Spatial Information Systems yesterday, today and tomorrow

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Thursday 2 June 2011 - Facultad de Ingeniería, Universidad de Cuenca, Ecuador - …

Thursday 2 June 2011 - Facultad de Ingeniería, Universidad de Cuenca, Ecuador -
Beniamino Murgante

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  • Oltre a formalizzazioni e reti semantiche towntology può utilizzare strumenti multimediali per migliorare la spiegazione dei concetti.
  • Protégé also allows to immediately visualize relationships among classes, sub-classes and instances different from the IS_A ones
  • Starting from concepts strictly related to risk (e.g. hazard, vulnerability, exposure), more abstract concepts have been treated, such as deferred vulnerability, accessibility, etc.. These concepts are difficult to be unambiguously defined and often they have different meanings in different contexts. For instance the term “damage” is linked to the terms “vulnerability” and “exposure” by a relationship of “is related to” type
  • In order to better analyse relationships among concepts, ontologies may be graphically represented by tree structures, where concepts are nodes and relationships are arches. In graphical representations of ontologies, it is possible to show relationships among concepts through proximity, connected lines or colour coding, as well as to visualize only a part of the ontological scheme.

Transcript

  • 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.