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Category: Data visualization

New tool for discourse network analysis

EJPR has just published an article introducing a new tool for ‘discourse network analysis’. Using the tool, you can measure and visualize political discourses and the networks of actors affiliated to each discourse. One can study the actor congruence networks (based on the number of statements actors share), concept congruence networks (based on whether statements are used by an actor in the same way) and trace the evolution of both over time. Here is a graph taken from the paper which illustrates the actor congruence networks for the issue of software patents in the EU (click to enlarge): The discourse networks analysis tool is free and available from the website of Philip Leifeld, one of the co-authors of the article. I can’t wait to get my hands on the program and try it out for myself. The tool promises to be an interesting alternative to evolutionary factor analysis – another new method for studying policy frames and discourses that I recently discussed – with the added benefit of being able to present actors and frames in an integrated analysis.   Here is the abstract of the EJPR article (there are more resources at this website): In 2005, the European Parliament rejected the directive ‘on the patentability of computer-implemented inventions’, which had been drafted and supported by the European Commission, the Council and well-organised industrial interests, with an overwhelming majority. In this unusual case, a coalition of opponents of software patents prevailed over a strong industry-led coalition. In this article, an explanation is developed based on political…

Visualizing left-right government positions

How does the political landscape of Europe change over time? One way to approach this question is to map the socio-economic left-right positions of the governments in power. So let’s plot the changing ideological  positions of the governments using data from the Manifesto project! As you will see below, this proved to be a more challenging task than I imagined, but the preliminary results are worth sharing nonetheless. First, we need to extract the left-right positions from the Manifesto dataset. Using the function described here, this is straightforward: lr2000<-manifesto.position(‘rile’, start=2000, end=2000) This compiles the (weighted) cabinet positions for the European countries for the year 2000. Next, let’s generate a static map. We can use the new package rworldmap for this purpose. Let’s also build a custom palette that maps colors to left-right values. Since in Europe red traditionally is the color of the political left (the socialists), the palette ranges from dark red to gray to dark blue (for the right-wing governments). library (rworldmap) op <- palette(c(‘red4′,’red3′,’red2′,’red1′,’grey’,’blue1′, ‘blue2′,’blue3’, ‘blue4’)) After recoding the name of the UK, we are ready to bind our data and plot the map. You can save the map as a png file. library(car) lr2000$State<-recode(lr$State, “‘Great Britain’=’United Kingdom'”) lrmapdata <- joinCountryData2Map( lr2000,joinCode = “NAME”, nameJoinColumn = “State”, mapResolution=’medium’) par(mai=c(0,0,0.2,0),xaxs=”i”,yaxs=”i”) png(file=’LR2000map.png’, width=640,height=480) mapCountryData( lrmapdata, nameColumnToPlot=”position”,colourPalette=op, xlim=c(-9,31), ylim=c(36,68), mapTitle=’2000′, aspect=1.25,addLegend=T ) dev.off() The limits on on the x- and y-axes center the map on Europe. It is a process of trial and error till you get it right, and…

Creating Data Maps

There are several online tools for data visualization including IBM’s ManyEyes and Google’s Chart Tools. For a recent post on the other blog to which I contribute I wanted to map the distribution of a variable on a geographical map of Europe. I decided that’s a good opportunity to try a site called Target Map which promises free, high-quality, customizable data maps. The result of my efforts can be seen below: The link to the map is here. Altogether, I can’t say that I am too happy with the mapping utility. My main quibble is that there are no default color palettes that translate well continuous variables into color hues. By default, the program offers highly contrasting color choices for the different categories but ones that don’t suggest the ranking of categories. And I couldn’t find an easy way to customize the color palette. Data entry is OK, although once you select Europe as the geographical scope of your data, you can’t have any values for Turkey, for example, even if you try to supply them manually. Altogether, Target Map might be useful for some very small and inconsequential projects but for serious staff one should bite the bullet and get familiar with R’s map utilities (something I have been planning to do for a while).