Principal Component Analysis (PCA)

Principal component analysis (PCA) is a frequently used method in geographic data analysis. Especially with the large-scale application of geographic big data and hyperspectral remote sensing, data dimensionality reduction supported by PCA approach becomes one of the key steps. In this chapter, PCA and cluster are identified as key exploratory methods in regionalization of temperatures in China, emphasizing the importance of understanding and interpreting their results from a geographic research perspective. Although PCA is effective in reducing dimensionality and enhancing the effectiveness of clustering, its application should not be considered universal. Direct clustering may be more appropriate when the original feature possesses clear interpretative or significant value.