Space abnormal extreme value unbiased isogram drawing method

Abstract

The invention provides a space abnormal extreme value unbiased isogram drawing method. Extreme point extracting is carried out on an original sampling point set A, and an extreme point set A' is obtained; extreme points are points with space attribute values higher or lower than all points around in a local neighborhood analysis range; the original sampling point set A is subjected to regular rectangular gridding, and a regular rectangular grid node point set B is obtained; the point set A' and the point set B are merged, and a point set C is obtained; abnormal grid nodes which cause generation of ill-conditioned triangles in the point set C are deleted, and a point set D is obtained; and the point set D is used for establishing a triangular irregular network, and an isogram G is drawn. According to the isogram drawn through the method, a space abnormal center and an extreme value point position are matched, a curve is smooth and attractive, visualization effect is guaranteed, and meanwhile isogram accuracy is improved.

Description

technical field [0001] The invention relates to a method for drawing an unbiased equivalence map of a spatial anomaly extremum, belonging to the field of geographic information systems. Background technique [0002] It is one of the important means to indicate spatial anomalies by using discrete sampled data to draw contour maps. The ideal equivalent map is as figure 2 (b) shown. Traditional contour drawing is mainly based on regular rectangular grid (Grid) and irregular triangulated network (Triangulated Irregular Networks, TIN). [0003] Grid data is usually interpolated from discrete point data. Thanks to the regularity and uniformity of the rectangular grid, the contour map drawn by the Grid method is more smooth and beautiful. However, this method uses the grid nodes as the reference points for contour tracking, ignores the position information of the original sampling points, will cause data distortion near the sampling points, and is difficult to accurately represe...

AI Technical Summary

Problems solved by technology

The above improved method of making contour maps based on TIN improves the visualization effect of cartography, but it does not properly solve the problem of expanding edges and filling gaps at the boundaries of contour maps.

[0006] The deviation between the spatial anomaly center and the sampling extremum points will cause the mapping visualization results to be inconsistent with the objective facts, which will bring great difficulties to the tracking and verification of anomalies, and even mislead

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