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Spatial Statistical Analysis (SSA) and Geographical Information Systems (GIS) are instrumental in many areas of geographical study. However, their use tends to be separate one from another. This has prevented their potential in many application areas from being realised. This research is an attempt to bring the two technologies together for a specific application area - health research. There are two research objectives. The first and main objective is to construct a software package - SAGE - by integrating necessary SSA techniques with ARC/INFO, a GIS, to enable the user to undertake a coherent study of area-based health-related data. The second objective is to evaluate and demonstrate SAGE through a case study. A range of SSA techniques was identified to be useful for addressing typical health questions. A three-tier client-server model was suggested and argued to be the most appropriate for integration as it takes advantages of both the loose-coupling and close-coupling approaches. Under this model, a SSA component forms the client, while ARCH/INFO functions as the server. They are linked through the middle tier - the linking agent. The development of SAGE provided experiences useful for developing a generic SSA module in the future for any GIS that confonns to a set of well-defined standard application interfaces. An empirical study of colorectal cancer (CRC) incidence for the city of Sheffield using SAGE is presented. It shows the usefulness of the SAGE regionalisation tool in constructing an appropriate regional framework for subsequent data analyses and of both exploratory and confirmatory spatial data analysis in exploring the characteristics of CRC incidence. Some weaknesses of SAGE are identified, while remedies for them are suggested. Future work is recommended. The SAGE User Guide, related publications and the SAGE source and executable code as well as the data used in the case study are enclosed for reference.

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Geographical Information Systems (GIS) are used for several types of spatial planning but so far they have not been used for optimal patch design. Optimal patch design is a generic spatial problem in which the objective is to design spatially explicit landuse maps when both the composition and configuration of patches are important criteria. There are many applications in conservation, forestry management, watershed management and the management of large military estates. This thesis describes a new autonomous computer program, the genetic algorithm for optimal patch design (GAPD). GAPD combines four components: a genetic search algorithm, a parameterised region growing (PRG) program, raster GIS measurement functions and multi-criteria decision-making methods. The key component is the PRG which translates between the aspatial domain of the search algorithm and the spatial domain of the GIS. GAPD generates landuse maps that optimise the configuration and composition of patches to meet multiple objectives for a given set of input maps and criteria. The theories of landscape ecology are used to establish a framework for formulating optimal patch design problems. The thesis describes the conceptual design of GAPD and its implementation and test, first as a prototype for solving single patch problems and then as a fully functional system for solving multi-objective multi-patch problems. The feasibility of GAPD was established by investigations of issues concerning the representation and measurement of configuration in raster data structures and by testing the efficiency and effectiveness of GAPD with simple problems. GAPD was further evaluated in five hypothetical problems designed to cover a range of different scenarios. The results are promising and show that GAPD has potential as a decision support tool. The final section recommends a number of topics for further research covering technical developments of GAPD, the application of GAPD to real problems and investigations of general issues of optimal patch design.

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Considerable interest remains in capturing once geographical information at the fine scale, and from this, automatically deriving information at various levels of detail and scale via the process of map generalisation. This research aims to develop a methodology for transformation of geographic phenomena at a high level of detail directly into geographic phenomena at higher levels of abstraction. Intuitive and meaningful interpretation of geographical phenomena requires their representation at multiple levels of detail. This is due to the scale dependent nature of their properties. Prior to the cartographic portrayal of that information, model generalisation is required in order to derive higher order phenomena typically associated with the smaller scales. This research presents a model generalisation approach able to support the derivation of phenomena typically present at 1:250,000 scale mapping, directly from a large scale topographic database (1:1250/1:2500/1:10,000). Such a transformation involves creation of higher order or composite objects, such as settlement, forest, hills and ranges, from lower order or component objects, such as buildings, trees, streets, and vegetation, in the source database. In order to perform this transformation it is important to model the meaning and relationships among source database objects rather than to consider the object in terms of their geometric primitives (points, lines and polygons). This research focuses on two types of relationships: taxonomic and partonomic. These relationships provide different but complimentary strategies for transformation of source database objects into required target database objects. The proposed methodology highlights the importance of partonomic relations for transformation of spatial databases over large changes in levels of detail. The proposed approach involves identification of these relationships and then utilising these relationships to create higher order objects. The utility of the results obtained, via the implementation of the proposed methodology, is demonstrated using spatial analysis techniques and the creation of ‘links’ between objects at different representations needed for multiple representation databases. The output database can then act as input to cartographic generalisation in order to create maps (digital or paper). The results are evaluated using manually generalised datasets.