Yupi: Generation, tracking and analysis of trajectory data in Python. (May 2023)

• Record Type: Journal Article
• Authors: Reyes, A.; Viera-López, G.; Morgado-Vega, J.J.; Altshuler, E.
• Is Part Of: Environmental modelling & software. Volume 163(2023)
• Publication Date: 2023-05
• Subjects: Trajectory analysis -- Modeling -- Tracking -- Python; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2023.105679
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: 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.