Ghalya Alsanea – LO-07 – What the street!?

MOVE LAB- Resident Researcher/Artist
Michael Szell & Stephan Bogner
Direction
Benedikt Groß
Full Credits
whatthestreet
–Data derived from OpenStreetMap (OSM)
This project looks into wasted public spaces in cities. The tool creates awareness about the disparity between the distribution of space for bikes, cars, parking lots, rails, etc.

Who “owns” the city?

They first looked at how much space there is in a city for moving around (ie the allocation/distribution ratios between cars, bikes, etc). The tool, “What the Street!?” is labelled as “The Mobility Space Report”, where the designers created an open-source public tool for exploring urban mobility spaces. They created a fun way to look at inequality of mobility spaces systematically AND interactively.

Play with the tool here!

Cover for About What the Street!?
I choose this project because of how the resulting shapes give a mesmerizing, never before seen perspective of urban spaces. For example, the image above shows all parking spaces in NYC, packed tightly together.

How does it work?

They use the data visualization tools of unrolling, packing, and ordering irregular shapes.  Therefore, they packed and rolled all mobility spaces into rectangular bins to visualize the areas they take up, then the tool allows you to see the same shape on a regular satellite map.

The mobility triangle shows how people move in each city – how much by car, bike, or public transport. Each dot stands for one city, and this specific image is highlighting Berlin (in blue). A dot in the very middle means an even share of these 3 forms of transportation is being used.

With each large dot there is a second dot that shows how much space a city has allocated for moving around with this particular transit mode. If first and second dots coincide, this would mean that city space is allocated in a fair way towards all forms of mobility. The triangle shows that space allocated is unfair: cars get much more space than they “deserve” – and bikes typically get much less. The team hope this tool shines a light on this disparity.

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