1. Build a data visualisation: scientific, sociological, ecological, personal (your own life,
for example, is a trove of data: blog posts, browser history, last.fm, iTunes, books
read, TV watched, meals eaten…). If the data is not your own, ensure that it is used
under an appropriate license. The visualisation can be either algorithmically
generated or hand-crafted, but consider how its design can help the user discern
patterns hidden in the “raw” data. Demonstrate an awareness of how different types
of visualisations are more or less useful at achieving their aims. Consult with your
tutor on possible techniques and forms to present your visualisation.
I have chosen to build a data visualization and in doing so, I interviewed a university student-Tamara Candy to find out what common activities she does on a weekly basis. I used Microsoft excel to present the raw data and then using this data, generated several different graphs. The activities chosen are based on what Tamara considers to be important in her life:
Activities per week:
Snacks eaten
Music downloads
Facebook login
Exercise
Editing journalism stories
Books read
Assignments due
Alcohol intake
I then monitored how many times Tamara done these activities over a period of 5 weeks starting with week 10 and ending in week 15. I done this by asking Tamara to write down how many times she done the activities in one day and then multiplied the number by 7 (7 days in a week).
This data was then made into a data visualization by using different graphs to convey the data.
The first graph is the Pie graph. The pie graph shows the data being represented through a percentage. Each week being represented through a different color and having a different percentage. By comparing a slice of the pie with the overall pie, one can see which activities Tamara does more commonly than others.
The second graph is an absolute area graph displaying the way the data has progressed or not progressed over time and emphasizing on quantity.
The third graph is a bar graph. The data displayed in the form of a bar graph is used to compare the amount or frequency of occurrence of different characteristics of data and it is used to compare groups of data.
The fourth graph is a column graph. The data represented in the form of a bar graph shows each weekly activity compared to each other overtime. One could see how Tamara has progressed with one activity one week in comparison with another week.
The purpose of displaying Tamara’s weekly activities in the form graphs was to show how often she does her activities per week and how this changes over a period of time. One could take the raw data and use it to investigate further. Presenting the raw data in the form of graphs would be the most appropriate way to convey data to someone effectively. Michael Friendly, a professor of psychology and editor of the Journal of Computational and Graphical Statistics, is an expert in the field of statistics, graphics and macro writing. Friendly claims that data visualization is made up of two main parts-Statistical graphics and thematic cartography. In the 1800’s, statistical graphs were being used all over France, Germany, England and elsewhere to distribute data from economical and national interest. Overtime, turning statistical data into a visual element has become an integral part of teaching, research and development.
The aim of the graphs is to compare and contrast Tamara’s weekly activities over a period of time. The raw data can be used for a survey, research or simply to find out about Tamara’s personal life. This form of design is similar to information graphics and statistical graphics. This form of data visualization is different to other works in the field because it is not as complex and it best represents a person’s personal activities.
