Eine journalistische Einschätzung zur E-Voting-Schwachstelle, die er Chaos Computer Club Schweiz veranschaulicht hat.
In this blog post, I explain step by step how I (eventually) achieved a nice thematic map with pure ggplot2 – from a very basic, useless, ugly, default map to the publication-ready and (in my opinion) highly aesthetic choropleth.
As georeferenced data from social media, be it in the form of Tweets, Foursquare Check-Ins, Instragram photos, Flickr pictures, etc., are increasingly available, so do (geospatial) analyses and visualizations done with them become more and more popular. Often, such studies and applications claim to be able to infer social, cultural, and even political insights from these data, spatially fine-grained and referenced down to the level of countries and cities.
I haven’t seen a single one which actually succeeded in plausibly explaining the how to me.
I implemented this interactive visualization for a university course about geovisualization. It allows the user to visually compare two variables (either the outcome of a vote or a socio-demographic factor, per district). Two maps are used to show the geographical distribution of the variables, and quartiles are computed to distribute the values into four classes.
A scatterplot allows the user to contrast the two chosen variables and explore possible associations. Both views (map and scatterplot) are linked, so the user always knows which district he or she is dealing with.
On top of that, the user has the possibility to draw a rectangle around the points in the scatterplot and thus select a set of districts (“brushing”). After this, the corresponding districts are highlighted in the map. This helps, for instance, to quickly identify regions where certain variables have high or low values, etc.
In order to better compare differences between subsequent variables, a linear, visual transition of half a second is used. This helps to mitigate the problem ofchange blindness.