Nicolas Feltron is a designer who specialises in data visualisation. Between the years of 2005 and 2014, Feltron would produce a document each year which detailed aspects of his life recorded over a year. I managed to get my hands on some physical copies of the reports from 2007 to 2010 and getting to see them in person really made me realise how stunning these documents are. I particularly like the years 2007 and 2009 for their usage of typography. 2007 is pretty typographically heavy but uses typography in a really engaging way. While there may be an assumption that data visualisation shouldn’t rely on typography, I think this shows that if it is used in engaging ways, it is just as effective as visuals. I also really like the 2009 report, which uses Swiss Style typography and visuals which match the typographies distinct style. This marrying of visuals and type work exceptionally well and is something I will definitely consider when doing my own data visualisation in my project. As a side note, 2009 is particularly beautiful in person as it was printed by letterpress, which gives the pages minor faults in the colouration and solidity. I would be interested in seeing if this appearance of letterpress could be replicated digitally as it could make for a really aesthetically interesting user experience.
As my project is going to focus on my travels throughout the week, I thought it could be useful to look at some transit maps to see how they handle route-related data. I looked at the NYC transit by Massimo Vignelli last semester, so I decided to focus on other maps I hadn’t studied yet. The London and Dublin map both use very similar organisation styles, using 45-degree increments on angles, and they both use more accurate geography than the likes of the NYC map but they do take certain liberties for the sake of clarity. I also found the Paris Metro map particularly interesting as it appears so chaotic. The Paris map is the most accurate geographically meaning is has a less organised layout. I really like this approach as it is a more accurate representation of the data but still finds a balance between clarity and realism.
This article took me a bit to get my head around but I think I understand it now. The main point is not to add or remove data to create evenly spaced data as this won't represent it. For example, if a light record as being on at 10 am, off at 11 am, off at 1 pm, on at 1:30 pm, and off at 2 pm, the instinct may be to space this data evenly to show the light’s state every hour. To do this, you would have to add an off instance at 12 am which would bloat the data, and remove the on the instance at 1:30 pm which would falsely represent the data by having it show the light was off from 1 pm to 2 pm. Essentially, what this means for me in my project is to not sacrifice the data’s accuracy in favour of the design, which is something I will make sure I am considering.
This week has started me off well for my new project. I really enjoyed looking at the Feltron Reports and they have given me some great ideas to get me thinking about the project. It has also been useful looking at some more utilitarian uses of data visualisations through transit maps. I am also pleased I found the IDEO article, as I think having read it this early in the project will really increase the effectiveness of my project.