City in Flux.
800 Routes of Diversity and Contrast

»City in Flux« visualizes the informal and formal mobility networks in Cape Town. It reveals hidden patterns and dividing lines throughout the city and tells meaningful stories that help to understand the developments in the city’s physical and social mobility networks.

This project was created during my master studies together with Fabian Ehmel, Fabian Dinklage and Valentina Troendle.

March 2018
Data Visualization, Webdesign, Graphic Design, Storytelling

The mobility API provided us with different data for the different transportation types. For the public transport routes, we retrieved the position of the first, intermediate and final stops of each route. We then used a routing service (HERE routing API) to calculate the optimal/fastest route along these points.
For the taxi routes, we only retrieved the location of the start- and endpoint of each route. Therefore, we used the routing service to calculate the whole route between these points without knowledge about any intermediate points. The taxi routes should, therefore, be seen as estimates—we do not know exactly which way they go in reality.

Census Data

In addition to the mobility data, we used data from the South African 2011 census, provided by »Statistics South Africa«, South Africa’s national statistical service. The data was retrieved from the »Code for South Africa Data Portal« as one big ShapeFile and contains a diverse set of societal data.

Although the census data provided us data for multiple attributes, we focused on four specific ones: income, housing, education, and diversity. To make the different census districts of the city comparable with each other, we calculated a specific index for each of these datasets.

Bringing the datasets together

Both datasets are geolocated, which means that their data attributes are referenced to a specific geographical point or area. The mobility routes consist of line-based data, while the census data is area-based.

As we wanted to show the difference and development of the four census attributes along a transportation route, we projected the census data onto the lines that we extracted from the mobility data.
For each line, we calculated a row of points with a distance of one kilometer between them. To each of these points, we applied a buffer of one kilometer to generate an area which we see as this point’s catchment area.

We now looped each of these buffered areas over our list of census districts to find all districts that overlap with the catchment area. Then, we used the indexes of all relevant census areas and weighted them based on the overlapping area and the population density. Out of this values, we calculated specific indexes for each of the points along the route.

Story Routes – Explore the relationships of transport routes and societal circumstances.

Playing with the filters reveals hidden patterns of the »urban divide«: It provides clear insights of the invisible borders and structural issues the city is facing. Without dealing with the underlying data, the user can deconstruct the societal segregation along the routes, which split the major city.

Eventually, we came up with the idea of adding insights from people who know Cape Town, the needs and joys of the people there and also the resulting initiatives and projects better than we do: By adding their contributions in the form of »story snippets« and hence let the platform grow with the help of its users, we hope to shed light on the opportunities arising from political and social change.

Learn more about the co-designer: