  Spatial data is ubiquitous in our data-driven society. The Logic Programming
community has been investigating the use of spatial data in different settings.
Despite the success of this research, the Geographic Information System (GIS)
community has rarely made use of these new approaches. This has mainly two
reasons. First, there is a lack of tools that tightly integrate logical
reasoning into state-of-the-art GIS software. Second, the scalability of
solutions has often not been tested and hence, some solutions might work on toy
examples but do not scale well to real-world settings. The two main
contributions of this paper are (1) the Relation Based Programming paradigm,
expressing rules on relations instead of individual entities, and (2) Geolog, a
tool for spatio-logical reasoning that can be installed on top of ArcMap, which
is an industry standard GIS. We evaluate our new Relation Based Programming
paradigm in four real-world scenarios and show that up to two orders of
magnitude in performance gain can be achieved compared to the prevalent Entity
Based Programming paradigm.
