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Tag: data visualization

The ‘Global South’ is a terrible term. Don’t use it!

Published by demetriodor on November 6, 2018

The Rise of the ‘Global South’ The ‘Global South‘ and ‘Global North‘ are increasingly popular terms used to categorize the countries of the world. According to Wikipedia, the term ‘Global South’ originated in postcolonial studies, and was first used in 1969. The Google N-gram chart below shows the rise of the ‘Global South’ term from 1980 till 2008, but the rise is even more impressive afterwards. Nowadays, the Global South is used as a shortcut to anything from poor and less-developed to oppressed and powerless. Despite this vagueness, the term is prominent in serious academic publications, and it even features in the names of otherwise reputable institutions. But, its popularity notwithstanding, the ‘Global South’ is a terrible term. Here is why.   There is no Global South The Global South/Global North terms are inaccurate and misleading. First, they are descriptively inaccurate, even when they refer to general notions such as (economic) development. Second, they are homogenizing, obscuring important differences between countries supposedly part of the Global South and North groups. In this respect, these terms are no better than alternatives that they are trying to replace, such as ‘the West‘ or the ‘Third World‘. Third, the Global South/Global North terms imply a geographic determinism that is wrong and demotivational. Poor countries are not doomed to be poor, because they happen to be in the South, and their geographic position is not a verdict on their developmental prospects.   The Global South/Global North terms are inaccurate and misleading Let me show you just how…

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Visualizing asylum statistics

Published by demetriodor on May 30, 2014

Note: of potential interest to R users for the dynamic Google chart generated via googleVis in R and discussed towards the end of the post. Here you can go directly to the graph. An emergency refugee center, opened in September 2013 in an abandoned school in Sofia, Bulgaria. Photo by Alessandro Penso, Italy, OnOff Picture. First prize at World Press Photo 2013 in the category General News (Single). The tragic lives of asylum-seekers make for moving stories and powerful photos. When individual tragedies are aggregated into abstract statistics, the message gets harder to sell. Yet, statistics are arguably more relevant for policy and provide for a deeper understanding, if not as much empathy, than individual stories. In this post, I will offer a few graphs that present some of the major trends and patterns in the numbers of asylum applications and asylum recognition rates in Europe over the last twelve years. I focus on two issues: which European countries take the brunt of the asylum flows, and the link between the application share that each country gets and its asylum recognition rate. Asylum applications and recognition rates Before delving into the details, let’s look at the big picture first. Each year between 2001 and 2012, 370,000 people on average have applied for asylum protection in one of the member states of the European Union (plus Norway and Switzerland). As can be seen from Figure 1, the number fluctuates between 250,000 and 500,000 per year, and there is no clear trend. Altogether, during this 12-year period, approximately 4.5 million…

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Scatterplots vs. regression tables (Economics professors edition)

Published by demetriodor on July 10, 2012

I have always considered scatterplots to be the best available device to show relationships between variables. But it must be even better to have the regression table and a full description of the results in addition, right? Not so fast: A new paper shows that professional economists make largely correct inferences about data when looking at a scatterplot, but get confused when they are shown the details of the regressions next to the scatterplot, and totally mess it up when they are shown only the numbers without the plot! Wow! If you needed any more persuasion that graphing your data and your results are more important than those regression tables with zillions of numbers, now you have it. P.S. The authors of this research could have done a better job themselves in communicating visually their findings… [via Felix Salmon]   The illusion of predictability: How regression statistics mislead experts Emre Soyer& Robin M. Hogarth Abstract Does the manner in which results are presented in empirical studies affect perceptions of the predictability of the outcomes? Noting the predominant role of linear regression analysis in empirical economics, we asked 257 academic economists to make probabilistic inferences given different presentations of the outputs of this statistical tool. Questions concerned the distribution of the dependent variable conditional on known values of the independent variable. Answers based on the presentation mode that is standard in the literature led to an illusion of predictability; outcomes were perceived to be more predictable than could be justified by the model. In particular, many respondents failed to take…

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Visualizing left-right government positions

Published by demetriodor on March 19, 2012

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…

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Creating Data Maps

Published by demetriodor on March 5, 2012

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).