Smart GIS Vlad Tanasescu @ KMi January 23, 2006

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

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

What is Spatial Data?

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

'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

Data Spatial Related Data

Spatial Object Data Spatial Related Data

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

Whereness?

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

Where is the vase?

… on the table

Table-Top Space (TTS)

David Hockney, Nichols Canyon , 1980

Where are the mountains?

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

Where is the canyon?

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

Can we count the forests?

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

Geographic Space (GS)

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

Vagueness + Multirepresentation

Candidate solutions?

GIS

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)

Vagueness + Multirepresentation

Web2.0 Maps

APIs + Reality Effect

Reality Effect

APIs

Geo mashups

housingmaps.com

popular

Limitations?

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

… lacking semantics

integrating data sources understanding them understanding the user

integrating data sources

understanding them

understanding the user

What can we do?

What is a Spatial Objects

grab count move smell

Affordances

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

In an information medium?

opportunities to collect useful data and representations

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

? ? ?

Lack of context

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



How to get there? Get access roads



Where ? Get accessible areas Get access paths



How to accommodate people? Get habitable parts Get catering facilities



Key to integration 1: Context

- user role - task - object - localization Context:

? ? ? context +

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

context +

Semantic Web Services

Objectives that a client wants to achieve by using Web Services Semantic description of Web Services: Capability ( functional) - Interfaces ( usage ) Provide the formally specified terminology of the information used by all other components Connectors between components with mediation facilities for handling heterogeneities WSMO MEDIATORS GOALS WEB SERVICES ONTOLOGIES

Semantic

description

of Web Services:

Capability ( functional)

- Interfaces ( usage )

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

Client Services IRS-III

Client IRS-III

DIP : a 17M Euro integrated project

DIP WP9 Scenario: Weather Emergency Planning

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

Reception

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

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

Thank you