This latest visualization has its genesis in this reddit thread. I wanted to represent the data therein in a way that would be easier to compare than a print-screen of an Excel table. As time went by, I found more detailed and accurate data, and I started looking at a way in which I could represent the relationship between the UK and the EU27 from the angle of the Four Freedoms :
1. Free Movement of Goods and 2. Services – The main focus of my visualization. Talk about Britain’s future relationship with the EU has often revolved around how free the trade will be and how high the risk of barriers will be. While the flow of goods has been much easier to free than that of services, and will be much easier to keep unrestricted in the post-Brexit world, I treated the two as somewhat two sides of the coin called Trade. A challenge of this infographic was to visualize both the absolute values, and the relative values to each other (imports vs exports, goods vs. services, UK-to-EU27 trade vs. global national trade)
3. Free Movement of People – Easily the most controversial of the Freedoms, at least in the United Kingdom, and a somewhat thorny subject in the early stages of negotiations, the size of Immigrant / Emigrant communities can inform on which countries might have strong incentives to protect their diaspora during negotiations.
4. Free Movement of Capital – By far the one I grasp the least, I limited myself to showing which countries are members of the Eurozone, and which ones still use their national currencies.
Made with Python (svgwrite module) and Inkscape. Data from Eurostat
A spin-off from the previous blogpost. Initially, I wanted to include this series in the previous visualization, but found it too busy, so I ended up replacing it by a simple heatmap.
Made in Python w/ Matplotlib and Inkscape.
The Free Movement of People within the European Union has become one of the hot topics surrounding the whole Brexit debate, and the following graph was born out of the desire to explore the relationship between intra-EU work restrictions on new members, and the growth dynamics of the number of immigrants from these new Eastern European states.
The initial idea was to compare the evolution of the number of New EU citizens in the Old EU member states, and especially between the the Big Three – Germany, Britain and France -, who each imposed different levels of restrictions on the 2004 wave of new member states – the wave that gave the world the image of the infamous “Polish plumber”. I wanted to see how much the presence or absence of work-restrictions slowed down immigration from the East.
Here are some of the findings:
- The UK is the only country where immigrants from EU-8 (the 2004 wave) grew as fast as those from EU-2 (Romania and Bulgaria). This later wave tended to be bigger in all countries, except the UK (and the 2013 wave, i.e. Croatia, tended to be between the two when it comes to growth)
- Work restrictions don’t seem to be the only factor in the rate of growth, but accession is clearly a tipping point when immigration accelerates. The removal of work-restrictions however are noticeable only in some cases (in Austria most clearly)
- I was surprised to see much calmer growth post-accession into Germany and Italy, but that is also due to already having larger numbers of immigrants from said countries before those countries joined the EU. The UK and Germany both ended up with 1+ million Central Europeans after 9 years, but they started out out from different base populations (136k vs. 481k)
One immediate problem that prevented me from a broader analysis was the lack of available data in some countries due to different methodologies. France, for example, does not, to my knowledge, publish an estimate on a country by country basis, the EU immigrants being divided solely into “Spain, Portugal, Italy, rest of the EU”, while other countries don’t go far enough into the past to be useful. True to stereotype, the most rigorous seem to be the Germanic nations, which is somewhat fortunate since German-speaking countries and Scandinavian ones are preferred destinations of intra-EU migration. Also, the numbers in Italy after 2011 are based on the census of 2011, but the data before that year, overestimating the number of immigrants, hasn’t been revised, and I had to revise the data myself, so as to not have an odd sudden drop around 2011.
Made in Python w/ Matplotlib (lineplots), LibreCalc (work restriction viz) and Inkscape.
Inspirat din articolul „O Transilvanie, două lumi” și observații proprii cum că cele două Wikipedii (Ro și Hu) tind să se axeze pe părți diferite ale istoriei, am făcut un experiment. Am scris un progrămel în python care să intre pe articolele localităților din Bihor de pe cele două wikipedii și să identifice anii menționați în articole. Am grupat anii pe decenii, și am făcut graficul de mai sus cu frecvența mențiunilor (mai pe larg: am introdus anii in Excel, am facut un grafic, exportat ca pdf, l-am deschis cu Inkscape si l-am editat ca imagine vectoriala .svg).
Ce se observă:
* lipsa datelor importante înainte de sec. 13
* accent pus de wiki maghiar pe sursele documentare medievale (mai ales cel din 1332-7) și pe recensămintele austriece și austro-ungare
* accent pe istoria dualistă la wiki-maghiar, accent pe istoria post-unire la wiki-român
Slobozíe, slobozii, s. f. (Înv.) Sat de coloniști (băștinași sau străini) care aveau pe o perioadă oarecare scutire de bir sau de prestații. – Slobod + suf. -ie.
Primele experimentări cu un program de tip GIS (QGIS). Preluarea datelor de pe Wikipedia s-a făcut cu un script personal în python.
Post-producție în Inkscape.
Varianta-n engleză uploadată și pe Wikimedia Commons.