An Approach to Visualization and Clustering-based Analysis on Spatiotemporal Data

Abstract

Currently, there is a considerable amount of spatiotemporal data available in various media, especially on the Internet. The visualization of spatiotemporal data is a complex task that requires suitable visual resources that can enable users to have a correct interpretation of the data. Apart from the use of visualization techniques, the use of techniques of knowledge discovery in databases has proven to be relevant for the exploratory analysis of spatiotemporal data. The state-of-the-art in the visualization of spatiotemporal data leads to the conclusion that the area is still deficient in solutions for the viewing and analysis of those data. Many approaches cover only spatial issues, ignoring the temporal characteristics of such data. In this context, the main objective of this research work is to improve the user experience in spatiotemporal visualization and analysis, going beyond the universe of the visualization of spatiotemporal raw data by considering the importance of the visualization of spatiotemporal data derived from a knowledge discovery process, more specifically, clustering algorithms. This goal is achieved by defining an innovative approach for the analysis and visualization of spatiotemporal data, and its implementation, called GeoSTAT (Geographic Spatiotemporal Analysis Tool). GeoSTAT includes important features of the main existing approaches and adds specific visualization techniques that are geared to the temporal dimension and the use of clustering algorithms, enhancing unexplored features in the spatiotemporal data. The validation of the proposed approach occurs through a case study that addresses spatiotemporal data from a specific domain, to demonstrate the end-user experience of the visualization techniques that are combined in the proposed approach presented in this article.