Conditioned and Manipulable Matrix For Visual Exploration

this paper. Based on a traditional matrix view of data, such as a scatterplot matrix, this paper enhances the matrix in two aspects. One is to support the direct manipulation of data representation in row/column position (Siirtola, 1999) and ease the selection of data to visualize using variable fields and observed data ranges. Users can easily select the variables or a range of observations they want to focus on and change the row/column position in the matrix dynamically via a graphical interface. The second enhancement to a "traditional" scatterplot matrix is to add the capability to condition the data depicted in the matrix on a related variable (conditioning is described below). Our goal is to offer an intuitive interface, through which users can visualize and manipulate any subsets within large geospatial datasets using familiar statistical tools and thus help analysts in partner U.S. government agencies to identify outliers, errors, spatial clusters and patterns, and possible multivariate correlations from both the users and datasets perspectives. Furthermore, using a component-based design for the matrix allows it to be coordinated with other software components, in order to enable more comprehensive and flexible information visualization (e.g., linked brushing) and database querie