Neogeography: the challenge of channelling large and ill-behaved data streams Maurice 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 WORLD LOCATION 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

Example neogeo sites

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 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) Maurice van Keulen (UT) Jan Flokstra (UT) Clarisse Kagoyire (ITC) Mena Badieh Habib (UT) PhD Background: about “Web geoprocessing services on GML with a fast XML database” She proved the feasibility of some this project’s ideas. PhD Background: about “Web geoprocessing services on GML with a fast XML database” She proved the feasibility of some this project’s ideas. PhD Background: Shams University, Cairo about “Automated Arabic Text Categorization” Strong background in natural language processing and text/data mining. PhD Background: Shams University, Cairo about “Automated Arabic Text Categorization” Strong background in natural language processing and text/data mining.

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