SketchMapia: A Framework for Collaborative Mapping
Final Report Abstract
Sketch maps are informal drawings that reflect a person’s perception of spatial areas. As such they are a unique source of data for researchers interested in subjective perception of space. They are a widely used method of measuring the memory of larger environments. Compared to other methods, such as distance estimation or judgment of relative direction tasks, drawing a sketch map gives the participant relatively large freedom in choosing what to externalize from their memory and how to do it. This property makes sketch maps an appropriate research tool when researcher is interested in different strategies with which participants orient themselves specifically because sketch maps can capture diverse types of spatial knowledge. Over the last 12 years, we investigated sketch maps as research tool to investigate human spatial memory. We identified that the biggest challenge lies in evaluating the information represented in sketch maps systematically as it tends to be incomplete, generalised, and only partially correct. Since sketch maps are commonly used to communicate spatial information in everyday life, interpreting these properties is manageable and intuitive for people. However, doing so consistently and completely across a set of sketch maps is a very hard task. Thus, most approaches to sketch map analysis rely on human-based ratings, and therefore remain inefficient, unsystematic, non-reproducible, and often non-generalisable beyond single studies. Although some computational methods have been introduced, their impact has been limited to a narrow problem (memory of landmark configurations) that is irrelevant or secondary to most researchers who collect sketch maps. The current situation limits the potential of sketch maps as a research method: it raises costs of running research with sketch maps, impedes high-quality analysis by non-expert researchers who already do or could collect them (sketch maps are fast to collect but take long to analyse), and hinders open data sharing. SketchMapia II improves the state-of-the-art with a software-supported method for comprehensively and consistently analysing sketchmaps in comparison to the metric base map of the relevant area. Our easy-to-use software uses qualitative calculi known from geoinformatics to calculate each sketchmap's • degree of completeness: A participant with a more complete sketch map is considered to have a better memorization performance than participants with a less complete map. • degree of generalization: Does the participant represent the information at the same level of generalization as the base map or do they recall the information at a more abstract level. • qualitative spatial accuracy: How accurately do participants capture the spatial configuration, e.g. are landmarks placed along the correct street segments which themselves are connected in the correct way. We proposed a step-by-step approach to digitize base and sketch maps, align features, determine generalization, completeness and assess the qualitative accuracy. An important aspect that had to be developed is a formal classification of different ways in which sketch map information can be generalized. This method was implemented in a web-based application allowing researchers to easily access it and evaluate sketch maps in their experimental studies. It is a step forward to enhance sketch map analysis to take the rich spatial information of sketch maps into account and turn research on sketch maps into a reproducible and traceable task. This project has a high potential to advance research in psychology, cognitive science, medical diagnosis as well as geography by the standardized and comprehensive sketch map analysis method.
Publications
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Are Psychological Variables Relevant to Evaluating Geoinformatics Applications? The Case of Landmarks (Vision Paper). In 15th International Conference on Spatial Information Theory (COSIT 2022)
Krukar, J. & Schwering, A.
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Generalized, inaccurate, incomplete: how to comprehensively analyze sketch maps beyond their metric correctness. In 15th International Conference on Spatial Information Theory (COSIT 2022)
Schwering, A., Krukar, J., Manivannan, C., Chipofya, M. & Jan, S.
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Spatial generalization in sketch maps: A systematic classification. Journal of Environmental Psychology, 83, 101851.
Manivannan, Charu; Krukar, Jakub & Schwering, Angela
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Route effects in city-based survey knowledge estimates. Cognitive Processing, 24(2), 213-231.
Krukar, Jakub; Navas, Medrano Samuel & Schwering, Angela
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Task-dependent sketch maps. Spatial Cognition & Computation, 23(4), 263-292.
Krukar, Jakub; van Eek, Antonia & Schwering, Angela
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An algorithmic approach to detect generalization in sketch maps from sketch map alignment. PLOS ONE, 19(6), e0304696.
Manivannan, Charu; Krukar, Jakub & Schwering, Angela
