its maximum: $$A(t)=\left(\frac{1}{\sum_{i,j}\delta(i,j,t)}\!\!\sum_{\scriptstyle i,j=1\atop\scriptstyle\delta(i,j,t)\neq0}^N\!\!\left(\frac{d_4(i,j,t)}{d_{4,\max}(t)}\right)^{p_A}\right)^{1/p_A}$$

This normalization puts $A$ in $[0,1]$ and allows comparing configurations of different sizes. Like $S$, “better” urban configurations are characterized by a lower $A$.

#### Economic performance

It was shown by Banos [@banos2012network] that the Schelling segregation model, a standard ABM of socio-economic dynamics [@schelling1969models], was highly sensitive to the spatial structures in which it could be embedded, since segregation rules depended on proximity. This justifies the use of this model as an evaluation of *economic performance* of our urban configurations, measuring how much structure influences segregation. To this aim, we implemented a model of residential dynamics based on the work of Benenson et al. [@benenson1998multi]. The output function is a segregation index $H(t)$ calculated on the residential patterns that emerge inside a distribution of built patches. For urban structures produced in a practical case (Section \[sec\_practapp\]), we obtained densities of mobile agents between 0.1 and 0.2. Following Gauvin et al. [@gauvin2009phase], the phase diagram of the Schelling model indicates that in such a density range, tolerance thresholds of 0.4 to 0.8 lead to clustered frozen states, where the calculation of a spatial segregation index is indeed relevant. The detailed description of this economic model is 