The document discusses key issues in combining real world data with abstract data visualizations. It addresses descriptive semantics, underlying assumptions when integrating different data sources and scales, and integrating spatial and temporal data as well as multiple datasets from different sources and scales while maintaining data provenance.

1. Combining the real world and abstract visualization Stuart Dunn Centre for e-Research King’s College London VizNet conference Loughborough, 8th May 2008

2. Key issues > Descriptive semantics > Underlying assumptions > Integration of space and time > Integration of multiple datasets (source, scale, provenance, etc…)

3. Descriptive semantics

4. ‘ We cannot take control of a disciplinary vocabulary and impose it on other researchers. We are are not all logicists. I am certainly not a logicist, and I never will be.’ Descriptive semantics

5. Descriptive semantics Ecotricity wind turbine, Reading

6. Underlying assumptions

7. Underlying assumptions Google Earth  [v. 4.2 (beta)] Berkshire [v. 1.0] Ecotricity Asda, Lower Earley

8. Underlying assumptions

9. Integration of space and time

10. Integration of space and time

11. Integration of space and time

12. Geographical Information Systems: (vector) GIS Integration of multiple datasets (source, scale, provenance, etc…) e.g. roads, rivers e.g. places e.g. countries, territories etc

13. Integration of multiple datasets (source, scale, provenance, etc…)

14. Integration of multiple datasets (source, scale, provenance, etc…)

15. Integration of multiple datasets (source, scale, provenance, etc…) Map 5.6 (NB: insets not to scale)

16. Integration of multiple datasets (source, scale, provenance, etc…)