Detecting Evidence of Organization in groups by Trajectories
This work addresses the problem of detecting criminal or organized groups from movement data for public safety applications, but it is incremental as it builds on existing methods with new techniques.
The paper tackled the Network Structure Inference challenge by introducing two new approaches based on graph entropy and clustering indices to detect organizations from agent trajectories, and demonstrated that these approaches more clearly identify network inferences in simulated scenarios compared to a prior method.
Effective detection of organizations is essential for fighting crime and maintaining public safety, especially considering the limited human resources and tools to deal with each group that exhibits co-movement patterns. This paper focuses on solving the Network Structure Inference (NSI) challenge. Thus, we introduce two new approaches to detect network structure inferences based on agent trajectories. The first approach is based on the evaluation of graph entropy, while the second considers the quality of clustering indices. To evaluate the effectiveness of the new approaches, we conducted experiments using four scenario simulations based on the animal kingdom, available on the NetLogo platform: Ants, Wolf Sheep Predation, Flocking, and Ant Adaptation. Furthermore, we compare the results obtained with those of an approach previously proposed in the literature, applying all methods to simulations of the NetLogo platform. The results demonstrate that our new detection approaches can more clearly identify the inferences of organizations or networks in the simulated scenarios.