While the infinite complexity and dynamics of geographic worlds have long been recognized in the geographic information science (GIScience) literature, current geographic information systems (GIS) technology has not yet incorporated data models, query functions, or analytic tools that can adequately handle geographic complexity and dynamics. This research project aims to integrate these features into GIS data models, query, and analysis. Such integration will lay a foundation for the next generations of GIS technology to further empower GIS support for scientific understanding and discovery of geographic worlds. To achieve this integration, the investigators will examine geographic complexity and dynamics. The basic premise is that geographic conceptualizations need to go beyond separate field- and object-based views of geographic worlds. The investigators will focus on geographic complexity that arises from the interwoven properties of fields and objects embedded in phenomena and relationships at different spatial and temporal scales. They will consider geographic dynamics that reflect on propagation and evolution in space and time as analyzed by Lagrangian (focusing on the stationary action of flows) or Hamiltonian (focusing on the motion of a particle of mass) dynamics. Field- or object-based conceptualization alone cannot capture complexity and dynamics critical to an accurate representation of geography. The investigators will incorporate two additional views of geographic worlds: fields of objects (o-fields) and objects of fields (f-objects) to incorporate geographic complexity and dynamics. They therefore expect to extend the dual geographic conceptualization to a spectrum of objects, f-objects, o-fields, and fields, with scale and resolution are as functions that allow a shift in perspective along the spectrum. With the spectrum of geographic conceptualizations, they will develop a data model that incorporates geographic complexity and dynamics with uncertainty, formulates queries and analytical functions, and builds a prototype system for proof of concepts.
The collaborative project brings together researchers from the University of Oklahoma, University of California-Santa Barbara, and University of Utah to expand on their work on geospatial data modeling. In separate ventures, they have examined the use of combined fields and objects on geographic representation and demonstrated that such combination effectively extends geographic representation to incorporate much richer, more complex geographic semantics. Central to the research project is the idea of modeling GIS data based on geographic complexity and dynamics, as an alternative to the conventional data models that are built upon how data are captured. The research project promises a broader, more comprehensive inspection of issues related to the integration of fields and objects and the development of a holistic theory of the representation of geographic complexity and dynamics. This new approach to GIS data modeling extends static representation to a complex and dynamic view of the world and thus enhances GIS technology to be better suited for scientific research.
