1. [Elkin-Frankston, S., McIntyre, J., Brunyé, T.T. et al. (2025). Beyond boundaries: a location-based toolkit for quantifying group dynamics in diverse contexts. Cogn. Research 10, 10 (2025).](https://doi.org/10.1186/s41235-025-00617-6) - *"we first segmented time periods when the group was in motion by identifying break periods using the stop detection feature from the MovingPandas Python package"*

1. [Koszewski, K., Olszewski, R., Pałka, P. et al. (2025). Utilizing IoT Sensors and Spatial Data Mining for Analysis of Urban Space Actors’ Behavior in University Campus Space Design. Sensors, 25(5), 1393.](https://doi.org/10.3390/s25051393) - *"trajectories were processed by the MovingPandas Python library, which offers several valuable processing algorithms, like calculation of velocity, direction of movement, intersections, as well as enabling easy data exchange with other GIS tools using built-in exporters" & "One useful solution turned out to be the MovingPandas’ built-in TrajectoryStopDetector"*

2. [Garcez Duarte, M. M., & Sakr, M. (2025). An experimental study of existing tools for outlier detection and cleaning in trajectories. GeoInformatica, 29(1), 31-51.](https://doi.org/10.1007/s10707-024-00522-y) - *"MovingPandas uses the Kalman Filter (KF) algorithm for outlier detection"*

[Full Google Scholar list](https://scholar.google.com/scholar?oi=bibs&hl=en&cites=10366998261774464895)