An algorithmic approach to detect generalization in sketch maps from sketch map alignment

Charu Manivannan, Jakub Krukar and Angela Schwering

PLOS ONE, 2024, vol. 19, issue 6, 1-21

Abstract: Sketch maps are valuable tools used across various disciplines including spatial cognition, environmental psychology, and spatial reasoning. A common approach to evaluate sketch maps in research is to align and compare them with metric maps. However, sketch maps are highly abstract and contain generalized information causing difficulty in their alignment. Current approaches to study sketch maps cannot handle generalized information. They require a one-on-one correspondence between features in the metric map and features in the sketch map. But memory is often generalized. This paper makes two contributions to the research on sketch maps: (i) we present an algorithmic approach to detect generalization in sketch maps (ii) we present an online tool that creates a generalized metric map corresponding to features in sketch maps. Previously, we identified nine types of generalization in sketch maps. In this paper, we develop formal operators to detect these generalizations and implement them as an online tool. We evaluated our algorithm with a set of 11 sketch maps containing 84 instances of generalization. The results indicated that our algorithm consistently detects instances of generalization in sketch maps.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0304696

DOI: 10.1371/journal.pone.0304696

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