Alternative Visualization of Large Geospatial Datasets

Abstract

Exploring large volumes of geospatial data is difficult. This paper presents an approach that combines visual and computational analysis to make this process easier. This approach is based on the effective application of computational algorithms, such as the Self-Organizing Map (SOM). These are used to uncover the structure, patterns, relationships and trends in the data, and for the creation of abstractions where conventional methods may be limited. In addition, graphical representations are applied to portray extracted patterns in a visual form that allows for better understanding of the derived structures and possible geographical processes, and should facilitate knowledge construction.