This paper explores the use of probabilistic and conventional qualitative spatial reasoning (QSR) in the context of geospatial question answering (GeoQA) systems. The paper presents a thorough empirical investigation of the performance of a probabilistic and a conventional qualitative spatial reasoner, across a range increasingly sophisticated scenarios with real data and synthetically generated questions. The results indicate the potential of probabilistic QSR to provide more detailed information about spatial configurations than conventional QSR; but at the cost of less frequent errors in estimating the relative likelihood of different reasoning conclusions. Errors in probabilistic reasoning also tend to be systematically associated with lower probability conclusions. The results have implications for reliable and flexible automated spatial reasoning systems, especially where neither conventional geographic information retrieval (GIR) techniques nor large language models (LLMs) are able to provide a satisfactory solution to GeoQA problems.

Although geospatial question answering systems have received increasing attention in recent years, existing prototype systems struggle to properly answer qualitative spatial questions. In this work, we propose a unique framework for answering qualitative spatial questions, which comprises three main components: a geoparser that takes the input questions and extracts place semantic information from text, a reasoning system which is embedded with a crisp reasoner, and finally, answer extraction, which refines the solution space and generates final answers. We present an experimental design to evaluate our framework for point-based cardinal direction calculus (CDC) relations by developing an automated approach for generating three types of synthetic qualitative spatial questions. The initial evaluations of generated answers in our system are promising because a high proportion of answers were labelled correct.

In most coordinated view geovisualization tools, a transient visual effect is used to highlight observations across views when brushed with a mouse or other input device. Most current geovisualization and information visualization systems use colored outlines or fills to highlight observations, but there remain a wide range of alternative visual strategies that can also be implemented and compared to color highlighting to evaluate user performance. This paper describes the results of an experiment designed to compare user performance with two highlighting methods; color and leader lines. Our study methodology uses eye-tracking to capture participant eye fixations while they answer questions that require attention to highlighted observations in multiple views. Our results show that participants extract information as efficiently from coordinated view displays that use leader line highlighting to link information as they do from those that use a specific color to highlight items. We also found no significant differences when changing the color of the highlighting effect from red to black. We conclude that leader lines show significant potential for use as an alternative highlighting method in coordinated multiple view visualizations, allowing color to be reserved for representing thematic attributes of data.

This issue of IJGIS showcases research activities related to how map displays can support users in visuospatial decision making for solving complex spatiotemporal problems. It represents a selectio...