IDEAS Factory - Global View
IDEAS Factory - Global View
批准号:
EP/I005986/1
负责人:
Nick Jones
金额:
$25.4万
依托单位:
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
项目状态:
已结题
起止时间:
2010 至 --
中文摘要
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英文摘要
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.
期刊论文(10)
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科研奖励(0)
会议论文
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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:
--
作者:
[Bui-Xuan BM, Jones NS]
通讯作者:
Jones NS
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.
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:
--
作者:
[Heaton LL, López E, Maini PK, Fricker MD, Jones NS]
通讯作者:
Jones NS
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。
DOI:
10.1098/rsif.2013.0048
发表时间:
2013-06-06
期刊:
Journal of the Royal Society, Interface
影响因子:
--
作者:
[Fulcher BD, Little MA, Jones NS]
通讯作者:
Jones NS
共 7 条
Functional Phylogenies
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批准号:EP/H046917/1
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项目类别:Research Grant
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资助金额:$3.5万
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财政年份:2010
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负责人:Nick Jones
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依托单位:
海外基金