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

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

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

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  1. Neogeography: the challenge of channellinglarge and ill-behaved data streams Maurice van Keulen and Rolf de By

  2. 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. for serious applications in the less developed world Location intelligence Kick-off Neogeography

  3. SOCIAL NETWORK APPLICATIONS • Crime and neighbour-hood vigilance • Urban utility monitoring • Trucking and road availability • Farming and field suitability • Traffic and car-pooling • Emergency response Kick-off Neogeography

  4. 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 neogeography Kick-off Neogeography

  5. Example neogeo sites

  6. 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).

  7. 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?

  8. System Objective sms / sensor & satellite data / data from official bodies Open source XML-based spatial data infrastructure capable of orchestrating & processing ambiguous/vague semi/unstructured geodata workflows deliveringpersonalized geoservices XML geoservices Kick-off Neogeography

  9. 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 scientific challenges Kick-off Neogeography

  10. Space and time issues Uncertainty and trust Role of the volunteered information Difference: handling the map versus handling the data CONNECTION WITH OTHER NEOGEO PROJECT Kick-off Neogeography

  11. The team PhD student @ITC Background: 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 @UT Background: Master @Ain Shams University, Cairo about “Automated Arabic Text Categorization”Strong background in natural language processing and text/data mining. Mauricevan Keulen(UT) Rolf de By(ITC) ClarisseKagoyire (ITC) Mena BadiehHabib (UT) Jan Flokstra(UT) Kick-off Neogeography

  12. Think outside the box

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