on December 30, 2022

Principal Component Analysis in QGIS

Geographic Information Systems

What is Principal Component Analysis in GIS?

The Principal Components tool is used to transform the data in the input bands from the input multivariate attribute space to a new multivariate attribute space whose axes are rotated with respect to the original space. The axes (attributes) in the new space are uncorrelated.

How do you analyze principal components?

How Do You Do a Principal Component Analysis?

  1. Standardize the range of continuous initial variables.
  2. Compute the covariance matrix to identify correlations.
  3. Compute the eigenvectors and eigenvalues of the covariance matrix to identify the principal components.

What is a Principal Component Analysis plot?

A PCA plot shows clusters of samples based on their similarity. Figure 1. PCA plot. PCA does not discard any samples or characteristics (variables). Instead, it reduces the overwhelming number of dimensions by constructing principal components (PCs).