Representation of continuously changing data over time and space: Modeling the shape of spatiotemporal phenomena

There are numerous technologies and tools to acquire data related to the evolution of spatial phenomena over time. These data are typically organized as sequences of 2D geometric shapes obtained from observations taken at different times. The transformation of such sequences of 2D geometric shapes into spatiotemporal data representations, which can be easily processed and interpreted, has the potential to enable novel applications in fields as diverse as environmental sciences, climate sciences, biology or medicine. This paper focuses on the representation of moving 2D geometric shapes acquired at discrete times using continuous models of time and space. Using morphing techniques based on compatible triangulations, issues regarding the representation of spatiotemporal data in databases, as well as the influence of different design strategies on the fidelity of the approximations with respect to the modelled phenomena, are investigated. An experimental study using synthetic and real data was performed. The findings show that the use of triangulation based interpolation is a promising approach, because it allows creating continuous spatiotemporal representations that are more realistic than those obtained using the solutions proposed in previous work. Open issues regarding the representation of spatiotemporal data in information systems are also highlighted.