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Soil is one of the most precious resources on Earth because of its role in storing and recycling water and nutrients essential for life, providing a variety of ecosystem services. This vulnerable resource is at risk from degradation by erosion, salinity, contamination and other effects of mismanagement. Information from soil is therefore crucial for its sustainable management. While the demand for soil information is growing, the quantity of data collected in the field is reducing due to financial constraints. Digital Soil Mapping (DSM) supports the creation of geographically referenced soil databases generated by using field observations or legacy data coupled, through quantitative relationships, with environmental covariates. This enables the creation of soil maps at unexplored locations at reduced costs. The selection of an optimal scale for environmental covariates is still an unsolved issue affecting the accuracy of DSM. The overall aim of this research was to explore the effect of spatial scale alterations of environmental covariates in DSM. Three main targets were identified: assessing the impact of spatial scale alterations on classifying soil taxonomic units; investigating existing approaches from related scientific fields for the detection of scale patterns and finally enabling practitioners to find a suitable scale for environmental covariates by developing a new methodology for spatial scale analysis in DSM. Three study areas, covered by detailed reconnaissance soil survey, were identified in the Republic of Ireland. Their different pedological and geomorphological characteristics allowed to test scale behaviours across the spectrum of conditions present in the Irish landscape. The investigation started by examining the effects of scale alteration of the finest resolution environmental covariate, the Digital Elevation Model (DEM), on the classification of soil taxonomic units. Empirical approaches from related scientific fields were subsequently selected from the literature, applied to the study areas and compared with the experimental methodology. Wavelet analysis was also employed to decompose the DEMs into a series of independent components at varying scales and then used in DSM analysis of soil taxonomic units. Finally, a new multiscale methodology was developed and evaluated against the previously presented experimental results. The results obtained by the experimental methodology have proved the significant role of scale alterations in the classification accuracy of soil taxonomic units, challenging the common practice of using the finest available resolution of DEM in DSM analysis. The set of eight empirical approaches selected in the literature have been proved to have a detrimental effect on the selection of an optimal DEM scale for DSM applications. Wavelet analysis was shown effective in removing DEM sources of variation, increasing DSM model performance by spatially decomposing the DEM. Finally, my main contribution to knowledge has been developing a new multiscale methodology for DSM applications by combining a DEM segmentation technique performed by k-means clustering of local variograms parameters calculated in a moving window with an experimental methodology altering DEM scales. The newly developed multiscale methodology offers a way to significantly improve classification accuracy of soil taxonomic units in DSM. In conclusion, this research has shown that spatial scale analysis of environmental covariates significantly enhances the practice of DSM, improving overall classification accuracy of soil taxonomic units. The newly developed multiscale methodology can be successfully integrated in current DSM analysis of soil taxonomic units performed with data mining techniques, so advancing the practice of soil mapping. The future of DSM, as it successfully progresses from the early pioneering years into an established discipline, will have to include scale and in particular multiscale investigations in its methodology. DSM will have to move from a methodology of spatial data with scale to a spatial scale methodology. It is now time to consider scale as a key soil and modelling attribute in DSM.

The production of geographic data has traditionally been the purview of institutions such as the Ordnance Survey and US Geological Survey. The past three decades have seen a technological revolution brought about by mobile computing resources and the World Wide Web. Ordinary citizens now have the tools to produce geographic information en-masse. OpenStreetMap, one of the world's most important and extensive geographic datasets has arisen out of this Volunteered Geographic Information phenomenon. Freely available to be both used and produced by ordinary people, it represents a paradigm shift which has changed our relationship with geographic information. The free and open nature of OpenStreetMap has given rise to novel and often mission-critical uses, often among people with little or no interest in traditional quality assurance frameworks. The shift away from authoritative data sources and traditional quality assurance paradigms raises problems for geospatial data consumers who still need to make informed trust judgements. In a milieu characterised by a diverse and dynamic range of use cases, large volumes of data and no established quality assurance paradigms, we need new ways of understanding Volunteered Geographic Information. One of the most difficult components to document all the contributors and their contribution practices, which operate at a scale and diversity not found in traditional science. Provenance data encodes much of this information and is useful for providing localised data documentation. Provenance Network Analytics is a methodology which has the potential to provide a principled automated means of analysing provenance data at scale. However, it has only been implemented in relatively simple, smaller scale use cases. OpenStreetMap does not explicitly record provenance data. There is also no framework for understanding of the necessary measurement and interpretation strategies. In this thesis we address these issues by providing a novel method of provenance reconstruction which produces a provenance dataset in an interoperable standard. We provide a framework for provenance measurement using metrics which allow the analysis of large volumes of data. Using OpenStreetMap provenance extracted from the Southampton area in the UK, we conduct a descriptive analysis of OpenStreetMap provenance data. The results provide an understanding of the drivers of variation in OpenStreetMap contribution practices. This work repositions VGI provenance as a new and novel form of geographic data which can provide insights into the nature of Volunteered Geographic Information and the human and physical environment it describes.