Time and space in geographic information: toward a four-dimensional spatio-temporal data model

The advent of computers has created significant new opportunities for geographers to analyze the spatial and temporal aspects of geographic information. To make greater use of this, geographers have expended considerable effort in understanding the relations that exist in space. More recently, the issue of including temporal data in geographic information systems has become an important concern. To adequately model temporal data with the spatial data, the nature of our concepts of space and time must be better understood. This study examines the concepts of space and time interdisciplinarily and finds that there are significant similarities in the concepts of space and time in Western thought. These similarities are used to build a typology of spatio-temporal concepts. These concepts, in turn, provide a variety of structures, which when combined with an evaluation of the linguistic fine structures, identify the relationships necessary for spatial and temporal topologies. The result of these analyses and an examination of the models currently in existence for geographic information lead to the development of a new conceptual spatio-temporal data model that combines vector, tessellation, and feature type models. Both the vector and tessellation structures are extended into the four-dimensional spatio-temporal environment. Time is also incorporated into the feature structure with feature trajectories being considered rather than merely static features. This combination of vector, tessellation, and feature structures provides an opportunity to realize the advantages of each while avoiding some of the difficulties inherent in modeling and analyzing the complexity of a four-dimensional universe. Many of the issues involved with the development of such a model are described and explored. A number of functions currently in use in geographic information systems are extended into the spatio-temporal realm and other new functions, unique to spatio-temporal data are proposed.