1. Vlasios Voudouris, Jo Wood, Peter Fisher giCentre, Department of Information Science, City University, London, UK Collaborative geoVisualization: Object-Field Representations with Semantic and Uncertainty Information

2. overview Field and Object Conceptual Models Conceptual Background of Object-Fields Formalised Object-Field Framework Application Example Conclusions

3. object model We mainly model the world as a series of point, line and area objects The same object can be represented by different object classes at different scales of analysis (multiple representation problem?) If objects are either poorly defined or have uncertain boundaries, then we need (but not necessarily)…

4. …field model The world is seen to be made up of continuously varying properties spread out in space. Key factors: continuity and self-definition. And although we can have a collection of locations [resolution elements] taken together to represent an object, the GI database has NO knowledge of such an object without processing! In other words, we can construct a model of some field independently of the characteristics of that field. A more recent approach is …

5. … object-field: conceptualization A model of space that combines the object and the field view of space using resolution elements [pixel?] that contain pointers to objects rather than the attributes of a field The data structures appropriate for storing such models will depend in part, on the types of relationship that exist between the field and object representations. In other words, the world is represented using the field approach and features within this field are identified and visualised as collections of locations It is, therefore, largely based on an augmented interpretation of the field model And …

6. …object-field: conceptualization figures Cova & Goodchild (2002) Winter (1998)

7. …and Formalising the Approach The model is implemented using the object-oriented paradigm and the Java programming language and its implementation is grounded in the raster data structure. It enables the identification of objects from continuous phenomena with the retention of variable levels of properties such as uncertainty within the object and with semantic description of the user’s understanding of the object.

8. ...so why uncertainty & semantics? To inform the information systems on how the, for example, land cover representation was produced. To identify where uncertainty informs the spatial description of the object and how the semantics are informed by this. To communicate conceptualization/knowledge To introduce invariable and fixed definitions of fuzzy units To transfer information that is not mappable in an integrated, single and combined data model To give, in collaborative environments, the collaborators a context within which the analysis is conducted

9. …and how? Using a land cover case: –By grouping cells together –By forming land cover FieldObjects [woodland] –And by attaching uncertainty [fuzzy memberships] and semantics [revised methodology, political initiative] objects.

10. conclusions We propose: –The implementation of a workable formalized framework from which coordinated object-based and field-based models can be systematically merged. –The modelling of three-level semantic and uncertainty information using object-fields Why? –To improve the output quality of datasets by allowing a group of people to attach, modify and share their semantics. –To enable, in collaborative environments, the transferring of geospatial conceptualizations and associated metadata by realizing human-centred objects as if these were detached/real objects while field-based continuum representations are used.

11. Thank you! Questions back to you: 1)Suggestions for improvements? 2)Applicability and usefulness of the model? 3)…?