Abstract
Traditional Geographic Information Systems (GIS) represent the environment under reductionist thinking, which disaggregates a geographic environment into independent geographic themes. The reductionist approach makes the spatiotemporal characteristics of geo-features explicit, but neglects the holistic nature of the environment, such as the hierarchical structure and interactions among environmental elements. To fill this gap, we integrate the concept geographic scenario with the fundamental principles of General System Theory to realize the environmental complexity in GIS. With the integration, a geographic scenario constitutes a hierarchy of spatiotemporal frameworks for organizing environmental elements and subserving the exploration of their relationships. Furthermore, we propose geo-characterization with ontological commitments to both static and dynamic properties of a geographic scenario and prescribe spatial, temporal, semantic, interactive, and causal relationships among environmental elements. We have tested the utility of the proposed representation in OWL and the associated reasoning process in Semantic Web Rule Language (SWRL) rules in a case study in Nanjing, China. The case study represents Nanjing and the Nanjing presidential palace to demonstrate the connections among environmental elements in different scenarios and the support for information queries, evolution process simulation, and semantic inferences. The proposed representation encodes geographic knowledge of the environment, makes the interactions among environmental elements explicit, supports geographic process simulation, opens opportunities for deep knowledge mining, and grounds a foundation for GeoAI to discover geographic complexity and dynamics beyond the support of conventional theme-centric inquiries in GIS.
Highlights
Geographic Information Science (GIScience) contributes knowledge and computing frameworks to understand dynamic processes and develop solutions to geographic problems [1,2,3,4,5]
Conventional Geographic Information Systems (GIS) representation models neglect the holistic nature of the environment and separate the environmenQtuienrtyo different themes
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Summary
Geographic Information Science (GIScience) contributes knowledge and computing frameworks to understand dynamic processes and develop solutions to geographic problems [1,2,3,4,5]. Major progressions throughout the past three decades have been shifting the foci of GIScience research from static distribution patterns to dynamic phenomena, space-time interactions, and evolution processes [6,7,8,9,10] Such shifts demand re-examining the nature of geographic environments with considerations of environmental processes and geographic cognition to construct a holistic representation framework [11]. Event-based models provide opportunities to elicit geographic dynamics and discover new knowledge beyond what is attainable from layer-confined objects Both categories of traditional GIS data models subscribe to the reductionist’s thinking in representing geography and neglecting the holistic nature of the environment. The scenario-based representation encompasses spatial, temporal, semantic, attribute, interactions, and processes This framework makes explicit connections among geographic objects, events, and processes and, opportunities for mining knowledge about geographic dynamics, and it serves as a blueprint for holographic information systems in the future.
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