We're thorugh the looking glass people...

Okay, so it just occurred to me that I never finished last week’s post. Frankly I straight up forgot about it, that and I’ve been battling a severe case of procrastination. Though I do intend to go back and pick at it some more, perhaps when there are less pressing matters at hand- such as this week’s blog post.

Data Visualization:

Or dataviz if you want to be all jargon-professional. So what is dataviz? Essentially it is a means of representing abstract data visually, in a way that exploits fundamental elements that humans can detect (…i.e. colour, shape, pattern, scale) and ultimately leads to the uncovering of new information or assumption.
So, the premise of an effective data visualisation is that it exploits the user’s ability to see. The user should be able to ‘experience’ (or interact with) a dataviz, to further explore and uncover information for themselves through correlations or parallels.

I discovered the merit of a dataviz with this week’s tutorial exercise. Looking at the delicious links kinda solidified what data visualization should entail, more so than it would have had I gone searching for a dataviz on my own.

THIS was the data visualisation that I decided to talk about. I found appeal in its simplicity, the fact that data was accessible in a fashion that was both easy to uncover and understand. It’s also incredibly valuable in terms of representing data because it provides both a broad overview of information and a more intimate or specific insight into the data presented.

(a) So what is shown? The fertility rate or an average of how many children per woman/family, of a country is represented on the y axis. This dataviz takes on the convention of the grid, and utilizes numbers as a means of specific communication. (Upon mouse over the specifics of the item can be found, i.e. 8.3.)The x axis is the life expectancy in a number of years, and again mouse over provides the specifics. Countries use the visual element of colour, shape and placement/position to translate their information. The closer the country’s (represented by a circle) colour is to the colour red, the higher the average of births per woman there are to that country, the further along the x axis the longer the lifespan. A marker can be placed on a specific country, in the form of a hovering bubble, which thus allows an easily deciphered (or “seen”) visual representation of the information.

Once more this dataviz is valuable because of the broad spectrum of information it contains. It not only provides life expectancy and fertility rate for a number of countries but also has a “time slider”, to observe the changes that have occurred within that country over an amount of time. 1960 -2008 to be exact.

(b) I imagine the algorithm for this dataviz would go a little something like this…
Date, = country: x axis, y axis.

Maybe there would be another algorithm for colour: the colour depends on the position on the x and y axis. And of course there would be the stats coded to appear upon mouse over.

(c) Although I think I’ve already had a stab at explaining the visual elements of this dataviz…I can’t think of some kind of way to expand stab into a clever analogy…

Anyway…As I mentioned before the use of the grid is a convention and more importantly a means of clarifying. The general mass knows what function of a x/y axis grid is, the further away from the grid line, in most cases, indicates the higher the value of the entity. So here (time 2008), on first glance we can see that Niger the highest fertility rate, (not doing so well with the life expectancy) while China, Japan and Lichtenstein have the top three life expectancies.

If the concept on the grid is lost on you, colour reinforces what the grid may not. Though our own associations of colour may alter our interpretations of attitude towards the information. For example red for me represents pressure, anger and frustration- fittingly corresponding with my idea of having anywhere between five to seven or eight kids. Another person may associate the colour red with love, yes, lots of love produced that many children. That same person might also believe that the blue for 2- 3 kids is depressing, yes depressing that you don’t have seven or eight children to give your copious amounts of love. Or not, just a theory, or a tangent…

Okay, so those are the two key visual elements that I stabbed before further examined. I have nothing more to say on it.

(d) The interesting finds, included the general decrease in the fertility rate, and increase of life expectancy as the years go on.
Rwanda’s life expectancy dropped from 46.6 (1981)years back to 26.4 from 1988. There was also a significant drop in the fertility rate.

In 2008 the country with the lowest life expectancy is in Afghanistan. At a whopping low 48.9.

In 1960 Europe’s countries had the highest life expectancy. Afghanistan has the lowest.
Timor and Cambodia hit a low in life expectancy in the 70s.

Where most of the countries in earlier times sat within the red to orange range in terms of fertility rate, in 2008 only 3 countries sat in the orange range of fertility rate.

I now realise, that this would never have been found out (by me) and retained in the back of my little mind without data visualisation.

THE SYSTEM WORKS! And isn’t that, ladies and gentlemen, the most important thing?