Time interval information modeling in two- dimensional space using the regular grid structure
Despite their pervasiveness in daily life, time and space continue to be problematic issues with various analytical viewpoints in many domains. From a philosophical standpoint, GIS spatial-temporal modeling is divided into four quadrants: absolutism-materialism, absolutism-idealism, relationalism-materialism, and relationalism-idealism (Yuan et al. 2014). While the absolutism-materialism viewpoint on space and time has been utilized mainly in GIS research, the remaining three perspectives on space and time deserve more consideration. Extending beyond these spatial and temporal perspectives, the spatiotemporal representation and modeling of geographic phenomena and their dynamics have long been a primary emphasis of the GIS discipline, which has achieved tremendous strides in this area (Goodchild 2019). Due to the long history of studying temporal information in geography (Miller 2005), many spatiotemporal data models have been presented in GIS (Siabato et al. 2018). Although temporal information is widely employed in spatiotemporal representation and modeling, the one-dimensional (1D) linear idea is mostly regarded in terms of time instants (Qiang et al. 2014a), and the timestamp is primarily used to implement it. To address the issue of massive data access challenges associated with the 1D representation and the implicit temporal semantics of time, this article proposed a method for explicitly representing the time interval information of geographic objects in two-dimensional (2D) space using a regular grid structure. Two essential concerns are specified and discussed, namely the modeling technique and the spatiotemporal relations in 2D space for time interval information. The suggested modeling technique results in a Temporal GIS solution that is readily implementable, expandable, and compatible. It has the potential to become a novel technique for spatiotemporal modeling and geovisualization in GIS.
Caihui Cui, Feng Liu, Qirui Wu, Gaohan Ban, Zhaoxin Xie
Geographic Information Modelling and Simulation in the Era of Big Data