Meteorological information in a spatial (3D) graph

The Royal Netherlands Meteorological Institute (KNMI) provides their data to the public. Those data are very detailed and go back over a hundred years. Meteorological means it’s all about the weather and because of this the data have an annual pattern. It is interesting to take the data from a range of months and years and put them in a spatial (3D) graph.

KNMI frontFor the readers who don’t know yet: VRBI (a Scientassist label) offers generators to the market, able to convert a set of numbers into such a spatial graph. To demonstrate the power of our generators – especially their output – this blog provides a couple of examples every now and then. We are convinced the era of the flat graphs will end, because Big Data becomes too complex to be presented in an old-fashioned way.

The last couple of years we have demonstrated the power of spatial graphs, but used VRML as the language. Not everybody wants to install a plugin or download a viewer, so the new generation of VRBI-generators is HTML-oriented. Now the same set of numbers will be converted to a simple .htm webpage, to be shown in a modern browser. The 3D-graph will appear like a website, although it’s a local file. Manipulating the graph is easy (see explanation below).

A piece of the KNMI-data used:

2010; January; -1.6
2010; February; 0.1
2010; March; 5.2

When looking at these data the y-value (vertical direction) is clear: we preferred the temperature for our illustration. Of course it is possible to put all values (high, low everage) in the graph for every single day but then the graph would be too crowded, so only averages  for every month within a year were taken.

For the x-values  (left to right) we used the years and the months became the z-values (front to back). The “fourth coordinate” – the size of the balls shown – was not really used this time:  all balls got the same size. Some labels were added as a kind of legend in the graph. Above, the front-view of the graph was already shown. The blue balls in the front  represent January with its low temperatures in the Netherlands. The second set of blue balls, more to the back, represent July with its higher temperatures. All sets (months – six different colours were used two times) are widely scattered, meaning large differences thoughout the years occured.Below some other screenshots are presented, but the real spatial graph (in html) is available here.

KNMI_bottomKNMI with scale-values







Looking from the side (left graph above), twelve “planes” of balls become visible. The planes represent the different months. One or two of the purple outliers in August are higher than the high-values for July, but the lowest values for August are definately above the July-level! Of course temperatures will be negative during the winter. To show this, the origin of the graph was set lower (can be done in the browser, by clicking in the graph). This time values were added for the temperature (black balls with white numbers). The lowest monthly averages(!) were even below -10 (Celcius) for a couple of years.

The resulting graph is very useful for visual mining. Look at the original graph in html and write down a couple of hypotheses. These hypotheses can be tested with sophisticated statistical software, but the human eye is able to select the real interesting areas immediately. Try it by looking at the original graph instead of some screenshots :instead of the fixed view of a screenshot, the full spatial graph will be shown directly in your web-browser (or download the file first by right-clicking).

To experience the full power of a spatial graph, click on the screen and then move your mouse to manipulate the graph: rotating and tilting is done easily this way. Translation is done by right-clicking and moving. Double clicking will change the origin. To return to the starting position, just refresh your screen!

For more information look at

Next time the free demo-generator will be available for download!


Over AnRep3D

AnRep3D is the new company, founded after the handover of Scientassist (together with VRBI) to one of my sons. From now I will focus on three-dimensional graphs for the financial markets, showing the main figures from annual reports in comparison. As per 2021 a second product is available: EnRep3D. It is meant to visualise energy. Although the engine is the same, the texts, manual, website and examples (including blogposts) are focused at energy.
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