Visualizing spatio-temporal data as an animation vs. as static maps: black coal mine shafts in the ruhr region

The ruhr region (Ruhrgebiet) in Germany was a center of black coal mining and the steel industry. In the last decades all black coal mines and most heavy industry sites have been shut down. Nowadays the low rents in this densely populated urban region attract startups, artists, retaurants, and immigrants. Still, the heritage of coal mining is visible everywhere. So I was keen to do a data visualization dealing with this history.

This project was done in collaboration with Peter Dodenhoff, who supplied me with the data he collected for his mapping project The discussions with Peter helped me to better interprete the historical trends visible in the data visualizations. Peter also wrote an article about this project which you can read here.

The animation below shows the development of black coal mining shafts in the ruhr region from 1800 until 2019. The color of the dots shows the maximal depth of each shaft over its operating time. All maps shown here were designed with QGIS and the Time Manager Plugin.

If you want to read an interpreation of these visible historical trends now, skip right down to the text. I also prepared a set of four maps which show the same data statically. The first map shows the year in which each mine shaft was dug:

The following map shows the year in which the mine shaft was shut down:

The following map shows how long each mine shaft was in operation:

The following map shows the maximal depth of each mine shaft:

The animation and the set of static maps nicely show the big historic epochs of black coal mining in the ruhr region:

  • 1800: Obervation: The animation starts in this year with only very few existing coal mining shafts. Explanation: Since the middle ages coal had already been mined in the small scale in pits and in horizontal tunnels in the Ruhr valley, not shown in the map. Deeper vertical mine shafts became possible when steam engines were used to pump the groundwater out of the shafts. The first such pump was put in operation in 1801 in Bochum by Franz Dinnendahl. Only after that did coal mining in the big scale in vertical shafts really take off.
  • 1800-1930: Observation: Mine shafts are first being mainly dug near Essen, Bochum, and Dortmund. The development then expands further north. Shafts tend to get deeper the further north they are located. Explanation: The axis Duisburg-Mühlheim-Essen-Bochum-Dortmund is the route of the Hellweg, a medieval trade route. Then and now the most populated cities lie along this axis. The need for coal strongly rose after 1800 with the introduction of steam engines in factories and of the steam locomotives for transport (in Germany since 1835). Steel manufacturing requires lots of coal, thus steel factories were built up near coal mines, also leading to higher demand. Coal mines were first dug in the south were coal lies nearer to the surface. Later mines opened further north were the coal lay deeper in the ground. Mines were shut as the coal reserves in the corresponding areas were depleted or mines became economically unviable. (Note: It is interesting that the first world war 1914 – 1918 is not detectable in the data visualization)
  • 1930: Observation: Larger sets of coal mines can be seen to shut down at once, notably south of Dortmund. Explanation: In the world economic crisis beginning in late 1929, several coal mines were shut down.
  • 1930-1960: Observation: Further coal mines are opened. Explanation: There was a high demand for coal before and during the second world war for steel and arms manufacturing. In the postwar „economic miracle“ there was also a high demand.
  • 1960-2018: Observation: Coal mines are quickly shut down. First in the south then in the north. Explanation: The coal crisis in Germany arose in the 1960s because black coal from other countries was cheaper, demand for black coal sunk, and other factors. Technologically older mine shafts in the south were shut down first, then more modern shafts in the north.
  • 2018: Observation: The last coal mine shafts in the ruhr region shut down. Explanation: These belonged to the mine Prosper-Haniel in the north of Bottrop.

This is an example of a project where I designed both an animated map and a series of static maps for a spatio-temporal dataset. After receiving the dataset I first explored the data using quickly prepared static maps. These maps I already discussed with Peter Dodenhoff. The overall historic trends could be seen in these first maps Then I designed the animation. Looking at the animation revealed several patterns on the small scale not so apparent in the static forms. Finally I redesigned the static maps to have them in a presentable form. Having the dynamic and static formats side by side allows a discussion of their benefits:

Animated maps of spatio-temporal data have the following advantages:

  • They provide entertaining content for presentations, websites, and social media. People tend to find dynamic content more interesting than static pictures.
  • Seeing a development in animated time is intuitive, i.e. is easily understood without explanation.
  • Animated maps allow to show more complex spatio-temporal patterns, for instance events of different durations recurring at the same geographic locations.
  • The visualization can usually be displayed in a single frame as a single file.
  • If the video is shown with control panel the user can skip back and forth in time (this is not possible using the simple gif file format). An animated map is a sequence of static maps each of which can be viewed on its own.

Static maps of spatio-temporal data have the following advantages:

  • All data is shown at once and can thus be better compared and analyzed. This only holds if events do not spatially overlap in the chosen visualization. (Visualization formats such as heatmaps also allow to show spatially overlapping events.)
  • Maps can be easily be prepared with standard GIS applications. The simple picture file formats can easily be used in all contexts and printed.

The above lists name more advantages for animated maps than for static ones for visualizing spatio-temporal datasets. In my opinion static maps are especially useful for analytical applications and initial data exploration. The choice of format should be based on the purpose of the visualization (entertaining, presenting vs. analyzing, exploring), the audience (general public vs. visually literate analysts), and nature of the data set (recurring spatially overlapping events vs. one-time spatially dispersed events). The ideal case obviously is to have both formats and to see what patterns can be seen in the different visualizations.

For the sake of transparency I want to add some notes on the underlying data. As already mentioned, the data was collected by Peter Dodenhoff for his project, which shows a spatial map of mine shafts and provides additional information. For some of the mine shafts in this dataset the begin or end year of mining operations is unknown. I could not include these in the animation and also omitted them for the static maps, to get an uniform set of maps. The map below shows these mine shafts in red. The visualisations above show the 1004 mine shafts with time data out of 1168 mine shafts in the inital dataset. There is also bound to be some uncertainty in the data per se. The inital dataset probably does not include every existing mine shaft and a few entries might be incorret. Nevertheless, I am convinced that the visualization and the underlying data reflect the overall historic trends very well.