A minimalistic data distribution system to support uncertainty-aware GIS

Error and uncertainty are inherent in all digital elevation models (DEMs) - representations of the Earth's terrain. It is absolutely essential to account for this uncertainty in any GIS operations that rely on this data because uncertainty propagates through any derived products. This can have very serious consequences such as the potential invalidation of model results. Geostatistical methods like conditional stochastic simulation have been developed to mitigate this problem, but they require expert knowledge to apply them to a project. Despite the fact that uncertainty propagation has been discussed in geographic literature for nearly three decades, there has been very little progress in making such analysis accessible to those who are not geostatistics experts--the majority of GIS users. This research uses open source software to build a system that makes the results of complex error models accessible to researchers worldwide without the need for expert knowledge. Then, I use this system to acquire data and perform a basic analysis, demonstrating how the average researcher might incorporate uncertainty propagation in own their work. In doing so, I hope to elucidate the ways in which conditional stochastic simulation changes the traditional spatial data model and set an example for others to follow. -- Abstract.