Visualization of multi-dimensional sparse spatial-temporal data

Multi-dimensionality and sparseness of spatial-temporal data are major challenges for data analysis. Data visualization can effectively address certain data analysis challenges and has increasingly drawn attention from both industry and academia. A hybrid approach for the visualization of multi-dimensional sparse spatial-temporal data was proposed. The method combined multiple data view models and human-machine interaction mechanisms in order to intuitively express the multi-dimensional features, statistical group features, as well as typical individual behavior patterns. Furthermore, a visual analysis method was introduced for the identification and detection of abnormal individual behaviors. A data visualization system based on gas filling data gathered from gas stations in Xinjiang Province was implemented. By using different view models (parallel coordinates, map view, calendar matrix, Sankey