14 crowdsourcinggi
TRANSCRIPT
CROWDSOURCING AND GI
JAVIER MORALES
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 2
AGENDACROWDSOURCING AND SPATIAL DATA INFRASTRUCTURES
Background
Crowdsourcing Principles
Examples
Conclusions
© Manuel Ramos
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 3
BACKGROUNDTHE ROLE OF GI
Geographic information (GI) was for generations produced and consumed by professionals
Societal processes land transfer, planning and development, risk management …that affect organisations and individuals.
Trend to develop mechanisms to bring GI closer to non-professional users
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 4
BACKGROUNDMODERN TOOLS
Web 2.0 high interactivity, sharing and collaboration, Interoperability, and real-time user-generated content
Web 2.0 apps social networking, blogging, wikis, video sharing, and Mashups
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 5
BACKGROUNDWEB 2.0
The users’ role has changedfrom looking for and retrieving content to active participation
everyone contributes to the common knowledgeof the group they interact with
© www.techscreens.com
Wikipedia, YouTube, Flickr, Wikimedia
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 6
CROWDSOURCINGWHY?
Organisations today have to operate in information-rich environments
They can no longer afford to rely entirely on their own ideas They cannot bet their success to a single product to the market
Traditional development which largely focused on
intra-organisational skills, closed off from outside ideas and technologies
is becoming obsolete
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 7
CROWD-WHAT?
Crowdsourcing
is the act of taking a job traditionally performed by a designated agent (usually an employee) and outsourcing it to an undefined,
generally large group of people in the form of an open call.
Jeff Howe
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 8
CROW-WHAT?
The crowdsourcing approach
a recognised entity posts a problem online a large number of individuals reacts they provide a small part of the solution to the problem solutions offered are exhaustive and not disjoint
This approach is popular because
web-based social technology makes it feasible & affordable to collect data using groups of individuals
such data is often more accurate indicator of current conditions in the real world than what can be obtained from data stored in databases
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 9
CROWDSOURCING PRINCIPLES
1. Formulate the problem properly Scope & purpose
2. State deliverables concretely (quality) let the crowd know exactly what is expected from them leave space for their creativity
3. Connect with the right crowd diversity (the question is answered from multiple points of view) scientists or specialists and a significant number of hobbyists
with knowledge in the problem domain
4. Deploy the appropriate crowd management scheme moderate discussion boards post provocative challenges & publish milestones
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 10
EXAMPLES
Ushahidi
GeoNames
Geonode
Google MapMaker
OpenStreetMaps
Aim at providing open data through the Creative Commons Attribution – ShareAlike license
data can be used freely and if you alter or build upon it, you need to share those alterations back to the community
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 11
OPEN DATA
Data is considered to be open if
it is and publish online, updated as often as possible, provided in a way that allows for its legal use for any purpose, and that allows easy processing with any arbitrary software program
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 12
OPENSTREETMAP
The OpenStreetMap project is a crowdsourced geospatial data repository, with a global cast of volunteers.
With the mission to create a free editable dataset of the world
It has been very successful especially In producing data fro places where it was very scarce
(rural & peri-urban areas) In keeping up-to-date datasets of rapidly evolving urban areas
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 13
OPENSTREETMAPA YEAR OF EDITS
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 14
OPENSTREETMAPPROJECT HAITI - 2010
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 15
OPENSTREETMAP
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 16
SPATIAL DATASETSCROWDSOURCING IMPACT
Maps
Chia, Colombia
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 17
OPENSTREETMAPCOMPARISON
Enschede
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 18
OPENSTREETMAPCOMPARISON
GuatemalaCity
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 19
SPATIAL DATASETSCROWDSOURCING IMPACT
Lahore, Pakistan in Google Maps
(before MapMaker)
Lahore, Pakistan in Google Maps(after MapMaker)
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 20
SPATIAL DATASETSCROWDSOURCING IMPACT
Bolivia
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 21
USHAHIDIHISTORY
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 22
USHAHIDIWORKING APPROACH
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 23
USHAHIDIDATA INPUTS
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 24
USHAHIDIEXPLOITATION
Disaster Response
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 25
USHAHIDIEXAMPLES http://ushahidi.internewskenya.org/
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 26
USHAHIDIEXAMPLES http://haiti.ushahidi.com/
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 27
CONCLUSIONSCROWDS & SDI
Work on something relevant (or at least has the promise of being useful relatively soon)
Put the users at the center View users as important contributors Give them responsibility Enable ratings Derive metadata from usage
Make customization as easy as possible Enable mashups Unlock the visualizations
Index your data and become searchable
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 28
CHALLENGESRESEARCH ISSUES
Automatic validation an filtering of data inputs
Indirect geo-tagging (mining of social networks)
Automatic aggregation & summarizing of similar data entries
© Community FixIt
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 29