parameters and display the 2D slice corresponding to the other two parameters set to $(0,0)$. Horizontal axes are reoriented in each case to minimize visual clutter. This exhaustive exploration has an intrinsic explanatory value (see text), and allows us to predict with some level of confidence how the model responds to certain input parameters.[]{data-label="fig_plots3d"}](3DWithArrowsLast){width="\columnwidth"}

![image](RasterAtlantisLast){height="45.00000%"}![image](ExampleActivitiesRealSituation){height="45.00000%"}

Practical example {#sec_practapp}
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In this section, we apply our model to the optimization of activities on top of a real-world urban structure obtained from a geographic file, as opposed to an randomly generated, artificial configuration. This type of scenario occurs in a planning problem where one must decide about the possible land use of predefined zones.

The practical example under study here concerns the planning of a new district. It is based on a real-world neighborhood, Massy Atlantis (Paris metropolitan area), built in 2012 (Fig. \[fig\_atlantis\]). We would like to investigate whether a more efficient planning could have been achieved. The goal of this exercise is to find an optimal assignment of two types of activities, “residential” (apartments) or “tertiary” (offices), to the centers of 9 areas located on a map. The transportation infrastructure is already in place and the train station is also 