Spatially Simplified Scatterplots for Large Raster Datasets

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Spatially Simplified Scatterplots for Large Raster Datasets

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Title: Spatially Simplified Scatterplots for Large Raster Datasets
Author(s):
Bin, Li;
Griffith, Daniel A. (UT Dallas);
Becker, Brian
Item Type: article
Keywords: Scatter diagrams
Autocorrelation (Statistics)
Resampling (Statistics)
Abstract: 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.
Publisher: Taylor & Francis
ISSN: 1009-5020
Persistent Link: http://dx.doi.org/10.1080/10095020.2016.1179441
http://hdl.handle.net/10735.1/4835
Bibliographic Citation: Bin, 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.
Terms of Use: CC BY 4.0 (Attribution) License
©2016 Wuhan University

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