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.

中文翻译：

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Geolog：空间数据的可扩展逻辑编程

空间数据在我们数据驱动的社会中无处不在。逻辑编程社区一直在研究空间数据在不同环境中的使用。尽管这项研究取得了成功，但地理信息系统 (GIS) 社区很少使用这些新方法。这主要有两个原因。首先，缺乏将逻辑推理紧密集成到最先进的 GIS 软件中的工具。其次，解决方案的可扩展性通常没有经过测试，因此，一些解决方案可能适用于玩具示例，但不能很好地扩展到现实世界的设置。本文的两个主要贡献是 (1) 基于关系的编程范式，表达关于关系而不是单个实体的规则，以及 (2) Geolog，一种可以安装在 ArcMap 之上的空间逻辑推理工具，这是一个行业标准的 GIS。我们在四个实际场景中评估了我们新的基于关系的编程范式，并表明与流行的基于实体的编程范式相比，可以实现多达两个数量级的性能提升。