The UX Visualization Diaries · Number 1

Someone once accused me of not doing visualizations. Although that is not actually true (I’ve done more than one and so that means there are a lot of people out there with a bad memory) However, I have to admit that it’s not a really my job.

My job, my function in the Visualization department – apart from designing user friendly interfaces, of course– is to make the visualisations of my team more understandable:

Trying to prevent them putting 20 variables in the sae graphic in an attempt to demonstrate that it can be done, just for the sake of it.

Sometimes I managed to do it. Sometimes I didn’t.

That’s the reason why I decided to write this series of blog entries dedicated to analyzing things that are not clear or aspects that could be improved upon, always from the UX point of view.

Taking Tuftte’s work as a base, Fernanda Viegas and Martin Wattenberg wrote a blog entry titled Design & Redesign which suggested that Data Scientists should not only criticize other people’s work but improve on it with suggestions on how to redesign the visualization and  I’ll try to analyze them respecting their original style.

Journal visualization, the number of publications are represented by the volume of circles

First I’ll confess that I chose this representation: Jounals, because I thought, at first glance, that it was appealing and easy to analyze. I also wanted to prove that my point of view coincided with, or at least complemented, the view of experts (my boss basically). And they did.

The first problem we see is that depending of the subject or type of publication we see a different time period (from 2004 to 2013, from 1970 to 2010…)

The question is why not represent the same time period for all graphs to show  that they don´t have any data in some years.

A different problem is the time step which is changing throughout the different graphs: every two years, every five years…

different step in timelinedifferent step in timelineAfter thinking about the color range, in the end, I deduced it was not relevant. The color range selected only tries to differenciate one line from another, but some users could have thought: Does the range (Blue, red, green) mean something? Does the color intensity mean something else? Is the light blue more relevant than the dark one?
And the last and most important design error: Why are some totally different values represented with the same/similar radius?

diapo_values

The basic problem is that for every line they have changed the relative radius. So if you don´t see the values beside two similar circles you might think they are hiding a similar value, but they don’t. One circle could have a value of 20, while a similar circle could have a value of 2. So at first glance, and without any interaction you can’t compare the two graphs (or even two lines) easily.

Big Data Evolution with TimeMapper

Today we will review an easy way to display some data according to a timeline in three steps:

Although sometimes it’s possible to use another type of visualization, if you don’t have too much time, TimeMapper may be a good option.

  1. In this example, you only have to prepare a spreadsheet on google drive with the following columns:

https://docs.google.com/spreadsheets/d/1gQSS4qHq9tqbadibaxyxg-VlI8H7wOcPuQhPYlnkzu8/edit#gid=0

I collected the information from Dezyre web:  web: http://www.sabatebarcelona.com/productos/decoracion-de-interiores/wall-papers-vinilo-3m-hp-latex-decoracion-interiores/

2. Once you publish your file from drive:

publishing your document

3.  Then you simply have to enter the link in the configuration page of  TimeMapper and decide a title for your timeline:

Time Mapper configuration

And at the end, just publish it and here it is your timeline:

BigData timeline

Quick, easy and a different option to explain a story.

This is not an amazing visualization

This does not pretend to be an deep and extensive visualization experiment. I just wanted to share with you a simple exercise of data visualization using Tableau.

I have to admit that my first contact with the tool was few months ago, and I also have to say that it has improved a lot this past year, adding some useful functionalities. (I have to admit that the video tutorials might have had something to do with this, but to tell the truth I’m falling in love again)

The visualization shows a representation of the offenses committed during 2013 by the bias motivation from the crime datase of the FBI.

The offenses (committed in 2013 in the USA) were grouped depending on the type of the offense: by race, religion, sexual orientation, even gender. Every type was subdivided in subtypes:  by talking about religion we have more information about if the incident was done against catholics, jews, islamics, etc.

My first representation was just about the number of the incidents depending on the incident type:

Sheet 2

Then I added the subtype variable to the color filter to add more deep information to every listed type:

Sheet 3
Not many conclusions about this , I simply want to say that is rather sad that the incidents related to race are still the most frequent, and that the number of offenses against the afro-american population triples the number of offenses against white people. The number of incidents related to race are followed by offenses linked to sexual orientation. Most of them against the gay community.

My boss is a troll

Well, not really a troll. A Troll by definition is a person that publishes wind-up messages in an online community with the main intention of annoying or provoking an emotional answer in the users or readers.

  • Provocative message? Guilty.
  • Trying to provoke an emotional answer? Guilty.
  • Message Irrelevant? Not at all.

Troll image

The conflict began on 4th of August with this first tweet in response to an infographic published on the El País twitter account:

football_signings_tweet

in which apparently the most expensive signings from 1998 to 2015, between the English league and the Spanish league clubs were compared.

I say apparently, because it seems the information was wrong: Someone called Bale hadn’t been included. I have to admit I don´t know anything even about his existence and much less about football signings.

The second tweet (persistent) was this:

tweet_football_signings_2

showing some Tableau created graphics using some data taken from The Guardian: Totally different results.

And the last tweet:

tweet_football_signings_3

Giving as a reference and article with the same (and correct) numbers.

Wrong processing? Some kind of error? May be something deliberate?

After sitting in front the great Julio Pomar for months I know that’s not the best way to deal with a troll. By ignoring him, I mean, specially when the troll is telling the truth and we have published some wrong data.

The best way to react would had been to admit the error, say sorry and rectify the data.

Talking with my boss about that two weeks ago, I knew the reason for this unpleasant comment (I have to say that’s not his usual way of doing things. He is actually nice, respectful and always ready to help anybody):

“I think that journalists have access to a lot of information, information that most of us don’t normally have access to, so they have a commitment with the society to be honest and unbiased. Those things made me indignant.”

So I decided to write this post, and I might mention the author (@rodrigo0silva) in a tweet linking to this article. I probably won’t get an answer.