Visualising a mix of large companies in a spatial graph (I)

Before the “abuse” of  the AnRep3D generator to visualise the average, individual energy-usage in different countries, normal AnRep3D spatial graphs were shown and discussed. With “normal” I mean visualising key financial data from the annual report (or more precisely: the Financial Statements in the Annual Report).  That’s what AnRep3D was developed for, although derivatives for other purposes could exist in the future, hence the demonstration of the “abuse”.

This time we go back to the original purpose, like we did in the series about relatively small companies. The difference is, the companies picked now are much larger. Tesla, EasyJet and USG People have revenues with a magnitude of billions, but the companies I selected this time, have annual revenues of tens of billions. At the same time, the AnRep3D-graphs are relative and a graph comparing small companies only won’t be very different from a graph comparing large companies. In a mix the difference would be huge, of course. Much like we saw with the energy-graphs, where the USA and India were in the same graph.

AnRep3D is all about comparing companies by looking at relative dimensions. It’s not important how large a company is, but the ratio of profit to revenue is, as is the ratio of equity and total assets (and e.g. the ratio of the revenue to the total assets for that matter). Even more important is the possibility to see companies change over time.

For this new series, I picked five very large companies in Europe and the USA: Tesco, PSA, E.on, Maersk and IBM. The most recent year – 2016 – is interesting but 2006, before the onset of the crisis, would make a good comparison. So I had to select ten sets of data: two years for five companies. Each set consists of four values: revenue, profit, equity and (total) assets. The abbreviation being R-PEA (i don’t know a real “pea” of this type, but its rather easy to remember).

For now I won’t present anything, except for the raw data I took from the annual reports (still a mix of USD, GBP and EUR). Some annual reports have a slightly unusual format and I don’t hope I made a mistake in picking the right values. Yet a warning: don’t take my numbers to work with when looking for financial information. Always go back to the source and look for yourself. If you want to visualise the data, please purchase an AnRep3D  licence 🙂 or download the free demo-set (only one “building”) at

raw input data large companies

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Abusing the AnRep3D-generator: visualising energy-sources (IV)

The last time I didn’t provide the readers with a real spatial graph, because it had not been uploaded yet. During the week after the post, I realised the full power of the AnRep3D generator – even if it’s abused for energy instead of money – needs more than just a single year. We already had eight countries for which we showed the usage of coal, oil, natural gas and non-fossil sources (renewables and nuclear combined). For every “building” in the graph the width represented the coal-consumption, the depth oil, the yellow part of the height the natural gas and the green part of the height the usage of non-fossil  sources. But in the end it was only a single row of eight buildings. However, energy usage is not static and AnRep3D allows us to show several rows of buildings, each row representing a single year for all the countries.

So instead of just showing last week’s graph, I collected a lot of additional data, for previous years. We already had 2014, but now 2004 and 1994 were added to cover two jumps of a decade. The last decade (2004 – 2014) covered the financial crisis, where the first one holds the millennium-boom. Interesting periods!

To start with, I will provide you with the original spatial graph through the links below. One is in text, the other is the picture of the graph itself. Remember, we divided the total consumption by the population, to obtain the individual usage for every source of energy. This allows us to compare large and small countries. The total consumption of energy will be the sum of width, depth and total height of the building, so not the volume!

This is the text-link to the original spatial graph on energy-usage per country (remember: WebGL should be enabled). Below a screenshot is presented.

Frontview 8 countries 3 years

This front-view presents the coal-usage per individual (width of the buildings), for the eight countries over three years – the most recent year in front. We already saw France and Italy are hardly using any coal, where the USA and the People’s Republic of China are using a lot. The relative contribution to its total energy-usage is higher for China, but the absolute amount of coal consumed per individual, is more or less the same for China and the USA!

Remember the spatial graph in your browser can be manipulated easily: clicking the right mouse-button, moving the mouse up and down will zoom the graph in and out. Clicking left and moving the mouse will tilt the graph in different directions (or move the observer’s viewpoint around a fixed graph). Double clicking in the graph translates it and moves the centre at the same time. As a result the way the graph tilts will change. Just try it. If you don’t know how to get the normal position back, refresh the graph.

If we fly up a little bit, we can observe the graph from above and in addition to the coal-values (width) now the oil-values  (depth) become clearer.

Tilted view for 8 countries 3 years

The individual oil-consumption is not very different for the inhabitants of EU-countries and the Russian federation. On the other hand,

Topview 8 countries 3 years

we can see people in the USA use about twice this amount individually, while China and India hardly use oil at all. The impression is even clearer with a real top-view.



Side-view 8 countries 3 years

Looking from aside, we can see the individual consumption of natural gas and non-fossil sources (nuclear, solar, wind, hydro and biomass). Now we can also see why France doesn’t need coal. The green part represents a lot of nuclear energy there! Italy doesn’t use a lot of coal either, but the green part is smaller and actually they banned nuclear energy long ago. So the increasing green part comes from other sources and biomass is growing throughout the years in Italy.

By the way: we placed 2014 in the front as the current consumption is more relevant than the historical values. Yet we can see the crisis had its impact and reduced the individual energy-consumption for a lot of countries. Especially the yellow height (so the usage of natural gas) decreased slightly between 2004 and 2014.

In China however, the population grew and therefore the country increased its energy-consumption, but the individual consumption grew as well! The bottom-view shows China’s coal-consumption per individual was more than doubled during the last twenty years, while the USA decreased the individual coal-consumption. Of course it’s an average and in China a lot of the coal will go to e.g. railways and the generation of electricity.

Bottom-view 8 countries 3 years

Interesting is the USA increasing its oil-usage (most likely the result of the shale-oil revolution) and the Russian Federation even more.


Finally, if we compare China to India, we see a huge difference. Although India’s energy-consumption increased, the population grew quicker and as a result the individual consumption decreased a little bit. The individual coal-consumption in India is much lower than in China. For the other fuels, like oil and natural gas the difference is not so impressive, but non-fossil is close (a lot of nuclear energy). Remember: the total usage is the sum of the three dimensions.


With this post we complete the series about energy – abusing our generator. The AnRep3D generator was meant to visualise data from Annual Reports, but other opportunities cannot be ignored!

For people who want to check the individual consumption of coal, oil, natural gas and non-fossils: have a look at my input-file. It was generated by Excel hence all the semicolons. The order is: country, year, natural gas+non-fossil, non-fossil, coal, oil (replacing company, year, revenue, profit, total assets, equity).

Input-file (csv)

The sources used were mainly the for the energy and for the population-sizes. For more information about the AnRep3D-generator itself, visit our website

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Abusing the AnRep3D-generator: visualising energy-sources (III)

My oldest son looked at the graphs and thought it was unfair to combine coal, oil and natural gas + non-fossil in three directions as the volume would suggest a total. Used for the original purpose (visualisation of four different values from an annual report), this objection won’t apply. Total assets and equity are related and provide for a solid base under the revenue and profit, so it made sense to combine them this way. Nobody would think something about the volume – only the shapes of the surfaces have meanings.

If the AnRep3D-generator is used (or abused) for energy, there will be something like “total consumption”, but it is not related to the volume of the building. The total usage is the sum of the three dimensions of the building: depth, width and height.

His second comment was a useful one, because it will allow me to present a different view on energy: countries with a large number of inhabitants will use more energy and therefore the comparison of coal, oil, gas and non-fossil should be “per capita” (by the way: I never understood why it’s not “per caput” as it’s about a single head). Dividing all the values by the number of inhabitants would be an interesting one, because e.g. China uses a lot of coal, but a lot of people live there as well – more than in the European Union and the USA together. We presented only seven countries, so it will be easy to present the average individual consumption for all the fuels. However, I should have added Italy, as it’s a large industrialised country. So this time we will have eight countries, but the amount of Joules shown will be the average per inhabitant. That’s why it’s in GigaJoule (GJ) now, rather than PetaJoule (PJ = 1000,000 GJ). Individuals usually consume less energy than whole countries.

The numbers of inhabitants below are not all exactly for 2014 (as some are ultimo 2015 and some are rounded as well), but it’s only for demonstration purposes and the differences will be quite small:  UK:  65,382,556,  France: 66,759,950,  Germany:  82,175,684,  Italy: 60,665,551, Russian Federation: ‎143,457,000,  China: 1,371,000,000,  India: 1,311,000,000

After dividing all values by the number of inhabitants, the average usage per inhabitant is obtained.This time I used Excel and saved a .csv to be used as an input-file. That’s were all the decimals and semicolons came from. For the meaning of all the numbers, please read the explanation in previous posts (or read the manual in the free demo-package at AnRep3D.)

csv as inputfile

I generated the new graph, but this spatial graph will only be available next time, as we discuss the numbers. At the moment we haven’t uploaded it to our server yet.So for now I put in two screenshots, taken from different angles.

energy-sources per capita

energy per capita tilted

Suddenly China’s individual usage of coal is less impressive if compared to the USA value! The shape of a single building won’t be different but this time the size can be compared as it’s all about the energy-consumption of an average individual in a country.

Remember: the total usage (this time per individual – on average of course) is the sum of all three dimensions of a building: depth, width and height. Width is coal, depth is oil, the yellow height is natural gas and the green height is “non-fossil” (a combination of nuclear energy, wind, solar, hydro and biofuels).

For more details about our generator, able to create spatial graphs or to get the free demo-package: please visit our company’s website: AnRep3D.

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Abusing the AnRep3D-generator: visualising energy-sources (II)

Now it’s time to have a closer look at the full graphs. Not just one country, like the graph for the UK  created with the demo-generator last time. All selected countries have to be shown together. A complication is that some countries are much larger than others. The graph can cover them all, but will be less informative (remember: WebGL should be enabled in your browser to be able to view the original spatial graph behind the link in the picture).

3D graph for energy-sources

To get a better overview the graph should be split: one covering for very large countries (or rather: countries consuming a lot of energy) like India, China and the USA.

Energy-sources in larger countries

The other one will show a the European countries we selected: UK, France, Germany and – although not completely European – the Russian Federation.

Energy-sources smaller countriesFrance is very interesting. It uses hardly any coal (its bar is very narrow), more oil than natural gas (the depth of its yellow block – oil – is about twice the height – natural gas). The height of the green  part represents the “carbon-dioxide-free” energy and in France it’s mostly nuclear.

Looking at the UK, we can see immediately that more coal (width) is used and slightly less oil (depth). The height of the yellow block is higher than for France, meaning the UK uses more natural gas. The green part for the UK contains more renewables, but also nuclear energy. Both don’t emit CO2. Adding width, depth and total height we will get a higher value for France, meaning its energy-consumption  is higher than the UK’s usage.

Germany is a bit of a surprise. Of course we expect the green part to be mainly renewable energy, but nuclear energy is still around at the moment. The surprising part, however, is the high usage of coal (the width of the Germany-bar is larger than the width of the UK-bar and much, much larger than France’s bar). Actually, Germany uses more coal than the Russian Federation! The Russian Federation has a high energy consumption, but it’s mainly natural gas.

Let’s switch to the large comsumers!

Energy-sources in larger countries

The bars for China and India show a large green part and only little yellow. This means natural gas is not very important, but nuclear energy and renewable energy are! Yet the amount of coal used in China is impressive. Remember the graph is now at a different scale and cannot be compared to the European part. Yet the width of the Chinese bar exceeds the combined widths of India and the USA – meaning China uses more coal than both countries together.

We could go on discussing the graphs, but it’s better to manipulate the 3D graphs yourself and really understand what is presented:

The total energy (in e.g. PetaJoules or Barrel Of Oil equivalents/boe) is the sum of width, depth and height. It’s all relative so the measures are not important as long as they are all the same thoughout a graph.

The sources (mainly “fuels”) used to obtain the energy, are represented by

  • width: coal,
  • depth: oil and the
  • height of the yellow part: natural gas.
  • the height of the green section represents non CO2 energy like nuclear and renewables.

After some exercise it will be easy to interpret a bar at once and to pick the special ones you’re interested in from a whole graph.

Of course time-ranges will be even more interesting, so the next time we will the abuse the AnRep3D generator once more!

For direct links to the spatial graphs on energy-consumption at a country-level:

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Abusing the AnRep3D-generator: visualising energy-sources!

Originally the AnRep3D graph generator was meant to visualise financial data from annual reports. Recently I realised other values can be visualised the same way. I performed a lot of energy-calculations the last couple of years and it’s clear we all try and get away from fossil fuels, especially coal, because of the climate-change.

Natural gas produces about half the amount of CO2 per unit of energy (let’s say per PetaJoule). It’s still a lot of CO2 and that’s why natural gas is considered to be a “transition” fuel. In the end we will move towards energy-sources without a net CO2 emission at all.

We all know about solar-, wind- and hydro-power but for a lot of countries nuclear power is an important alternative and the operational production of energy doesn’t generate CO2. On top of it there is bio-fuel and other forms of biological materials used as energy-sources.

Because of this set, I decided to use coal for the width of the building (replacing “equity”), oil for the depth and gas + non-fossils as height (revenue in the normal situation). The non-fossil part would become the roof (serving as “profit”) and the yellow part of the height representing gas (in a profit and loss report it would be the “costs” to be subtracted from the revenue to get the profit). Using countries instead of companies, the picture gets completed!

Suddenly we will see “good” and “bad” buildings. If a building is taller, the gas and non-fossil part of the total energy-usage is high. A plump building shows a country is relying on coal and oil. A broad plump building is the worst, because it represents high coal usage. A deep building is about a focus on oil. Of course tall building with a small roof still shows a lot of natural gas consumption and less non-fossil energy. A thick roof could means a lot of renewables but also a lot of nuclear energy (like e.g. in France).

Other choices would have been valid as well. E.g. taking coal for width, but oil+gas for depth. This would have left the height to be used for nuclear (yellow part) and renewables (green roof). We will explore those options in upcoming posts about “the abuse of AnRep3D”. If people are interested, an alternative version of the generator could be made available, called something like Energy3D.

Where do I get my data from? It’s not in the annual reports and countries don’t publish them either. The International Energy Agency however, has very nice charts showing all the energy-flows. Excluding exports, all data can be collected from the chart at the right point. To the top the measure can be chosen: either PetaJoule or MMboe. It doesn’t matter as both are objective units to present amounts of energy. I collected the information for a couple of countries and I will generate a graph. Below an example of the value for oil in France.


For now I didn’t want to leave you without a 3D-graph at all. That’s why I used the free demo-version of AnRep3D (to be found at: – one building only) and created a 3D energy-graph for the UK using it.

Remember: this time the width is energy from coal, the depth is energy from oil, the yellow part of the height is energy from natural gas and the green roof is nuclear + renewables (solar, wind, hydro, bio). All sources were presented as PetaJoules to be able to compare all sources (but MMboe would have done as well). Electric is not a part of the graph as it’s either generated from one of the other sources or the primary form of energy produced by renewables (solar, wind and hydro).


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Visualising a mix of companies in a spatial graph (III)

Before we talk about the graph, let me tell you the actual spatial graph I am talking about is available at our AnRep3D website:  Any modern browser with WebGL enabled will show this X3DOM-based graph, which actually is just a webpage. We will show some screenshots for people who are not able to look at the graph for whatever reason, but if possible, keep the original graph at hand.

The distance between the “buildings” was increased, because after the correction of the values for Easyjet and Elecnor the buildings became narrower and extra space had to be created for the labels. As a result, the graph is further away, more in the background. in the background

By clicking the right mouse-button and moving down at the same time, the graph will come closer.

Zooming in

To re-adjust the position of the graph in the screen, double-click in the graph. When clicking in the middle of the Elecnor-building, the graph will move up because now Elecnor was selected as the new centre. Click on USG-People and the graph will move to the left. Remember: when the graph is in a very strange position, click the refresh-button to reload the page and the original position will be shown again.

Back to original position

To be able to see equity and assets at the same time, we have to look at the top of the building (or the bottom). The graph can be tilted easily by clicking the left mouse-button and moving the mouse downward (or upward) at the same time. Using the middle of Elecnor to click at will keep the graph aligned horizontally.

Top view

Now it’s time to have a closer look at the companies. Zalando doesn’t have a lot of debt: the building is not much deeper than its width – not even two times. As the equity (width) is part of the total assets (depth), the extra part represents the debt.

For Tesla it’s completely different: the debt is probably five times as high as the equity, so the equity ratio is rather low but the company still doesn’t look like a bank! (see previous post about Dutch banks).

Looking at their revenues, the difference is small, but Tesla manages to lose a lot of money, while Zalando makes a small profit.


Comparing Elecnor to Tesla, we see it has more or less the same shape, so a rather high debt as well. The revenue is only half as high as Tesla’s but there is some profit after all. Of course their business-models are very different. Tesla will spend a lot of money at R&D without caring about profit in the short term.


EasyjetEasyjet is surprising. To generate a revenue in the same magnitude as Tesla, they need much more money, because it is expensive to own airplanes and to be compliant with all the regulations in aviation. The equity-ratio is somewhere between the values of Zalando and Tesla, but what really surprised me is the profit. I thought airlines don’t make a lot of profit, but the firm green roof tells me otherwise.

USG PeopleFinally USG people is able to generate a rather high revenue (somewhere between Zalando and Elecnor), without having a lot of money. Neither the equity nor the value for total assets is high. Of course they will hardly have any tangible assets and almost no stock. Most of their expenses will be related to people, so hardly any investment to be made (relatively speaking).

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Visualising a mix of companies in a spatial graph (II)

We already had a look at the revenue, profit, equity and total assets of five very different companies (the well-know Zalando, Tesla and Easyjet, the probably less-known temp agency USG-People and then the Spanish technology company Elecnor). Yet we only presented a range of numbers. Although some people will understand the characteristics of a company – or even recognise the company – from those numbers, it’s better to visualise them. That’s what AnRep3D does: the numbers in the input-file are converted to spatial graphs. Once again, I started the generator and got the screen below:

Start-screen AnRep3D

Then I selected the files for input and output and got the message that the graph was generated. As I double-clicked the output-file (an .htm-file), the graph appeared in my browser. Let’s have a look!

First graph of five companies

The five companies are represented by their buildings, standing abreast. The front-view shows their equities (width) and revenues (height). If the roof is green, there is a profit but a red roof means a loss. All companies show a smaller or larger profit, except for Tesla with it’s very clear red top! So its total revenue was needed to cover all the expenses and then some (the red part).  Yet Tesla’s equity is rather low as the narrow building emphasises! On the other hand, Tesla is able to generate a lot of revenue with a small relative small amount of own money. Of course a lot of other capital (not shares, but loans and bonds) could be available. If we click in the graph, hold the mouse-button an move the mouse downwards, we will see the graph tilt.

Tilted graph of five companies

Now we have a better view at the depth of the building (total assets). If we consider the depth of the building –  from front to back that is – we see the total assets. It should be equal to the combination of equity and liabilities. Looking from this position I knew immediately something went wrong! Both Easyjet and Elecnor showed equities higher than their assets but this doesn’t make sense! Was it a typo or did I switch assets and equity? Well, both actually.

For Easyjet equity and assets were switched indeed. I only noticed when observing the graph. It is stupid, I know but at the same time it shows how powerful a visualisation can be! After correction the Easyjet building will remain the same, only rotated by 90 degrees.

For Elecnor I just put the decimal point at the wrong position. Stupid as well and I’m happy I wasn’t presenting graphs for a meeting of shareholders. After correction this building will get only 10% of the width in the graph shown above. I adjusted the input-file and then the graph had to be generated again of course, but it’s a matter of seconds. This time I used the version with billions instead of millions and as a result  the “scaling factor” cannot be 500 but is 0.5. So actually the values will be doubled. At the same time I had to increase the spacing (to 100) and reduce the font-size (to 3), because of the buildings would become narrower and the labels would overlap as a result of it. Below the corrected input-file – this time with semicolons instead of comma’s as both are allowed as a separator (I’m very sorry I presented a misleading set of values earlier):

Zalando; 2015; 2.96; 0.12; 1.27; 2.12
Tesla; 2015; 3.69; -0.81; 0.83; 5.32
Elecnor; 2015; 1.88; 0.066; 0.74; 3.49
Easyjet; 2015; 6.47; 0.76; 3.10; 6.67
USG_People; 2015; 2.55; 0.020; 0.487; 1.28

Looking at the new graph, based on the improved set of numbers, we can see Elecnor and Easyjet have changed (but the other three still being the same).

Adjusted graph

Because of the increased spacing, the buildings moved into the back as well. By right-clicking the mouse and moving it at the same time, the graph will zoom.

Zooming in

Tilting again (left-clicking the mouse, holding the button and moveing down) we can see total assets being higher than the equity for all companies shown. Easyjet only turned by 90 degrees, but Elecnor only being much smaller than previously.

Tiling zoomed graph

If we would go on, this post would be too long to read, so I keep the in depth discussion for the next time. Meanwhile have a look at

The only advice for now: if you really messed up with tilting or zooming, don’t worry! By clicking the “refresh” button in your browser, the graph will get back to its original position!

Back to original position

Back to original position using the “refresh” button

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