A surface-based approach to the handling of uncertainties in an urban-orientated spatial database

The work presented in this thesis is built on three assertions: (1) uncertainties should be perceived as integral components of GIS spatial databases; (2) as such and given the importance of uncertainties at all stages of processing spatial data by digital methods, an integrating strategy is needed to provide more direct access to the uncertainties of spatial data during data collection, update, spatial analysis and during the creation of output products; (3) surface-based models and methods are capable of such an integral strategy, by which many kinds of uncertainties of spatial data can be well represented and handled. A cumulative description is given of various uncertainties occurring in geographical abstraction and spatial data acquisitions with special reference to one common area of geographical studies, that is, land cover mapping. Two alternative forms of geographical abstraction or spatial data modelling are introduced: discrete objects and continuous fields. The uncertainties are then discussed with respect to their description, estimation and representation under object and field perspectives. For categorical data, in particular, uncertainties are represented as fuzzy surfaces, whose derivation and analysis are described in detail. To provide an evaluation of the integrated approach and to show how such an integrated strategy can be used to advantage, a case study is developed in the context of suburban land cover mapping, based on a local Edinburgh area. The case study begins with the construction of a co-registered hierarchy of test data with a corresponding hierarchy of accuracies, and continues to the generation and analysis of fuzzy surfaces using the suite of methods introduced previously. The various graphical maps and quantitative data produced show that surface based approaches are well suited to the representation and handling of uncertainties of spatial data, because they are effective and flexible.