The document discusses the evolution of Geographic Information Systems (GIS) from historical perspectives to modern challenges, emphasizing the issues surrounding data accessibility and interoperability. It highlights the importance of the National Spatial Data Infrastructure and the role of ontologies in improving data exchange and semantic matching in GIS. Additionally, the document touches on collaborative data production methods such as volunteered geographic information and crowdsourcing.

1. Beniamino Murgante Spatial Information Systems yesterday, today and tomorrow e-mail: [email_address] url: http://www.unibas.it/utenti/murgante/Benny.html Skype: beniamino.murgante Linkedin: http://www.linkedin.com/pub/beniamino-murgante/7/960/9aa

2. My books

3. My books

4. Other books and special issues Analysing, Modelling and Visualizing Spatial Environmental Data NeoGeography and WikiPlanning"

5. Network co-founder

6. Network co-founder

7. Network co-founder

8. Network co-founder

9. Network co-founder 1,403 Members

10. Network co-founder 1,680 Members

11. History of GIS Ian McHarg 1969

12. History of GIS 1969 Physiographic obstructions Social aspects

13. History of GIS 1969 Physiographic obstructions

14. Origin of GIS

15. Evolution of GIS Cost of the GIS software (Longley, et al. 2001)

16. Evolution of GIS Increase of GIS software functions (Longley, et al. 2001)

17. Evolution of GIS Growth of personal computers power (Moore's law ) (Longley, et al. 2001)

18. Evolution of GIS

19. Evolution of GIS

20. Evolution of GIS GIS users tend to develop their own data sets for many reason: 1. they may not know available existing data sets that could be appropriately used for their applications; 2. access to these data sets is difficult; 3. they are not used to sharing data sets with other sectors and/or organizations; 4. existing geospatial data sets stored in a certain GIS system may not be easily exported to another system.

21. Evolution of GIS As a result, the new age of GIS is still characterized by: 1. many actors involved in data collection and distribution; 2. a proliferation of GIS applications , product types, and formats; 3. duplication as a consequence of the difficulties to access the existing data, and the highly specific quality of the data collected; 4. increasing difficulty in the exchange and use of data that came from different organizations ;

22. CANRI 1999 Evolution of GIS

23. Executive Order 12906 Coordinating Geographic Data Acquisition and Access: The National Spatial Data Infrastructure (NSDI) " National Spatial Data Infrastructure" means the technology, policies, standards, and human resources necessary to acquire, process, store, distribute, and improve utilization of geospatial data. "Geospatial data" means information that identifies the geographic location and characteristics of natural or constructed features and boundaries on the earth. This information may be derived from, among other things, remote sensing, mapping, and surveying technologies. Statistical data may be included in this definition at the discretion of the collecting agency. The "National Geospatial Data Clearinghouse" means a distributed network of geospatial data producers, managers, and users linked electronically.

24. Executive Order 12906

25. Executive Order 12906

26. Executive Order 12906 Spatial Data Infrastructures: geographic data and attributes, catalogues and Web mapping (as a means to discover, visualize, and evaluate the data), enough documentation (metadata)

27. Nebert, The SDI Cookbook Information Resource 1. Document Metadata Data Server Metadata Server 2. Publish User Registry 3. Register 4. Query 5. Access

28. Nebert, The SDI Cookbook Web Client Clearinghouse Servers Gateway(s) User Z39.50 protocol HTTP protocol Service Registry Web Server One Search across many servers Metadata is the key

29. National Spatial Data Infrastructure

30. National Spatial Data Infrastructure

31. National Spatial Data Infrastructure

32. National Spatial Data Infrastructure

33. National Spatial Data Infrastructure

34. National Spatial Data Infrastructure

35. National Spatial Data Infrastructure

36. National Spatial Data Infrastructure

37. National Spatial Data Infrastructure

38. Geoportals

39. Interoperability The main barriers towards a full interoperability are determined by three factors (Murgante 2011); Bureaucratic: due to a poor practice to share data which, in most cases, leads to a sort of personal property right by the employer, who provides to its management; Technological: mainly produced by differences between systems, structures and format of data; Semantic: due to the lack of correspondence in meanings.

40. Information-Explosion Era The complete elimination of bureaucratic barriers could be achieved with a “ wiki ” approach to data production and management. (Murgante 2011); Volunteered Geographic Information (Goodchild, 2007) where distributed masses create, manage and disseminate spatial data , Crowdsourcing (Goodchild, 2009) organizations or companies externalize production using a mass collaboration (OpenStreetMap), Neogeography (Turner, 2006), citizens produce data integrating maps with geo-tagged photos, videos, blogs, Wikipedia, etc.. Cloud Servicies

41. Information-Explosion Era

42. Information-Explosion Era

43. Information-Explosion Era Google Earth bing virtual earth openstreetmap

44. Information-Explosion Era

45. Information-Explosion Era

46. Information-Explosion Era http://www.giscloud.com/map/11766/timatongis/tourism-in-matera-on-geographic-information-system

47. Information-Explosion Era

48. Semantic Matching http://slashgeo.org/2011/05/24/Geospatial-Semantic-Web-Part-Inevitable-Future

49. Semantic Matching Neither a standard data format nor a common data model allows for the transfer of the meaning of information The more complex issue of what is represented instead of how it is represented needs to be addressed

50. Semantic Matching  Users   Table Vegetation Table Fauna … Logical Model  Fauna wolf wildcat fox … Database Vegetation Conifers Hardwood Species, Mediterranean macchia…   fauna forest live Conceptual Model Vegetation Fauna observer Real world

51. Semantic Matching Perception Lake??? Body of water??? Lagoon??? Basin???

52. ONTOLOGY: definitions An ontology is “An explicit specification of a conceptualization” (Gruber, 93) An ontology is a formal and explicit specification of a shared conceptualization ( Borst 1997) In order to implement an ontology, it is necessary to formalize some mental models within a community, aiming to capture domain knowledge and providing a common vision, which can be reused and shared by other groups (Chandrasekaran et al., 1999).

53. ONTOLOGY: definitions Eco (2005) reviewing the book "Where are you? Mobile Ontology" (Ferraris 2005) high-lights that the most interesting aspect of the volume is not the fact that Ferraris developed a mobile phone ontology, but that its ontology allowed us to understand what a mobile phone is. Ontologies, aiming at ensuring system interoperability, allow to clarify in a deepened way several concepts, often considered known, but affected by a certain degree of superficiality (Murgante 2008).

54. ONTOLOGY: definitions When a wider semantic matching is reached considering many communities, a certain detail of the original meaning is loosed (Fonseca et al., 2002) An ontology defines, then, the basic conditions, the relationships, the vocabulary of a domain , the rules for combining words and relationships to define any vocabulary extension (Neches et al., 1991). An ontology can be seen as a set of hierarchically structured terms for a domain description and can be used as a skeleton for a knowledge base (Swartout et al., 1997).

55. ONTOLOGY Four phases of Ontology building : the definition of concepts related to domain, which led to the realization of a glossary finding main concepts of the domain, associating definitions included in our glossary; hierarchically organizing concepts, finding super-classes and sub-classes of concepts with relationship IS_A; studying other types of relationships associated to concepts.

56. ONTOLOGY Fundamental steps in ontology building, represented through a layer cake (adapted from Cimiano, 2006).

57. ONTOLOGY Terms are a fundamental aspect in ontology building; if an object has not been represented by a term, it means that it lacks in importance. Synonyms, on one hand are enrichments of language, while from an interoperability point of view are factors which may generate confusion. Many kinds of relationships exist: taxonomic (IS_A) , meronimic (PART_OF) telic (PURPOSE_OF..) etc.

58. Ontologies Vs Database The ontology is a formal explicit model for a domain An Entity relationship scheme can be translated in an ontology In the case of particularly complex domains an ontology allows a more effective representation Domain concepts Concepts property Property constraints Entity Attributes and relationships Constraints Ontologies Database

59. Building ontologies for disaster management: seismic risk domain Damage definition B. Murgante, G. Scardaccione, G. Las Casas

60. Building ontologies for disaster management: seismic risk domain Damage definition B. Murgante, G. Scardaccione, G. Las Casas

61. Building ontologies for disaster management: seismic risk domain Definition of emergency area and strategic elements B. Murgante, G. Scardaccione, G. Las Casas

62. Building ontologies for disaster management: seismic risk domain Relationship IS_A is a link generalization / specialization among entities Super-class entities generalize the sub-classes Sub-class entities are Super-class specialization Sub-classes inherit Super-class attributes B. Murgante, G. Scardaccione, G. Las Casas Attribute 1 Sub-class 1 Super-class Sub-class 2 Attribute 2 Attribute 3 IS_A

63. Super-class Sub-class Building ontologies for disaster management: seismic risk domain B. Murgante, G. Scardaccione, G. Las Casas

64. Relationship Contribuisce a Vulnerabilità Rischio sismico Building ontologies for disaster management: seismic risk domain B. Murgante, G. Scardaccione, G. Las Casas

65. Relationship IS_A

66. Building ontologies for disaster management: seismic risk domain B. Murgante, G. Scardaccione, G. Las Casas

67. Building ontologies for disaster management: seismic risk domain B. Murgante, G. Scardaccione, G. Las Casas

68. Building ontologies for disaster management: seismic risk domain Representation of relationships between concepts B. Murgante, G. Scardaccione, G. Las Casas

69. Attributes inherited from type of vulnerability Specific attribute of physic vulnerability Defining properties Building ontologies for disaster management: seismic risk domain B. Murgante, G. Scardaccione, G. Las Casas

70. Ontological Network Tree structure concerning seismic risk.