A context-sensitive correlated random walk: a new simulation model for movement

Ahearn, Sean C.; Dodge, Somayeh; Simcharoen, Achara; Xavier, Glenn; Smith, James L.

doi:10.1080/13658816.2016.1224887

Cited by 33 publications

(39 citation statements)

References 46 publications

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“…Context-awareness has recently received attention in many movement research directions in the areas of geographic information science (GIScience), [12][13][14][15] geocomputation (i.e., a wide array of spatial analytical methods and tools 16 ), visual analytics 17,18 (i.e., analytical reasoning facilitated by interactive visual interfaces 19 ), remote sensing 20 and tracking, 21 spatial data mining and knowledge discovery, or a combination thereof. The majority of this research has thus far merely used context as ancillary information to better understand mobilities, such as event-based movement analysis, 22 similarity measurement of trajectories, 23,24 uncertainty reduction and ranking in road networks, 25 uncertainty modeling associated with moving objects, 26 modeling spatial relevancy in context-aware systems, 27 determining significant places from mobility data, 28 visual analysis of movement behavior, 29 simulation models for movement, 30 analysis of human mobility patterns 31 and predictions, 32 and location prediction, 33 among others.…”

Section: Introductionmentioning

confidence: 99%

“…The majority of this research has thus far merely used context as ancillary information to better understand mobilities, such as event-based movement analysis, 22 similarity measurement of trajectories, 23,24 uncertainty reduction and ranking in road networks, 25 uncertainty modeling associated with moving objects, 26 modeling spatial relevancy in context-aware systems, 27 determining significant places from mobility data, 28 visual analysis of movement behavior, 29 simulation models for movement, 30 analysis of human mobility patterns 31 and predictions, 32 and location prediction, 33 among others.…”

Section: Introductionmentioning

confidence: 99%

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Context‐aware movement analytics: implications, taxonomy, and design framework

Sharif

Alesheikh

2017

WIREs Data Min & Knowl

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Movement of an entity is greatly affected by its internal and external contexts. Such consequential influence has created new paradigms for context-aware movement data mining and analysis. The significance of incorporating contextual information and movement data is becoming quite evident because of the growing interest in context-aware movement analysis. Despite such importance, there is limited consensus among researchers on the definition of context and context-aware system design in movement studies. Therefore, this paper comprehensively reviews current concepts of context and provides a definition and a taxonomy for context in movement analysis. The paper proceeds by providing a definition of context-aware systems in the movement area after a complete review and comparison of the present definitions present in the literature. Inspired by related works, the paper further suggests a holistic three-layer design framework tailored to context-aware systems in movement studies to examine in greater depth the techniques applied during the design stages. The paper outlines the challenges and emergent issues in future research directions in context-aware movement analysis. The present study is an attempt to bridge the gap between solely using context and developing context-aware systems, thus paving the way for further research in movement applications. © 2017 Wiley Periodicals, Inc. How to cite this article:WIREs Data Mining Knowl Discov 2018Discov , 8:e1233. doi: 10.1002Discov /widm.1233 INTRODUCTIONM ovement is intrinsically a continuous phenomenon and is normally recorded as discrete snapshots at different temporal resolutions. The three fundamental sets pertinent to movement are space S (i.e., set of locations/places), time T (i.e., set of instants/intervals), and object O (i.e., set of entities). 1 In this paper, by movement we mean the change in the spatial location of one or more entities (e.g., people, animals, and vehicles) over time, not a change in entities' geometries (e.g., an individual's body parts). Such changes can be represented as a function μ: T ! S, which maps any time to a location in space. The purpose of analyzing movement data in the majority of research fields (e.g., geography, ecology, sociology, and transportation) has focused on the development of insights into the behavior of moving entities. However, the movement behavior of entities is generally associated with their internal states and external factors. 2 In other words, understanding entities' movement greatly depends on recognizing the characteristics attributed to the entity itself (e.g., the entities' purposes and intentions for movement, the entities' perceptions of space) as well as the discovery of geographical circumstances during the movement (e.g., weather conditions and traffic). We, respectively, term these influential variables internal and external contexts in this paper. of 19Because of the progress in sensing, imaging, tracking, and navigation technologies and infrastructures in recent years, unprecedented amount...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Context‐aware movement analytics: implications, taxonomy, and design framework

Sharif

Alesheikh

2017

WIREs Data Min & Knowl

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“…The approach we develop here is to use the time interval that corresponds to the same frequency at which the data were collected and to either use actual step-size and turning-angle data, or smoothed/idealized functions that the are fitted to or theoretically represent these data. As part of the simulation, considerations of directional biases, step-size and turning-angle serial correlations, step-size/turning-angle cross-correlations, and context-dependent environmental and individual internal-state step-size and turning-angle distributions may be considered (Ahearn et al 2017, Langrock et al 2014). Although, individuals may also take the locations and directions of heading of other individuals into account as they move across landscapes (Couzin et al 2005, Conradt et al 2009, Langrock et al 2014), we do not consider this level of complexity here.…”

Section: Deconstruction Of Dar Ensemblesmentioning

confidence: 99%

Simulation and Analysis of Animal Movement Paths using Numerus Model Builder

Getz

Vissat

Salter³

2019

Preprint

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Animal movement paths are represented by point-location time series called relocation data. How well such paths can be simulated, when the rules governing movement depend on the internal state of individuals and environmental factors (both local and, when memory is involved, global) remains an open question. To answer this, we formulate and test models able to capture the essential statistics of multiphase versions of such paths (viz., movement-phase-specific step-length and turning-angle means, variances, auto-correlation, and cross correlation values), as well as broad scale movement patterns. The latter may include patchy coverage of the landscape, as well as the existence of home-range boundaries and gravitational centers-ofmovement (e.g., centered around nests). Here we present a Numerus Model Builder implementation of two kinds of models: a high-frequency, multi-mode, biased, correlated random walk designed to simulate real movement data at a scale that permits simulation and identification of path segments that range from minutes to days; and a model that uses statistics extracted from relocation data-either empirical or simulated-to construct movement modes and phases at subhourly to daily scales. We evaluate how well our derived statistical movement model captures patterns produced by our more detailed simulation model as a way to evaluate how well derived statistical movement models may capture patterns occurring in empirical data.

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“…The goal is often to understand the behaviourial state of the individual by analysing its trajectory and patterns of movement. A level of complexity that needs to be accounted for is the fact that behaviour occurs at different spatial and temporal scales reflected in the movement of an individual (Ahearn, David, Joshi, & Ding, ; Ahearn & Smith, ; De Weerd et al., ; Soleymani, Pennekamp, Dodge, & Weibel, ). Movement is a composite of multiple patterns that occur over different temporal frequencies.…”

Section: Introductionmentioning

confidence: 99%

“…For example, in their agent‐based model of tiger, Ahearn et al. () described several scales of behaviour that occur over multiple temporal frequencies: daily movement based on a correlated random walk at the local scale, hunting on average every 7 days at intermediate scales, and tiger patrolling its boundary on average every 3 weeks (i.e., at longer time periods). Using segmentation to capture such patterns at different scales from a movement trajectory is an ongoing challenge and one that is addressed by this research.…”

Section: Introductionmentioning

confidence: 99%

Recursive multi‐frequency segmentation of movement trajectories (ReMuS)

Ahearn

Dodge

2018

Methods Ecol Evol

Self Cite

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The quantity of GPS trajectories on the movement of individuals has far outstripped our ability to analyse them. Segmentation, a first step in trajectory analysis, is fundamental to imputing behaviour from patterns of movement. Behaviour occurs at multiple scales that are captured at different temporal frequencies. To identify behaviour from a complex trajectory it is essential to use a segmentation procedure that captures different frequency ranges. A limitation of existing segmentation algorithms is that they require training and parameterisation that can lead to special use segmentations that are not generalisable. This paper presents a new approach to the automated segmentation of movement trajectories at multiple scales. It has two distinguishing characteristics: it can capture movement patterns at multiple temporal frequencies through its recursive application; and requires no training or thresholding. The methodology is tested on two sets of simulated data and GPS tracking data of two species, the Turkey Vulture (Cathartes aura) and the Galapagos Albatross (Phoebastria irrorata). For the Turkey Vulture dataset, segments and transition points are evaluated using behavioural segmentations delineated by domain experts, and results are also compared with an existing approach. For the Albatross dataset, the segmentation results are evaluated using the geographic context of the movement tracks through visualisation. An Albatross trajectory is used to illustrate the application of recursive segmentation for extracting movement patterns at multiple temporal scales. The evaluation suggests that the segmentation technique is capable of extracting the right number of segments and in estimating transition points for most of the evaluated datasets. The proposed methodology has the potential to automate the segmentation process by enabling researchers to analyse a broad range of heterogeneous datasets without training. It will enable them to segment datasets recursively to derive multiple scales of movement patterns in a trajectory that may relate to different scales of behaviour.

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A context-sensitive correlated random walk: a new simulation model for movement

Cited by 33 publications

References 46 publications

Context‐aware movement analytics: implications, taxonomy, and design framework

Context‐aware movement analytics: implications, taxonomy, and design framework

Simulation and Analysis of Animal Movement Paths using Numerus Model Builder

Recursive multi‐frequency segmentation of movement trajectories (ReMuS)

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