Abstract: Trajectory data have been extensively used in human mobility studies. Activities, especially conducted on a static and local space are basic elements of people's daily life, and they are represented as stays in trajectories. Hence detecting stays from trajectories has become a base for many activity-oriented studies. The temporal sampling interval (TSI) of trajectory data can impact the result of stay detection. However such impacts have not been systematically studied yet. This study proposes a probability-based framework, which aims to quantify the probability of an activity that with a specific duration time can be detected as a stay with different TSIs. Moreover, this framework can support further analysis on the evolution of daily movement network with different TSIs. We demonstrate the impacts of TSIs on stay detection and movement networks construction by using a trip survey dataset and a mobile phone location dataset of Shenzhen, China respectively. This study provides both metho-dological and empirical guidance on the decision-making of a TSI selection as well as the estimation of the results of activity-oriented studies.