Data Visualisation

Data visualization is increasingly used by data journalists and engineers of all kinds. The main reason for this is the Open Data initiative, which requires that data be made publicly available by government organizations and universities, free of charge. The examples also use publicly available data from the Federal Statistical Office.

Please note and respect the license conditions.

At the moment there the following maps available:

Swiss Communities

• The timeline view of the gini index of all swiss communities from the year 2001 to 2015.
• A partial view of the same maps limited to the region of Zuerich
• Map of the Swiss communities with the Average, pure equivalised income, in Swiss francs
• Map of the Swiss communities with the Gini coefficient of pure equivalised income and the Average, pure equivalised income, in Swiss francs of year 2015
• Map of the swiss communities with the Gini coefficient of pure equivalised incomeof year 2015
• Map of the swiss communities with the Gini coefficient of pure equivalised incomeof year 2015 with other colors used.

Swiss Cantons

• Map of the Swiss Cantons with the Average, pure equivalised income, in Swiss francs
• Map of the Swiss Cantons with the Gini coefficient of pure equivalised income and the Average, pure equivalised income, in Swiss francs of year 2015
• Map of the swiss Cantons with the Gini coefficient of pure equivalised incomeof year 2015
• Map of the swiss Cantons with the Gini coefficient of pure equivalised incomeof year 2015 with other colors used.

Classification of Gini ranges and colors

There are not many infomation available about ranges of gini values and their colors. The World Bank use a color schema which ends for gini value 6.5. In Switzerland you can find communities which has a gini index value over 0.9 (Anières. 0.914). I make the colors a little bit political with three main areas from green over blue to red. In the red communities you can find many pepole with extrem high income  but with low income people as wenn and this leads to an high gini index.

Initial idea

The idea of this work was based on the excellent work of Timo Grossenbacher, timogrossenbacher.ch/2019/04/bivariate-maps-with-ggplot2-and-sf/ which has initially created these maps with R. I played and tested a lot of time with these R code and was amazed about it.

I use GIS Software as well and I took the challenge to rebuild the work wiih a GIS system in my case with QGIS. The result is more or less the same. Really amazing as well for me. The code can be downloaded from Github, github.com/tgdbepe4/bivariate-maps-qgis-recreate-sf.

The maps 3 to 5 are re engineered versions of what Timo and his colleagues had produced. Maps 1, 2 & 5 are inspired works from me.