Voxel-based probabilistic space-time prisms for analysing animal movements and habitat use

Habitat Use and Behaviours of Introduced Muscovy Ducks (Cairina moschata) in Urban and Suburban Environments

“…Johnson and Hawk (2012) also provide a brief extension factsheet about Muscovy ducks as an introduced species in the State of Florida. Downs et al (2013) presented one of the first studies of Muscovy duck movements and habitat use patterns in the United States, where they quantified the home range and habitats used by a single Muscovy duck tracked by GPS in Florida. This male duck occupied a home range of 0.17 km 2 and utilized water, grass, tree, shrub, and urban habitats.…”

Space–Time Analysis: Concepts, Quantitative Methods, and Future Directions

“…For instance, comparisons between space-time paths can be conducted through the use of path similarity indexes including Hausdorff distance and Frechet distance, the dynamic time warping algorithm, the multiobjective optimization evolutionary algorithm Probabilistic time geography @ @ Survival analysis models b @ @ Others @ Process-based simulation Agent-based models c @ @ Others (e.g., Markov models) @ Spatial panel data analysis Pattern revelation Multiple space-time metrics @ Multiple space-time tests @ Others @ Space-time statistical models Panel regression models @ S-T autoregressive models & variants @ S-T weighted regression models @ Latent trajectory /multilevel models c @ Survival analysis models c @ @ Others (e.g., hybrid models) @ Process-based simulation Agent-based models @ @ Cellular automaton @ Spatial Markov models @ Others @ a We do not provide detail due to decent coverage in Long and Nelson (2013) Space-Time Analysis: Concepts, Quantitative Methods, and Future Directions (MOEA), and the longest common sequence algorithm (Chen et al 2011;Long and Nelson 2013;Kwan, Xiao, and Ding 2014). Other methods allow for grouping together individuals who have similar spacetime paths or activity density surface (Kwan 1999;Chen et al 2011), quantitatively modeling movement probabilities that incorporate object kinetics (Long, Nelson, and Nathoo 2014), and creating probabilistic space-time prisms depicting an individual agent's daily movement (Downs et al 2014). For details about these quantitative methods, see Long and Nelson (2013).…”

“…He concluded that STGIS is not likely to emerge in the near future. Nevertheless, GIScientists are making rapid progress in various areas, including dealing with a complex structure of space-time data (e.g., Yuan 1999), using different space-time units (e.g., Downs et al 2014), and implementing more tools in a GIS environment (Rey and Janikas 2006;Shaw, Yu, and Bombom 2008). We expect that more effort will be devoted to development of STGIS, which might greatly advance space-time analysis.…”

“…A current research frontier in individual movement data analysis is the so-called probabilistic time geography, which has arisen due to the understanding that although points within a space-time prism are accessible, not all points have an equal probability of being visited (Winter and Yin 2011;Downs and Horner 2012;Song and Miller 2014). A number of methods have been developed to compute these probabilities or spacetime fields (contours), including the use of kernel density estimation to create density surfaces characterizing a moving object's spatial distribution over a fixed time interval (or space-time discs), random walk or correlated random walk methods that are used with discrete space and time intervals, Brownian bridges used for continuous space and time, and voxel-based geocomputational approaches (Downs 2010;Downs et al 2014;Song and Miller 2014). Future research might revolve around integrating time geography and statistical density estimation to allow better portrayal of movement over a long time period for objects and phenomenon at various spatial scales (Downs 2010;Downs and Horner 2012).…”

“…For details about these quantitative methods, see Long and Nelson (2013). Some specific research efforts have focused on development and evaluation of space-time measures or algorithms that are "sensitive to person-specific situations and gender-role constraints" (e.g., Kwan 1998, 211), assessment of the similarity among individual activity patterns using the sequence alignment method that was originally developed to analyze DNA sequences (Shoval and Isaacson 2007;Kwan, Xiao, and Ding 2014), and exploration and visualization of large space-time trajectory data sets in the GIS software environment for both human (e.g., Kwan 2004) and nonhuman agents (e.g., Baer and Butler 2000;Downs et al 2014). In parallel with such efforts, individual movement data analysis also aims at better understanding individual people's activity-travel scheduling behavior subject to space-time prisms (e.g., Liao, Rasouli, and Timmermans 2014) as well as revealing how individual accessibility constraints might factor into personal or social decision making (e.g., Neutens et al 2008).…”