Context-aware movement analysis in ecology: a systematic review. Issue 2 (1st February 2022)

• Record Type: Journal Article
• Authors: Brum-Bastos, Vanessa; Łoś, Marcelina; Long, Jed A.; Nelson, Trisalyn; Demšar, Urška
• Is Part Of: International journal of geographical information science. Volume 36:Issue 2(2022)
• Page Start: 405
• Page End: 427
• Publication Date: 2022-02-01
• Subjects: Movement analysis -- tracking data -- environmental data -- context -- movement ecology; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2021.1962528
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: Research on movement has increased over the past two decades, particularly in movement ecology, which studies animal movement. Taking context into consideration when analysing movement can contribute towards the understanding and prediction of behaviour. The only way for studying animal movement decision-making and their responses to environmental conditions is through analysis of ancillary data that represent conditions where the animal moves. In GIScience this is called Context-Aware Movement Analysis (CAMA). As ecology becomes more data-oriented, we believe that there is a need to both review what CAMA means for ecology in methodological terms and to provide reliable definitions that will bridge the divide between the content-centric and data-centric analytical frameworks. We reviewed the literature and proposed a definition for context, develop a taxonomy for contextual variables in movement ecology and discuss research gaps and open challenges in the science of movement more broadly. We found that the main research for CAMA in the coming years should focus on: 1) integration of contextual data and movement data in space and time, 2) tools that account for the temporal dynamics of contextual data, 3) ways to represent contextualized movement data, and 4) approaches to extract meaningful information from contextualized data.