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IDEAS Factory - Global View

IDEAS 工厂 - 全球视野

基本信息

批准号：
EP/I005986/1
负责人：
Nick Jones
金额：
$ 25.4万
依托单位：
University of Oxford
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2010
资助国家：
英国
起止时间：
2010 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FI005986%2F1
关键词：
IDEAS Factory Global View

项目摘要

The aim of this project is to scope a form of dash-board that gives policy makers an integrated view of the state of the UK, both at the current time, and into the past. If we are equipped with a better view of the UK, we can ensure that it is more resilient to shocks.We might like to have policy makers who are aware of the implications of their actions on others and who understand how independent parties relate to each other. Since relationships change we might also hope our decision makers are up-to-date. Ideally they would be sensitive to subtle changes in the UK today and understand how changes could affect predictions of the future and relate to experiences in the past. But to really have a good perspective on the whole of the UK is next to impossible for any one individual. Our proposal investigates how novel tools from network science, statistical inference, text mining and signal processing can help those making policy decisions.Our scoping project aims to use three types of raw materials: 1) a set of key, live, social, environmental and economic signals, 2) live streams of phrases and keywords extracted from the internet, 3) a collection of interested parties. A signal might be any time-varying quantity and relevant examples are oil prices, internet traffic or rainfall. The keyword information might inform us about what people are searching for on the internet and which terms are suddenly popular.We can ask how the signals relate to each other at the moment by using tools from statistical inference. We can, after a fashion, draw a network map where any pair of signal labels are linked by a line if the signals are related. Since we have records into the past, we can also find similar maps, or networks, for earlier periods. These networks of how relevant signals connect could be useful for policy makers who are trying to understand how different features of the UK relate to each other (like how oil prices depend on how rainy it is). However, having a sequence of networks, or a set of unfolding signals, can be made more useful if they can be associated with known events. Alongside the quantitative signals that we record, we will also record our keyword data which allows us to give a qualitative signature for each period of time. We can then use the keywords, coupled to the quantitative data to attempt to make statistical predictions about keywords, networks or signals in the future. As well as making predictions, one can also attempt to search the past for similar events or carefully scan the present to see if there is anything unusual about current signals, network or keywords.This is a one year scoping project with the aim of establishing a firm base for future development and further grants. Our plan is to assemble the basic algorithmic, human and data components over the year and scope out the possible ways in which these components can be harnessed. We will first establish what specifics policy makers might want from this tool and extend our existing network of interested parties. We will then collect and label relevant signals and write computer programs to use the internet to return to us both timely keywords and meaningful phrases. Given the signals, we will advance methods to infer sequences of networks. We will then scope out how we can use our tool to: detect subtle changes in the UK; make predictions about future networks, signals or keywords; and associate current information with past periods in the UK for which we have data. A major aim is to consider ways in which data of this kind can be visualized and made useful for policy.

该项目的目的是确定一种仪表板的范围，使政策制定者对英国当前和过去的状况有一个综合的看法。如果我们对英国有更好的认识，我们就能确保它更能抵御冲击。我们可能希望政策制定者能够意识到自己的行为对他人的影响，并理解独立各方之间的关系。由于关系的变化，我们也可能希望我们的决策者是最新的。理想情况下，他们应该对英国今天的微妙变化敏感，了解变化如何影响对未来的预测，并与过去的经验联系起来。但要真正对整个英国有一个好的视角，对任何一个人来说都是不可能的。我们的提案探讨了网络科学、统计推断、文本挖掘和信号处理的新工具如何帮助决策者做出决策。我们的范围界定项目旨在使用三种类型的原材料：1）一组关键的、实时的、社会的、环境的和经济的信号; 2）从互联网上提取的短语和关键字的实时流; 3）感兴趣的各方的集合。信号可以是任何随时间变化的量，相关的例子是油价，互联网流量或降雨量。关键词信息可能会告诉我们人们在互联网上搜索什么，以及哪些术语突然流行起来。我们可以通过使用统计推断工具来询问这些信号此时是如何相互关联的。我们可以画一个网络图，如果信号是相关的，任何一对信号标签都由一条线连接起来。既然我们有过去的记录，我们也可以找到更早时期的类似地图或网络。这些相关信号如何连接的网络可能对政策制定者有用，他们试图了解英国的不同特征如何相互关联（例如油价如何取决于降雨量）。然而，如果一系列网络或一组展开信号可以与已知事件相关联，则可以使它们更有用。除了我们记录的定量信号外，我们还将记录我们的关键字数据，这使我们能够为每个时间段提供定性签名。然后，我们可以使用这些关键词，再加上定量数据，试图对未来的关键词、网络或信号进行统计预测。除了预测之外，还可以尝试搜索过去的类似事件，或者仔细扫描现在，看看当前的信号，网络或关键字是否有任何异常。这是一个为期一年的范围项目，旨在为未来的发展和进一步的赠款建立坚实的基础。我们的计划是在一年内组装基本的算法、人力和数据组件，并研究利用这些组件的可能方法。我们将首先确定政策制定者可能希望从这一工具中获得哪些具体信息，并扩大我们现有的利益相关方网络。然后，我们将收集和标记相关信号，并编写计算机程序，使用互联网向我们返回及时的关键字和有意义的短语。给定信号，我们将提出推断网络序列的方法。然后，我们将研究如何使用我们的工具：检测英国的细微变化;预测未来的网络、信号或关键词;将当前信息与我们拥有数据的英国过去时期相关联。一个主要目标是考虑如何使这类数据可视化并对政策有用。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

How modular structure can simplify tasks on networks: parameterizing graph optimization by fast local community detection.

DOI：
10.1098/rspa.2014.0224
发表时间：
2014-10-08
期刊：
Proceedings. Mathematical, physical, and engineering sciences
影响因子：
0
作者：
Bui-Xuan BM;Jones NS
通讯作者：
Jones NS

Highly Comparative Feature-Based Time-Series Classification

DOI：
10.1109/tkde.2014.2316504
发表时间：
2014-12-01
期刊：
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
影响因子：
8.9
作者：
Fulcher, Ben D.;Jones, Nick S.
通讯作者：
Jones, Nick S.

Advection, diffusion, and delivery over a network.

DOI：
10.1103/physreve.86.021905
发表时间：
2012-08
期刊：
Physical review. E, Statistical, nonlinear, and soft matter physics
影响因子：
0
作者：
Heaton LL;López E;Maini PK;Fricker MD;Jones NS
通讯作者：
Jones NS

The State of Vaccine Confidence 2016: Global Insights Through a 67-Country Survey.

DOI：
10.1016/j.ebiom.2016.08.042
发表时间：
2016-10
期刊：
EBIOMEDICINE
影响因子：
11.1
作者：
Larson, Heidi J.;de Figueiredo, Alexandre;Zhao Xiahong;Schulz, William S.;Verger, Pierre;Johnston, Iain G.;Cook, Alex R.;Jones, Nick S.
通讯作者：
Jones, Nick S.

Highly comparative time-series analysis: the empirical structure of time series and their methods.

高度比较的时间序列分析：时间序列的经验结构及其方法。