Slides describing the eMerges approach at the <a href="http://www.ordnancesurvey.co.uk/oswebsite/partnerships/research/research/terracognita.html">Terra Cognita Workshop</a>, collocated with ISWC.

2. Smart GIS Vlad Tanasescu @ KMi January 23, 2006

3. Vlad Tanasescu @ KMi Dr. John Domingue Prof. Enrico Motta Prof. Marc Eisenstadt Supervision team:

4. Gugliotta, A., Domingue, J., Villarías, L., Davies, R., Rowlatt, M., Richardson, M., Stincic, S.

6. What is Spatial Data?

7. 'up to 80 percent of data have a spatial component’

8. 'up to 80 percent of data have a spatial component' is now part of our geographical lexicon (e.g., Boulos and Roudsari 2000; Francica 2000; McKee 2001; Swartz 2001). Klinkenberg (2003), The true cost of spatial data in Canada . The Canadian Geographer 47 (1), 37-49. This is more significant knowing the fact that about eighty percent of all data have a spatial component Dent et al. (2000), Using GIS to Study the Health Impact of Air Emissions , Drug and Chemical Toxicology, Volume 23 It is estimated that eighty percent of all data have a spatial component ; therefore… Radford University, Geoinformatics information page, http://www.radford.edu/~informatics/geoinformatics.shtml Moreover, while it is estimated that up to 85% of all data have a spatial component … Brown University (2001), Project Description, http://envstudies.brown.edu/thesis/2001/james/intro.html Estimates suggest that up to up to 80% of all applications have a geographical component Terra Cognita 2006 - Directions to the Geospatial Semantic Web

9. Data Spatial Related Data

10. Spatial Object Data Spatial Related Data

11. … things with ‘whereness’… James Hood and Antony Galton, Implementing Anchoring , GIScience 2006

12. Whereness?

13. David Hockney, Still Life on a Glass Table , 1971-72

14. Where is the vase?

15. … on the table

16. Table-Top Space (TTS)

17. David Hockney, Nichols Canyon , 1980

18. Where are the mountains?

19. David Mark and Gaurav Sinha, Ontology of Landforms: Delimitation and Classification Of Topographic Eminences , GIScience 2006

21. Where is the canyon?

22. Antony Galton and James Hood, Anchoring: A New Approach to Handling Indeterminate Location in GIS , COSIT 2005

24. Can we count the forests?

25. Brandon Bennett, What is a Forest? On the Vagueness of Certain Geographic Concepts , Topoi, 20:189-201, 2001C

27. Geographic Space (GS)

28. = Table-Top Space (TTS) Geographic Space (GS)

29. Vagueness + Multirepresentation

30. Candidate solutions?

31. GIS

32. SELECT C1.Name, R1.Name FROM City C1, River R1 WHERE Distance(C1.Shape,R1.Shape) <= ALL (SELECT Distance(C2.Shape) FROM City C2 WHERE C1.Name <> C2.Name)

33. Vagueness + Multirepresentation

34. Web2.0 Maps

36. APIs + Reality Effect

37. Reality Effect

40. APIs

42. Geo mashups

43. housingmaps.com

44. popular

46. Limitations?

47. Points on a map… None of the GS specificities are handled

48. … lacking semantics

51. What can we do?

52. What is a Spatial Objects

53. grab count move smell

54. Affordances

55. what the environment &quot;provides or furnishes, either for good or ill&quot; J.J. Gibson, 1979

56. In an information medium?

57. opportunities to collect useful data and representations

58. The actual work of establishing a useful affordance hierarchy is formidable. Jordan, Raubal, Gartrell, Egenhofer, An Affordance-Based Model of Place in GIS , SDH 1998 We limit the presentation to type classes, representing categories as the set of types sharing some behavior, i.e., offering common affordances. Werner Kuhn, Modeling the Semantics of Geographic Categories through Conceptual Integration , GIScience 2002

59. ? ? ?

60. Lack of context

62. Where are adapted rest centres and open supermarkets Get rest-centres in area Get-supermarkets in area

63. 

64. How to get there? Get access roads

65. 

67. Where ? Get accessible areas Get access paths

68. 

69. How to accommodate people? Get habitable parts Get catering facilities

70. 

71. Key to integration 1: Context

72. - user role - task - object - localization Context:

73. ? ? ? context +

74. Key to integration 2: Data Spatial Related Data Affordances Spatial Object

75. context +

76. Semantic Web Services

78. WSMO 2 GS MEDIATORS GOALS WEB SERVICES ONTOLOGIES Data Spatial Related Data Affordances Spatial Object

79. Client Services IRS-III

80. Client IRS-III

81. DIP : a 17M Euro integrated project

82. DIP WP9 Scenario: Weather Emergency Planning

84. BuddySpace Server BuddySpace Services Google Maps API AJAX Accommodation Goal Environment Goal Presence Goal Archetypes SGIS-Spatial Emergency-GIS-Domain Emergency-GIS-Goals BuddySpace Goals Smart Filter Services ViewEssex Services Environment Services

86. Reception

87. singled out by the 3 external reviewers as the best exemplar for SWS technology in DIP shortlisted as a finalist for the Semantic Web Challenge at ISWC 2006 Won Semantic Web Scripting Challenge at a ESWC 2006

88. http://irs-test.open.ac.uk/sgis-dev/

89. Thank you