Spatially simplified scatterplots for large raster datasets

作     者：Bin Li Daniel A.Griffith Brian Becker 

作者机构：Department of GeographyCollege of Science and EngineeringCentral Michigan UniversityMount PleasantMIUSA School of EconomicPolitical and Policy SciencesUniversity of Texas at DallasRichardsonTXUSA 

出 版 物：《Geo-Spatial Information Science》 (地球空间信息科学学报（英文）)

年 卷 期：2016年第19卷第2期

页      面：前插1-前插1,81-93页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

主　　题：Scatterplot spatial autocorrelation effective sample size 

摘      要：Scatterplots are essential tools for data exploration. However, this tool poorly scales with data-size, with overplotting and excessive delay being the main problems. Generalization methods in the attribute domain focus on visual manipulations, but do not take into account the inherent nature of information redundancy in most geographic data. These methods may also result in alterations of statistical properties of data. Recent developments in spatial statistics, particularly the formulation of effective sample size and the fast approximation of the eigenvalues of a spatial weights matrix, make it possible to assess the information content of a georeferenced data-set, which can serve as the basis for resampling such data. Experiments with both simulated data and actual remotely sensed data show that an equivalent scatterplot consisting of point clouds and fitted lines can be produced from a small subset extracted from a parent georeferenced data-set through spatial resampling. The spatially simplified data subset also maintains key statistical properties as well as the geographic coverage of the original data.