{"title":"在解释点数据集中的空间模式时分析用户行为。","authors":"Martin Knura, Jochen Schiewe","doi":"10.1007/s42489-022-00111-9","DOIUrl":null,"url":null,"abstract":"<p><p>Volunteered geographic information is often generated as voluminous point data, leading to geometric and thematic clutter when presented on maps. To solve these clutter problems, cartography provides various point generalization operations such as aggregation, simplification or selection. While these operations reduce the total number of points and therefore improve the readability, information preservation could be harmed when specific spatial patterns disappear through the generalization process, possibly leading to false interpretations. However, sets of map generalization constraints that maintain spatial pattern characteristics of point data are still missing. To define constraints that support synoptic interpretation tasks, user behaviour while solving these tasks has to be analysed first. We conduct a study where participants have to perform such interpretation tasks, using a new method that combines think-aloud interviews and techniques from visual analytics. We reveal that the point density of a dataset has the biggest impact on the user behaviour and the respective task-solving strategy, independently from the actual task type executed. Furthermore, our results show that the graphical map complexity only has a minor impact on the user behaviour, and there is no evidence that point data cardinality influences task execution and the solution-finding strategies.</p>","PeriodicalId":36860,"journal":{"name":"KN - Journal of Cartography and Geographic Information","volume":"72 3","pages":"229-242"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9205413/pdf/","citationCount":"3","resultStr":"{\"title\":\"Analysis of User Behaviour While Interpreting Spatial Patterns in Point Data Sets.\",\"authors\":\"Martin Knura, Jochen Schiewe\",\"doi\":\"10.1007/s42489-022-00111-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Volunteered geographic information is often generated as voluminous point data, leading to geometric and thematic clutter when presented on maps. To solve these clutter problems, cartography provides various point generalization operations such as aggregation, simplification or selection. While these operations reduce the total number of points and therefore improve the readability, information preservation could be harmed when specific spatial patterns disappear through the generalization process, possibly leading to false interpretations. However, sets of map generalization constraints that maintain spatial pattern characteristics of point data are still missing. To define constraints that support synoptic interpretation tasks, user behaviour while solving these tasks has to be analysed first. We conduct a study where participants have to perform such interpretation tasks, using a new method that combines think-aloud interviews and techniques from visual analytics. We reveal that the point density of a dataset has the biggest impact on the user behaviour and the respective task-solving strategy, independently from the actual task type executed. Furthermore, our results show that the graphical map complexity only has a minor impact on the user behaviour, and there is no evidence that point data cardinality influences task execution and the solution-finding strategies.</p>\",\"PeriodicalId\":36860,\"journal\":{\"name\":\"KN - Journal of Cartography and Geographic Information\",\"volume\":\"72 3\",\"pages\":\"229-242\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9205413/pdf/\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"KN - Journal of Cartography and Geographic Information\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s42489-022-00111-9\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2022/6/17 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"Earth and Planetary Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"KN - Journal of Cartography and Geographic Information","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s42489-022-00111-9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/6/17 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"Earth and Planetary Sciences","Score":null,"Total":0}
Analysis of User Behaviour While Interpreting Spatial Patterns in Point Data Sets.
Volunteered geographic information is often generated as voluminous point data, leading to geometric and thematic clutter when presented on maps. To solve these clutter problems, cartography provides various point generalization operations such as aggregation, simplification or selection. While these operations reduce the total number of points and therefore improve the readability, information preservation could be harmed when specific spatial patterns disappear through the generalization process, possibly leading to false interpretations. However, sets of map generalization constraints that maintain spatial pattern characteristics of point data are still missing. To define constraints that support synoptic interpretation tasks, user behaviour while solving these tasks has to be analysed first. We conduct a study where participants have to perform such interpretation tasks, using a new method that combines think-aloud interviews and techniques from visual analytics. We reveal that the point density of a dataset has the biggest impact on the user behaviour and the respective task-solving strategy, independently from the actual task type executed. Furthermore, our results show that the graphical map complexity only has a minor impact on the user behaviour, and there is no evidence that point data cardinality influences task execution and the solution-finding strategies.
期刊介绍:
KN - Journal of Cartography and Geographic Information is dedicated to theoretical, applied and empirical approaches of cartography and geovisualization. We understand cartography as a science and technique to analyze, visualize and communicate spatial information. Cartography is the cross-over discipline in the field of spatial and geo sciences, including geoinformation science. Cartography addresses spatial questions from a variety of disciplines, including geography, environmental sciences and social sciences, using methods and tools developed at the interface with neighboring domains such as geodesy, GI Science, and spatial cognition.These questions can put different emphasis on theoretical fundamentals, methods, techniques and applications.KN - Journal of Cartography and Geographic Information is the only cartographic journal of the German language area. The journal is among the oldest cartographic periodicals worldwide. It was established in 1951 as the journal of the German Society of Cartography (DGfK). In 1976, the journal has become the joint periodical publication of DGfK, the Cartographic Commission of the Austrian Geographical Society (ÖKK), and the Swiss Cartographic Society (SGK).The journal publishes four issues per year. All articles are peer-reviewed. Furthermore, there are short articles on recent technical developments in practical applications with geodata. The journal reports on national as well as international conferences and other events concerning the above-mentioned fields. Supplementary sections comprise regular accounts of the activities in the German, Austrian and Swiss cartographic societies and business news from private-sector-companies, government agencies and academia. In addition, there are book reviews and a calendar of cartographically relevant events. Since 2009, the journal is indexed in Scopus.