Landing the fridge-sized probe Philae on 67P/Churyumov-Gerasimenko was without a doubt one of the most fascinating achievements of science and aeronautics this year. We were glued to the screens during the dramatic hours that followed the landing and impressed by the stream of pictures arriving from this distant and unexplored world. The European Space Agency (ESA) did a great job not only with the mission but also by using a Creative Commons License to publish the pictures of the comet.
This gave us the possibility to create a 3D-model of the first landing site. Admittedly the ESA published a complete yet low-resolution 3d-model of the comet a few weeks ago but we wanted to try some software anyway: Using 123D Catch from Autodesk we stitched the pictures together and created a meshed and textured *obj file which we then uploaded to Sketchfab. Although not perfect we were impressed by the results. The process is quite simple and we will surely use it on more down-to-earth mapping projects in the future.
Yesterday we’ve had the pleasure to hold a presentation at Netzwerk Recherche’s Jahreskonferenz. Germany’s most important investigative journalism conference. The aim of our session was to give guidance to (data) journalists and went by the title: “Mapping Data: So gelingen Geovisualisierungen” (“A guide for making geodata visualizations”).
You can take a look at our presentation by clicking one of the links below (we’ve made a German and an English version)
As an add-on to our presentation we produced two more things, that some of you out there mind find helpful too:
Mappable Toolset: The number of tools to process data, make maps, interactive visualizations etc. is continuously growing. While we love new tools, this leads to a situation that makes it quite hard to keep an overview of which tools are good for a certain tasks, where to find them and how much they cost. To keep track of the tools we’ve used so far and as a guide for others we thus collected our toolset. Have a look at it here: English version, German version.
Mappable Cheat-Sheet: Making maps and other visualizations with a geospatial component is certainly not a trivial tasks. There are many pitfalls, take alone spatial reference systems as an example, that might completely mess up your visualization if you don’t handle them correctly. We thus created a checklist for making geodata visualizations in (data-driven) journalism. You can find it here: English version, German version.
Last week the German weekly newspaper ‘Die Zeit‘ published an article on the distribution of physicians in major German cities. They used maps to show the relationship between income and physician density. Their main claim was that physicians tend to open their offices in wealthier districts.
First of all – we love the style and user interface of the article. It is really well done and the Zeit’s new customized mapbox style blends in really neat into their overall design. However we believe that the Zeit could have done better in one respect: They did not discuss the significance of the correlation between income and physicians density.
Yes, it is! The diagram with it’s trend line show that there in deed is a strong correlation between income and the density of physicians. Another determinant is visualized by the colors: the population density. Even though we’ve already normalized the number of physicians per capita, those districts with a higher population density still tend to have a higher density of physician’s offices. In our eyes this is the case, because those districts in general are closer to the city center and are characterized by mixed-use.
How was this done?
We had a deeper look at the Zeit website and found a JSON-file containing the information behind their interactive explorer. We extracted the locations of the physicians and joined them to a district shape with QGIS. Afterwards we used public data from the Statistikamt Nord to add income statistics. Finally, the scatterplot was realized tableau-public.
Ulla Jelpke, Jan van Aken and Christine Buchholz (German MPs – die Linke), today published a document about public recruiting events of the German army. Events like this, especially if targeted on school children and teenagers, have been the subject of fierce public discussions for quite some time.
We filtered the given information to show the location of the people attending the events and not the venue. A first look at the map shows a slight accumulation of events in rural regions (possibly related to higher unemployment rates). It would be interesting to correlate the data with other sources.
We extracted the tables from the pdf file using tabula, a helpful little tool, which lets you convert pdfs directly on your computer (great for sensitive data). The next step was to clear and categorize the data, geocode it and upload it to cartodb.
The aim of this blog post is not to show you the best possible map design but to point out that with today’s tools everything is mappable – even on a very short notice: The whole process took us about 1,5 hours (so there could still be errors in the data!).
Some of you may have heard of Code for America. It’s an awesome program that encourages civic hackers, developers and designers to use their skills for the public good. Similar programs exist in several other countries – and now there is a German divison, too!
Mappable was excited to support the launch by organizing the Hamburg Open Data Day on February, 22 together with Marco Maas of OpenDataCity. The event was a full success with close to 30 people coding, mapping and discussing the whole day (and into the night). Some results can be found on the Open Data Day Hackdash.
Encouraged by the event we are now in the process of organizing a regular monthly meetup and forming an ‘OK Lab Hamburg’ (equivalent to the Code for America Brigades). The first one will take place on April 7. So drop by if you’re in town.
One weakness of the presentation was the lack of information about the relative number of nonvoters for each voting district. Basically, by mapping every nonvoter we produced a map which looks almost the same as a visualization of the current population density.
As a consequence we’ve decided to vary the style of our map for the results of the 2013 election by additionally displaying the percentage of nonvoters for each district by diverging colors.
The results are quite interesting: More than two decades after the reunification there is still a significant gap between voters in the eastern and western parts of Germany. Especially rural parts of eastern Germany have a considerably low turnout. This is sad but in no way surprising given their demographic, economic and social situation. The refusal to cast a vote is directly connected to factors like wealth and education. The suburban regions around cities like Berlin, Hamburg or Munich therefore show higher turnouts.
Interesting and somehow surprising is the fact that some counties in Bavaria had quite low turnouts as well. Maybe voters were overly convinced that the CSU-party (who received over 50 % of the votes in Bavaria) would win anyway.
As Patrick already wrote, I’m now officially part of mappable.info and excited about the things lying ahead. My name is Achim Tack and my professional background is quite similar to Patrick’s: just like him I’m an urban planner. I’m currently working for a small Hamburg based consulting company. I spend most of my time on research projects, especially focusing on technical and social infrastructure systems in the context of demographic change.
To me it`s all about data – ways to acquire, clean and structure it and finally draw conclusions from it. Today data is generated everywhere – from mobile phones to sensor systems for traffic control or environmental monitoring – and a lot of it is mappable. This is why I’m joining this blog.
As well as complex, long-term projects, I enjoy working on spontaneous ideas and mini projects for fun (some of them can be found on my personal website) and from today on I will publish some of them here.