This chapter examines how modelling and processing data describing entities of geographical interest such as land use and landscapes can lead to meaningful results for both humans and machines by using methods of Symbolic AI. Next, from building a simple expert system in Symbolic AI for assessing the outcomes of landscape change to tackling the Traveling Salesman Problem with the Ant Colony Optimization algorithm, it is shown how concepts and methods of Evolutionary AI (Artificial Swarm Intelligence and Agent-Based Models in particular) can serve in the creation of spatially-enabled AI that can help in spatial problem solving.