Using eigen decomposition and sequence-based representation to extract movement patterns from contextualized tracking data

Abstract

Abstract. State sequences are a new paradigm to encode and represent contextualised movement data. A state sequence is a temporal succession of characters representing categorical states of the moving entity or its surrounding environment. Eigen decomposition, a principal components analysis method, is an option to reduce and find patterns in such multi-dimensional categorical data through dimensionality reduction. Recurrent patterns can be found by identifying the most relevant eigenbehaviours, which are a set of vectors that characterize the variation in the behaviour of an entity during a time period. Dimensionality reduction techniques have so far not been widely used in movement analytics and in this paper we demonstrate how they could help analyse responses of a moving entity to the dynamic environmental conditions. Specifically, we use sequence-based representation and eigen decomposition to investigate movement patterns of maned wolves (Chrysocyon brachyurus) in relation to vegetation vigour in their habitat. We use a set of GPS-trajectories from a group of maned wolves to which we link multi-source NDVI data as a proxy for the state of vegetation. We find that eigenbehaviours can identify patterns in the wolves’ responses to dynamic environmental conditions that align with the current literature on the species. Our research highlights the potential for dimensionality reduction and sequence-based methods to identify patterns in large tracking databases linked to contextual data.

Leveraging similarity analysis to understand variability in movement behavior

“…After preprocessing, each tracking point is enriched with a series of environmental variables identified as potentially effective factors based on domain knowledge. The environmental variables can be obtained through a trajectory enrichment process by interpolating and integrating remote sensing and weather reanalysis models data with movement data (Brum‐Bastos et al, 2016), for example, using the Env‐DATA service in http://movebank.org (Dodge et al, 2013, 2014). For human movement, remote sensing data such as land use and land cover, weather information, as well as census data capturing demographic structures of the communities can be integrated through data fusion (Dodge, 2022).…”