STA: Spatio-Temporal Aggregation with Applications to Analysis of Diffusion-Reaction Phenomena

Spatio-temporal data sets arise when time-varying physical fields are discretized for simulation or analysis. Examples of time-varying fields are isothermal regions in the sea or pattern formations in natural systems, such as convection rolls or diffusion-reaction systems. The analysis of these data sets is essential for generating qualitative interpretations for human understanding. This paper presents Spatio-Temporal Aggregation (STA), a system for recognizing and tracking qualitative structures in spatio-temporal data sets. STA algorithms record and maintain temporal events and compile event sequences into concise history descriptions. This is carried out at several levels of description, from the bottom up: first, low level events are identified and tracked, and then a subset of those events, relevant at the next description level, is identified. The process is iterated until a high level description of the system’s temporal evolution is obtained. STA has been demonstrated on a class of diffusion-reaction systems in two dimensions and has successfully generated high-level symbolic descriptions of systems similar to those produced by scientists through carefully hand-tuned computational experiments.