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Geographic space point data sampling method based on semi-variation function

A technology of geographic space and variation function, applied in the field of information, to achieve the effect of achieving accuracy

Active Publication Date: 2021-12-14
HANGZHOU DIANZI UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, there are still three technical problems in considering spatial interpolation in the sampling process: (1) how to characterize the geostatistical characteristics of attributes, to dynamically constrain the sampling process in real time, and generate points that are more suitable for spatial interpolation; (2) how to define a point based on The sampling model of the semivariogram further ensures the accuracy of the attribute interpolation results on the basis of maintaining the spatial distribution of geospatial point data; (3) How to evaluate the effectiveness of the sampling results in maintaining the spatial distribution and interpolation quality

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  • Geographic space point data sampling method based on semi-variation function
  • Geographic space point data sampling method based on semi-variation function
  • Geographic space point data sampling method based on semi-variation function

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Embodiment Construction

[0028] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0029] like figure 1 As shown, a sampling method of geospatial point data based on semivariogram, the specific steps are:

[0030] Step (1) uses the semivariogram to capture the geostatistical characteristics of the geospatial point data; this method uses the semivariogram as the premise and important input of the interpolation model to characterize the geostatistical characteristics, so as to achieve the interpolation quality by maintaining the geostatistical characteristics improvement. The specific method is:

[0031] (1-1) Empirical semivariogram modeling for geospatial point data: Among them, i and j are any two points whose Euclidean distance is h, γ(h) is the average semivariance between i and j, and N(h) is all point pairs whose Euclidean distance is h Set, |N(h)| is the number of point pairs in N(h), z i and z j are the attrib...

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Abstract

The invention discloses a geographic space point data sampling method based on a semi-variation function. The method comprises the following steps of: firstly, capturing geographic statistical characteristics of geographic space point data by utilizing a semi-variation function, and dynamically counting the difference of the semi-variation function before and after sampling in real time in a sampling process; and then with difference minimization as a target, driving a simulated annealing optimization algorithm to replace an initial sampling point generated by Z-order sampling, maintaining geostatistical characteristics. A sampling result not only maintains spatial distribution of geospatial point data, but also improves the accuracy of attribute interpolation. According to the method, visual evaluation and quantitative comparison of sampling results are carried out from two aspects of spatial distribution and attribute interpolation, and the visual performance of differences before and after sampling is enhanced through a contour map. According to the method, a sampling model for large-scale geographic space point data is realized, so that a user can easily restore original data characteristics through sampling points, and the geographic space is visually explored and analyzed.

Description

technical field [0001] The invention belongs to the field of information technology and relates to a method for sampling geospatial point data based on a semivariogram. Background technique [0002] With the rapid development of geospatial information technology, geospatial point data has been widely collected in the fields of epidemiology, economics, and climatology. Scatterplots are commonly used to visualize geospatial point datasets, describing spatial location and data attributes with coordinates and visual elements, respectively. However, the scatterplot obtained by using large-scale geospatial point data often has serious overdrawing and visual confusion problems, making it difficult to perceive the spatial distribution and attribute relationship. To this end, many studies have proposed a variety of sampling methods considering data characteristics to simplify large-scale geospatial point data to alleviate the overdrawing problem of scatter plots. For example, Parad...

Claims

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Application Information

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IPC IPC(8): G06F16/29G06F16/2458G06F16/248G06T11/20G06N7/00
CPCG06F16/29G06F16/2462G06F16/248G06T11/203G06T11/206G06N7/01
Inventor 周志光郑凤玲温晋陈圆圆刘玉华苏为华
Owner HANGZHOU DIANZI UNIV