Datavisualisation

This is generally about the visual representation of abstract information. (This versus the depiction of physical artefacts) This means that maps, photos are not a part of datavisualisation because they have no start or end.

It is about exploiting our abilities as human beings to see things and we are using computers to help us to do this is, ie; colour, shape, patterns, movement, etc. Using visual wetware, we can see these things.

Datavisualisation is translation. Quantity is translated into colour, scale, shape, position, movement.

Datavisualisation gets interesting when you reveal correlations.

There is a difference between datavisualisation and info graphics, in that datavisualisation is done by computers while info graphics is done with more use of the human brain than technically.

Some problems are hard to datavisualise because of complex systems with non-linear responses to multiple inputs, ie; democracy, climate change, etc.

How it is done

· Find some data

· Think about how to do it algorithmically and translate the data. Use scale, colour, shape, position, movement.

· So that we can see correlations, connections, relationships.

· Exploration, make it interesting to find the data you’ve done put up.

· Layering-being able to layer things over each other to establish connections.

· Interactivity-relationship between data.

· Availability of data- show the data you were using.

· Comparison of data sets, for example; use of correlation.

Note

Correlation is not equivalent to Causation.