Resources on Visualization

Enrico Bertini and Jorge Camoes presented their favorite resources on information visualisation. Surfing the free courses mentioned in Bertini's article,
I read about some interesting techniques I wish to learn more about (later)
  • edge bundling when visualizing graphs
  • Dorling circular cartograms named after Danny Dorling. The algorithm is described in Dorling, D. (1996) Area Cartograms: Their Use and Creation. Concepts and Techniques in Modern Geography, 59, 69 pages. Norwich: Geobooks
  • determining the optimal aspect ratio of a line graph by the shape of the graph


Regional differences on what drives CO2 emissions

If you are investigating the change of CO2 emissions, then you might ask: Where do the changes occur? Well here is the answer.

The staircase plots show the contributing factors to CO2 emissions for each continent. population refers to population effects, gdp_pcap refers to income per capita, energy_intensity refers to energy used per dollar added value, and carbon intensity refers to CO2 emissions per energy unit. The numbers are Gt CO2 and come, not very carefully curated, from Worldbank.

As you can see, europe’s emissions stay at nearly the same level, mostly because there is no population effect. On the other hand, Asia’s economic rise seems to explain much of the change of CO2 emissions in the time from 1995 to 2007.

Here is how to produce the chart:


Reproducible blogging

As a fact-based blog, the posts here contain very often diagrams and data tables. To enable you to reproduce the results and insights, I include the computations as computer code.

Most blogposts I write are markdown text combined (or weaved) with computer code written in the R language. I created a small package mdtools that puts the tools together and smoothes the workflow.

This post gives an short introduction to the mdtools package: how to install it, the first post, caveats, and future directions.


Index decomposition with R

Few days ago, I finally finished a small package ida. It enables you to analyse contributions of underlying factors to the change in an aggregate, using methods based on index number theory. These methods have become popular by, but are not restricted to, investigating the change of CO2 emissions.

Here is a chart that shows what the change of population, welfare, efficiency and fuel substitution contributed to CO2 emissions:

The numbers refer to Gt CO2. The data comes from Worldbank, however we treated missing values rather uncautious here. So the result may or may not be valid. However, it puts the efforts in perspective: Clearly the reduction of the energy use per GDP has not been capable to compensate the additional emissions from population growth and income per capita growth. The carbon intensity, i.e. the emissions per energy unit, remained nearly unchanged.

Here is how to produce the chart:

Favicon CC-license backlink

The nice favicon of this blog was created by fatcow and found at iconarchive. Changing the favicon in blogger is nicely explained by Chester Alan Tagudin.