Based on ArcGIS platform, 9 vulnerability index layers such as regional crustal stability, fracture zone distribution, elevation, surface undulation, vegetation cover, surface wetness index, soil erodibility, soil erosion intensity and karst distribution are normalized by linear transformation, so that the results fall into the interval of [0,100], and get the layer of standard value of each index; applying factor correlation analysis to Analyze the correlation between the 9 vulnerability indicators; apply principal component analysis to delete the repetitive elements with significant correlation and recombine them into a new set of comprehensive vulnerability elements that are not related to each other; take the variance contribution rate of the principal component elements as the weights, and apply the comprehensive index model to complete the comprehensive evaluation of the vulnerability of the geo-environment; carry out the partitioning with the support of the locational theory and the spatial statistics, and classify the whole country as The country was divided into six categories of vulnerability zones: slight, mild, light, moderate, severe and extreme.
(I) Normalization
Normalization processing, linear transformation transformation function is as follows:
Strategic Research on Geo-Environmental Survey from the Perspective of Ecological Civilization
In the formula: X is the standard value of indicator x; xmax is the maximum value of the sample data of indicator x; and xmin is the minimum value of the sample data of indicator x.
(ii) Factor correlation analysis
Wave set statistics are performed in the multivariate analysis of the SPATIAL ANALYST tool to analyze the direction of correlation and the degree of correlation of the above nine indicators. Pi product moment correlation coefficient is a measure of the degree of linear correlation between two random variables. It was proposed by Carl Pearson in 1880 and is now widely used in various fields of science. For variables x, y, Pearson's product moment correlation coefficient is:
Strategic Research on Geo-Environmental Survey under the Perspective of Ecological Civilization
In the formula: rxy is Pearson's product moment correlation coefficient; is the mean value of variable x; is the mean value of variable y. If the two are positively correlated, r is positively correlated, and r is positively correlated. If the two are positively correlated, r is positive, and r=1 is completely positive; if the two are negatively correlated, r is negative, and r=-1 is completely negative. Usually, the strength of the correlation of the variables is determined by the following ranges of r: 0.8 to 1.0 extremely strong correlation; 0.6 to 0.8 strong correlation; 0.4 to 0.6 moderate correlation; 0.2 to 0.4 weak correlation; 0.0 to 0.2 extremely weak correlation or no correlation.
(C) Principal component analysis
Based on the ArcGIS platform, principal component analysis was performed in the multivariate analysis of the spatial analyst tool. The principal component analysis tool is used to transform the data within the input bands in the input multivariate attribute space to a new multivariate attribute space that rotates the axes relative to the original space. The axes (attributes) in the new space are uncorrelated with each other. The first principal component V1 (the first linear combination, i.e., the first composite indicator) will have the largest variance, and the larger Var(V1) is, the more information V1 contains; if the first principal component is not enough to represent the information of the original m indicators, and then consider selecting V2, i.e., selecting the second linear combination, the information already in V1 does not need to be present in V2, which is expressed in the mathematical language by requesting Cov( V1,V2)=0, then V2 is called the second principal component, the second principal component will have the second largest variance not described by the first principal component; and so on, other principal components can be constructed.
(IV) Composite index model
Geological environmental vulnerability is calculated by the following formula
Strategic research on geological environmental survey in the perspective of ecological civilization
In the formula: V is the vulnerability of the regional geological environment; Vi is the ith composite variable obtained by using the method of principal component analysis; n is the number of composite variables; m is the number of first-level indexes; and λ is the eigenvalue corresponding to the principal component. variable corresponds to the eigenvalue.