Geolog: Scalable Logic Programming on Spatial Data

TL;DR

Geolog introduces a scalable logic programming tool integrated with GIS software, enabling efficient spatial reasoning through a novel relation-based paradigm that significantly outperforms traditional entity-based methods.

Contribution

The paper presents a new relation-based programming paradigm and a practical tool, Geolog, for scalable logical reasoning on spatial data within industry-standard GIS software.

Findings

Up to 100x performance improvement over entity-based methods.

Successfully applied to four real-world spatial scenarios.

Seamless integration with ArcMap enhances practical usability.

Abstract

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.