EasyXplorer: A Flexible Visual Exploration Approach for Multivariate Spatial Data

Abstract

Exploring multivariate spatial data attracts much attention in the visualization community. The main challenge lies in that automatic analysis techniques is insufficient in discovering complicated patterns with the perspective of human beings, while visualization techniques are incapable of accurately identifying the features of interest. This paper addresses this contradiction by enhancing automatic analysis techniques with human intelligence in an iterative visual exploration process. The integrated system, called EasyXplorer, provides a suite of intuitive clustering, dimension reduction, visual encoding and filtering widgets within 2D and 3D views, allowing an inexperienced user to visually explore and reason undiscovered features with several simple interactions. Case studies show the quality and scalability of our approach in quite challenging examples.