High-dimensional spatial simulation ensemble analysis

Leanna L. House

Abstract

Advancements in computational power enable scientists to study complex spatial systems using varying models or initial conditions in what is known as spatial ensemble. Analyzing all facets of such high dimensional spatial ensembles poses challenges for its representation and visualization. This requires the design of interactive analysis tool(s) that supports visualization, analysis, and exploration of spatial ensembles in multiple ways and at multiple levels of abstraction. The difficulty in such representation is the need to consider both data dimensionality and spatiality simultaneously. Much research has been done for visually analyzing spatial ensembles; however, the majority of these visualizations capture differences in the spatial ensemble through descriptive statistics, neglecting spatial data characteristics, which severely limits the exploration process. In this paper, we propose a visual analysis approach that employs statistical and visual analytics methodologies for a more effective understanding and exploration of spatial ensembles without the need for in-depth knowledge of the underlying computational models and ensemble features and association.

Mai Dahshan, Leanna House, Nicholas F. Polys: High-dimensional spatial simulation ensemble analysis. BigSpatial@SIGSPATIAL 2020: 2:1-2:4