The M³ massive movement model: a distributed incrementally updatable solution for big movement data exploration

2020 ◽  
Vol 34 (12) ◽  
pp. 2517-2540 ◽  
Author(s):  
Anita Graser ◽  
Peter Widhalm ◽  
Melitta Dragaschnig
Keyword(s):  
Data Exploration ◽  
Movement Model ◽  
Movement Data

scholarly journals Detection of Breathing Movements of Preterm Neonates by Recording Their Abdominal Movements with a Time-of-Flight Camera

Pharmaceutics ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 721
Author(s):  
Felix C. Wiegandt ◽  
David Biegger ◽  
Jacob F. Fast ◽  
Grzegorz Matusiak ◽  
Jan Mazela ◽  
...  
Keyword(s):  
Time Of Flight ◽  
Physiological Model ◽  
Preterm Neonates ◽  
Great Effort ◽  
Breathing Patterns ◽  
Flow Sensors ◽  
Movement Model ◽  
Movement Data ◽  
Respiratory Parameters ◽  
Abdominal Movement

In order to deliver an aerosolized drug in a breath-triggered manner, the initiation of the patient’s inspiration needs to be detected. The best-known systems monitoring breathing patterns are based on flow sensors. However, due to their large dead space volume, flow sensors are not advisable for monitoring the breathing of (preterm) neonates. Newly-developed respiratory sensors, especially when contact-based (invasive), can be tested on (preterm) neonates only with great effort due to clinical and ethical hurdles. Therefore, a physiological model is highly desirable to validate these sensors. For developing such a system, abdominal movement data of (preterm) neonates are required. We recorded time sequences of five preterm neonates’ abdominal movements with a time-of-flight camera and successfully extracted various breathing patterns and respiratory parameters. Several characteristic breathing patterns, such as forced breathing, sighing, apnea and crying, were identified from the movement data. Respiratory parameters, such as duration of inspiration and expiration, as well as respiratory rate and breathing movement over time, were also extracted. This work demonstrated that respiratory parameters of preterm neonates can be determined without contact. Therefore, such a system can be used for breathing detection to provide a trigger signal for breath-triggered drug release systems. Furthermore, based on the recorded data, a physiological abdominal movement model of preterm neonates can now be developed.

scholarly journals Facilitating insights with a user adaptable dashboard, illustrated by airport connectivity data

2018 ◽  
Vol 1 ◽  
pp. 1-6
Author(s):  
Ieva Dobraja ◽  
Menno-Jan Kraak ◽  
Yuri Engelhardt
Keyword(s):  
Temporal Patterns ◽  
Data Representation ◽  
Data Exploration ◽  
Temporal Data ◽  
Movement Data ◽  
Graphical Data ◽  
Multiple Data ◽  
Multi Temporal ◽  
Spatio Temporal ◽  
Heterogeneous Multiscale

Since the movement data exist, there have been approaches to collect and analyze them to get insights. This kind of data is often heterogeneous, multiscale and multi-temporal. Those interested in spatio-temporal patterns of movement data do not gain insights from textual descriptions. Therefore, visualization is required. As spatio-temporal movement data can be complex because size and characteristics, it is even challenging to create an overview of it. Plotting all the data on the screen will not be the solution as it likely will result into cluttered images where no data exploration is possible. To ensure that users will receive the information they are interested in, it is important to provide a graphical data representation environment where exploration to gain insights are possible not only in the overall level but at sub-levels as well. A dashboard would be a solution the representation of heterogeneous spatio- temporal data. It provides an overview and helps to unravel the complexity of data by splitting data in multiple data representation views. The adaptability of dashboard will help to reveal the information which cannot be seen in the overview.

scholarly journals Visual Analytics for Characterizing Mobility Aspects of Urban Context

2021 ◽  
pp. 727-755
Author(s):  
Gennady Andrienko ◽  
Natalia Andrienko ◽  
Fabian Patterson ◽  
Siming Chen ◽  
Robert Weibel ◽  
...  
Keyword(s):  
Visual Analytics ◽  
Research Topic ◽  
Data Exploration ◽  
Spatiotemporal Data ◽  
Multiple Perspectives ◽  
Numerous Application ◽  
Urban Context ◽  
Spatial Objects ◽  
Movement Data ◽  
Spatial Locations

AbstractVisual analytics science develops principles and methods for efficient human–computer collaboration in solving complex problems. Visual and interactive techniques are used to create conditions in which human analysts can effectively utilize their unique capabilities: the power of seeing, interpreting, linking, and reasoning. Visual analytics research deals with various types of data and analysis tasks from numerous application domains. A prominent research topic is analysis of spatiotemporal data, which may describe events occurring at different spatial locations, changes of attribute values associated with places or spatial objects, or movements of people, vehicles, or other objects. Such kinds of data are abundant in urban applications. Movement data are a quintessential type of spatiotemporal data because they can be considered from multiple perspectives as trajectories, as spatial events, and as changes of space-related attribute values. By example of movement data, we demonstrate the utilization of visual analytics techniques and approaches in data exploration and analysis.

scholarly journals Integrating animal movement with habitat suitability for estimating dynamic landscape connectivity

2017 ◽  
Author(s):  
Mariëlle L. van Toor ◽  
Bart Kranstauber ◽  
Scott H. Newman ◽  
Diann J. Prosser ◽  
John Y. Takekawa ◽  
...  
Keyword(s):  
Habitat Suitability ◽  
Disease Ecology ◽  
Animal Movement ◽  
Landscape Connectivity ◽  
Population Level ◽  
Tracking Data ◽  
Migratory Connectivity ◽  
Ecological Processes ◽  
Movement Model ◽  
Movement Data

AbstractContextHigh-resolution animal movement data are becoming increasingly available, yet having a multitude of empirical trajectories alone does not allow us to easily predict animal movement. To answer ecological and evolutionary questions at a population level, quantitative estimates of a species’ potential to link patches or populations are of importance.ObjectivesWe introduce an approach that combines movement-informed simulated trajectories with an environment-informed estimate of the trajectories’ plausibility to derive connectivity. Using the example of bar-headed geese we estimated migratory connectivity at a landscape level throughout the annual cycle in their native range.MethodsWe used tracking data of bar-headed geese to develop a multi-state movement model and to estimate temporally explicit habitat suitability within the species’ range. We simulated migratory movements between range fragments, and calculated a measure we called route viability. The results are compared to expectations derived from published literature.ResultsSimulated migrations matched empirical trajectories in key characteristics such as stopover duration. The viability of the simulated trajectories was similar to that of the empirical trajectories. We found that, overall, the migratory connectivity was higher within the breeding than in wintering areas, corresponding to previous findings for this species.ConclusionsWe show how empirical tracking data and environmental information can be fused for meaningful predictions of animal movements throughout the year and even outside the spatial range of the available data. Beyond predicting connectivity, our framework will prove useful for modelling ecological processes facilitated by animal movement, such as seed dispersal or disease ecology.

scholarly journals A New Type of Eye Movement Model Based on Recurrent Neural Networks for Simulating the Gaze Behavior of Human Reading

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaoming Wang ◽  
Xinbo Zhao ◽  
Jinchang Ren
Keyword(s):  
Neural Networks ◽  
Eye Movement ◽  
Conditional Random Fields ◽  
Short Term Memory ◽  
Training Data ◽  
Gaze Behavior ◽  
Movement Model ◽  
Movement Data ◽  
Movement Models ◽  
New Type

Traditional eye movement models are based on psychological assumptions and empirical data that are not able to simulate eye movement on previously unseen text data. To address this problem, a new type of eye movement model is presented and tested in this paper. In contrast to conventional psychology-based eye movement models, ours is based on a recurrent neural network (RNN) to generate a gaze point prediction sequence, by using the combination of convolutional neural networks (CNN), bidirectional long short-term memory networks (LSTM), and conditional random fields (CRF). The model uses the eye movement data of a reader reading some texts as training data to predict the eye movements of the same reader reading a previously unseen text. A theoretical analysis of the model is presented to show its excellent convergence performance. Experimental results are then presented to demonstrate that the proposed model can achieve similar prediction accuracy while requiring fewer features than current machine learning models.

scholarly journals Simulation and Analysis of Animal Movement Paths using Numerus Model Builder

2019 ◽  
Author(s):  
Wayne M. Getz ◽  
Ludovica Luisa Vissat ◽  
Richard Salter
Keyword(s):  
Animal Movement ◽  
Internal State ◽  
Step Length ◽  
Movement Model ◽  
Model Builder ◽  
Movement Data ◽  
Test Models ◽  
Detailed Simulation ◽  
Biased Correlated Random Walk ◽  
Open Question

ABSTRACTAnimal 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-of-movement (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.

scholarly journals The “Lévy or Diffusion” Controversy: How Important is the Movement Pattern in the Context of Trapping?

2018 ◽  
Author(s):  
Danish A. Ahmed ◽  
Sergei V. Petrovskii ◽  
Paulo F.C. Tilles
Keyword(s):  
Brownian Motion ◽  
Traditional Approach ◽  
Mean Field ◽  
Movement Pattern ◽  
Movement Model ◽  
Pest Monitoring ◽  
Movement Data ◽  
Conflicting Evidence ◽  
Modelling Techniques ◽  
And Diffusion

Many empirical and theoretical studies indicate that Brownian motion and diffusion models as its mean field counterpart provide appropriate modelling techniques for individual insect movement. However, this traditional approach has been challenged and conflicting evidence suggests that an alternative movement pattern such as Lévy walks can provide a better description. Lévy walks differ from Brownian motion since they allow for a higher frequency of large steps, resulting in a faster movement. Identification of the ‘correct’ movement model that would consistently provide the best fit for movement data is challenging and has become a highly controversial issue. In this paper, we show that this controversy may be superficial rather than real if the issue is considered in the context of trapping or, more generally, survival probabilities. In particular, we show that almost identical trap counts are reproduced for inherently different movement models (such as the Brownian motion and the Lévy walk) under certain conditions of equivalence. This apparently suggests that the whole ‘Levy or diffusion’ debate is rather senseless unless it is placed into a specific ecological context, e.g. pest monitoring programmes.

Determination of Fish Movement Patterns from Tag Recoveries using Maximum Likelihood Estimators

1990 ◽  
Vol 47 (3) ◽  
pp. 635-643 ◽  
Author(s):  
Ray Hilborn
Keyword(s):  
Population Dynamics ◽  
Likelihood Function ◽  
Nonlinear Function ◽  
Spatial Location ◽  
Movement Model ◽  
Movement Rates ◽  
Movement Data ◽  
Specific Vulnerability ◽  
Changes Over Time

A general method for analysis of movement data from tag returns is proposed which has four major components: (1) a population dynamics and movement model that describes how the number of tagged individuals in each spatial location changes over time; (2) an observation model which describes how the tags are recovered and reported; (3) a likelihood function that specifies the likelihood of observing a specific number of recoveries in each space/time stratum as a function of the number thought to be there under a specific set of parameters of the population dynamics, movement and observation models, and (4) a nonlinear function minimization computer algorithm. This approach is applied to movements of skipjack tuna (Euthynnus pelamis). When tagging and recapture take place in each spatial stratum, reliable estimates of movement rates can be obtained. The approach described is completely general and can be used in cases where movement takes place continuously, or only once in the life history. Methods for determining confidence limits and evaluation of residuals are presented and extensions that include tagging mortality, tag shedding, and size specific vulnerability are discussed.

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