Month: October 2015

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.