{"title":"Visually comparing eye movements over space and time","authors":"Ayush Kumar, Michael Burch, K. Mueller","doi":"10.1145/3317958.3319810","DOIUrl":null,"url":null,"abstract":"Analyzing and visualizing eye movement data can provide useful insights into the connectivities and linkings of points and areas of interest (POIs and AOIs). Those typically time-varying relations can give hints about applied visual scanning strategies by either individual or many eye tracked people. However, the challenging issue with this kind of data is its spatio-temporal nature requiring a good visual encoding in order to first, achieve a scalable overview-based diagram, and second, to derive static or dynamic patterns that might correspond to certain comparable visual scanning strategies. To reliably identify the dynamic strategies we describe a visualization technique that generates a more linear representation of the spatio-temporal scan paths. This is achieved by applying different visual encodings of the spatial dimensions that typically build a limitation for an eye movement data visualization causing visual clutter effects, overdraw, and occlusions while the temporal dimension is depicted as a linear time axis. The presented interactive visualization concept is composed of three linked views depicting spatial, metrics-related, as well as distance-based aspects over time.","PeriodicalId":161901,"journal":{"name":"Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications","volume":"108 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3317958.3319810","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Analyzing and visualizing eye movement data can provide useful insights into the connectivities and linkings of points and areas of interest (POIs and AOIs). Those typically time-varying relations can give hints about applied visual scanning strategies by either individual or many eye tracked people. However, the challenging issue with this kind of data is its spatio-temporal nature requiring a good visual encoding in order to first, achieve a scalable overview-based diagram, and second, to derive static or dynamic patterns that might correspond to certain comparable visual scanning strategies. To reliably identify the dynamic strategies we describe a visualization technique that generates a more linear representation of the spatio-temporal scan paths. This is achieved by applying different visual encodings of the spatial dimensions that typically build a limitation for an eye movement data visualization causing visual clutter effects, overdraw, and occlusions while the temporal dimension is depicted as a linear time axis. The presented interactive visualization concept is composed of three linked views depicting spatial, metrics-related, as well as distance-based aspects over time.