Parallel and Distributed Computing for large raster-based Spatial Multicriteria Decision Analysis Problems: A Computational Performance Comparison

Abstract

This article focuses on a cluster-based parallel and distributed approach for large raster datasets in the context of Spatial Multicriteria Decision Analysis (S-MCDA). The research addresses a land-prioritization model with respect to conservation practices. The reliability of the model results is examined using a variance-based Spatially-Explicit Uncertainty and Sensitivity (SEUSA) framework. The original case study area to which we applied the model was located in southwest Michigan, USA, and incorporated millions of mapping units (pixels). As part of the model sensitivity analysis, several thousand intermediate raster datasets representing suitability surfaces are generated by means of a Monte Carlo Simulation (MCS). The creation of the suitability surfaces represents the most timeconsuming and memory-intensive step within the SEUSA framework. Sequential computational approaches to implementing SEUSA often have to accept a compromise with respect to problem size and the number of simulations, resulting in the low quality of the model sensitivity measures. This article presents the concept and implementation of a distributed and parallel solution based on the Python-Dask framework in order to improve the quality of SEUSA results for computationally-intensive spatial models.