An article on big think examines a project to find the unique qualities of urban street grids as an exercise in the potential of Big Data.
Frank Jacobs writes about the project of two French researchers working to develop a mathematical of the unique "fingerprint" of cities. The research of Rémi Louf and Marc Barthelemy examined the street grids of 131 cities around the world for their paper "A Typology of Street Types," finding that they could, in fact, describe street grids as distinct types.
Here is the core of the project's methodology, as described by Jacobs:
If one considers street grids as networks, with intersections as the nodes and street segments as the links between them, it becomes clear that classification should not just rely on proximity (i.e. the spatial distribution of betweenness), but also on geometry (i.e. the spatial distribution of the nodes). The researchers extracted information on city blocks (easier to define than streets) from 131 maps of cities on all inhabited continents, and defined these by area (A) and shape (Φ). The value of Φ is always smaller than one, and the smaller it is, the more anisotropic it is. Meaning that its properties differ according to the direction of measurement.
The article includes several compelling data visualizations as well as some discussion of the taxonomy that emerges from the analysis: according to the researchers, cities fall into four groups, exemplified by Buenos Aires, Athens. New Orleans, and Mogadishu. The third group, exemplified by New Orleans, dominates North America (every city but Vancouver) and Europe (every city but Athens).
FULL STORY: How to Fingerprint a City

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