Figure 4
From: A global empirical study on how street networks facilitate driving longer distances
Spatial distribution of \(\beta 's\) for different cities. In (a–e), we calculate the values for \(\beta\) (on the left) and \(\langle \beta _s \rangle\) (on the right) for each of those six cities. We take all the nodes from the street network as origin points. The points are colored according to their exponent value and their color is painted by the color scheme at the center. The black lines represent the larger SWDP. The function for probability density for each experiment’s \(\langle \beta _s \rangle\) is shown in both sides of the color scale to represent each result from the execution, and a dashed line also shows its average distribution. This figure also shows the linearity of the \(\langle \beta _s \rangle\) exponent for a random experiment, which stays around 1 for all cities. It also shows how SWDPs are important to ensure the non-linear characteristic of \(\langle \beta _s \rangle\)’s, indicating there is a spatial correlation between values with high exponents and SWDP. This figure was generated using the Python open source library Matplotlib (v3.7.1).
