Assessing Resampling Accuracy of Categorical Data Using Random Points
Author: Seong, Jeong Chang
Source: Cartography and Geographic Information Science, Volume 32, Number 4, October 2005 , pp. 393-400(8)
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Abstract:
Tissot's Indicatrix and regular grids have been used for assessing map projection accuracies. Despite their broad applicability for accuracy assessment, they have limitations in quantifying resampling errors caused by map projections. This is due to the structural uncertainty with regard to the placement and pattern of grids. It is also difficult to calculate the absolute amount of resampling error in each projection. As an alternative to traditional testing methods, the use of random points was investigated. Specifically, random point generation, resampling with spherical block search algorithms, resampling accuracy with a perfect grid, and resampling accuracy with eight projections were investigated and are discussed here. Eight global referencing methods were tested: the equal-area cylindrical, sinusoidal, Mollweide, Eckert IV, Hammer-Aitoff, interrupted Goode homolosine, integerized sinusoidal projections, and the equal area global gridding with a fixed latitudinal metric distance. The resampling accuracy with a perfect grid is about 75 percent. Results showed the sinusoidal and the integerized sinusoidal projections and equal-area global gridding to achieve the highest accuracies.Keywords: RESAMPLING ACCURACY; MAP PROJECTION; RANDOM POINTS; GLOBAL RASTER DATABASE
Document Type: Research article
DOI: 10.1559/152304005775194764
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