Question Answering (QA), the process of computing valid answers to questions formulated in natural language, has recently gained attention in both industry and academia. Translating this idea to the realm of geographic information systems (GIS) may open new opportunities for data scientists. In theory, analysts may simply ask spatial questions to exploit diverse geographic information resources, without a need to know how GIS tools and geodata sets interoperate. In this outlook article, we investigate the scientific challenges of geo-analytical question answering, introducing the problems of unknown answers and indirect QA. Furthermore, we argue why core concepts of spatial information play an important role in addressing this challenge, enabling us to describe analytic potentials, and to compose spatial questions and workflows for generating answers.
Scheider et al. (Thu,) studied this question.
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