One of the main tasks in analyzing pedestrian movement is to detect places where pedestrians stop, as those places usually are associated with specific human activities, and they can allow us to understand pedestrian movement behavior. Very few approaches have been proposed to detect the locations of stops in positioning data sets, and they often are based on selecting the location of candidate stops as well as potential spatial and temporal thresholds according to different application requirements. However, these approaches are not suitable for analyzing the slow movement of pedestrians where the inaccuracy of a nondifferential global positioning system commonly used for movement tracking is so significant that it can hinder the selection of adequate thresholds. In this article, we propose an exploratory statistical approach to detect patterns of movement suspension using a local indicator of spatial association (LISA) in a vector space representation. Two different positioning data sets are used to evaluate our approach in terms of exploring movement suspension patterns that can be related to different landscapes: players of an urban outdoor mobile game and visitors of a natural park. The results of both experiments show that patterns of movement suspension were located at places such as checkpoints in the game and different attractions and facilities in the park. Based on these results, we conclude that using LISA is a reliable approach for exploring movement suspension patterns that represent the places where the movement of pedestrians is temporally suspended by physical restrictions (e.g., checkpoints of a mobile game and the route choosing points of a park).