Biodiversity Mapping

The projects we’ve published so far are all visualizations and analyses of human activity, mostly on a city scale. But there are so many more fascinating data sets out there and it’s about time for us to broaden our activities. Therefore, it was a welcome opportunity to do some mapping together with Julia Griehl (@JulieDeLaMer) who is writing her master thesis on the protection of terrestrial mammal species. As a sneak preview into her work – she’s currently adding the finishing touches to her thesis – we want to share this map on mammal distribution with you:

Click on the image for a zoomable high-res version.

Plain and simple, the map shows the distribution areas of almost all terrestrial mammals – more than 5,000 species in total. The distribution data was obtained from the IUCN Red List of Threatened Species (which in fact lists all known mammals, classified into different categories spanning from low concern to a high risk of extinction). The map we present here is part of Julia’s data source description. We’ve simply mapped all distribution areas on top of each other with a very low opacity (2%) using QGIS. Areas of full saturation consequently have a density of more than 50 species. Only hitch was the enormous data size and processing time (e.g. to exclude extinct species and delete marine distribution areas of some terrestrial mammals, such as seals, by clipping along coastlines using Natural Earth data).

Even tough the map is rather simple and descriptive we still like it for it’s unique style. It rather has the appearance of a precipitation map than a biodiversity map.

As a little extra, we’ve calculated statistics per latitude and visualized them as bar charts with Adobe Illustrator’s graph functionality. The bars visualize the size of land area (left axis) and the number of species (right axis). It’s quite easy to spot that the highest diversity of terrestrial mammals can be found in the equatorial belt – areas with tropical climate – whereas the peak values for landmass are between 25° – 50° N.

But enough text and time for some visuals. You can explore the map in a full-sized zoomable view when you click the world map image above. It’s fun to zoom in and see how diversity is changing rapidly – for example between the the Gangetic Plain, Nepal and the Himalayas. Or how the mountain range of ‘Tassili n’Ajjer’ stands out in the middle of the Sahara Dessert.

Recruitment Events of the German Army

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!).

Mappable goes


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.

What’s your Travel Score?

Today we’re releasing a new project: Travel Score. It’s an interactive map which, by selecting those areas of the world that you’ve visited, calculates how much of the earth you’ve already explored.

The geographic data, gathered from Natural Earth & SEDAC/CIESIN, was processed in QGIS and finally visualized in D3 for making everything interactive. The project page includes a detailed description of the way everything works and how I’ve built the map as well as some words about the dataviz / cartography journey I’m currently planning. Or if you, like most of the people on the web, are just interested in the fancy, interactive content, take this shortcut to reach the full-sized, interactive version of the map or simply click on one of the images.

BTW: My travel score is 6.91…still a lot to explore! what’s yours?

The limited accessibility of public transport

Taking the tube or suburban railway for your daily commute is quite comfortable if you live in a city with a well developed public transport network. But as so often in life we quickly take things for granted and tend to forget that such amenities are not accessible for everyone. And I’m not referring to people living in cities without efficient public transport, but those who are, due to limited mobility, not able to use parts of the public infrastructure. Even though there certainly have been a lot of improvements in the accessibility of public transport stations during the last decade or two, there are still far too many stations in almost any bigger city which are not accessible for someone who is e.g. in a wheelchair. And even if there are big efforts to improve the situation, very old stations in densely populated areas just make the construction works very complicated and costly.

We are all disabled – sometime or another

Fortunately there are projects like Raul Krauthausen’s phenomenal wheelmap, which collects accessibility details for all kinds of points of interests by crowdsourcing and maps them on top of an OpenStreetMap base layer. He states that everyone is certain to be in need for a barrier-free environment sometime or another: Whether you are sitting in a stroller as a baby, using a wheelchair or crutches when injured, or a walker as an elderly. 

While this and other tools help to improve the situation of handicapped people, we nevertheless think that from time to time it’s useful and necessary to remind ‘the public’ about the limitations of ‘public transport’. In order to do so we chose a quite simple approach and remapped the public transport network in Hamburg, London and New York together with Julia Griehl (@JulieDeLaMer). 

How did we do this? Well most public transport networks publish maps which quite clearly symbolize the accessibility of every station. Our approach was to take open licensed versions of these maps and remove the name of every station which is not marked as wheelchair accessible (we used information from the official maps to identify them – knowing that each city might have a different definition of accessibility). The results are maps which show how thinned out those networks suddenly look from the perspective of a handicapped person. Just click on one of the animated images to get to a larger view with an interactive slider to swipe between the two maps.

Create a map for your city!

For those of you who are interested in the techniques involved in creating these maps or want to draw your own map for the city you live in, here’s how it’s done:

First you need a map of the chosen transit network with a license that let’s you modify it and publish your work. Finding such a map might already be the hardest step, as the official maps mostly are released under quite restrictive copyright (looking at you, London!). You therefore need to be lucky to find an alternative version released by someone who put quite some effort in drawing his/her own version. Probably the best place to search for such maps is wikimedia commons. In our case we used the maps of Lars Hänisch, Jake Berman and Matthew Edwards. Thanks a lot for making those maps and releasing them under open licences!

Next, we suggest to look at an official map in order to identify stations indicated as accessible. You can use the drawing software of your choice to remove the names of those stations that are not accessible. We used Adobe Photoshop and Illustrator (for bitmap- or vector-images respectively) but any other drawing tool will be just fine too. Additionally, we altered some map details (e.g. removed unnecessary labels, changed some colors and stroke-widths) to improve the readability, but that’s an optional step. 

To get this fancy visual diff view there’s the jquery-plugin TwentyTwenty. Just as the maps, this little piece of software is released under an open license and requires only a few very simple steps to setting everything up. It won’t be a challenge for you, even if you’re not familiar with coding and there are step-by-step instructions to be found here.

Finally, if you want to publish your results to the web, you need some webspace. There are tons of possibilities to do this. If you are not familiar with this kind of stuff, check out the options dropbox has to host your own website. It might not be very professional – but hey, it’s for free and it works reliably. We use it too, if we want to publish content which conflicts with the content-management-system of Squarespace (our hoster).

Join the Open Data Day 2014

If you like these maps and want to produce some of them on your own, or you have other ideas what could be done with open data, why not join in on the Open Data Day at February, 22nd? There are going to be meet-ups with friendly people who do awesome stuff with public data in many different cities all around the world. If you want to join us in Hamburg, you’re welcome and can find all the necessary information here.

The potential of phone directories for urban analytics

One of our main interests at mappable is to find creative ways to use (geo-)data for mapping urban dynamics. In our newest project we will explore how phone directories can serve as a data source for various analytical tasks, starting with urban migration patterns. 

For this purpose we bought German CD-ROM phone directories for the years 2004 – 2012 and exported all datasets for Berlin. We subsequently identified approximately 50.000 individual intra-city relocations and started to visualize and analyze the derived migration data. The first result of our work is an interactive, explorative visualization that let’s you explore Berlin’s intra-city migration patterns with high spatial granularity. You can take a closer look at it and explore the dataset on your own by clicking on the image bellow.


The migration patterns generated with our approach resemble those of the city’s official migration statistics. Thanks to the fact that our raw data are addresses, we are even able to analyze intra-city migration on a more detailed level than with the officially released data, which is aggregated to the county (‘Bezirk’)-level. 

To sum things up: we are quite enthusiastic about the potential of phone directories as a data source and there are definitely more research questions that can be answered with these data sets besides only migration patterns (e.g. monitoring gentrification processes, identifying ethnicity patterns).
We will continue to publish short updates about this project here on our blog. If you want to take a more in depth look, see our project page, where you can find some words on how we processed the data, created the visualization and how we interpret the migration patterns we’ve found.

‘one week of carsharing’ back online

In July 2013 we’ve released a project named ‘one week of carsharing’, for which we’ve tracked and analyzed one week of carsharing usage in 19 cities throughout Europe and North America.  

Unfortunately, just one days after the initial release of our project, in which we visualized key facts like the number of vehicles, number of trips per day and vehicle, average rental duration and maps of the spatial distribution for all cities in which car2go was operating at that time, Daimler (car2go’s parent company) requested us to take the project offline. They argued that we had violated their terms of use during the process of data collection. Since then we’ve taken several attempts to contact Daimler and find a solution in accordance with them to re-publish our project. Unfortunately their reactions were very scarce and we’ve got the impression they were just waiting it out. As a consequence we thought about a way to present our project without harming Daimler’s interests: simply by showing what we did and how the results looked like, this time not with real data but with a synthetic data set for an imaginary city.

You can take a closer look at our work and the description how we realized it on our project page or by simply swiping through the images below.

nonvoters 2013

We’ve received a lot of (mostly positive) feedback for our last project “Atlas der Nichtwähler / The Geography of Nonvoters” which visualized the spatial distribution of nonvoters for the German federal election in 2009.

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.


new project: the geography of nonvoters

The German election campaign steps into it’s final phase. On September 22th the Germans will elect their new Parliament.

During the weeks before the election maps, graphics and data visualizations are probably more widespread in the media than at any other time. Consequently there’s a peak in the demand for data driven journalism every four years. We want to contribute to the coverage and therefor release a map that focuses on one single aspect: the nonvoters of the last federal election.

The basic idea behind our map is to raise awareness to the number on nonvoters by visualizing turnouts for certain areas in another way than by the ordinary choropleth map. Our focus is not on the percentage of nonvoters but on the sum and spatial distribution of individual persons that did not vote.

That’s why we chose to use a dot density map, displaying one point for every ten nonvoters during the 2009 election. For our home state Hamburg we were able to get more detailed data and realized a second version with higher zoom levels, displaying one point for every single nonvoter. Of course the points do not represent real persons and their home address. Moreover, the exact location of every point was determined by a random distribution of all nonvoters in each district. For a closer & interactive look at our maps, as well as on how we realized them, take a look at our project page. 


mapping all airbnb listings in Hamburg

So far, our mapping tourism project visualized the spatial patterns of tourism in Hamburg by showing all hotels & hostels. But even when we only look at the places where tourists stay, this view is incomplete. Holiday rental accommodations and camp sites for example could also be considered – and of course there are online marketplaces / community platforms like airbnbThey bring together hosts and guests and thereby offer a completely new way to explore a destination. Having stayed in great airbnb apartments in Switzerland and China, meeting wonderful people, I’m personally a big fan of this concept.

At the same time the hotel business is not amused about their new competitor. Whereas around 1,000 accommodations with a capacity of approx. 2,500 beds is a quite moderate number compared to more than 50,000 hotel beds in Hamburg, the situation in other cities (e.g. Barcelona with more than 11,000 airbnb listings) is different.

But how about the spatial distribution of those accommodations? Are there similarities to the patterns of hotel locations or do airbnb listings show different agglomerations? 
To explore these questions we’ve collected location data for every airbnb accommodation in Hamburg and visualized it on an interactive map, using cartoDB once again. We’ve slightly modified the style of our hotel map and used different colors and another basemap this time (mainly because we really love the stamen toner map). Of course this relatively abstract kind of visualization, using effects like transparency and overlay, doesn’t produce a very clean look, but it might help to identify patterns. Take a look and explore the airbnb hotspots in Hamburg here: (for full screen click here)

But how about the direct comparison: patterns of hotels vs. pattern of airbnb accommodations

Let’s take a closer look (this time working with the same map style in both cases) at Hamburg’s most touristy areas.

  • The Reeperbahn (bottom centre of the image) is Hamburg’s famous amusement district. It offers a wide range of bars & clubs, hosts the red light district and seems to offer a concentration of hotels as well as airbnb accommodations.
  • Totally different is the situation at Sternschanze (top centre of the image, use the arrows to change between the two images). The close-by neighborhood is a residential area which in recent years more and more developed into a tourist destination with a huge variety of bars, restaurants and fashion stores. While there are hardly any hotels to be found, there’s a strong agglomeration of airbnb accommodations. 

In our opinion this distribution can be explained by the following:

  1. Sternschanze is Hamburg’s ‘hipster district’. It’s only logic that we see the highest agglomeration of airbnb listings in an area that’s especially popular with young people that frequently use social networks and the like.
  2. A system like airbnb can easily adopt to changing preferences of tourists (to be more precise, it’s of course not airbnb itself that acts, but the collectivity of apartment host). Providers like airbnb thus benefit from an accommodation demand gap in quarters with limited space for new building projects whereas tourists benefit from the adaptability of these platforms.