In recent years, the widespread deployment of sensors and communication technologies has enabled the collection of large-scale trajectory data in open, unconstrained environments. One prominent example is the Automatic Identification System (AIS), which transmits real-time navigational data such as location, speed, and heading between moving entities like ships and external observers, including terrestrial stations and satellites. The accumulation of such data opens new avenues for analyzing mobility patterns and supports a variety of applications-ranging from environmental impact assessments to activity detection and transport modeling. This chapter uses AIS as a case study to explore the potential of trajectory data analytics. We focus on two illustrative applications: detecting domain-specific activities (e.g., fishing behavior) and estimating CO \(_2\) emissions. In addition, we address a major challenge that arises across many free-range tracking systems—the presence of large spatial or temporal gaps in the collected trajectories. We outline common causes of such missing data and review methods for addressing them, including trajectory imputation and the integration of complementary data sources such as coastal cameras. We conclude with a discussion of open research problems and promising directions for future work in free-range trajectories.