GPU-Accelerated Rendering Methods to Visually Analyze Large-Scale Disaster Simulation Data

Abstract

Emerging methodologies for natural hazard risk assessments involve the execution of a multitude of different interacting simulation models that produce vast amounts of spatio-temporal datasets. This data pool is further enlarged when such simulation results are post-processed using GIS operations, for example to derive information for decision-making. The novel approach presented in this paper makes use of the GPU-accelerated rendering pipeline to perform such operations on-the-fly without storing any results on secondary memory and thus saving large amounts of storage space. Particularly, algorithms for three frequently used geospatial analysis methods are provided, namely for the computation of difference maps using map algebra and overlay operations, distance maps and buffers as examples for proximity analyses as well as kernel density estimation and inverse distance weighting as examples for statistical surfaces. In addition, a visualization tool is presented that integrates these methods using a node-based data flow architecture. The application of this visualization tool to the results of a real-world risk assessment methodology used in civil engineering shows that the memory footprint of post-processing datasets can be reduced at the order of terabytes. Although the technique has several limitations, most notably the reduced interoperability with conventional analysis tools, it can be beneficial for other use cases. When integrated into desktop GIS applications, for example, it can be used to quickly generate a preview of the results of complex analysis chains or it can reduce the amount of data to be transferred to web or mobile GIS applications.