Data Visualisation (DV) is the result of processing raw data to help communicate information with greater ease. The reason we need this is because humans take in new ideas in two ways; shallow and deep. Shallow learning would be considered reading an article or block of information and memorizing nothing but what has been written. They are “isolated facts” (Chew, S, 2011) with no connection to the real world. It’s been proven that humans “are wired to make sense of visual images” (Reas & McWilliams, 2010, pg121) and this would be considered deeper learning. Taking on ideas that have a connection imagery or our emotions. The ultimate aim of making Data Visualisations is to create a level of distinctiveness that cannot be confused with any other information, it is more recognizable and easier to understand then plain text on a page.
The charts above are interactive graphs available from FlowingData (2015) and are an example of dynamic filtering. This is a tool used in data visualisation which helps to pinpoint information about a specific place, time or statistic. Click on a specific U.S county, and it compares the time taken to commute to work against all the other regions, with various shades of red signalling its worse, and shades of green means the traffic is lighter in those areas. There are many visual forms that DV’s can take and they are not interchangeable, there is potential within the wrong form to create misleading or unintelligible information.
Reas, C. & McWilliams, C. (2010). Form + Code in Design, Art, and Architecture.
Chew, S (2011). How to Get the Most Out of Studying
FlowingData (2015). Compare Best and Worst Commutes in America
TED, Hans Rosling, (2006). The best stats you’ve ever seen
TED, Aaron Kobling, (2011). Visualizing ourselves…with crowd-sourced data