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Stacked space-time densities: a geovisualisation approach to explore dynamics of space use over time

Research output: Contribution to journalArticle


Urska Demsar, Kevin Buchin, Emiel E. van Loon, Judy Shamoun-Baranes

School/Research organisations


Recent developments and ubiquitous use of global positioning devices have revolutionised movement ecology. Scientists are able to collect increasingly larger movement datasets at increasingly smaller spatial and temporal resolutions. These data consist of trajectories in space and time, represented as time series of measured locations for each tagged animal. Such data are analysed and visualised using methods for estimation of home range or utilisation distribution, which are often based on 2D kernel density in geographic space. These methods have been developed for much sparser and smaller datasets obtained through very high frequency (VHF) radio telemetry. They focus on the spatial distribution of measurement locations and ignore time and sequentiality of measurements. We present an alternative geovisualisation method for spatio-temporal aggregation of trajectories of tagged animals: stacked space-time densities. The method was developed to visually portray temporal changes in animal use of space using a volumetric display in a space-time cube. We describe the algorithm for calculation of stacked densities using four different decay functions, normally used in space use studies: linear decay, bisquare decay, Gaussian decay and Brownian decay. We present a case study, where we visualise trajectories of lesser black backed gulls, collected over 30 days. We demonstrate how the method can be used to evaluate temporal site fidelity of each bird through identification of two different temporal movement patterns in the stacked density volume: spatio-temporal hot spots and spatial-only hot spots.


Original languageEnglish
Pages (from-to)85-115
Issue number1
Early online date3 Apr 2014
Publication statusPublished - Jan 2015

    Research areas

  • Animal Movement, Space-time Density, Space-time Cube, Visual data exploration, Home range estimation, Utilisation distribution

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ID: 108603444