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Mapping Cities – Making Cities

The data visualization project “Berlin in 15 Minuten? – Perspektiven auf die Stadtentwicklung” (Berlin in 15 Minutes? – Perspectives on Urban Development) was developed as part of the “Mapping Cities – Making Cities” course by Prof. Marian Dörk. The result of the team effort is an interactive data visualization website, showing the reachability of different social categories throughout the German capital.

Assignment

The objective of the course was to process and visualize data sets on a selected topic. Also – if doable – the project should be made accessible to a wide audience through a publication on the web and interaction options.

Topic

As a team we chose the subject of mobile inclusion as our semester topic. Our data partner was the MobileInclusion Project, a joint research collaboration between the Technical Universities of Berlin and Hamburg.

The project examines the connection between social exclusion and mobility. Everyday life in large German cities clearly shows that low-income social classes can only participate in public life to a limited extent, and thus clearly less. Those affected travel shorter distances and get around less often; moreover, they often live in neighborhoods that are not easily accessible by public transport.

Data

The project partner has kindly provided us with an extensive data set. However, the data had an extremely complex structure, so that it took us a long time to understand it in its entirety. Several consultations with the project partner were necessary in order to obtain final clarity and be able to work with the data independently. However, this phase is by no means rare in extensive data visualization projects.

Our data pipeline eventually looked like this: Structural analysis of the dataset by looking at it in Excel or Google sheets and making sense of it, visual data exploration (spatial and in chart form) and data analysis in QGIS and tableau, and finally transferring the datasets into code.

Approach & Methodology

We wanted to work on the topic of mobile inclusion with our own questions in mind and not simply continue the work of the project partner. For this reason, we first researched existing works that appealed to us thematically or aesthetically. After a joint discussion, we decided on the 15 Minute City approach by Carlos Moreno.

Other methods we have used include data sketching, shared ideation with post-it clusters on miro, and the development of concept and usage stories.

Concept

Our final concept is a fusion out of the socially designed dataset from the MobileInclusion Project and the 15-minute city approach. 

How would we like to live in the near future? With regard to the transformation of urban spaces, the concept of the 15-minute city is currently a much-discussed model, stating that all basic everyday needs should be located within 15 minutes walking distance.

The goal of such a city structure is to make the urban space more livable for everyone and at the same time more sustainable and environmentally compatible. The model focuses primarily on short distances, which should enable residents to do their errands, access services and cultural offerings, and enjoy local recreation within short distances.

How far away is Berlin from this idea, this ideal structure? On our website you can  explore Berlin from this perspective.

Implementation

Work process

The work process was iterative. After we had prototyped the first ideas, we showed our work to different expert groups. On the one hand, we conducted an expert interview with an organization that already successfully operates a related online platform – FixMyBerlin. On the other hand, we subjected our work to a group review in which our work was constructively criticized and at the same time we were able to view and comment on other work from the course. Both parts of the group review were very helpful in further developing our own project. The UI design as a separate work step went through several iterations.

Data visualization structure & design

The data visualisation is geo-location based. We divided Berlin into hexagons. Each hexagon represents a certain area with a center, a radius and a isochron line. The data for this was partially already predefined in the underlying dataset and partially collected and added by ourselves. The colors of the hexagons correspond to the number of categories reached within it.

With a click on any hexagon a detailed view appears on a corresponding mini map, showing the detailed location and category distribution. On hover one can see which categories exactly are reached and which are not and how the hexagon scores overall on a spider chart.

The chart view provides an alternative data visualization of the same data set. The hexagons are arranged here according to the number of categories reached. At the same time, the hexagons remain interactive and continue to provide the information on hover and on click.

Website

Here you can try out the final website for yourself. Please enjoy!

Outlook

At the end of a project one is (hopefully) always a little bit wiser than when one goes in. ;)

If our team had had more time, we would definitely have conducted more expert interviews and enriched the data set and the story with conversations with those directly affected.

A possible outlook for the project, like it is completed in its current form, would be to send the work to newspaper editors and see if there is interest in publishing it.

Conclusion

The extensive project provided an excellent opportunity to become familiar with all the steps of a data visualization project. Starting with the acqusition of data, over the collaboration with project partners to the concept development and implementation, everything was part of the project.

Working in an interdisciplinary team has shown us all our individual strengths and weaknesses. As a result, we now have a clearer idea of what each of us still needs to work on. A very valuable data set ;) ! In this sense, thank you to the project and the team!

Special Thanks

Our special thanks goes out to our data partner, the MobileInclusion Project and Prof. Marian Dörk for his continued support.