Derivation and implementation of a semantic GIS data model informed by principles of cognition

Abstract The purpose of this research is to develop a new kind of semantic GIS data model that is better able to represent users’ conceptual models of geographic domains than the conventional vector and raster data models. To this end, I look to the principles of cognition, how humans represent geographic information in their minds, to inform the development of this semantic data model. A three-stage methodology for the derivation and implementation of the semantic data model is presented. First, a conceptual framework of geographic cognition is developed. This framework incorporates principles of cognitive categorization and ‘top-down’ and ‘bottom-up’ information processing. Second, by replacing those elements of cognition with their database representation counterparts, often drawing from the object-oriented modeling paradigm, a semantic data model is derived. Finally, a prototype implementation of the semantic data model is presented using the Java programming language and the object-oriented database Poet as a development platform. This implementation utilizes a rule-based approach for representing categorical information and for the extraction of semantic entities from observational data.

[1]  Peter Milne,et al.  Geographical Object-Oriented Databases - A Case Study , 1993, Int. J. Geogr. Inf. Sci..

[2]  Barry Smith,et al.  Ontology and Geographic Kinds , 1998 .

[3]  Mark Gahegan,et al.  The Integration of Scene Understanding within a Geographic Information System: A Prototype Approach for Agricultural Applications , 1999, Trans. GIS.

[4]  Fangju Wang,et al.  Fuzzy Representation of Geographical Boundaries in GIS , 1996, Int. J. Geogr. Inf. Sci..

[5]  Ivar Jacobson,et al.  The Unified Modeling Language User Guide , 1998, J. Database Manag..

[6]  Christian Freksa,et al.  On the Relations between Spatial Concepts and Geographic Objects , 2020 .

[7]  E. Rosch,et al.  Cognition and Categorization , 1980 .

[8]  M. S. Mayzner,et al.  Cognition And Reality , 1976 .

[9]  Ulric Neisser,et al.  From direct perception to conceptual structure. , 1987 .

[10]  Oliver Günther,et al.  Progress in computational methods for representing geographical concepts , 1999, Int. J. Geogr. Inf. Sci..

[11]  Giorgio Gambosi,et al.  Geographic Database Management Systems , 1992 .

[12]  Stephen M. Kosslyn,et al.  Tutorials in learning and memory : essays in honor of Gordon Bower , 1984 .

[13]  Daniel R. Montello,et al.  Spatial Information Theory A Theoretical Basis for GIS , 1995, Lecture Notes in Computer Science.

[14]  Eleanor Rosch,et al.  Principles of Categorization , 1978 .

[15]  S. Kosslyn.  Image and Brain: The Resolution of the Imagery Debate , 1994, Journal of Cognitive Neuroscience.

[16]  Donna Peuquet,et al.  A conceptual framework for incorporating cognitive principles into geographical database representation , 2000, Int. J. Geogr. Inf. Sci..

[17]  U. Neisser.  Concepts and Conceptual Development: Ecological and Intellectual Factors in Categorization , 1989 .

[18]  Karla A. V. Borges,et al.  Ontologies and Knowledge Sharing in Urban GIS , 2000 .

[19]  Marvin Minsky,et al.  A framework for representing knowledge , 1974 .

[20]  P. Burrough,et al.  Geographic Objects with Indeterminate Boundaries , 1996 .

[21]  Marvin Minsky,et al.  A framework for representing knowledge" in the psychology of computer vision , 1975 .

[22]  E. Lynn Usery,et al.  A Spatial Data Model Design for Feature-Based Geographical Information Systems , 1996, Int. J. Geogr. Inf. Sci..

[23]  Andrew U. Frank,et al.  Concepts and Paradigms in Spatial Information: Are Current Geographical Information Systems Truly Generic? , 1995, Int. J. Geogr. Inf. Sci..

[24]  D. Peuquet.  It's About Time: A Conceptual Framework for the Representation of Temporal Dynamics in Geographic Information Systems , 1994 .

[25]  David M. Mark,et al.  Experiential and Formal Models of Geographic Space , 1996 .

[26]  David Howard,et al.  Interface Design for Geographic Visualization: Tools for Representing Reliability , 1996 .

[27]  Agnès Voisard,et al.  Object-Oriented Database Systems for Geographic Applications: an Experiment with O2 , 1992 .

[28]  Refractor.  Vision , 2000, The Lancet.

[29]  Patrick Henry Winston,et al.  The psychology of computer vision , 1976, Pattern Recognit..

[30]  Timothy L. Nyerges,et al.  Geographic Information Abstractions: Conceptual Clarity for Geographic Modeling , 1991 .

[31]  B. Tversky,et al.  Categories of environmental scenes , 1983, Cognitive Psychology.

[32]  Peter A. Burrough,et al.  Natural Objects with Indeterminate Boundaries , 2020 .

[33]  Dimitris Papadias,et al.  Processing fuzzy spatial queries: a configuration similarity approach , 1999, Int. J. Geogr. Inf. Sci..

[34]  M. Egenhofer,et al.  Cognitive Aspects of Human-Computer Interaction for Geographic Information Systems , 1995 .

[35]  Barbara Tversky,et al.  Cognitive Maps, Cognitive Collages, and Spatial Mental Models , 1993, COSIT.

[36]  S. Pinker,et al.  Visual cognition : An introduction * , 1989 .

[37]  Nicola Guarino,et al.  Formal ontology, conceptual analysis and knowledge representation , 1995, Int. J. Hum. Comput. Stud..

[38]  Andrew U. Frank,et al.  Spatial Information Theory A Theoretical Basis for GIS , 1993, Lecture Notes in Computer Science.

[39]  David Mckeown,et al.  The Role of Artificial Intelligence in the Integration of Remotely Sensed Data with Geographic Information Systems , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[40]  Ray Jackendoff,et al.  Languages of the Mind: Essays on Mental Representation , 1992 .

[41]  E. L. Usery.  Category Theory and the Structure of Features in Geographic Information Systems , 1993 .