Self-adaption spatial interpolation method and system based on spatial feature analysis

Abstract

The invention discloses a self-adaption spatial interpolation method based on spatial feature analysis. The method comprises the steps as follows: a pollutant monitoring value is extracted; an interpolation module is established on the basis of a variation function; a direction feature is introduced to the interpolation module and the variation function; the spatial distribution of a pollutant is judged to be isotropic or anisotropic; a search strategy is set based on all the isotropy and all the anisotropy, so that a reference point is determined; and a pollutant concentration attribute value of an interpolating point is determined according to variation function expression formulas in all directions and the reference point. Compared with the prior art, the self-adaption spatial interpolation method is generated after the spatial features of sample point data are analyzed on the premise that the spatial distribution features of the sample point data are considered, the calculation amount is small, and the interpolation is improved, so that a attribute value of an unknown point of a study area is predicted more accurately. The invention discloses a self-adaption spatial interpolation system based on the spatial distribution features.

Description

technical field [0001] The invention relates to the technical field of environmental monitoring, in particular to an adaptive spatial interpolation method based on spatial feature analysis and a system thereof. Background technique [0002] The point layout method stipulated in the current automatic detection specification cannot observe all points in the space. However, based on the monitoring network that has been deployed, a certain number of spatial samples can be obtained. These samples reflect all or part of the characteristics of the spatial distribution. According to these Known samples, using appropriate spatial interpolation methods, can more accurately predict the characteristics of unknown geographic spaces. [0003] Common spatial interpolation methods include Inverse Distance Weighted Interpolation (IDW) and Kriging. The above two spatial interpolation methods have the following defects: IDW method only considers the distance between two points in space, but d...

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Problems solved by technology

The above two spatial interpolation methods have the following defects: IDW method only considers the distance between two points in space, but does not consider the spatial distribution characteristics, so the interpolation accuracy is usually low; although the Kriging method considers the spatial distribution characteristics, it involves the calculation of solving equations The problem of large amount and slow interpolation speed

Atmospheric environmental monitoring stations are usually discrete, uneven, and limited in number. Using the above-mentioned spatial interpolation algorithm will have low interpolation accuracy or cannot accurately reflect the characteristics of unknown point attributes. This is also an important problem facing the spatial distribution of regional air quality.

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