Summary

In this chapter, we introduced you to different types of spatial optimization that you can solve using Python. We kicked this chapter off by discussing LSCPs, where you found the optimal number of facilities needed to serve the emergency service demand of a community.

Then, we transitioned our focus to discussing route-based optimization problems, including TSP, VRP, and CVRP.

You explored three different case studies in this section using two different integer linear programming formulations (MTZ and DFJ), which help the optimization abide by the global constraint that a person or vehicle can only visit each stop once.

You were also introduced to a handful of new packages, including Spopt, which is PySAL’s spatial optimization library. You also learned about PuLP for solving integer optimization problems. Lastly, you set up a Google Maps API to gather real-world distances in the form of an O-D Cost Matrix from Google Map’s street network.

We covered...