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Abstract
Recent developments in sensing and tracking technologies have enabled large geographical databases to be established that represent spatial dynamics of ‘behavioral entities’. Within this type of dynamics there are several levels and modes of organization that need to be revealed. Clusters are high‐level groupings of entities, where change in their location and form, including split and merge events, represents self‐organization and functioning patterns. Such information may contribute for better understanding spatially complex dynamic patterns. The main objective of this article is to develop an adaptable methodology that facilitates exploration of spatial order and processes in point pattern dynamics. The approach presented here utilizes data‐clustering at each snapshot of the moving pattern, and then involves pairwise linking between the clusters identified at each snapshot and those identified in the following snapshot. Such linking is based on a new methodology that defines well globally optimized solutions for numerous possible linking combinations based on Linear Programming. A preliminary assessment of the approach was conducted with an existing Ants' simulation tool, capable of creating data sets covering in detail a substantial portion of the nest's life cycle.
