Geospatial Modelling of Indeterminate Phenomena: The Object-Field Model with Uncertainty and Semantics

Abstract

The need for a conceptually unifying data model for the representation of geospatial phenomena has already been acknowledged. Recognising that the importance of the data model employed by and large determines what can be done by way of analysis and the methods by which the analysis can be undertaken, there has been some activity in developing unifying data models for geospatial representation in digital form. Some successes have been reported. Nevertheless, progress has been slow, especially at the conceptual and logical levels of abstraction of geospatial data models.Concepts and ideas from cognitive and perceptual psychology as well as GIScience and GISystems literature are examined within the context of geospatial data modelling and reasoning. Drawing on and combining these concepts, ideas and successes with an empirical approach which proposes generalities by induction, this thesis suggests the fused Object-Field model with uncertainty and semantics at the conceptual, logical and physical levels of abstraction. The logical level has been formalised in the Unified Modelling Language (UML) class diagram and the physical level has been implemented in Java programming language. The purpose of the Object-Field model is to better support the representation and reasoning of geospatial phenomena, particularly indeterminate phenomena such as town centres and land cover changes. It is shown that many of the concepts required to better represent geospatial phenomena can be derived from a single foundation that is termed the elementary-geoParticle which is regarded as indivisible, has no parts and serves as the standard for integrating the dual continuous-field and discrete-object conceptualisations by means of aggregation. A second concept is introduced, termed Traditional Scientific and Concept spaces of the Object-Field model and shown to provide a useful foundation for collaborative reasoning. The traditional scientific space is a mathematical representation of observational data and the concept space is a representation of conceptualisations, meanings and interpretations of the traditional scientific space. A third concept is also introduced, termed the Hierarchical Uncertainty and Semantic components of the Object-Field model that 'populates' the concept space with variable levels of uncertainty and semantics. Sketching is also suggested as a way to represent, record and manage conceptualisation uncertainty as it is an element of uncertainty that is frequently overlooked, yet has a significant impact in the way in which subjects understand and use geospatial data. Given that conceptualisation uncertainty is a subjective process that varies between individuals, this form of uncertainty has particular importance in the collaborative decision-making of indeterminate phenomena.This thesis constructs technical and theoretical scientific knowledge for the design and development of geospatial models that aim to support the human decision-making process of indeterminate phenomena by means of multiple conceptualisations and interpretations. The theoretical knowledge is embodied in the UML formalization of the Object-Field model and the technical knowledge is embodied in the Object-Field GISystems Prototype.