Weather factors such as temperature, moisture, and air pressure are considered as geographic phenomena distributed continuously in space and without boundaries. Weather factors have field characteristics, meanwhile their data are collected discretely at nodes which are considered as spatial objects. In this article, the model of multivariate cube is employed to visualize the data of weather factors in two modes, object-based visualization and field-based visualization. On a multivariate cube, the 2-D Cartesian coordinate systems representing various factors at a node are embedded in a space-time cube at the position of the node on map plane, where the data of each factor are represented as histogram bars with respect to time. The representation of factors on a multivariate cube supports the object-based visualization and the field-based visualization. The mode of object-based visualization displays the variation of one or more factors over time at one or more nodes, the difference between the values of a factor at various spatial positions, as well as the correlation between various factors at one or more spatial positions at the same time. The mode of field-based visualization displays each factor on layers associated with time. Each factor layer is constituted by converting point data of the factor recorded at nodes to surface data. The mode of field-based visualization approaches the models of stopped process and dynamics to infer surface data from point data. The mode of field-based visualization indicates the value of factors at a certain spatial position, where the mode of object-based visualization may be applied to display data similarly to at nodes. The mutual transformation of data between two modes of object-based visualization and field-based visualization on a multivariate cube expands analytical problems from some locations of nodes to every point in space.
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