Stop@: A framework for scalable and noise-resistant stop-move segmentation of large datasets of trajectories in outdoor and indoor spaces
Fatima Hachem, Maria Luisa Damiani · SoftwareX · 2024
Capturing the mobility behavior of moving entities from their traces is a prominent theme in mobility data science.Stop@ supports behavior analysis by providing a generic framework for the mining of stop-move patterns in spatial trajectories across animal and human mobility scenarios.The framework is built around a stop detection method, successfully used in diverse applications in animal ecology.The method has been recently validated against accurate ground truth stops collected in a museum, proving to be effective and robust, also for the study of human mobility.Stop@ provides a rich set of functionalities to facilitate the stop-move analysis, including the parallel processing of large datasets of trajectories collected outdoor and indoor.