Authors: Yi Huang*, Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, PRC/The University of Texas at Dallas, May Yuan, The University of Texas at Dallas, Yehua Sheng, Key Laboratory of Virtual Geographic Environment, Nanjing Normal University
Topics: Geographic Information Science and Systems
Keywords: Geographic system, Geographic event, Holism, reasoning, ontology
Session Type: Poster
Start / End Time: 9:55 AM / 11:35 AM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: No File Uploaded
Traditional GIS represents the environment under reductionist thinking, which disaggregates a geographic environment into independent geographic themes. The reductionist approach highlights the spatiotemporal characteristics of geo-features but neglects the holistic nature of the environment, such as the hierarchical structure and interactions among environmental elements. To fill this gap, we integrate ideas from geographic ontology and general system theory to represent the environment with geo-system ontology, geo-feature ontology (including geo-fields), and geo-event ontology. Geo-system ontology captures the hierarchical structure of the environment, while the other two ontologies constitute the components of the environmental system. In addition, each of the ontologies of the environment, features, and events ascribes to the geo-information ontology of time, space, semantics, attribute, relation and process. The geo-information ontology supports both static and dynamic characterizations and prescribes spatial, temporal, interactive and causal relationships among environmental elements. We implement a case study in Mount Lu, China to test the utility of the proposed representation in OWL. The case study demonstrates a representation of Mount Lu as an environmental system, how heavy rainfall triggers a sequence of events in the system, and furthermore how the integrated GIS representation supports spatiotemporal queries and environmental process simulation. This study provides a GIS representation that encodes geographic knowledge of the environment, makes explicit the interaction mechanisms among environmental elements, supports high-level spatiotemporal queries and process simulation, and grounds a foundation for GeoAI to discover geographic complexity and dynamics beyond the current theme-centric inquiries in GIS.