Neogeography: the challenge of channelling large and ill-behaved data streams Maurice van Keulen and...
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Transcript of Neogeography: the challenge of channelling large and ill-behaved data streams Maurice van Keulen and...
Neogeography: the challenge of channelling large and ill-behaved data streamsMaurice van Keulen and Rolf de By
Spatial information is becoming an ordinary commodity
Google Earth & Maps, MS Bing, NASA’s WorldWind
Geo-tagging of visited places, meetings, activities; automatic geo-
tagging by personal devices: photo/video camera, cell phone
Social networks with location intelligence
In the less developed world, serious applications are slowly becoming
a reality
Location intelligence for agriculture, health, transportation and
traffic, education, emergency mitigation, electronic payments,
election monitoring, market prices etc.
12 Mar 2010Kick-off Neogeography 2
FOR SERIOUS APPLICATIONS IN THE LESS DEVELOPED WORLDLOCATION INTELLIGENCE
12 Mar 2010Kick-off Neogeography 3
SOCIAL NETWORK APPLICATIONS
Trucking and road availability
Farming and field suitability
Traffic and car-pooling
Emergency response
Crime and neighbour-
hood vigilance
Urban utility monitoring
Neogeography: applications in which geographic information derives
from end-users, not only from official bodies like mapping agencies,
cadastres or other official, (semi-)governmental entities.
Central problems User community is dynamic Users contribute information and expect something in return Contributed information is not necessarily of good quality or trust Contributed information is somewhat unstructured
(contributors cannot be expected to follow strict data schemes and they may only have access to a cell-phone operated network)
Need for a new brand of location-based information management
12 Mar 2010Kick-off Neogeography 4
NEOGEOGRAPHY
Importance of neogeography in disaster response
In disaster events: In situ real-time data
may be scarce, may be mutually inconsistent, and may change over time is needed to augment partial knowledge and understanding.
Communication infrastructure may be damaged. All data is welcome, all kinds of data also:
witness reports photos audio videos human and machine sensor readings
General public is a powerful information source, and generally has an incentive to report (911).
The neogeographers in disasters
People on site
People affected
Rescuers and other professionals
Mobile telephone providers
Press
Biggest challenge: how to make sense of large amounts of not very
trustworthy information:
Can you rely on what unknown sources inform you about?
12 Mar 2010Kick-off Neogeography 8
SYSTEM OBJECTIVE
XML
sms / sensor & satellite data / data from official bodies
geoservices
Open source XML-based spatial data
infrastructure capable of
orchestrating & processing
ambiguous/vague semi/unstructured
geodata workflows delivering
personalized geoservices
Spatiotemporal features
Extend XML database technology to fully include spatial feature
support (OGC) and support for fully XML-based development of
geoservices and spatiotemporal analysis
Spatiotemporal vagueness
Extend information extraction technology to handle ambiguity and
spatiotemporal vagueness in sensor data and explicit natural
language references to the where and when
Data augmentation and data quality improvement
Spatiotemporal profiling
Provide better understanding of user’s information needs by
analyzing historic requests and offered neogeographic data
User profile pattern matching: finding like-minded users
12 Mar 2010Kick-off Neogeography 9
SCIENTIFIC CHALLENGES
Space and time issues
Uncertainty and trust
Role of the volunteered information
Difference: handling the map versus handling the data
12 Mar 2010Kick-off Neogeography 10
CONNECTION WITH OTHER NEOGEO PROJECT
12 Mar 2010Kick-off Neogeography 11
THE TEAM
Rolf de By(ITC)
Mauricevan Keulen
(UT)
Jan Flokstra(UT)
ClarisseKagoyire (ITC)
Mena Badieh Habib (UT)
PhD student @ITCBackground: Master @ITC about “Web geoprocessing services on GML with a fast
XML database”She proved the feasibility of some this project’s ideas.
PhD student @UTBackground: Master @Ain Shams University, Cairo about “Automated Arabic
Text Categorization”Strong background in
natural language processing and text/data mining.