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Abstract

The advances in locational technologies have resulted in a huge amount of geo-spatial data. The abundance of such data has led to the research area of movement analysis (Andrienko et al., 2013). This research area investigates the surrounding condition of a moving object to understand the factors and context under which a specific movement takes place (Parent, et al., 2013). One of the applications is in studying animals’ movement to understand their interaction with each other and with the environment (Slingsby and Emiel, 2017). Researchers have developed a variety of methods that enable them to consider the context when analysing trajectories. Most of these methods use interpolation to reconstruct the object’s trajectoriesfor unmeasured locations and times. This approach is acceptable for analysing movement data with fine spatial and temporal resolution (quasi-continuous); however, many movement data types do not allow interpolation, including data with large or irregular spatial and temporal gaps (episodic) (Andrienko et al., 2012) (Chen et al.,2015). The reason is that when the measurements are irregular or the temporal gaps are large, an accurate estimation of trajectories and their geometry is not possible. Hence in episodic movement data, estimation of object’s positions between the measured positions (interpolation) is not valid. Episodic data are very common. They are usually produced by event-based and location-based data collection methods. They may also be produced by time-based methods when the position measurements cannot be recorded sufficiently frequently (Andrienko et al., 2013).

In this research, we aim to understand the differences between quasi-continuous and episodic movement data. For this purpose, we started with a quasi-continuous dataset and then performed downsampling and investigated its impact. We used the juvenile lesser black-backed gulls’ dataset1 as our case study to investigate the effects of using temporally coarser data on contextual data visual analysis. The dataset has 271807 records for 50 birds in a time period of six months (July to December 2020), approximately one record per bird every 20 minutes. We performed the following steps.

Publication Type:	Conference or Workshop Item (Poster)
Additional Information:	© Author(s) 2022. This work is distributed under the Creative Commons Attribution 4.0 License.
Publisher Keywords:	Movement Analysis, Visual Analysis, Animal Tracking, Episodic Data
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology School of Science & Technology > Computer Science
SWORD Depositor:	Symplectic Administrator

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