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

1. Urbanopoly – a Social and
Location-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. 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
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3. Context: Gamification
Definition:
“The integration of the mechanics that make games
fun 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 2000
embrace 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
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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
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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. 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. 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
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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
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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
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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
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11. Urbanopoly Input Data
OpenStreetMap (OSM)
http://www.openstreetmap.org/
data as key-value pairs + pre-defined tags
LinkedGeoData (LGD)
http://linkedgeodata.org/
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. Urbanopoly "game mood"
http://bit.ly/urbanopoly
Collect information about your city by playing with the
neighborhood around you.
Create your venues' portfolio and become the greatest
landlord ever!
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13. Urbanopoly gameplay
the map with the the player’s venue the “wheel of the leaderboard
close-by venues portfolio fortune” when with the best
to be visited visiting an players
occupied venue
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14. Urbanopoly mini-games for Data Collection
data acquisition challenges as data validation challenges to check
contributions 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
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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 statement's confidence score
overcomes a threshold, after the evaluation false
of 2+ distinct players' actions, the statement when <
lower
becomes either true or false threshold
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16. Urbanopoly Data Publication
True statements published as linked data
If a statement's confidence becomes greater that the upper
threshold, the statement is asserted: <venue> <feature> <value>
This is the approach adopted by OpenStreetMap/LinkedGeoData
But there's 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
(http://swa.cefriel.it/ontologies/gwap)
generatedBy
playedBy Game
provo:Agent provo:Activity
Cf. http://swa.cefriel.it/linkeddata/
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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 ☺
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18. Urbanopoly Evaluation (2/2)
"Playability" of the game
Evaluation survey at http://bit.ly/u-survey, with questions about
usability, social aspects, physical presence, motivation, etc.
First feedbacks very encouraging ☺
"Sociability" through Facebook channel
With Facebook Insights (http://www.facebook.com/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 ☺
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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.
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20. Thanks for your attention!
Questions?
Irene Celino – CEFRIEL, ICT Institute Politecnico di Milano
email: Irene.Celino@cefriel.it – web: http://swa.cefriel.it
SoHuman Workshop @ SocialCom 2012 - 2012/09/03