Christian Beilschmidt, T. Fober, Michael Mattig, B. Seeger
{"title":"A Linear-Time Algorithm for the Aggregation and Visualization of Big Spatial Point Data","authors":"Christian Beilschmidt, T. Fober, Michael Mattig, B. Seeger","doi":"10.1145/3139958.3140037","DOIUrl":null,"url":null,"abstract":"The visualization of spatial data becomes increasingly important in science, business and many other domains. In geography, data often corresponds to a large number of point observations that should be displayed on a constrained screen with limited resolution. This causes, however, a loss of information due to an overloaded and occluded visualization. In this paper we present a new visualization algorithm that avoids this problem by aggregating point data into a set of non-overlapping circles that capture all important information. Our algorithm based on a quadtree computes the circles in linear time with respect to the number of points.","PeriodicalId":270649,"journal":{"name":"Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3139958.3140037","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
The visualization of spatial data becomes increasingly important in science, business and many other domains. In geography, data often corresponds to a large number of point observations that should be displayed on a constrained screen with limited resolution. This causes, however, a loss of information due to an overloaded and occluded visualization. In this paper we present a new visualization algorithm that avoids this problem by aggregating point data into a set of non-overlapping circles that capture all important information. Our algorithm based on a quadtree computes the circles in linear time with respect to the number of points.