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Building a Visual Analytics Tool for Location-Based Services
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Author(s): Erdem Kaya (Sabanci University, Turkey), Mustafa Tolga Eren (Sabanci University, Turkey), Candemir Doger (Sabanci University, Turkey)and Selim Saffet Balcisoy (Sabanci University, Turkey)
Copyright: 2015
Pages: 31
Source title:
Geo-Intelligence and Visualization through Big Data Trends
Source Author(s)/Editor(s): Burçin Bozkaya (Sabanci University School of Management, Turkey)and Vivek Kumar Singh (Rutgers, The State University of New Jersey, USA & Massachusetts Institute of Technology, USA)
DOI: 10.4018/978-1-4666-8465-2.ch006
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Abstract
Conventional visualization techniques and tools may need to be modified and tailored for analysis purposes when the data is spatio-temporal. However, there could be a number of pitfalls for the design of such analysis tools that completely rely on the well-known techniques with well-known limitations possibly due to the multidimensionality of spatio-temporal data. In this chapter, an experimental study to empirically testify whether widely accepted advantages and limitations of 2D and 3D representations are valid for the spatio-temporal data visualization is presented. The authors implemented two simple representations, namely density map and density cube, and conducted a laboratory experiment to compare these techniques from task completion time and correctness perspectives. Results of the experiment revealed that the validity of the generally accepted properties of 2D and 3D visualization needs to be reconsidered when designing analytical tools to analyze spatio-temporal data.
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