Spatiotemporal Visual Analysis in Parallel Coordinate Plots (STPCPs): A Case Study of Meteorological Data Analysis

Significant changes in the global climate are a focus of widespread concern, with profound implications for economies, daily life, and sustainable development. Analyzing and forecasting these trends relies heavily on meteorological data, which typically possess high-dimensional spatiotemporal attributes. Effectively extracting underlying patterns and meaningful information from such complex data is crucial for informed decision-making. This study addresses the challenge of visually representing temporal sequences within geospatial contexts, a process often hindered by the separate visualization of spatial and temporal dimensions. We propose a method that embeds a geographic map within a parallel coordinate plot: time is represented on the parallel axes, and high-dimensional attributes are encoded using color channels. This integrated view, combined with a suite of interactive techniques, enables detailed, multi-perspective, and holistic visual exploration and enhances the understanding of high-dimensional spatiotemporal meteorological data.