Spatial sampling method based on multi-target multi-data type variability model

Abstract

The invention discloses a spatial sampling method based on a multi-target multi-data type variability model. The method comprises the following steps: a, pre-researching a sample area, and determining the weight of each sampling influencing factor according to an analytic hierarchy process; b, collecting factors influencing sampling layering, drawing a spatial rank distribution diagram of each factor, and acquiring a sample area integrated factor layering diagram according to a GIS (geographic information system) spatial analysis function; c, processing pre-researching data, calculating a variability value of each layer through the variability model, and replacing variance parameters in a sandwiched spatial sampling method with the variability values to obtain a spatial sampling scheme based on the multi-target multi-data type variability model.

Description

technical field [0001] A spatial sampling method based on a multi-objective multi-data type variability model relates to a spatial sampling method. This spatial sampling method based on the multi-objective multi-data type variability model not only solves the multi-objective and multi-data type problems in sampling, but also effectively reduces the sampling cost and improves the sampling accuracy. The objective multi-datatype sampling problem provides an efficient solution. Background technique [0002] In social science and natural science, sampling is a very important theory and technology, which is widely used in agriculture, ecology, environmental science, social survey and other fields. Sampling is to take a part of individuals from the population as samples, and use mathematical and statistical knowledge to process and refine the sample information, so as to make a more scientific inference about the overall situation. In practical work and scientific research, we ar...

AI Technical Summary

Problems solved by technology

Traditional sampling theory has played a good role in promoting social and economic development and natural science progress, but with the development, traditional sampling theory has been unable to meet the actual needs, and there are two obvious shortcomings in traditional sampling theory: 1. Traditional sampling theory Variation quantification and spatial sampling can only be performed on one sampling target. When there are multiple sampling targets, effective variation quantification and spatial sampling cannot be performed.

2. Traditional sampling theory can only perform variation quantification and spatial sampling on numerical data types. When the sampling target includes categorical data types, effective variation quantification and spatial sampling cannot be performed.

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