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How context influences the segmentation of movement trajectories - an experimental approach for environmental and behavioral context


Lautenschütz, A K (2010). How context influences the segmentation of movement trajectories - an experimental approach for environmental and behavioral context. In: GIScience 2010: sixth International Conference on Geographic Information Science, Zurich, Switzerland, 14 September 2010 - 17 September 2010.

Abstract

In the digital information age where large amounts of movement data are generated daily through technological devices, such as mobile phones, GPS, and digital navigation aids, the exploration of moving point datasets for identifying movement patterns has become a research focus in GIScience (Dykes and Mountain 2003). Visual analytics (VA) tools, such as GeoVISTA Studio (Gahegan 2001), have been developed to explore large amounts of movement data based on the contention that VA combine computational methods with the outstanding human capabilities for pattern recognition, imagination, association, and reasoning (Andrienko et al. 2008). However, exploring, extracting and understanding the meaning encapsulated in movement data from a user perspective has become a major bottleneck, not only in GIScience, but in all areas of science where this kind of data is collected (Holyoak et al. 2008). Specifically the inherent complex and multidimensional nature of spatio-temporal data has not been sufficiently integrated into visual analytics tools. To ensure the inclusion of cognitive principles for the integration of space-time data, visual analytics has to consider how users conceptualize and understand movement data (Fabrikant et al. 2008). A review on cognitively motivated work exemplifies the urgent need to identify how humans make inferences and derive knowledge from movement data. In order to enhance visual analytics tools by integrating cognitive principles we have to first ask to what extent cognitive factors influence our understanding, reasoning, and analysis of movement pattern extraction. It is especially important to comprehend human knowledge construction and reasoning about spatial and temporal phenomena and processes.
This paper proposes an experimental approach with human subject testing to evaluate the importance of contextual information in visual displays of movement patterns. This research question is part of a larger research project, with two main objectives, namely

* getting a better understanding of how humans process spatio-temporal information
* and empirically validating guidelines to improve the design of visual analytics tools to enhance visual data exploration.

Abstract

In the digital information age where large amounts of movement data are generated daily through technological devices, such as mobile phones, GPS, and digital navigation aids, the exploration of moving point datasets for identifying movement patterns has become a research focus in GIScience (Dykes and Mountain 2003). Visual analytics (VA) tools, such as GeoVISTA Studio (Gahegan 2001), have been developed to explore large amounts of movement data based on the contention that VA combine computational methods with the outstanding human capabilities for pattern recognition, imagination, association, and reasoning (Andrienko et al. 2008). However, exploring, extracting and understanding the meaning encapsulated in movement data from a user perspective has become a major bottleneck, not only in GIScience, but in all areas of science where this kind of data is collected (Holyoak et al. 2008). Specifically the inherent complex and multidimensional nature of spatio-temporal data has not been sufficiently integrated into visual analytics tools. To ensure the inclusion of cognitive principles for the integration of space-time data, visual analytics has to consider how users conceptualize and understand movement data (Fabrikant et al. 2008). A review on cognitively motivated work exemplifies the urgent need to identify how humans make inferences and derive knowledge from movement data. In order to enhance visual analytics tools by integrating cognitive principles we have to first ask to what extent cognitive factors influence our understanding, reasoning, and analysis of movement pattern extraction. It is especially important to comprehend human knowledge construction and reasoning about spatial and temporal phenomena and processes.
This paper proposes an experimental approach with human subject testing to evaluate the importance of contextual information in visual displays of movement patterns. This research question is part of a larger research project, with two main objectives, namely

* getting a better understanding of how humans process spatio-temporal information
* and empirically validating guidelines to improve the design of visual analytics tools to enhance visual data exploration.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, further contribution
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Event End Date:17 September 2010
Deposited On:27 Jan 2011 07:07
Last Modified:07 Dec 2017 06:27
Official URL:http://www.giscience2010.org/index.php?page=empirical-methods-2
Related URLs:http://www.giscience2010.org/index.php?

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