meaningful measure of fitness in this application. The calculation of the economic segregation index $H$ involved a secondary agent-based simulation on top of the main urban development model (details not provided here).

Results are shown in Fig. \[fig\_pareto\]. We obtained a Pareto front of “optimal solutions” trying to minimize both $H$ and $A$, while observing that the actual configuration is not far from being optimal itself, and appears to be a compromise between accessibility and economic performance. After closer examination of the Pareto front and its vicinity, we found that the distribution of activities was highly mixed in these points. More precisely, we defined a spatial heterogeneity index of center activities by $$\lambda=a_\mathrm{max}\frac{\sum_{\scriptstyle c\neq c'\atop\scriptstyle a(c)\neq a(c')}d(c,c')^{-1}}{\sum_{c\neq c'}d(c,c')^{-1}}$$ where $c=(i,j)$ and $c'=(i',j')$ are two centers, $d(c, c')$ their Euclidean distance, and $a(c)$, $a(c')$ their activities. Points in the scatterplot were colored according to their level of $\lambda$. Highly heterogeneous configurations appeared in regions of the plot distinct from homogeneous configurations, which were for the most part located in the central cluster. Optimal solutions and their neighbors all corresponded to high heterogeneity. This interesting result is a step toward evidence-based justification of mixed land use in planning—a principle often invoked by 