Information Taxonomy Assignment One (1)

[Ellis Lum Mow]

FONTS

Flickr Complete Font Set (20)

The "object" that I settled upon to critically analyse was fonts found on the web. More specifically i have chosen to investigae the most popular fonts based on downloads, votes, ratings and clicks. The insights i am aiming to realise in some fashion are the relationships or trends that may be evident in what makes a particular font popular or successful. The goal is to apply and explore different characteristics of fonts and display that information visually to represent the results and also look for further insights that may be displayed.The information that i gathered came from a variety of websites on the internet. With the nature of my "objects" being an onine medium i believed websites to be the most relevant and effective means of research so i looked into the most used font websites and download sites. The ones that i found most helpful and functional were:

WEBSITES

fonts | fontriver | highfonts | fontreactor | 1001fonts

I decided that these were the most "reliable" sources of information because they were firstly high on the hit list in google. These sites also had some form of rating system which identified and highlighted the popularity of fonts and did this in numerous different methods. These sites also provided a forum for comments and seemed to have a collaboration involved in them with each site linking to each other and recommending the sites as "partners". The main use of these sites in gathering information was to level out and collaboratively highlight what were in fact the 20 most popular fonts. This of course resulted in comparisons, tallying and further exploration of sites across the web. The most definitive and logical solution to finding these fonts came to be using just one site and its top 20. There are numerous reasons behind this.

Firstly this site 1001fonts is extremely popular and possibly the "go to" website for font downloading. It was highly regarded by the other font websites and more importantly it had the best means for "rating" fonts. Fonts could be searched and listed by user ratings, votes and also popularity. The popularity system is explained here - basically it uses a system that "indicates the percentage of yesterday's downloads per unique visitor". It refers the amount of visitors to the website and how many of those downloaded a particular font. To me this was an extremely valid way to represent popularity. Another reason i chose this site was because it displayed the amount of votes these fonts were getting (NB: a font to be classified for popularity has to have at least 10 votes). These votes gave more insightful information that the other websites did not. Having said this i did not dismiss the other websites information. Rather i used them to re-enforce the arguement of using just 1 site for my core information. I realised that throughout the different sites there was relative consistency in which fonts appeared as the most popular - noteworthy ones such as "scriptina" and "walt disney" ranked highly everywhere. Most importantly using 1 site gives consistency in result analysis and the endeavour is to find further insights into this content.

CHARACTERISTICS

My data set and characteristics to analyse these fonts are as follows:

1. Category - Every font has a category e.g. calligraphic, graffiti etc.

2. Stroke Weight - Font thickness and black white ratio. This tells a lot about the style and form a font takes and i rated this in the form of either "light, medium or heavy" for simpicities sake.

3. Serif / San Serif - An obvious and critical observation of whether or not a font has "extra bits" on its "tails" or is solid and closed.

4. Votes - how many votes this "popular" font received.

5. Popularity - (explained above)

6. Context - A taxonomy idea to analyse the context of a downloaded/popular font. Split into just 2 forms - text or feature font.

VISUALISATIONS

Visualisation 1

This first visualisation allowed me to gain insight into the general relationships between the “category” of popular fonts and their relevant “context”. I used the information attained to visualise which fonts were “feature” fonts and those that are more suitable to text.

The research behind this came from looking into the roles and functions of fonts. Fonts are analysed and evaluated through a means of typographic clarity. The two tests of this clarity are legibility and readability. “Legibility is a function of typeface design. It’s an informal measure of how easy it is to distinguish one letter from another in a particular typeface. Readability, on the other hand, is dependent upon how the typeface is used. Readability is about typography. It is a gauge of how easily words, phrases and blocks of copy can be read.” fonts.com

With this in mind I constructed a personal test following this criteria to determine which fonts had credibility in the areas of legibility and readability. I decided to include this as a characteristic as it is thought about to a certain degree but not always immediately realised. I found that the large majority of the popular fonts didn’t posses much typographic clarity. In my opinion I found them mostly “feature” fonts – ones that would be used as headings or artistically as opposed to fulfilling the role of “text” and conveying large bodies of readable content. I deemed it suitable to therefore investigate the relationship between the context of where these fonts would be used and their labelled category.

The visualisation was quite straightforward however highlighted some interesting points. The main point being that out of the top fonts only calligraphic fonts and handwritten ones lent themselves to being “text” orientated. Even then some of them leant towards being a feature font. I think this gives insight into what people/downloaders are looking for on the web in fonts. They don’t desire regular text fonts, they want something new and distinct and not really useful for bodies of text. This makes sense considering the default fonts available to users cover this role more than adequately however it does pose the question as to what browsers are intending to do with the fonts that are being downloaded. To me it suggests that they are possibly designers, looking to expand their font collection for different purposes.



Visualisation 2

The second visualisation created is a treemap. The goal of the treemap is to map out all of my data set in one visualisation and observe relationships that might not otherwise have been recognised. What struck me was that my previous fixation was that the most popular fonts would probably be serif fonts. Fonts with extra detail and spice, but I found that there was a rather even spread of serif and sans serif fonts. What I also discovered was that the split in ratings and votes was also quite even amongst those 2 “styles” (serif/sans serif).



Visualisation 3

The logical progression from the second visualisation I believed was to then plot out the data and categorise it in a most efficient manner. This third visualisation illustrates where each category of font fits in the scheme of voting popularity but also in style. It was really quite interesting to note that out of the 20 most popular fonts 2 categories of fonts featured most prominently. This could just be because each had a respectively popular font but it also gives insight into what people are looking for. People want to find a “movie” font most likely because it is popular in society. They have exposure in the cinemas and on dvd and so people go to dowload them. The other category is “calligraphic” and I think this is more a testament to their design and aesthetics that people browse and see these fonts and like them. This seems to be the case as they wouldn’t get the same public exposure as movie fonts.

Visualisation 4

My fourth visualisation was a representation of the data that I had gathered. Because fonts are visual mediums by nature it was obvious that using the fonts as part of the visual representation would be a good idea. I just plotted the fonts height wise like a graph in accordance to their vote counts to see visually which fonts are popular and where insights could be gained and relationships seen.

I found this visualisation very helpful because it quite adequately and accurately showed how prominent some fonts were. My eyes were drawn to the public forum fonts once again such as “halo”, “spiderman”, “walt Disney” and “Beyond Wonderland”. I think this visualisation made it quite clear that those fonts that have been advertised do in fact get downloaded and voted for.

Visualisation 5

My fifth visualisation was really just a test and an aesthetic exercise. I plotted the two most popular fonts in a body of normal text to see how prominent they are against the usual fonts everyday users encounter. They were also given size in relation to their popularity and I did intend to put all 20 fonts on the page but realised it lost meaning when over cluttered. The idea was just to represent and visualise how different these popular fonts are to the norm. These fonts are by all means “feature” and not designed to be in bodies of text.
I wanted to visualise these things after looking into and researching “advancing and retreating terms” in font searches. I found it quite amazing that people are obviously looking for the common Helvetica and the like but also scripts, graffiti and popular fonts are most highly searched for. “Fancy Writing” really does seem to be the popular thing at present. Conclusions that may have been obvious before this research was carried out was confirmed to me that the popular fonts on the web really are the ones that are “featurable” in some context. It makes sense considering the standard fonts available to most users but it still leaves a massive opportunity for people to create basic, legible, readable and typographically clear fonts that the public will gobble up because that seems to be what’s missing in the current “font market”.

FINAL TAXONOMY

The final Taxonomy I created was therefore a representation of my findings in regards to font votes and their popularity ranking. I decided upon this because it clearly highlights the fonts that are “strongest” and therefore insights can be clearly seen. The taxonomy also provides a cross-section of the 20 most popular fonts and gives the viewer a chance to make their own assumptions and conclusions because the fonts are right there for them to see and analyse. I used the fonts as the tool to illustrate the information. This was probably the most effective way I could think of as it communicates the information visually. Characteristics such as stroke-weight, size and even category don’t need to be given a legend or explanation because they can be learnt through observation.

To be truthful I found it quite difficult to find many meaningful “trends” or “relationships” between the most popular fonts. The results are relatively dry but I still believe worthwhile insights were made that can be valuable. Its clear that exposure = downloads. Its notable that there is no consistency in font make up and creation. I think its insightful to realise that there doesn’t seem to be a “formula” for success when creating fonts. Medium stroke weight/black to white ratio is the most popular by far, not to overbear the user or leave them longing for more. This again is a rather obvious insight but re-enforced in this taxonomy. What is consistent is the stylistic and heading/feature/commercial fonts are by far the most popular on the web.

The main insight I believe to be realised from this taxonomy is that calligraphic fonts and movie fonts are the most voted for and popular/downloaded. To me that is something I hadn’t predicted or seen but when looking at the fonts in this relational set up makes you more aware of the similarities in some fonts. Calligraphic/Script fonts are possibly the most desirable and sought after category of font because of how they represent emotion, passion and lyrics. (fonts.com) This is a very valuable insight highlighted in the taxonomy that the majority of fonts (disregarding movie types) are downloaded. I think this is because of their dramatic nature and appeal and meaning behind them. I think the next step I would take in looking into fonts would be how Script fonts stir certain emotions and how that affects their popularity.

CONCLUSION

Through the process of representing data visually it became clear that data becomes information when given meaning. The visual representation of relationships and at times random characteristics provides a very effective forum of gaining insights. Although results can appear to be dry- on reflection and analysis these conclusions may not have been able to be formed without going through this process.

Looking into and understanding the process that goes into representing data visually also helped in critically analysing what information is important and can be useful. My findings were that through a visual medium there is a much grander opportunity to represent data not just clearly and innovatively but also aesthetically as a part of design.

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[Stephanie Patel]

You have some mad graphic design skills Ellis! Awesome stuff! (^-')b

[Nikash V. Singh]

This presentation just F$#*en ROCKED!
Very nice slides Pelvis!

[Ellis Lum Mow]

on it =)

[Andrea]

Hi Ellis, I know you're not finished yet, but don't forget that integrating your pictures and visualisation within the blog composition itself (to illustrate the point you're trying to make in the text) would be...handy =)