Mashed up data sets
In this project I have experimented with mashing up multiple data sets, which visualised together give greater context to the data than if viewed independently.
I have started with a data set from the wikipedia article on the G-20 major economies which sets out population and gross domestic product (GDP), total and per capita, both nominal and with purchasing power parity (PPP). This is a rich and interesting, concise, data set to explore in it self. It is probably already a mash up from various sources.
I have added to this set data about carbon emissions for the same countries, extracted from lists on wikipedia of all countries total emissions and per capita emissions. I then calculated emissions to GDP ratios, which is slightly flawed because the respective data was from different years, but very interesting as a indicative and prototype only exercise. This all took a bit of stitching together manually, but was very rewarding because quickly, visually, it was possible to see greater specific context than is usually available when considering carbon emissions - that is who was efficient or wasteful in generating money from emissions and who could most afford to reduce them.
There were two visualisation modes that the data could be explored in - ranked lists and scatter plot. The ranked lists can visualise more than two dimensions simultaneously, however the scatter plot can show clusters of data and outliers. Both are really useful.
|Ranked lists - carbon emissions total, per capita, and against GDP nominal and PPP - Australia is highlighted|
|Scatter plot - carbon emissions per capita vertical axis and against nominal GDP horizontal axis|