Integrating cross-scale analysis in the spatial and temporal domains for classification of behavioral movement

Since various behavioral movement patterns are likely to be valid within differ- ent, unique ranges of spatial and temporal scales (e.g., instantaneous, diurnal, or seasonal) with the corresponding spatial extents, a cross-scale approach is needed for accurate clas- sification of behaviors expressed in movement. Here, we introduce a methodology for the characterization and classification of behavioral movement data that relies on comput- ing and analyzing movement features jointly in both the spatial and temporal domains. The proposed methodology consists of three stages. In the first stage, focusing on the spatial domain, the underlying movement space is partitioned into several zonings that correspond to different spatial scales, and features related to movement are computed for each partitioning level. In the second stage, concentrating on the temporal domain, several movement parameters are computed from trajectories across a series of temporal windows of increasing sizes, yielding another set of input features for the classification. For both the spatial and the temporal domains, the "reliable scale" is determined by an automated procedure. This is the scale at which the best classification accuracy is achieved, using only spatial or temporal input features, respectively. The third stage takes the measures from the spatial and temporal domains of movement, computed at the corresponding reliable