Canonical Correspondence Analysis (CCA) is introduced as a multivariate extension of weighted averaging ordination, designed to visualize the relationships between species and environmental variables. CCA constructs linear combinations of environmental variables that maximize the separation of species distributions, with eigenvalues measuring this separation. It combines the features of correspondence analysis and direct gradient analysis, providing a direct method to detect species-environment relations and investigate species responses to environmental variables. Unlike traditional methods, CCA handles qualitative and zero-valued data, non-linear relationships, and highly correlated environmental variables more effectively. The paper provides an alternative derivation of CCA from the method of weighted averaging, demonstrating its effectiveness in analyzing complex species-environment data.Canonical Correspondence Analysis (CCA) is introduced as a multivariate extension of weighted averaging ordination, designed to visualize the relationships between species and environmental variables. CCA constructs linear combinations of environmental variables that maximize the separation of species distributions, with eigenvalues measuring this separation. It combines the features of correspondence analysis and direct gradient analysis, providing a direct method to detect species-environment relations and investigate species responses to environmental variables. Unlike traditional methods, CCA handles qualitative and zero-valued data, non-linear relationships, and highly correlated environmental variables more effectively. The paper provides an alternative derivation of CCA from the method of weighted averaging, demonstrating its effectiveness in analyzing complex species-environment data.