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Principal component analysis (PCA) is one of the commonly used methods to integrate multi-source geological data to enhance understanding of geo-information. Each principal component (PC) obtained by PCA reflects a different aspect of the geo-information contained in a dataset. Confined by statistical significance, some PCs are not acceptable for interpretation. In this paper, the same problem in PCA occurs in mapping potential areas of Fe mineralization in eastern Tianshan mineral district, China. By spatially weighting correlation coefficient matrixes between variables, a spatially weighted principal component analysis (SWPCA) can deal with the shortcoming of PCA, thus improving the statistical acceptability of eigenvectors and eigenvalues derived by ordinary PCA. Based on the geological model in the study area, a current weighting factor is defined to enhance the geo-information possessed by the ordinary PC1. Compared with the loading of input layers on ordinary PCA, SWPC1 shows more significant physical meaning than PC1. Meanwhile, remarkable increases on the eigenvalues of SWPC2 and SWPC3 are demonstrated to exist making these spatially weighted principal components more acceptable in a statistical sense. In comparison with both loadings and scores on ordinary PCs, the improved geo-information carried by SWPCs can help with better interpretations of the geological phenomena. (C) 2015 Elsevier B.V. All rights reserved.