Analysing the space-time structure of trajectory point patterns

C. Comas Rodríguez, J. Mateu

Currently, vast amounts of data and information emerge and evolve through space and time, forming linear structures (networks-graphs). The path left by a moving animal, the trajectory of hurricanes, taxi routes in a city, or airplane flight paths, are examples. In all these cases, these objects move through space and time, leaving a trace that can be tracked over time. This type of data is often referred to as trajectory tracking data. In this work, we analyse this type of data assuming that the resulting point trajectory is a space-time point pattern. We develop an algorithm to simulate trajectory point patterns and present an equivalent version of the Ripley's K function to analyse the space-time structure of this type of data. We apply this new summary function to analyse a case study evolving space-time trajectories of some marine mammals.

Keywords: Space-time point patterns, Trajectory tracking data, Trajectory point processes

Spatio-Temporal Statistics II
June 12, 2025  11:30 AM
MR 3

I. Hidalgo Arellano, G. Fernández-Avilés Calderón

A. Martínez Puig, À. Blanch Plana, C. Comas Rodríguez

I. Fuentes Santos, W. González Manteiga, J. Mateu Mahiques

T. E. Salcedo Palacios, M. J. Pacheco López