Cognition and semantic database representation in geographic information systems.

详细信息   

• 作者：Mennis ; Jeremy Laurence.
• 学历：Doctor
• 年：2001
• 导师：Peuquet, Donna J.
• 毕业院校：The Pennsylvania State University
• 专业：Geography.;Computer Science.;Information Science.
• ISBN：0493321861
• CBH：3020506
• Country：USA
• 语种：English
• FileSize：10382477
• Pages：280

文摘

The recent growth of data gathering technologies and organizations has produced an unprecedented amount of digital spatio-temporal data. However, the primary tool for spatio-temporal data handling, GIS (geographic information systems), does not adequately support the semantic representation—and thus the data exploration processes—necessary to extract meaningful information from these data. This inadequacy is due to the limitations of conventional vector and raster GIS database models. A number of authors have suggested that research in cognitive knowledge representation be used to inform the development of semantic representation and data exploration in a GIS environment; however a working implementation of such a GIS that is generic to a variety of geographic domains has yet to be developed. It is the purpose of this research to develop a semantic GIS database model, as well as a prototype implementation of that model, that is informed by principles of geographic cognition, supports data exploration, and is also extendable to a number of different geographic domains.;I identify certain elements of geographic cognition that are particularly relevant to GIS database representation: categorization, percept versus concept, image versus propositional knowledge structures, geographic knowledge acquisition, and cognitive mapping. From these elements I develop a conceptual framework of geographic cognition that describes how conceptual information that is stored in categorical hierarchies is used to interpret sensory information and recognize instances of geographic phenomena in the environment. This conceptual framework of cognition is used to derive a cognition-informed semantic GIS database model by substituting established techniques in computational symbolic knowledge representation, such as object-oriented modeling, for their analogous cognitive counterparts. In the semantic database model, a priori knowledge stored in categories is used to interpret multi-dimensional observational data in order to extract geographic features that may be captured by those observational data.;The semantic database model is implemented using the object-oriented database platform Poet (Poet Inc., San Mateo, California) and the Java programming language. The implementation provides classes to represent spatio-temporal observational data, geographic categories, and geographic entities, as well as their inter-relationships. The implementation also provides methods that use a rulebase approach to extract geographic features from the observational data. As a demonstration, the implementation is extended for the representation of storm phenomena in the Susquehanna River Basin using a spatio-temporal meteorological data set.;This research demonstrates how principles of cognition may be used to inform the development of semantic GIS database representation as well as how such semantic representation supports data exploration of large spatio-temporal data sets. Such database representation techniques may ultimately be applied to the exploration of geophysical remote sensing and social science data sets. Future research should focus on introducing more sophisticated aspects of categorization, as well as inductive learning techniques, into GIS database representation and data exploration environments.