Tag: map

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). (We’ve switched to github pages in the meantime)

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.

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 airbnb. They 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.

Adding legends to CartoDB maps

Today I want to write a few words about recent improvements of CartoDB maps. For those of you, who don’t know what CartoDB is good for: it is, at least in my eyes, the easiest and most intuitive framework for mapping and publishing spatial data online. For more detailed information take a look at their website and the awesome tutorial section.

Since the release of CartoDB 2.1 around a month ago there are some neat new features like multilayer support and legends. The whole package seems to evolve from a quick visualization tool to a different kind of web GIS environment (without looking ugly or having a confusing user interface). It goes without saying, that adding legends is a key feature to any mapping framework and we’ve been waiting for this feature ever since working with cartoDB in our Mapping Tourism project. Adding legends to our maps just took me some minutes yesterday. Currently the styling options are still limited, but they’ve announced further improvements and full HTML customization for the coming months.

Mapping Tourism – Identifying Hamburg’s most ‘touristy’ areas

Finally, I found some time to continue working on my mapping tourism project. The first map I published some weeks ago displayed, as you might remember, every hotel & hostel in the city of Hamburg. The map hereby enabled the viewer to get an impression of the spatial dimension of tourism at a glance. Not bad as a starting point. But one of the main reasons tourism has become a topic of public interest in Hamburg recently, is the complaint by some inhabitants that growing tourism is becoming a problem in their neighborhood for various reasons (e.g. noise, traffic, littering). To address these issues too, it was necessary to include an additional aspect in my visualization: population. I’ve thus made a second map, which shows the ratio between inhabitants and tourists on a fine-grained level – in my eyes a very good indicator how ‘touristy’ an area is.

It was’n quite easy to find publicly accessible data for the map. The smallest existing tract shapes (German: statistische Gebiete) are provided by the city of Hamburg, which has opened up this data set as part of their recent open data initiative, which of course is a good thing. The down side is, that this data set is only available via an WFS-Server. A technique, which is not really suitable if you aim at opening up data for the average user. The population data comes from an official  publication by the city of Hamburg, which unfortunately is only available as a pdf-Version, which meant quite some work for me to extract and process the data.

Finally having the data sets at hand, I calculated the number of tourists per area by spatially joining the hotel locations to each tract and counting the sum of provided rooms. The final visualization is once again done in CartDB – this time by using their API, which is way more flexible and allows you to integrate things like a switch between satellite-view and map-view. The final map makes it easy to see which are the most touristy parts of town (marked in blue). You can explore the interactive map by clicking here or on the preview picture below. Both lead to the updated project page, where I also added a new project summary (completely in English now).