Spatially Simplified Scatterplots for Large Raster Datasets

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dc.contributor.ISNI0000 0001 0872 2508 (Griffith, DA)en_US
dc.contributor.authorBin, Lien_US
dc.contributor.authorGriffith, Daniel A.en_US
dc.contributor.authorBecker, Brianen_US
dc.contributor.utdAuthorGriffith, Daniel A.en_US
dc.date.accessioned2016-06-03T15:35:45Z
dc.date.available2016-06-03T15:35:45Z
dc.date.created2016-05-24en_US
dc.date.issued2016-05-24en_US
dc.description.abstractScatterplots 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.en_US
dc.identifier.bibliographicCitationBin, Li, Daniel A. Griffith, and Brian Becker. 2016. "Spatially simplified scatterplots for large raster datasets." Geo-Spatial Information Science, doi: 10.1080/10095020.2016.1179441.en_US
dc.identifier.issn1009-5020en_US
dc.identifier.urihttp://hdl.handle.net/10735.1/4835en_US
dc.publisherTaylor & Francisen_US
dc.relation.urihttp://dx.doi.org/10.1080/10095020.2016.1179441
dc.rightsCC BY 4.0 (Attribution)en_US
dc.rights©2016 Wuhan Universityen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.source.journalGeo-Spatial Information Scienceen_US
dc.subjectScatter diagramsen_US
dc.subjectAutocorrelation (Statistics)en_US
dc.subjectResampling (Statistics)en_US
dc.titleSpatially Simplified Scatterplots for Large Raster Datasetsen_US
dc.type.genrearticleen_US

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