The 2018 Winter Olympic Games in PyeongChang have come to an end. Branded as the ‘Games of new horizons’, they were as much about politics as they were about actual sports. The following cartogram series focuses on the sports side of the games, showing the distribution of medals that were awarded during the games. The maps show each country resized according to the number of medals received by each country (with the Olympic Athlete from Russia shown as Russia):
Tag Archives: data
Destination: Iceland
1,767,726 people have visited Iceland through Keflavik Airport in 2016. These statistics from the Icelandic Tourism Board (Ferðamálastofa) confirm that there has been an exponential growth in tourism to the country. Six years ago, when this latest growth really started after the infamous Eyjafjallajökull eruption. It looks as if this incident triggered a new wave of tourism, Inspired by Eruptions?, further fueled by new flights not only from established European mostly low cost carriers but also from the new Icelandic carrier Wow air founded in 2011. Globally successful movies and TV series further helped putting Iceland on the map of the global tourism industry.
Rio 2016: Medals vs Athletes
Alternative ways of presenting the results of the Olympics has become more popular in recent years. Google – as other media outlets – did alternative medal counts allowing you to rank the medals not by their absolute numbers, but by other indicators such as population, GDP, or even more quirky themes such as fans or healthy eating. Continue reading
EU Referendum Statistics
“Sanity is not statistical.” The political rhetoric in the aftermath of the EU referendum in the United Kingdom has brought us closer to Orwell’s infamous state of Airstrip One then one could have possibly envisaged. Each side of the debate twists and turns the statistics and ‘facts’ to keep supporting their argument, while neither political party has yet managed to end the political stalemate in the country, which finds itself in a state of ‘post-truth democracy‘ that it slowly entered during the pre-referendum campaigns. All sides claim what can be best explained with the German word ‘Deutungshoheit’ (a form of prerogative of interpreting the numbers behind the result as the ultimate truth). The real truth perhaps is that there is no truth, and the deeper you delve into the results, the more complexity you find. So here are some more less-talked about findings that emerge when taking a second look at the EU referendum statistics.
As mentioned in my earlier piece on mapping the referendum outcome, of all those who were allowed to vote in this referendum, 13 million people did decide not to cast their vote, which – despite the higher than currently usual turnout – is a significant number that could have made a difference in the close outcome either way. Amongst those that voted the immediate picture that emerged from the polls published after the referendum was confusing. Several polls, such as those paid for by Lord Ashcroft and used for this analysis, agreed that the older people were those who were more likely to vote for Leave, while the youngest had the largest share voting for Remain. However, when taking the total electorate into account, and considering those who – according to SkyData – chose not to vote (or spoilt their ballot), this picture became far less clear than it first seemed:
Data skills in geography: Gridded cartograms
Data Skills in Geography is a two year programme at the Royal Geographical Society, supported by funding from the Nuffield Foundation. Continue reading
“Calling Abidjan” – estimating population distribution through analysis of mobile phone call data records
Big data, big challenge? Together with Harald Sterly of the University of Cologne I presented a little piece of research in the Extended Spatial Analytics session of the German Geography Congress (Deutscher Kongress für Geographie) in Berlin. The project “Calling Abidjan” that we worked on with Kouassi Dongo of Université de Cocody-Abidjan was started after we successfully applied for participation of the D4D Challenge. According to the initiator Orange telecommunications ‘Data for Development’ is “an innovation challenge open on ICT Big Data for the purposes of societal development”. The project allowed us to work with anonymised mobile phone data from individual call records by Orange in the country of Côte d’Ivoire (Ivory Coast).
We were interested in investigating, what non-computer scientists with a social science and urban planning background can do with such data in a more contextual rather that technically driven way and therefore explored how mobile phone call records can be used to better estimate population distribution.
For our analysis we used anonymised call data records consisting of information about the base station, timestamp, and caller ID produced by the approximately 500.000 Orange Télecom users in the country. There were 1079 base stations at the time the data was generated and we were able to work with data covering 183 days. The dataset consisted of 13GB of raw data which some would perhaps call ‘Big Data’ (though I personally do not like this term for many reasons).
The following two (draft) maps give an insight into the results. The purple circles show the distribution and density of population estimates that we derived using only mobile phone call records dataset. To better see the correlation with what other population data tells us about where people live, we did not only produce a normal land area map (on the left, also displaying some basic idea of the topography in the country) but also showed the data on a gridded population cartogram which we generated from the LandScan population grid, the perhaps most detailed population dataset currently available on a globally consistent high-resolution basis:
