The air transport sector lives in a permanent dynamic of operational challenges. One of the major challenges is the redistribution of routes operated by each company derived from the birth of low cost airlines companies. Before the existence of low cost airlines companies, offer prices were prohibitive for many citizens. When low cost airlines companies started operating routes, ticket prices decreased dramatically, which led to adjustments in the european air transport routes. Traditional airlines companies had to decrease their ticket prices and low cost airlines companies took control of many routes that were not profitable enough for traditional airlines companies.

This project shows how the European airspace is organized between different airlines companies, allowing the navigation through more than 9.000 routes operated by more than 100 airlines companies. You can analyze how many routes are operated by each airline company, filter by distance of routes and focus on a specific geographic area.

This project has a character exploration. Our first analysis show different operating models routes by airlines companies. These models are:

- Model “fly to all destinations” a model used by, among others, Ryanair and Easyjet. In this model, companies use various airports as major centers of distribution routes from which they establish connections with as many European cities as possible. They also cover short-haul routes in countries like Spain, which are routes operated historically by traditional companies that have come to be managed by low-cost airlines sometimes with funding of local authorities in the cities where they operate.

- Model “tour operator”. This model is used by companies belonging to tourism conglomerates, such as Thomas Cook or Thomson Fly. These companies follow am operational model based on few routes whose primary mission is to link tourist destinations in southern Europe (mainly the Canary Islands) with cities of central and northern Europe.

- Model “logistics centers”. This model is currently the most followed by traditional companies that have participated in the recent mergers in Europe. Examples of this models are Lufthansa and Alitalia.

- Model “local”. This model establishes a main transportation center, usually the capital of a country from which they operate different routes connecting the city with other cities of the same country. This model is identified in Air France, Iberia, Spanair and Air Europa.

- Model “radial”. This model has similarities to the local model, but in this case happens in countries with fewer internal connections. It runs based on establishing a primary transportation center from which you operate different routes to European countries. This model is identified in  KLM and Malev.

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Chirstophe Cariou is a researcher, teacher and consultant specialized in the content economy (news and videos). Christophe currently lives in Rennes (France).

His work is based in statistical analysis. Within his projects he usually includes data visualisation applications that stand out because of two personal features:
- A simple design but with attention to detail.

- The interactive applications are well integrated with statistical analysis, text reports, and also performances on exhibitions.

Christophe agreed to answer some questions during a remote interview about his work the 27th of May of 2013. This is a summary.

Q: Christophe, could you describe a little your current projects?

CC: I am currently writing an article for the review InaGlobal on Netflix that will contain some graphics. I have a pair of confidential contracts coming for this summer too. I want to finish soon some personal projects with data from Netflix or YouTube and about a book: The Theory of The Leisure Class by Thorstein Veblen (1899). I wanted to test a few things like sentimental analysis of sentences, view the book as a heatmap, provide quotes … it is almost ready.

Q: What consider as your better  skills? what tools usually manage your work? 

CC: I am not a software developer. I come from social sciences because I am an economist. I am specialised in the economy of the contents. About my software tools skills, apart for Quadrigram I usually manages MySQL and PHP to transform data. I manages Gephi for network analysis and R for statistical analysis.

Q: How Quadrigram contributes to your work?

CC: Previously, my contracts were more focused on data and statistics. Lately I’ve also integrated visualisation with Quadrigram for some contracts in progress with universities, foundations and digital companies. I also make personal projects to improve my skills, try new things and expand my portfolio.

Q: Quadrigram is a tool that is currently evolving. On the last update Quadrigram introduced a new system to overlap visualizers in order to build free style visualisations. It also included an inspector panel to set the module options faster. Did you test these new changes? what do you think of them?

CC: I think that the inclusion of the inspector panel where you can type directly the values is very positive. The new functionality to overlap visualizers are better than the previous one.

Q: Could you talk a little about some of your projects?

CC: The international Sciences Po Rennes  is designed for 250 students from Sciences Po Rennes who go every year in a university or an organization abroad. It identifies all student stays since 2001: name / address of university or organization (with geolocalisation) and contacts (name) / characteristics (year, specialty, duration). It also includes the same information for the 150 alumni who are currently working abroad. It was performed on 4 computers during the Sciences Po Rennes open day exhibition and used by the students and their families.

Link to Flickr portfolio.

Link to Institute of Political Studies,Rennes, France.

The French vs American cinema in France  is an illustration of my course dedicated to the economic history of French cinema. I use a lot of graphics in my economics courses, I thought an interactive visualization would be more interesting for students.
The visualization identifies the number of entries and exploitation films in French cinemas since 1945 (relative and absolute values, top and bottom  graphics ) as well as the 2697 films with more than one million entries (a square is a film with their name, rank and number of entries). It compares the French, U.S. and the rest of the world movies. It allows to identify important periods, competition between films, long tail of entries…

Link to the interactive visualization.

You can find more information about Chirstophe’s work on his website:

http://www.chcariou.fr/

The Failed States Index is an index built by the Fund for Peace using a methodology called CAST and consists of a series of indicators that measure to what extent a country can be described as a failed state. These indicators are divided into three categories (social, economic and political and military) and take into account different aspects that influence the quality of a state, such as population pressure, poverty, the quantity and quality of public services or the degree of power of the elites.

In order to build a project that would analyze the results of a particular country or set of countries
for each of the indicators that make up the index, we have developed a visualization that consists of 3 major elements

The first element is a visualization that groups countries by continent and in terms of four states: Sustainable, Stable, Danger and Alert. It is important to note that this type of work one position as more accurate visual attribute when communicating quantitative results. On the other hand, is a clear example of how to construct visualizations based on free design and use of simple geometric shapes such as circles or squares.

The second element is a world map showing the selected indicator values ​​for each country or set of countries. This visualization nicely complements the second element, providing a more comprehensive perspective and linked to a geographical context. The third element is a visualization in the form of radar, specially designed either to display time changes or to study sets of multiple dimensions, as in our case. The visualization in the form of radar shows the results of one or more countries in each and every one of the indicators that form the index, allowing comparability between different countries or between different indicators of various countries.

The economical power influences heavily the competence and chances of success of football clubs, similarly to other sectors (check our data story: La Liga 2003-2012, a broken “market”). In the case of the Champions League, despite the fact that money is a key success factor, there are several important patterns to reveal and discuss.

In fact, by comparing the sixteen richest clubs (Football Money League, Deloitte) with those with the sixteen highest ranking during 2003-2012 Champions Leagues, we show that the correlation between budget and classification is fuzzy.

An overview perspective on the last decade of Champions League shows that 9 out of the 10 titles were won by the Top 16 richest clubs: FC Barcelona on 3 occasions, AC Milan twice, and Manchester United, Chelsea, Internazionale and Liverpool once each. At the same time, 3 out of the 7 richest European clubs (Real Madrid, Juventus and Bayern München) did not achieve the expected success during these last ten years.

Only Porto, a club far from belonging to the class of richest clubs, was able to win the championship in 2004. Moreover, during these years, a noticeable segregation between “rich” and “poor” clubs is observable: the general growth of budget sizes, especially in the case of the most competent clubs, seems to have reduced the opportunity of more modest clubs to compete and secure any of the first four places in the league. In fact, since 2004, the year in which Porto, Monaco, and Deportivo reached the semi-finals, there is a noticeable decrease in “surprises” as fewer modest clubs managed to reach the semi-finals. PSV and Villareal were the last of such surprises in 2005 and 2006. Since then, each year there are five or six clubs with modest budgets that manage to compete in the league without reaching the quarter-finals.

The finals of the 2013 Champions League won’t be any different. After a Spanish-German semi-final in which the richest clubs in the World (Real Madrid and FC Barcelona) were eliminated, the championship is contested between Bayern München (the fourth richest) and Borussia Dortmund (the 11th richest).

See live project

The Spanish league or “la Liga” is one of the most prominent European football championships, and involves each year a considerable amount of investments. It also captures many international players with worldwide reputation, such as Messi and Cristiano Ronaldo.
We conducted a comparative analysis of the Spanish league clubs as companies: In this sense, the return-over-investment of a club is defined as the number of points acquired in the league VS the club budget. The results show that the clubs are classifiable into three categories whose behavior is comparable to “multinationals”, “medium size” enterprises, and “small size” companies and “start-ups”, fighting for market shares.

The “multinationals”, Real Madrid and FC Barcelona, dominated the league during the last decade. Both increasingly gained ground, leaving little for other clubs to dispute. This separation from other clubs became more evident after 2009 when most clubs had to adjust their budgets due to the European economic crisis, while Real Madrid and FC Barcelona kept on investing more, with ambitions of wining the European Champions League. Our analysis reveals that both Real Madrid and FC Barcelona ended up investing between 5 to 10 times more per point scored in the Spanish league than other clubs.

The “medium size” (or “middle class”) are clubs such as AT Madrid, ATH Bilbao, Sevilla or Valencia, which look at increasing their performance but are left to fight for the third position. Most have not substantially improved their point scores despite increasing their budgets significantly (from 50-60 to 100-130 million euros per year). Their overall euros-per-point curve remained flat: more investment does not translate to more points. However, there are few exceptions such as such as Malaga that have succeeded in improving their situations, but they still find it hard to compete with multinationals. Some, such as Deportivo followed a gradual nose dive, exhibiting a progressive loss in both economic and competitive capacities.

Finally, the “small size” and “start-ups” are those that struggle to stay afloat in the market, or in this case clubs that aim to stay in the first division and avoid falling back. Some such as Osasuna, Getafe and Mallorca have sustained a steadily profitable curve despite their lower budgets. Others such as Levante, Real Sociedad and Almeria were not so lucky and had to struggle dearly to compete in the Spanish league.

Comparing football clubs to companies tells a different league narrative. It shows that the Spanish league is segmented or partitioned into three categories: those in the top category will steadily increase their domination, those in the middle category work on preserving their status, and those on the bottom category struggle just to stay in the game.

Nevertheless, in sports as in economics, there is always new hope and new expectations. By chance as by talent, small clubs may rise steadily to the top and others from the top may tumble, although as revealed in our analysis, these exceptional changes are rare to happen.

You can access the interactive visualizations behind this comparative analysis here.

We just started an initiative called “data stories” that aims to tell stories where data visualisation plays a key role in decision making or problem solving. We want to publish on our website/blog specific cases where data visualisation has played a key role in solving a problem.

We would like to share with you this post recently published by Andy Kirk (visualisingdata.com) where he describes our “data stories” iniciative and shows an example of a “data movie” built with the “Data Visualisation Census” initiative led by Andy.