{"title":"视觉上比较眼睛在空间和时间上的运动","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":"{\"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}","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}
Visually comparing eye movements over space and time
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.