What can we learn from street networks?

Street #networks represent a layout of streets in a city and a degree of connectivity between places. Even though it is often analysed through rather abstract concepts based on a graph theory, these values can be super important in real life. 

Where would you open your shop? On a busy main street or a quiet residential one? Sounds obvious, right? But what about a corner shop in a local neighbourhood? Which corner would be the "right" one? And why? Street network analysis can help you answer questions like this and indicate what is going on.

Take an example of local closeness centrality you can see below (blue represents low while red represents high levels of centrality). The name is a mouthful (as we said, it tends to be a bit abstract), but it reflects the likelihood that people on a casual walk around a neighbourhood will go through an intersection. Now it sounds a bit more useful, right? 

Measurements like this one give us a perspective on human behaviour without dependency on either scarce real-time measurements or expensive and controversial GPS tracking. After all, every city is built to support the life of its residents. So it is only natural to expect that life itself is encoded in it.

And yes - this is one of the features that we have preprocessed for all of Switzerland and that we can serve via our feature-API!

#data #behaviour #locationintelligence #maps