Recurrence indicators for the identication of spatial patterns

This paper addresses the problem of identifying the main characteristics of Turing patterns by spatial recurrence properties. Changes in patterns shapes and spatial frequencies are estimated by the application of generalized recurrence quantification measures, establishing their relationships with the model parameters. Furthermore, variations of the recurrence measures respect to the spatial frequency are shown to fulfill theoretical results. A comparison with the standard two dimensional Fourier transform is carried out, showing that the recurrence indicators perform better in identifying a reliable connection with the spatial frequency of the patterns.