Imperfect data: accuracy, impacts and extraction of meaningful information

有意义的信息是知情、合乎逻辑和理性的活动的基本要求。然而，从数据中提取有意义的信息可能是一项挑战，尤其是考虑到数据可能是不准确、不完整的，并且可能是矛盾的，这是由于各种来源的可变质量和信任级别引起的。数据缺陷是信息提取和决策中的普遍问题，因此这项工作在许多学科中都是相关的。例如，不完美的数据在医学诊断中很明显(例如，患者的测试结果通常只是病情的不完美指示器)，在确定物种保护的自然保护区时(例如，物种分布图和模型通常对‘缺失’数据高度敏感--物种是否实际存在，但没有被观察到？)以及在安全和国防应用中(例如，应用于监视图像的亚像素目标检测算法在性能和实用性方面因环境而异)。最近，在对2010年海地地震的反应方面，数据不完善的一些问题非常明显，特别是在为救灾活动提供信息的损害测绘方面。许多专业和业余机构以前所未有的数据率提供了大量善意的援助，但数据量及其问题令人担忧。关键问题是地图不准确、不一致，有时甚至相互矛盾。因此，在如何使用这些数据方面出现了重大的绘图挑战。一个关键问题是需要关于数据源和方法的准确性的信息，以帮助使用不完美的数据。该项目旨在为这项任务做出贡献。它的目的是说明使用不完美数据的影响，探索表征数据质量的方法，以及组合数据源以产生已知精度的增强产品的方法。将使用一系列方法，但核心重点是使用潜在类别建模。这种类型的分析基于多个观察结果或来自各种来源的数据。观察员/数据来源之间的关系被用来试图解释它们的质量，并建议如何解释数据以产生信息。该方法是统计建模的一种形式，对于具体的研究方案非常有吸引力，因为如果可以形成一个符合观测数据的模型，则模型的参数定义了数据源的精度，其输出可以用于形成已知精度的新产品。因此，通过指出数据的质量并将其有效地组合以提取信息，模型分析可以增加数据的价值。由于数据不完善的问题是普遍存在的，这项提议具有广泛的潜在影响。对于具体的雏菊呼叫，在安全和防御方面有明显的影响。例如，能够从准确性、完整性和信任度不同的来源获得快速和合格的信息的方法将提高决策的效率和质量。此外，作为一种基于模型的方法，它消除/减少了获取参考数据以进行验证的需要，否则可能需要将人员部署到危险地点，因此对健康和福祉有相当大的好处。

Meaningful information is a fundamental requirement for informed, logical and reasoned activity. Extracting meaningful information from data can, however, be a challenge, especially given problems that data may, amongst other things, be inaccurate, incomplete, and possibly contradictory as arise from a variety of sources of variable quality and trust level. Data imperfections are a generic problem in information extraction and decision making and so the work is relevant in many disciplines. Imperfect data are, for example, evident in medical diagnosis (e.g. a patient's test results are typically only an imperfect indicator of a condition), in defining nature reserves for species conservation (e.g. the species distribution maps and models are often highly sensitive to 'absence' data - was the species actually present but not observed?) and in security and defence applications (e.g. sub-pixel target detection algorithms applied to surveillance imagery vary in performance and utility between environments). Some problems with imperfect data were recently highly apparent in relation to the response to the Haiti earthquake of 2010, especially in relation to damage mapping to inform relief activities. Vast amounts of well-intentioned assistance was provided by numerous professional and amateur bodies with unprecedented data rates but the volumes of data and the problems with them were a concerns. Key problems were that maps were inaccurate, inconsistent and sometimes contradictory. As such a major mapping challenges arises in how to work with such data. One key issue is the need for information on the accuracy of data sources and methods to help use imperfect data. This project seeks to contribute to this task. It aims to illustrate the impacts of using imperfect data, explore methods to characterise the quality of the data and methods to combine data sources to yield an enhanced product of known accuracy.A range of methods will be used but the core focus is on the use of latent class modelling. This type of analysis is based on multiple observations or data from a variety of sources. The relationships between the observers/data sources are used to attempt to explain their quality and suggest how the data could be interpreted to yield information. The approach is a form of statistical modelling and is highly attractive for the specific research proposal because if a model can be formed that fits the observed data, then model's parameters define the accuracy of the data sources and its outputs can be used to form new products of known accuracy. As such the modelling analysis may add value to data by indicating its quality and combining it usefully for extraction of information.As the problems of imperfect data are generic the proposal has broad potential impacts. For the specific DaISy call there are clear impacts in relation to security and defence. For example methods that enable rapid and qualified information to be derived from sources of variable accuracy, completeness and trust level will increase effectiveness and the quality of decision making. Additionally as a model based approach it removes/reduces the need for reference data to be acquired for validation which could otherwise require deployment of personnel to dangerous locations and so of considerable benefit to health and well-being.