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Big, Open Data: Mining and Synthesis (BODMAS)

大开放数据：挖掘和合成 (BODMAS)

基本信息

批准号：
ES/K009176/1
负责人：
James Cheshire
金额：
$ 30.98万
依托单位：
University College London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2013
资助国家：
英国
起止时间：
2013 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=ES%2FK009176%2F1
关键词：
Big Open Data Mining Synthesis

项目摘要

The volume and assortment of available data for research in the social sciences has dramatically increased in recent years- a trend that shows no sign of stopping. For the first time researchers can obtain large amounts of population data free of charge (so-called "open data") thanks to government websites such as data.gov.uk. When these data are combined with the computing power to perform complex calculations it creates an unprecedented opportunity for social science researchers. We are now in an era of big data and this is fundamentally changing the research environment for investigations across social science. The purpose of this project is to develop some of the new perspectives required to adapt to these changes in the practice of data modeling and synthesis. These new perspectives include the need to account for the increased uncertainty in data provenance and less thorough metadata, as the data provision philosophy has shifted away from careful collection and dissemination to an emphasis on expediency. Researchers increasingly have to temper gains in data volume against losses in data quality when they embark on a study. Extra caution is also required when combining datasets, especially if they contain geographic information, as it is not always case that the spatial scales are compatible. The proposed project will develop a web-based tool to help social scientists minimise or eradicate these issues by enabling the synthesis, mining and visualisation of open datasets in a more informed way. The project will also use the newly combined data to undertake more complex analyses of population processes using supercomputers to gain unprecedented insights into phenomena such as commuter flows. In addition the project is focused facilitating my personal ambition to become a Future Research Leader. A comprehensive list of world-leading collaborators (ESRI (UK), the Open Data Institute, University of Illinois and University of Zurich) each have a specialism of interest to me and that is integral to the project. Activities with these organisations and my mentor, Professor Michael Batty, form part of a comprehensive plan for knowledge transfer and personal development. I have the full support of my host department, the UCL Centre for Advanced Spatial Analysis, which ensures that the project activities are not confined to the time formally costed to it. As the proposal demonstrates, the proposed project is both ambitious and extremely timely and will strive for high impact social science.

近年来，社会科学研究中可用数据的数量和种类急剧增加，这一趋势没有停止的迹象。多亏了data.gov.uk等政府网站，研究人员第一次可以免费获得大量的人口数据（所谓的“开放数据”）。当这些数据与计算能力结合起来进行复杂的计算时，它为社会科学研究人员创造了前所未有的机会。我们现在处于一个大数据时代，这从根本上改变了整个社会科学调查的研究环境。这个项目的目的是开发一些新的视角，以适应数据建模和综合实践中的这些变化。这些新的观点包括，随着数据提供理念从谨慎收集和传播转向强调权宜之计，需要考虑数据来源的不确定性增加和元数据不太彻底。当研究人员开始一项研究时，他们越来越需要调整数据量的增长和数据质量的下降。在组合数据集时还需要格外小心，特别是当它们包含地理信息时，因为空间尺度并不总是兼容的。拟议中的项目将开发一个基于网络的工具，帮助社会科学家以更明智的方式合成、挖掘和可视化开放数据集，从而最大限度地减少或消除这些问题。该项目还将利用新合并的数据，利用超级计算机对人口过程进行更复杂的分析，从而对通勤流等现象获得前所未有的见解。此外，该项目的重点是促进我的个人抱负，成为未来的研究领袖。世界领先的合作者（ESRI（英国）、开放数据研究所、伊利诺伊大学和苏黎世大学）都有我感兴趣的专业，这是这个项目不可或缺的一部分。与这些组织和我的导师Michael Batty教授合作，是知识转移和个人发展综合计划的一部分。我得到了我的主办部门，伦敦大学学院高级空间分析中心的全力支持，这确保了项目活动不限于正式花费的时间。正如提案所表明的那样，拟议的项目既雄心勃勃又非常及时，并将努力实现高影响力的社会科学。