infovis and datageorge, laura, tjerk

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

data - what is it?

data is simple facts, lacking any context.

within infovis, the strategy is totransform data into a relation or set of relations that are more structured and easier to map to visual forms

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?

data - what is it?

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

from raw data to data tables

via data transformations

human interaction

creates data tables

data tables

dimensionality is the referral to the numberof input variables, the number of outputvariables, 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

data tables

data tables can consist of cases and variablesvariables describe relations between casesmathematically, a relation is a set of tuplesa tuple is a sequence (also known as an "ordered list") of objects, each of a specified type

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.

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 (=, !=, <, and > 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.

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

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

data tables

QuickTime™ and aGIF decompressor

are needed to see this picture.

data tables

data transformation can be performed

different transformations leadto different visualisations

values: derived valuesstructure: derived structurevalues: derived structurestructure: derived values

data transformations

an example

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

choices in visualisation

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’

chartjunk example

perception and interpretation

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

“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]