Question-Based Spatial Computing—A Case Study

Abstract

Geographic Information Systems (GIS) support spatial problem solving by large repositories of procedures, which are mainly operating on map layers. These procedures and their parameters are often not easy to understand and use, especially not for domain experts without extensive GIS training. This hinders a wider adoption of mapping and spatial analysis across disciplines. Building on the idea of core concepts of spatial information, and further developing the language for spatial computing based on them, we introduce an alternative approach to spatial analysis, based on the idea that users should be able to ask questions about the environment, rather than finding and executing procedures on map layers. We define such questions in terms of the core concepts of spatial information, and use data abstraction instead of procedural abstraction to structure command spaces for application programmers (and ultimately for end users). We sketch an implementation in Python that enables application programmers to dispatch computations to existing GIS capabilities. The gains in usability and conceptual clarity are illustrated through a case study from economics, comparing a traditional procedural solution with our declarative approach. The case study shows a reduction of computational steps by around 45 %, as well as smaller and better organized command spaces.