1. infovis and data george, laura, tjerk

2. http://www.isrc.umbc.edu/HCIHandbook/figures/28-10.jpg

3. data - what is it? data is simple facts, lacking any context. within infovis, the strategy is to transform data into a relation or set of relations that are more structured and easier to map to visual forms

4. Data are observational measurements that have been recorded in some way, whereas information is data that is generalized, ordered and contextualized in ways that give them meaning. Information thus is selective toward data, separating the important from the relatively unimportant. [Mennis et al., 2000] data - what is it?

5. in order for data to be infovis-ready, it needs to be: readable - supporting discourse about the subject data-driven information-driven, are there visible variations infovis concerns the gestalt of data

6. from raw data to data tables via data transformations human interaction creates data tables

7. data tables dimensionality is the referral to the number of input variables, the number of output variables, both together or even the number of spatial dimensions in the data 2d and 3d dimensionality creates understandable data tables. data tables can be multidimensional visualizations

9. data tables data tables can consist of cases and variables variables describe relations between cases mathematically, a relation is a set of tuples a tuple is a sequence (also known as an "ordered list") of objects, each of a specified type

10. data tables three types of variables are mentioned nominal - without natural order ordinal - obeys a lessthan relation quantitative - calculatable within these categories, different types of transformation can be processed.

11. data tables The data scale of a parameter is given by the statistic attributes of the values. - nominal: Unordered set (only = or != relations) example: film titles. - ordinal: Ordered set (=, !=, relations) example: film ratings. - discrete: Numeric range (Integer, arithmetic possible) example:film year. - continuous: Numeric range (Real/float numbers, arithmetic possible) example: film length. - binary: true or false (Boolean arithmetic) example: film available.

12. data tables metadata - descriptive information about data important for choosing visualisations metadata often informs about data tables and their structure a good structure creates better insights in data

13. data tables metadata is ”data about data” which allows computers to process information more effectively. [Dmoz, 2004] two meanings - catalogue (find &get it) - context data (understand it) and three applications - find data. that is the functionality of catalogue-systems. - get data. meta-data is needed in interchange and real transportation of data. - understand data. metadata help to interpret and understand the numbers, words and other things denoted as data

14. data tables

15. data transformation can be performed different transformations lead to different visualisations

16. values: derived values structure: derived structure values: derived structure structure: derived values data transformations

17. an example

18. visual structures and mappings goals of visualization are: - making invisible correlations visible - treat graphic aspects of display as the critical element - providing interfaces for asking questions of the data

19. choices in visualisation

20. visual structures and mappings problems with visualisation - technical constraints [screen] - human work patterns - chartjunk [Tufte] ‘Chartjunk consists of decorative elements that provide no data and cause confusion’

21. chartjunk example

22. perception and interpretation - must connect with the user via sensory and arbitrary conventions - does it allow modifications? - does it allow zooming [physical or semantic]?

23. “There is a trade-off between amount of information, simplicity, and accuracy. It is often hard to judge what users will find intuitive and how [a visualization] will support a particular task” [Tweedie et al]