Optimizing an SPT-tree for visual analytics

Despite the extensive work done in the scientific visualization community on the creation and optimization of spatial data structures, there has been little adaptation of these structures in visual analytics and information visualization. In this work we present how we modify a space-partioning time (SPT) tree - a structure normally used in direct-volume rendering - for geospatial-temporal visualizations. We also present optimization techniques to improve the traversal speed of our structure through locational codes and bitwise comparisons. Finally, we present the results of an experiment that quantitatively evaluates our modified SPT tree with and without our optimizations. Our results indicate that retrieval was nearly three times faster when using our optimizations, and are consistent across multiple trials. Our finding could have implications for performance in using our modified SPT tree in large-scale geospatial temporal visual analytics software.