As big data attracts attention in a variety of fields, research on data exploration for analyzing large-scale scientific data has gained popularity. To support exploratory analysis of scientific data, effective summarization and visualization of the target data as well as seamless cooperation with modern data management systems are in demand. In this paper, we focus on the exploration-based analysis of scientific array data, and define a spatial V-Optimal histogram to summarize it based on the notion of histograms in the database research area. We propose histogram construction approaches based on a general hierarchical partitioning as well as a more specific one, the l-grid partitioning, for effective and efficient data visualization in scientific data analysis. In addition, we implement the proposed algorithms on the state-of-the-art array DBMS, which is appropriate to process and manage scientific data. Experiments are conducted using massive evacuation simulation data in tsunami disasters, real taxi data as well as synthetic data, to verify the effectiveness and efficiency of our methods.