The Effects of Map Reading Expertise and Map Type on Eye Movements in Map Comparison Tasks
Notice bibliographique
Résumé
Comparing maps of different geographical phenomena, or maps of the same geographical phenomenon at different points in time, is a frequent task in many disciplines. The process of map comparison has been studied occasionally by cartographers since the 1970s, but recent improvements in neuropsychological testing equipment and in geographical information system (GIS) technology had us review this topic in a new light. We propose a cognitive approach using eye movement recording to understand the process of comparing two static maps displayed simultaneously on a screen. Two groups of subjects with different levels of expertise with map reading were shown pairs of maps and asked to judge their similarity or difference. We used three types of maps that differed in their spatial granularity: (A) randomly generated, 64-by-64 pixel, black-and-white images, (B) grayscale choropleth maps representing socio-economic variables for counties in lower Michigan, and (C) land-use maps of the surroundings of selected Canadian cities in different years resulting from classified satellite imagery. Subjects were asked whether two maps presented on the screen were similar (tests A and B) or different (test C). Response times, fixation durations and fixation counts differed significantly for the three map types. Land-use maps required the longest response times indicating that they were most difficult to compare. At the same time, land-use maps required more fixations than the other two types of maps, while the duration of these fixations was not different from the other map types. When comparing two maps of the same type, saccades between the two maps provide information on the subject’s decision-making process. We found that for the land-use maps, the number of these cross-saccades was significantly smaller than for the two other map types. Pairs of land-use maps were characterized by a fine raster grid and fewer pixel-by-pixel differences between the two maps, while both, random grids in test A and county maps in test B consist of clear-cut spatial units. We conclude that whenever spatial units can be distinguished on a map and corresponding units on a second map can be found easily, subjects will tend to compare the two maps in a unit-by-unit approach. In contrast, if maps consist of smoother spatial patterns, subjects will try to memorize patterns on one map (usually the one on the right-hand side), and make fewer saccades to compare these patterns with those on the other map. The results from this experiment could be used to provide context-adaptive tools for map comparison in GIS. The behavioral differences between groups (experts vs. novices) in this experiment were mostly not significant. This supports the notion of developing standard GIS tools that are offered to users with a wide range of expertise.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».