Gradually morphing a thematic map series based on cellular automata

Maps are often animated to help users make comparisons and comprehend trends. However, large and complex differences between sequential maps can inhibit users from doing so. This paper proposes a morphing technique to highlight trends without manual intervention. Changes between sequential maps are considered as the diffusion processes of expanding classes, with these processes simulated by cellular automata. A skeleton extraction technique is introduced to handle special cases. Experimental results demonstrate that the proposed morphing technique can reveal obvious trends between dramatically changed maps. The potential application of the proposed morphing technique in sequential spatial data (e.g. remote-sensing images) is discussed.