Study aim | Method | Analytical category | Description | Input signal | References |
---|---|---|---|---|---|
Movement pattern description | Thresholding | Topology-based | Applies thresholding schemes (cut-off values) to separate relocations into different groups based on single or multiple path parameters (e.g., short- vs. Long-range movements) | Primary and secondary signals | |
Supervised Classification | Topology-based | Relocations (steps) of a trajectory are assigned to certain classes of movement behavior based on a classification scheme fitted with a training dataset | Primary and secondary signals, additional information like activity data | ||
Clustering | Topology-based | Unsupervised classification for identifying distinctive groups within a multivariate set of path-signals | Primary and secondary signals, additional information like activity data | ||
Bayesian Partitioning of Markov Models (BPMM) | Topology- and time- series based | Classification algorithm for determining the number and sequence of homogenous classes within a sequential path-signal (time series) | Primary and secondary signals | ||
Change-point detection | Line Simplification | Topology- or time-series based | Tests whether reducing the number of vertices in a trajecotry significantly impacts path topology to determine change points (can also be applied with graphs of sequential path-signals) | Primitive signals (spatial position) | |
Change Point Test | Topology-based | Detects significant changes in the observed movement direction (orientation) between the starting point and an attraction point of a trajectory | Primitive signals (spatial position) | ||
Spatio-Temporal Criteria Segmentation | Topology-based | Special type of thresholding seeking optimal segmentation of a trajectory based on monotone criteria: relocations are included in a segment as long as they fullfill certain predefined requirements | Primitive, primary and secondary signals | ||
Piecewise Regression | Time-series analysis | Splits time-series model into representative segments based on a signficant change-point (fits a polynomial model for each segment) | Primary and secondary signals | ||
Penalized Contrast Method (PCM) | Time-series analysis | Non-parametric segmentation of a path-signal: the unknown number of segments is estimated by minimizing a penalized contrast function | Mostly secondary signals | ||
Behavioral Change Point Analysis (BCPA) | Time-series analysis | Likelihood-based method for detecting significant change points; applies moving window over continuous autocorrelated time series of a path-signal | Mostly secondary signals | ||
Pruned Exact Linear Time (PELT) Algorithm | Time-series analysis | Search method for detecting optimal number and locations of change points minimizing different cost and penalty functions | primary and secondary signals | ||
Behavioral Movement Segmentation (BMS) | Time-series analysis | Combined search algorithm which optimizes segmentation based on parsimony and subsequent clustering for assigning segments to similar behaviors | primary and secondary signals, additional information like activity data | [43] | |
Process identification | Hidden-Markov Models (HMM) | State-space models | Estimate the sequence and composition of a predifined number of discrete states (e.g., movement behaviors) as well as the switching-probabilities between these states | Primary signals, additional information like activity data | |
State-Space Models with Location Filtering | State-space models | More complex models which can model hidden movement states and also correct for errors in the observation process (e.g., GPS errors) | Primitive (spatial position) and primary signals, additional information like activity data | ||
Hierarchical State-Space Models | State-space models | Hierarchical models accounting for variability of number and composition of movement states between individuals (further making inferences at population level) | Primary signals | ||
Bayesian Partitioning of Markov Models (BPMM) | Topology- and time- series based | Can also be used as partitioning algorithm determining the number and sequence of homogenous models (“states”) within a sequential path-signal | primary and secondary signals |