Geographic knowledge engineering for territorial intelligence

In a lot of applications from agriculture to zoology, from environmental planning to territorial intelligence, actual systems of artificial intelligence are not very efficient, essentially because of a naïve representation of space. As spatial knowledge corresponds to conventional geometric and topological knowledge, geographic knowledge corresponds to knowledge about geographic features in the real world even if real features can have spatial relationships between them. In other words, spatial knowledge is based on topological, projective and distance relations; but if applied to geographic features, one must take earth rotundity and other characteristics (demography, physical geographic, economic geography, etc.) into account. After a rapid presentation of geographic relations and their properties, this paper will detail the 12 principles governing geographic knowledge. First emphasis will be given to various forms of geography knowledge, such as located facts, geographic clusters, flows, co-location rules and topological constraints. Then, based on ribbon theory spatial relations and earth rotundity, geographic relations will be defined. For instance, let us consider two features in the real world associated with a DISJOINT relation; when down-scaling, those objects can be associated with a TOUCHES relation. As a consequence, any reasoning mechanism must be transformed accordingly. Territorial intelligence is what business intelligence is for companies. Territorial intelligence can be defined as a cross-fertilization of human intelligence and artificial intelligence for sustainable development of any territory, countries, regions, cities, etc.