Assignment 3: Tools

The main task for this assignment was more of technical one.

The initial task I thought of was that to use the data across French Fries (considered fast or junk food) to assess if what is would have any trends to show that it has benefits of sorts.

I picked 4 tools to use eventually, but was actually only doing 2 in actuality.

Google Fusion Tables and Tableau allowed me to work with the data and filter only what I needed whereas the other 2 could not.

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Google Fusion Tables

GFT had some cons:

  • Limited viz options
  • Not much chart settings
  • Cannot export image/pdf & not for print (mainly web display)

GFT pros:

  • Tabbed interface; able to filter data
  • Good sorting options
  • Simple UI

Using GFT, I was able to find out that for all other things similar (namely the energy), there were no-salt alternatives (low in sodium) which makes French Fries less unhealthy in some sense.

However, the some classmates provided feedback that I was comparing supermarket versions of uncooked and unprepared fries with the likes of those from fast food chains like Wendy’s and McDonald’s. In my defence, I was only comparing Energy and Sodium which isn’t directly linked to deep frying frozen french fries. Of course, it was paramount that this detail was noted and adjusted accordingly later.

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Tableau Desktop

The cons:

  • Hard to learn and use (category and value is known as dimensions and measures respectively)
  • Cannot export image/pdf directly (locally), and not for print

The pros:

  • Drag & Drop
  • Simple visualisation recommendations (largely automated)
  • Many minute settings and can filter data
  • Almost limitless variables
  • Tabbed working interface with ability to make dashboards

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Many Eyes

Was not able to filter data within the software/application/online service and therefore no conclusions can be drawn. Non-conventional uploading of data into a textbox, not with csv or excel sheets.

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Nodebox

Don’t even try this. I believe it is meant for final level/stage visualisation and not meant for working with large data, at least for beginners. Would call it “visualisation software” rather than “data visualisation”.

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¶ Time Skip – Moving on to the later stage of the assignment ¶

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With the known issues in mind, I had manually filtered out the data and relabelled them from the data set.

I intended to focus on both French Fries and Ice Cream (since both were typically unhealthy foods)

Assignment 2: A Tour

So Assignment 2 came out, and finally everyone is on some common ground. We were asked to use the data from QS World Universities Ranking and formulate a question or a story to tell in an infographic piece.

  • Other than the provided piece of data for 2012/2013, the rest of the data was extremely hard to get
  • 2010/2011 data was only available in the supplementary PDF FILE which had to then be manually typed and compiled into another Excel sheet.

After hours of wrestling around with the data (read: Rankings of top 125 universities in 2013 with their rank progress since 2009) in Excel and Tableau Public, I decided to explore the prospect of the fastest rising universities in that 5-year time frame. One could even say I am taking the sociological approach by supporting the underdogs, but hey, whatever works right?

Initial intended layout

This was my intended layout at first because the graph rendered by tableau was of a very low resolution, so I had to limit its size across the page which created a large unused white space. I filled it with a grey box for future purposes.

3229_A2

Draft 1

This is draft one. Firstly, the topmost graph was an epic fail. I did not know how to combine the years together because learning tableau is a b*tch. However, making do with whatever I have, I made each column separate so that I could compare it with the chart below. However, this relationship was not exactly evident/visible.

It shows the 9/125 universities which have had large increases in rank from 2009 to 2013. I then isolated them and used colour intensity to label the net amount of change. The chart below shows a glimpse of why these universities have improved in terms of faculty to student ratio and academic reputation, both of which have a high contribution to the global rankings (20% and 40% respectively). Another point to note is that I chose these 2 factors because they are malleable (able to be manipulated through PR, publicity, hiring more faculty staff).

Although I colour-coded the text to correspond to the graph, it was still confusing to many.

The right column also seems to draw attention away from the main gist of the article as it has bold colours and large type. The removal of it was suggested during tutorial, and the text between graphs to be shifted below so that the correlation between graphs can be easily seen.

ALAS, after experimenting with different visualisations, I decided to go for the safer one – Time against Rank with colour-coded Institutions. Even after so long, I still think Tableau sucks as you have to learn how that crappy algorithm plots your crap together instead of being a WYSIWYG.

Sheet 1 Dashboard_1 (1)

Interactive version: http://public.tableausoftware.com/shared/FSCTQ3KPD?:display_count=no 

As you can see from the images above, I finally got my hands on the datavis output I intended to show. Therefore, after some assemblage in Illustrator (o m g vectoring is a waste of time imho) this should be my final piece!

Semi-Final Draft

Semi-Final Draft

Andddd….. Presenting my final piece.

A2_Samuel_Cho

To sum it up:

  • Number of institutions from 800+ to 125 to 9 to 3 featured
  • Flashy headline
  • Focuses on universities with an upward trend

Assignment 1: A Walkthrough

And thus I embarked on Assignment 1. When I first loaded the website, I could’ve sworn it was in an utter MESS. There was so much data to begin with (not to mention the fact that it was already in a visualisation in itself) and we had to pull out certain data to make an infographic.

It had to be a confirmative analysis (confirm or infirm hypothesis or structural relationships between data) piece of work and needed to be time-based.

With this in mind, I gobbled up all the data and let it sit inside my head over a few days, turning it about mentally and finally came up with a linear, “timeline”-esque infographic which was only in black and white. It took me approximately 5.5 hours in illustrator to create draft number one.

3229_A1-01

Draft 1

Allow me to explain:

  1. I only drew data from the timeframe of 1950 to 1974 (as stated in the infographic)
  2. I wanted to make it visually comparable with respect to the previous, hence the small column per milestone
  3. I added descriptive text to each column, to name and elaborate on each element
  4. Grey boxes were added to group certain time-periods to vaguely classify and clump some of them together
  5. Some information (or lack of complete data) was omitted
  6. Colour was left as black and white to prevent any elements from receiving undue focus or recognition but in the end it turned out like a huge chunk of data (i.e. poor data-ink ratio)

As I proceeded to draft number two, based on suggestions by my classmates and my tutor, I decided to add colour to the entire chart and rearrange some graphical elements.

3229_A1_d2

Draft 2 (with colour)

  1. Computer was tilted to create a “pop-up” effect with description added
  2. Desaturated colours were used to group the “sub-eras”. I have my doubts about the data-ink ratio now?
  3. An information textbox was added to show why is this data significant & the story it is supposed to tell
  4. Weird professor vector was added to make the infographic seem a little more playful and to cater to a larger audience
  5. All blurry JPEG images used were re-vectored and coloured to give a consistent aesthetic to the piece

Still thinking it needed refinement, I proceeded to make draft number three.

3229_A1_D3

Draft 3

Draft 3 seemed like it now had too many colours and the user would get confused with what to see and why it is relevant, therefore Draft 4 is my final piece.

3229_A1_Final

Draft 4 (Final)

I asked myself what would be negligible if removed, and the computer was taken out. The circles were shrunk a little and opacity reduced (Thanks, Jaime!) to give some legible white space around and the title was reworked to make it less ostentatious, since there were already so many darn shapes around.

Also, something I hadn’t elaborated was about the last column, 3-D Visualisations. I intentionally made it extend out of the defined space to give the visual effect of 3-Dimensions, i.e. occlusion to show the foreground depth of the idea being conveyed.

Also, British spelling of “visualisation” because WE’RE BRITISH AND HAVE BRITISH COLONIAL MASTERS. TAKE THAT AMURECA

Lecture 1 Exercise

ge.swarm.is was the visualization we were asked to ponder about.

Class exercise 01: What does this visualization try to say?

I believe that this tracker was made to classify, sort and make sense of the insane amount of chatter that was going around on the internet.

The few things that came up while discussing were:

  • Certain popular keywords/key terms began to surface over the period leading up to and over the Election Day
  • The data (key terms mentioned online) was sorted by days
  • The most shared content was also ranked
  • The coloured lines show a correlation between the day’s key terms and most shared content; the ranking of the most shared content share a positive relationship to the quantity of key terms. The higher the ranking, the more key terms it is related to.

In the most general sense, this visualization maps the changing concerns of the people over the course of the days leading to the Election Day and its importance!