yupi: Generation, tracking and analysis of trajectory data in Python

Studying trajectories is often a core task in several research fields. In environmental modeling, trajectories are crucial to study fluid pollution, animal migrations, oil slick patterns or land movements. This contribution addresses the lack of standardization and integration existing in current ap...

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Bibliographic Details
Published in:Environmental modelling & software : with environment data news Vol. 163; p. 105679
Main Authors: Reyes, A., Viera-López, G., Morgado-Vega, J.J., Altshuler, E.
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.05.2023
Subjects:
animals
computer software
data collection
Modeling
oil spills
pollution
Python
Tracking
Trajectory analysis
Tracking
Trajectory analysis
Modeling
Python
ISSN:1364-8152, 1873-6726
Online Access:Get full text
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Summary:Studying trajectories is often a core task in several research fields. In environmental modeling, trajectories are crucial to study fluid pollution, animal migrations, oil slick patterns or land movements. This contribution addresses the lack of standardization and integration existing in current approaches to handle trajectory data. Within this scenario, challenges extend from the extraction of a trajectory from raw sensor data to the application of mathematical tools for modeling or making inferences about populations and their environments. We introduce a framework that addresses the problem as a whole. It contains a tracking module aiming at making data acquisition handy, artificial generation of trajectories powered by different stochastic models to aid comparisons among experimental and theoretical data, a statistical kit for analyzing patterns in groups of trajectories and other resources to speed up data pre-processing. We validate the software by reproducing key results from published research related to environmental modeling applications. •Friendly and compact solution for research applications related to trajectories.•Designed for obtaining, processing and statistically analyzing trajectory data.•Allows the generation of trajectories based on parametric stochastic models.•Simplifies two-way conversions of data among existing software libraries.•Main features are illustrated by reproducing key results from published papers.•Highlights potential applications for environmental research.
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ISSN:1364-8152
1873-6726
DOI:10.1016/j.envsoft.2023.105679