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Imperfect data: accuracy, impacts and extraction of meaningful information

不完美数据：准确性、影响和有意义信息的提取

基本信息

批准号：
EP/J020230/1
负责人：
Giles Foody
金额：
$ 8.82万
依托单位：
University of Nottingham
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2012
资助国家：
英国
起止时间：
2012 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FJ020230%2F1
关键词：
Imperfect data accuracy impacts extraction

项目摘要

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.

有意义的信息是知情、逻辑和理性活动的基本要求。然而，从数据中提取有意义的信息可能是一项挑战，特别是考虑到数据可能不准确、不完整，以及可能由于各种质量和信任程度不同的来源而相互矛盾的问题。数据不完整性是信息提取和决策中的一个普遍问题，因此这项工作与许多学科有关。例如，不完美的数据在医学诊断中是显而易见的（例如，患者的测试结果通常只是一个不完美的状况指标），在定义自然保护区进行物种保护时（例如，物种分布图和模型通常对“缺失”数据高度敏感-物种实际存在但未被观察到吗？）以及在安全和防御应用中（例如，应用于监视图像的亚像素目标检测算法在不同环境之间的性能和效用不同）。最近，在2010年海地地震的应对工作中，数据不完善的一些问题非常明显，特别是在为救济活动提供信息的损害绘图方面。许多专业和业余机构以前所未有的数据速率提供了大量善意的援助，但数据量及其问题令人关切。关键问题是地图不准确、不一致，有时甚至相互矛盾。因此，在如何处理这些数据方面出现了重大的制图挑战。一个关键问题是需要关于数据来源和方法准确性的信息，以帮助使用不完善的数据。本项目旨在为这一任务作出贡献。它旨在说明使用不完美数据的影响，探索提高数据质量的方法，以及联合收割机数据源的方法，以产生已知准确度的增强产品。将使用一系列方法，但核心重点是使用潜在类建模。这种类型的分析是基于多个观察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其质量，并建议如何解释数据以产生信息。该方法是一种统计建模的形式，对于具体的研究建议非常有吸引力，因为如果可以形成一个模型，适合观察到的数据，那么模型的参数定义了数据源的准确性，其输出可以用于形成已知准确性的新产品。因此，建模分析可通过表明数据质量并将其有效地结合起来以提取信息，从而增加数据的价值，由于数据不完善的问题是普遍存在的，因此该建议具有广泛的潜在影响。对于具体的DaISy调用，在安全和国防方面有明显的影响。例如，能够从准确性、完整性和可信度各不相同的来源迅速获得合格信息的方法，将提高决策的有效性和质量。此外，作为一种基于模型的方法，它消除/减少了对获取用于验证的参考数据的需要，否则这可能需要将人员部署到危险位置，因此对健康和福祉有相当大的好处。