Sampling rate effects on measurements of correlated and biased random walks

Codling, Edward A.; Hill, Nicholas A.

doi:10.1016/j.jtbi.2004.11.008

Cited by 94 publications

(113 citation statements)

References 24 publications

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“…With less frequent sampling some of the d m become very small, whereas others remain almost unchanged. Similar results have also been previously explained and explored in velocity jump simulations of correlated random walks [39,40]. In a velocity jump process, an object moves with a fixed speed for a random time interval and then turns to a new direction, usually one drawn from a circular normal distribution; the iteration of these steps creates a correlated random walk.…”

Section: Evaluation Of Interpolation Error In Real-world Fcdsupporting

confidence: 78%

“…Rediscretization of a RW has a significant influence on the calculation of movement parameters [39,40]. When the sampling rate is decreased, the resulting increase in interpolation error causes the observed speed to decrease, the object appears to move more slowly.…”

Section: Related Workmentioning

confidence: 99%

“…A similar approach is used in movement ecology to analyse the effects of the sampling rate on simulated random walks [39,40]. We define the experimental FCD recorded at 1 Hz to be the reference movement.…”

Section: Rediscretizing the Trajectoriesmentioning

confidence: 99%

See 2 more Smart Citations

What is an Appropriate Temporal Sampling Rate to Record Floating Car Data with a GPS?

Ranacher

Brunauer

Spek

et al. 2016

IJGI

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Abstract:Floating car data (FCD) recorded with the Global Positioning System (GPS) are an important data source for traffic research. However, FCD are subject to error, which can relate either to the accuracy of the recordings (measurement error) or to the temporal rate at which the data are sampled (interpolation error). Both errors affect movement parameters derived from the FCD, such as speed or direction, and consequently influence conclusions drawn about the movement. In this paper we combined recent findings about the autocorrelation of GPS measurement error and well-established findings from random walk theory to analyse a set of real-world FCD. First, we showed that the measurement error in the FCD was affected by positive autocorrelation. We explained why this is a quality measure of the data. Second, we evaluated four metrics to assess the influence of interpolation error. We found that interpolation error strongly affects the correct interpretation of the car's dynamics (speed, direction), whereas its impact on the path (travelled distance, spatial location) was moderate. Based on these results we gave recommendations for recording of FCD using the GPS. Our recommendations only concern time-based sampling, change-based, location-based or event-based sampling are not discussed. The sampling approach minimizes the effects of error on movement parameters while avoiding the collection of redundant information. This is crucial for obtaining reliable results from FCD.

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Section: Evaluation Of Interpolation Error In Real-world Fcdsupporting

confidence: 78%

Section: Related Workmentioning

confidence: 99%

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What is an Appropriate Temporal Sampling Rate to Record Floating Car Data with a GPS?

Ranacher

Brunauer

Spek

et al. 2016

IJGI

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“…Experimental studies on microorganisms have shown that, typically, swimming speeds are either exponentially (e.g., Codling and Hill 2005;Hill and Häder 1997) or normally distributed (Bearon and Grunbaum 2008). Codling et al (2010) found that a variable swimming speed can significantly change the spatial distribution of microorganisms when implemented in CRW models.…”

Section: Introductionmentioning

confidence: 99%

Modelling the dispersal of riverine fish larvae: from a raster-based analysis of movement patterns within a racetrack flume to a rheoreaction-based correlated random walk (RCRW) model approach

Glas

Tritthart

Zens

et al. 2017

Can. J. Fish. Aquat. Sci.

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Recruitment of Chondrostoma nasus and similar fish species in rivers is related to spatiotemporal linkages between larval hatching and nursery habitats. Active swimming behaviour contradicts the assumption that passive particle tracing models can serve as a proxy for larval dispersal models. A racetrack flume with an inshore area of near-natural slope was created to observe individual larval trajectories. A new three-step, raster-based analysis was developed to distinguish four types of movement patterns: active upstream, active downstream, active-passive, and passive. Both larval developmental stage-specific and release site-specific occurrences of these movement patterns were experimentally found for nine flow velocity classes (≤0.225 m·s −1 ). These currentinduced movement patterns, and evaluated durations within them, were used to develop a biased and correlated random walk model that includes rheoreaction -a key behavioural response of fish to flow within rivers. The study introduces the concept and application of a rheoreaction-based correlated random walk model, which coupled with a 3D hydrodynamic model, allows prediction of the spatiotemporal effects of various river discharges, morphologies, and restoration scenarios on larval fish dispersal.Résumé : Le recrutement de Chondrostoma nasus et d'espèces de poissons semblables dans les rivières est relié à des liens spatiotemporels entre l'éclosion des larves et les habitats d'alevinage. Le comportement de nage active contredit l'hypothèse voulant que les modèles de suivi de particules passives puissent se substituer aux modèles de dispersion des larves. Un canal de nage ovale avec une zone côtière de pente quasi naturelle a été créé pour observer les trajectoires de larves individuelles. Une nouvelle analyse de mappage en trois étapes a été mise au point pour distinguer quatre types de déplacements, à savoir : actif vers l'amont, actif vers l'aval, actif-passif et passif. Des cas de ces types de déplacements tant à l'étape du développement des larves que pour des sites de lâcher précis ont été observés expérimentalement pour neuf catégories de vitesses d'écoulement (≤0,225 m·s -1 ). Ces motifs de déplacement induits par le courant et leurs durées évaluées ont été utilisés pour développer un modèle de parcours aléatoire biaisé et corrélé qui comprend la rhéoréaction, une réaction comportementale clé des poissons à l'écoulement dans les rivières. L'étude introduit le concept et l'application d'un modèle de parcours aléatoire corrélé basé sur la rhéoréaction qui, jumelé à un modèle hydrodynamique en 3D, permet de prédire les effets spatiotemporels de différents débits, morphologies et scénarios de restauration de rivière sur la dispersion des larves de poisson. [Traduit par la Rédaction]

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“…Often, the time scale in a discrete analysis is that given by the sampling scheme of the observations, leading to problems regarding irregular or missing observations (Patterson et al in press), along with concerns regarding suitability and interpretability (Codling and Hill 2005;Rowcliffe et al 2012;Nams 2013;Harris and Blackwell 2013). This lack of scale invariance places unwarranted importance on the chosen time frame, suggesting no way to combine multiple sources of data or compare analyses.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Inference for Multistate ‘Step and Turn’ Animal Movement in Continuous Time

Parton

Blackwell

2017

JABES

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Mechanistic modelling of animal movement is often formulated in discrete time despite problems with scale invariance, such as handling irregularly timed observations. A natural solution is to formulate in continuous time, yet uptake of this has been slow. This lack of implementation is often excused by a difficulty in interpretation. Here we aim to bolster usage by developing a continuous-time model with interpretable parameters, similar to those of popular discrete-time models that use turning angles and step lengths. Movement is defined by a joint bearing and speed process, with parameters dependent on a continuous-time behavioural switching process, creating a flexible class of movement models. Methodology is presented for Markov chain Monte Carlo inference given irregular observations, involving augmenting observed locations with a reconstruction of the underlying movement process. This is applied to well-known GPS data from elk (Cervus elaphus), which have previously been modelled in discrete time. We demonstrate the interpretable nature of the continuous-time model, finding clear differences in behaviour over time and insights into short-term behaviour that could not have been obtained in discrete time.

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Sampling rate effects on measurements of correlated and biased random walks

Cited by 94 publications

References 24 publications

What is an Appropriate Temporal Sampling Rate to Record Floating Car Data with a GPS?

What is an Appropriate Temporal Sampling Rate to Record Floating Car Data with a GPS?

Modelling the dispersal of riverine fish larvae: from a raster-based analysis of movement patterns within a racetrack flume to a rheoreaction-based correlated random walk (RCRW) model approach

Bayesian Inference for Multistate ‘Step and Turn’ Animal Movement in Continuous Time

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