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Building a Visual Analytics Tool for Location-Based Services

Building a Visual Analytics Tool for Location-Based Services

Erdem Kaya, Mustafa Tolga Eren, Candemir Doger, Selim Saffet Balcisoy
Copyright: © 2015 |Pages: 31
ISBN13: 9781466684652|ISBN10: 1466684658|EISBN13: 9781466684669
DOI: 10.4018/978-1-4666-8465-2.ch006
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MLA

Kaya, Erdem, et al. "Building a Visual Analytics Tool for Location-Based Services." Geo-Intelligence and Visualization through Big Data Trends, edited by Burçin Bozkaya and Vivek Kumar Singh, IGI Global, 2015, pp. 150-180. https://doi.org/10.4018/978-1-4666-8465-2.ch006

APA

Kaya, E., Eren, M. T., Doger, C., & Balcisoy, S. S. (2015). Building a Visual Analytics Tool for Location-Based Services. In B. Bozkaya & V. Singh (Eds.), Geo-Intelligence and Visualization through Big Data Trends (pp. 150-180). IGI Global. https://doi.org/10.4018/978-1-4666-8465-2.ch006

Chicago

Kaya, Erdem, et al. "Building a Visual Analytics Tool for Location-Based Services." In Geo-Intelligence and Visualization through Big Data Trends, edited by Burçin Bozkaya and Vivek Kumar Singh, 150-180. Hershey, PA: IGI Global, 2015. https://doi.org/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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