A GeoAgent-based framework for knowledge-oriented representation: Embracing social rules in GIS

Abstract

While current GISystems (geographic information systems) can represent observational spatial data well, they have limited capabilities in representing some non-observational social elements and goal-driven behaviors that can be important factors in a wide range of geographic issues. Such social elements and behaviors can include laws, regulations, polices, plans, culture and customs, as well as their relations and interactions with the geographic environment at different scales. Getting beyond traditional data-centered approaches, this research presents a knowledgeoriented strategy in order to address these issues within a GIS context. We incorporate two major conceptual elements. First, extending from conventional agent notions and their geographic applications, geographic agents (GeoAgents) are considered as a basic representation component to specifically address social and goal-driven behaviors that impact the Earth and environmental systems, as well as to represent higher-level knowledge. Second, in order to incorporate GeoAgents with current space-time representation, a new conceptual representation framework, called FOTAR, is introduced to address the cross-scale processes of both social and natural interactions. A Java-based prototype, GeoAgent-based Knowledge System (GeoAgentKS), is described to implements this framework by integrating agent technologies with multiple data and knowledge representation techniques, such as expert systems, concept maps, mathematical models, and geospatial databases. The application of this prototype in a case study is also presented, investigating scale-dependent human-environment interactions under different emergency situations for CWSs (community water systems) in Central Pennsylvania, USA. In this case study, a systematic set of

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Cite this paper

@article{Yu2009AGF, title={A GeoAgent-based framework for knowledge-oriented representation: Embracing social rules in GIS}, author={Chaoqing Yu and Donna Peuquet}, journal={International Journal of Geographical Information Science}, year={2009}, volume={23}, pages={923-960} }