time calculating the complete charts of all evaluation functions. Other parameters with a direct correspondence to the real-world, depending on the scale adopted, were set to fixed values. For example, the neighborhood radius $\rho$ or the road-triggering distance $\theta_2$ were both equal to 5 cells: this number could represent 50m, characteristic of a block at the scale of a district, or 500m for a district in a city, or 5km between cities in a region.

Examples of evaluation surfaces in 2D projection spaces are shown in Fig. \[fig\_plots3d\]. Each function, $D$, $I$, $S$, and $A$, was plotted against two parameters out of four, chosen for their higher “influence” (variations in amplitude) on the function. The economic index $H$ was not calculated here (see \[sec\_practapp\]). This exhaustive exploration of parameter space was necessary to gain deeper insight into the behavior of the model. It also represents a crucial step toward making computational simulations more rigorous [@banos2013HDR].

Altogether, we observed that outputs varied for the most part smoothly, except Moran’s index which appeared more chaotic. Variations were greater in cases where one parameter was dominant. For example, the measures of density $D$, speed $S$ and (global) accessibility $A$ all exhibited a significant 