Information Visualization Techniques for Big Data: Analytics Using Heterogeneous Data in Spatiotemporal Domains

Information Visualization Techniques for Big Data: Analytics Using Heterogeneous Data in Spatiotemporal Domains

William H. Hsu (Kansas State University, USA)
DOI: 10.4018/978-1-4666-9845-1.ch080
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Abstract

This chapter presents challenges and recommended practices for visualizing data about phenomena that are observed or simulated across space and time. Some data may be collected for the express purpose of answering questions through quantitative analysis and simulation, especially about future occurrences or continuations of the phenomena – that is, prediction. In this case, analytical computations may serve two purposes: to prepare the data for presentation and to answer questions by producing information, especially an informative model, that can also be visualized. These purposes may have significant overlap. Thus, the focus of the chapter is about analytical techniques for visual display of quantitative data and information that scale up to large data sets. It begins by surveying trends in educational and scientific use of visualization and reviewing taxonomies of data to be visualized. Next, it reviews aspects of spatiotemporal data that pose challenges, such as heterogeneity and scale, along with techniques for dealing specifically with geospatial data and text. An exploration of concrete applications then follows. Finally, tenets of information visualization design, put forward by Tufte and other experts on data representation and presentation, are considered in the context of analytical applications for heterogeneous data in spatiotemporal domains.

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