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From Introduction: In geography during the 1950's there was a definite move away from the study of unique phenomena to the study of generalized phenomena or pattern (Mather and Openshaw, 1974). At the same time interrelationships between phenomena distributed in space and time became the topic of much interest among geographers, as well as members of other disciplines. The changing emphasis initiated acceptance of certain scientific principles (Cole, 1973), and mathematical techniques became the recognized and respected means through which objective analysis of pattern, structure, and interrelationships between a really distributed phenomena could be achieved (Ackerman, 1972; Burton, 1972; Gould, 1973). Geographers, as do members of other disciplines, frequently borrow mathematical techniques developed for problems encountered in the pure sciences and apply these techniques to what are felt to be analogous situations in geography.

The Modifiable Areal Unit Phenomenon (MAUP) has traditionally been regarded as a problem in the analysis of spatial data organised in areal units. However, the approach adopted here is that the MAUP provides an opportunity to gain information about the data under investigation. Crucially, attempts to remove the MAUP from spatial data are regarded as an attempt to remove the geography. Therefore, the work seeks to provide an insight to the causes of, and information behind, the MAUP. The data used is from the 1991 Census of Great Britain. This was chosen over 2001 data due to the availability of individual level data. These data are of key importance to the methods employed. The methods seek to provide evidence of the magnitude of the MAUP, and more specifically the scale effect in the GB Census. This evidence is built on using correlation analysis to demonstrate the statistical significance of the MAUP. Having established the relevance of the MAUP in the context of current geographical research, the factors that contribute to the incidence of the MAUP are considered, and it is noted that a wide range of influences are important. These include the population size and density of an area, along with proportion of a variable. This discussion also recognises the importance of homogeneity as an influential factor, something that is referenced throughout the work. Finally, a search is made for spatial processes. This uses spatial autocorrelation and multilevel modelling to investigate the impact spatial processes have in a range of SAR Districts, like Glasgow, Reigate and Huntingdonshire, on the scale effect. The research is brought together, not to solve the MAUP but to provide an insight into the factors that cause the MAUP, and demonstrate the usefulness of the MAUP as a concept rather than a problem.

One of the key concerns in spatial analysis and modelling is to study and analyse similarities or dissimilarities between places over geographical space. However, ”global“ spatial models may fail to identify spatial variations of relationships (spatial heterogeneity) by assuming spatial stationarity of relationships. In many real-life situations spatial variation in relationships possibly exists and the assumption of global stationarity might be highly unrealistic leading to ignorance of a large amount of spatial information. In contrast, local spatial models emphasise differences or dissimilarity over space and focus on identifying spatial variations in relationships. These models allow the parameters of models to vary locally and can provide more useful information on the processes generating the data in different parts of the study area. In this study, a framework for localising spatial interaction models, based on geographically weighted (GW) techniques, has been developed. This framework can help in detecting, visualising and analysing spatial heterogeneity in spatial interaction systems. In order to apply the GW concept to spatial interaction models, we investigate several approaches differing mainly in the way calibration points (flows) are defined and spatial separation (distance) between flows is calculated. As a result, a series of localised geographically weighted spatial interaction (GWSI) models are developed. Using custom-built algorithms and computer code, we apply the GWSI models to a journey-to-work dataset in Switzerland for validation and comparison with the related global models. The results of the model calibrations are visualised using a series of conventional and flow maps along with some matrix visualisations. The comparison of the results indicates that in most cases local GWSI models exhibit an improvement over the global models both in providing more useful local information and also in model performance and goodness-of-fit.

Management and conservation of populations of animals requires information on where they are, why they are there, and where else they could be. These objectives are typically approached by collecting data on the animals’ use of space, relating these to prevailing environmental conditions and employing these relations to predict usage at other geographical regions. Technical advances in wildlife telemetry have accomplished manifold increases in the amount and quality of available data, creating the need for a statistical framework that can use them to make population-level inferences for habitat preference and space-use. This has been slow-in-coming because wildlife telemetry data are, by definition, spatio-temporally autocorrelated, unbalanced, presence-only observations of behaviorally complex animals, responding to a multitude of cross-correlated environmental variables. I review the evolution of techniques for the analysis of space-use and habitat preference, from simple hypothesis tests to modern modeling techniques and outline the essential features of a framework that emerges naturally from these foundations. Within this framework, I discuss eight challenges, inherent in the spatial analysis of telemetry data and, for each, I propose solutions that can work in tandem. Specifically, I propose a logistic, mixed-effects approach that uses generalized additive transformations of the environmental covariates and is fitted to a response data-set comprising the telemetry and simulated observations, under a case-control design. I apply this framework to non-trivial case-studies using data from satellite-tagged grey seals (Halichoerus grypus) foraging off the east and west coast of Scotland, and northern gannets (Morus Bassanus) from Bass Rock. I find that sea bottom depth and sediment type explain little of the variation in gannet usage, but grey seals from different regions strongly prefer coarse sediment types, the ideal burrowing habitat of sandeels, their preferred prey. The results also suggest that prey aggregation within the water column might be as important as horizontal heterogeneity. More importantly, I conclude that, despite the complex behavior of the study species, flexible empirical models can capture the environmental relationships that shape population distributions.