The interrelationship between transport, streets, and urban patterns, which has been the focus of multiple professions including urban planning, architecture, geography, and transport engineering is truly fascinating.
Journal of the Royal Society Interface propose a quantitative method to classify cities according to their street pattern. They use the conditional probability distribution of shape factor of blocks with a given area, and define what could constitute the `fingerprint’ of a city.
By using a simple hierarchical clustering method, these fingerprints can then serve as a basis for a typology of cities. At a lower level of the classification, the findings prove that most European cities and American cities fall in their own sub-category, highlighting quantitatively the differences between the typical layouts of cities in both regions.
This method provides a quantitative comparison of urban street patterns, which could be helpful for a better understanding of the causes and mechanisms behind their distinct shapes.
Accordingly to MIT Technology Review most older European cities have grown organically, usually before the advent of cars, with their road layout largely determined by factors such as local geography. By contrast, the growth of many American cities occurred after the development of cars and their road layout was often centrally planned using geometric grids.
However, despite the fact that the differences in the typology of streets are rather easy to notice, there has been no objective way to capture that difference yet.
Thanks to the work of Rémi Louf and Marc Barthelemy at the Institut de Physique Théorique, since they managed to decode the unique “fingerprint” of a city’s road layout as well as to classify and compare the unique layouts of cities all over the world for the first time.
Capturing the geometry of city blocks is not a straightforward task, nevertheless, Louf and Barthelemy learnt to do it by using using the ratio of a block’s area to the area of a circle that encloses it.
This quantity is always less than 1 and the smaller its value, the more exotic and extended the shape. The researchers then plot the distribution of block shapes for a given city.
However, this shape distribution by itself is not enough to account for visual similarities and dissimilarities between street patterns, because blocks can have similar shapes but very different areas. The resulting plot is the unique fingerprint that characterizes each city.
The ability to classify cities in this way will come as something of a revelation to travelers who have long noticed the visual similarities and differences between cities all over the world. The inability to classify these associations has always been something of an embarrassment for city planners.