Modeling Spatio-temporal Change Pattern using Mathematical Morphology

Abstract

Detection and assessment of spatio-temporal change pattern is a challenging task, and may provide insights into various spatio-temporal changes, like urban sprawl monitoring, surveillance of epidemics due to infectious diseases etc. The existing spatio-temporal pattern mining techniques mostly deal with the assessment of thematic change patterns. However, analyzing the spatio-temporal pattern of geometric changes is also important for analyzing such kinds of spatial changes on a temporal scale. This paper presents a novel framework for modeling such spatio-temporal change in geometry with the help of mathematical morphology and directional granulometric analysis. Morphological operators have been used to detect the various spatio-temporal change patterns in geometry, like spatial growth (due to Expansion and Merge), spatial shrinkage (due to Contraction and Split) etc. Further, the temporal changes in the orientations of these patterns have been modeled by performing granulometric analyses on them. The proposed framework for spatio-temporal change pattern modeling has been validated considering four cases of spatio-temporal change, namely (i) spatial expansion, (ii) spatial contraction, (iii) spatial merge, and (iv) spatial split in regional distribution of climate zones in Australia.