urbanopoly
  18. 18. Urbanopoly Evaluation (2/2) "Playability" of the game Evaluation survey at, with questions about usability, social aspects, physical presence, motivation, etc. First feedbacks very encouraging ☺ "Sociability" through Facebook channel With Facebook Insights (, tracking of installs, demographics, log-ins, content sharing, etc. Example of published "story" on Facebook Timeline: Statistics about "stories" and "impressions": Interesting results, but channel to be further exploited ☺ SoHuman Workshop @ SocialCom 2012 - 2012/09/03 18 Urbanopoly
  19. 19. Conclusions Gaming features and design are core to find the best trade-off between players engagement and effectiveness to achieve the GWAP purpose Getting both a high ALP and a high throughput is a challenge, while getting a high accuracy is relatively easy Citizen Computation games with a "social" flavour appear a powerful means to collect and validate urban data Meeting point of Human Computation, Social Computing and Crowdsourcing Smart Cities stakeholders have a number of needs that could be fulfilled by this kind of solutions Origin/destination matrixes, pressure on parking maps, etc. SoHuman Workshop @ SocialCom 2012 - 2012/09/03 19 Urbanopoly
  20. 20. Thanks for your attention! Questions?Irene Celino – CEFRIEL, ICT Institute Politecnico di Milano email: – web: SoHuman Workshop @ SocialCom 2012 - 2012/09/03