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.

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.

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).

mappable +1

Since today, mappable is not
a one man project any more. As it is quite hard to find enough time for pushing
it forward next to working full-time, I’m really happy to now join forces with
my friend and colleague Achim. For more information about us and our goals at
mappable see our updated about page. And the best thing is: we’ve already begun
working on a really neat new project. More information
will follow soon.

The future of subway station area maps

During a trip to Asia a few weeks ago I had the pleasure to spend some days in Seoul, South Korea. Even tough I knew that Korea is a very high-tech society, I was still amazed by the extend to which e.g. location based services are integrated into everyday life and used by people of all ages.

Especially an interactive, map centered information device, named ‘Digital View’, impressed me. Built around a  huge 46 touchscreen and operated by the popular Korean web portal Daum, these terminals offer probably any kind of information about the surrounding area you’ll ever need. Here’s a list of some of the available features:

  • information about news, weather, finance, etc.
  • entertainment services
  • ticketing, e.g. purchasing movie tickets
  • maps with diverse styles (road, satellite, hybrid, google Street View-like) and different layers with points of interests (shopping, accommodation, banks, real estate)
  • free phone calls
  • real-time bus schedules, subway maps and direction details

Features that make them extremely helpful for finding the fastest way through Seoul’s massive underground transportation network as well as for getting all the required information about the surrounding area.

Just for the record: here in Hamburg (and I don’t think the situation in other big German cities is different) we still rely on badly readable poster maps, showing nothing but a road-map of the surrounding and I don’t see anything comparable to Seoul’s Digital View popping up here in the coming years. Considering that the installation in Seoul began in 2010, we are already, in terms of technology, lacking behind five to ten years.

For more details, see:

https://en.wikipedia.org/wiki/Seoul_Metropolitan_Subway

http://www.daumcorp.com/DaumEng/about/service.daum#viewd_10http://english.chosun.com/site/data/html_dir/2010/02/19/2010021900782.htmlhttp://www.advancedtechnologykorea.com/303

http://www.advancedtechnologykorea.com/303

http://english.chosun.com/site/data/html_dir/2010/02/19/2010021900782.html

similar system in NYC coming up: http://cityroom.blogs.nytimes.com/2013/04/08/new-screens-in-the-subway-will-guide-riders-and-sell-to-them-too/ 

 

Mapping the growth of tourism in Hamburg

Tourism in Hamburg has been substantially and continuously growing during the last decade. The city has reached position ten among the most popular European city destinations last year with more than 10 million overnight stays.

Even tough the topic of tourism growth is drawing increasing public attention, especially when it comes to spectacular projects like the new concert hall ‘Elbphilharmonie‘ or the plans to build a cable car over the river Elbe, I’ve never come across a good visualization on that issue. Which, at least in my eyes, is quite astonishing, as most of the underlying information like hotel addresses or tourist attractions is space-related.

This is where I got started with my just for fun side project. I collected data of hotel locations, categories, years of construction and number of rooms from the free, crowd-sourced database hotelsbase. After quite a lot of data wrangling and searching further details from various hotel- and hotel-booking websites, the only thing missing was information about current building projects. Fortunately there is an up-to-date overview of hotel projects provided by Hamburg travel.

Finally, the fun part about the whole thing was visualizing the collected information by using CartoDB. A really powerful, yet easy to use web-based mapping platform. A big recommendation, even for those of you who are not too familiar with mapping. I started by showing the hotel locations on a dark base-map. In order to visualize the different hotel sizes and years of construction I made further adjustments to the default map style. As a result it is now quite easy to recognize which hotels were built in the last five years (marked in dark blue) and which are currently planned or under construction (purple).

You can explore the final and fully interactive map here.  Furthermore there is a more in depth analysis of the identified patterns and some additional interpretation (unfortunately only in German). I’m quite curious how people will react to this relatively abstract, but spatial perspective on tourism growth in Hamburg.