Clustering is one of important tools to study the multivariate spatial data. However, automatic clustering algorithms require the user to finely modulate parameters, imposing the need for an effective mechanism to manipulate the clustering process by dynamically changing the parameters and evaluating the results. This paper proposes a novel visual analysis approach for clustering multivariate spatial data. First, the underlying dataset is clustered in 3D using an automatic clustering algorithm. Second, the result is examined and refined on its 2D projection by leveraging a suite of visualization and analysis toolkits. The user is allowed to intuitively verify and adjust the clusters by referring to the visual encoding and visual patterns. The entire process is progressively performed in a raw-to-fine fashion. The case study on a high-dimensional symmetric tensor field verifies the effectiveness and robustness of the proposed approach.