Issues of Uncertainty and Scale in Derived Products

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When deciding how land should be utilised, planners require information about the status of that land and its relative suitability for different uses. For instance, planners might wish to compare the value of land for agricultural production (which might be determined from soil properties), for resource extraction (which might be determined from mineral resource information) and for housing (which will require information about flood and subsistence risk and the suitability of the land for building). The information required to make these assessments is often provided as spatial data products in which the relevant environmental property (e.g. soil pH or flood risk rating) is estimated on the nodes of a spatial-grid which covers the relevant landscape. These spatial data products are derived from the data and models which are available for the landscape under consideration. A soil pH spatial product might be produced by interpolation of the pH values that were measured in a soil survey of the region. Alternatively, a mathematical model that integrates information about climate, topography and drainage might be used to determine the flood risk.This project addresses two problems in such use of spatial data products. First, the planners are unlikely to be aware of or account for the uncertainty that is associated with the product. Some uncertainty will almost inevitably arise because it is rarely possible to measure at every relevant location. Second, the spatial product describes the expected value of the environmental property for a specific spatial scale and this might not be the scale at which information is required. For instance, in a mineral resource product, the horizontal scale of the estimates might correspond to the diameter of the boreholes in which the mineral concentrations were measured. However, planners might be interested in the mineral concentrations at a spatial scale equal to the size of a quarry. These issues propagate further if a spatial data product is used as an input to a mathematical or empirical model that leads to a further data product especially if the model has been designed to relate environmental properties at a scale which is inconsistent with the data products.In this project we will consider strategies for minimising, quantifying and communicating the uncertainty and scale related issues of spatial data products. We will relate these issues to two pertinent data sets regarding the carbon content of UK soils. We will determine how a spatial survey of such data might be cost-effectively designed to yield accurate estimates of the property of interest at different spatial scales. We will develop a statistical algorithm that can use the data that result from such a survey to produce data products at different spatial scales and explore the feasibility of making this algorithm available to end users of the data. Finally, we will consider the propagation of uncertainty when spatial data products are used to derive further products. In particular, we will quantify the uncertainty that results from using a spatial product of the radiometric properties of the soil as an input to a model of soil carbon concentrations. We will quantify this propagated uncertainty and explore the information that must be provided to users of the radiometric product if they are too are to be able to determine the degree of uncertainty that will appear in a resultant product.This project is primarily a statistical study of the issues of scale and uncertainty in spatial data products. However, in addition to statisticians the project team includes experts in product development, data science and earth science who will provide valuable information and advice regarding about the project findings can be communicated to users of spatial data products.