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Issues of Uncertainty and Scale in Derived Products

衍生产品的不确定性和规模问题

基本信息

批准号：
NE/T004169/1
负责人：
B Marchant
金额：
$ 8.02万
依托单位：
British Geological Survey
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2019
资助国家：
英国
起止时间：
2019 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FT004169%2F1
关键词：
Issues Uncertainty Scale Derived Products

项目摘要

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.

在决定如何利用土地时，规划者需要有关土地状况及其对不同用途的相对适合性的信息。例如，规划者不妨比较农业生产用地的价值（可根据土壤特性确定）、资源开采用地的价值（可根据矿物资源信息确定）和住房用地的价值（需要关于洪水和生存风险以及土地是否适合建房的信息）。进行这些评估所需的信息往往以空间数据产品的形式提供，其中在覆盖相关景观的空间网格节点上估计相关环境特性（例如土壤pH值或洪水风险等级）。这些空间数据产品是从所考虑的景观的现有数据和模型中得出的。土壤pH值的空间产品可能会产生插值的pH值，在该地区的土壤调查测量。或者，也可以使用一个综合了气候、地形和排水信息的数学模型来确定洪水风险。首先，规划者不太可能意识到或考虑到与产品相关的不确定性。几乎不可避免地会出现一些不确定性，因为几乎不可能在每个相关地点进行测量。第二，空间产品描述了特定空间尺度的环境属性的预期值，而这可能不是需要信息的尺度。例如，在矿物资源产品中，估计数的横向比例可能与测量矿物浓度的钻孔直径相对应。然而，规划者可能对与采石场大小相等的空间尺度上的矿物浓度感兴趣。这些问题进一步传播，如果空间数据产品被用作一个数学或经验模型，导致进一步的数据products.In这个项目中，我们将考虑最大限度地减少，量化和沟通的不确定性和空间数据产品的规模相关的问题，特别是如果该模型已被设计为在一个规模，这是不一致的环境属性。我们将这些问题与两个有关的数据集英国土壤的碳含量。我们将确定如何对这些数据进行具有成本效益的空间调查，以在不同的空间尺度上准确估计感兴趣的属性。我们将开发一种统计算法，该算法可以使用从这样的调查中得到的数据来生成不同空间尺度的数据产品，并探索将该算法提供给数据的最终用户的可行性。最后，我们将考虑传播的不确定性时，空间数据产品被用来获得进一步的产品。特别是，我们将量化的不确定性，结果从使用的土壤的辐射特性的空间产品作为输入到一个模型的土壤碳浓度。我们将量化这种传播的不确定性，并探讨必须提供给辐射产品用户的信息，如果他们也能够确定将出现在最终产品中的不确定性程度。然而，除了统计人员外，项目小组还包括产品开发、数据科学和地球科学方面的专家，他们将提供有关项目成果的宝贵信息和建议，以便将这些成果传达给空间数据产品的用户。