课题基金 / 基金详情

Urban Big Data Centre

Urban Big Data Centre
城市大数据中心
批准号:
ES/S007105/1
负责人:
Nick Bailey
金额:
$227.6万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2019
资助国家:
英国
项目状态:
未结题
起止时间:
2019 至 --
关键词:

项目摘要

项目成果

Nick Bailey的其他基金

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中文摘要
翻译
城市大数据中心旨在促进创新研究方法和大数据的使用,以改善城市的社会、经济和环境福祉。传统上,定量的城市分析依赖于为研究目的而设计的数据:特别是人口普查和社会调查。人们对它们的品质很了解,从它们中提取知识所需的技能也被社会研究人员广泛分享。随着数字时代的到来,我们在日常生活中产生了越来越多的数据,例如,来自物理传感器、商业和公共管理系统或社交媒体平台的数据。这些数据有可能为了解城市生活提供有价值的见解,但要从中提取有用的知识还面临更多挑战。有些是技术性的,源于数据的数量和种类,以及它的非结构化性质。有些是法律和道德问题,涉及数据所有权和个人隐私权。最重要的是,在大数据的使用中存在重要的社会科学问题。我们需要用对城市问题和背景的知情视角来塑造我们对这些数据提出的问题,而不是让数据驱动研究。有必要对数据本身以及它们如何影响城市生活的最终表现提出问题。有必要检查决策者采用这些数据并在决策中使用这些数据的方式。UBDC是一个研究中心,汇集了优秀的多学科团队来解决这些复杂而多样的挑战。我们是四种能力的独特组合:具有与城市研究相关的一系列学科背景的专业知识的社会科学家;在编程、数据管理、信息检索和空间信息系统以及大数据使用相关法律问题方面具有专业知识的数据科学家;数据基础设施,包括大量的数据收集和安全的数据管理和分析系统;在第一阶段和更广泛的工作过程中,建立了一个与政策、行业和民间社会组织有着密切联系的学术团体。在第二阶段,我们的目标是使第一阶段活动的社会和经济效益最大化。我们将特别通过与工业和政府利益相关者的伙伴关系来实现这一目标,共同努力产生满足其需求并具有更广泛应用的分析。我们将继续在多个学科领域发表世界领先的科学论文。我们将努力加强数据收集,开发新的分析方法。我们将进行研究,以了解这些新数据的质量,它们在多大程度上代表或歪曲了生活的特定方面,以及政策制定者在实践中如何使用它们。最后,我们将为研究人员和其他人在未来处理这类数据建立能力。我们的工作方案包括四个专题工作包。其中一个重点是了解城市交通系统的可持续性、公平性和效率,以及评估基础设施投资对这些系统的影响。特别关注公共交通的可达性以及积极出行和由此产生的健康结果。第二部分考察了城市居住结构的变化或空间隔离模式,以及它们对社会公平的影响,特别关注私人租赁的重新增长。第三部分研究城市系统如何塑造技能发展和生产力,特别是家庭和学校环境如何结合起来塑造中等教育成就。第四章探讨了政策制定者如何利用大数据。它询问更有效地利用这些数据的障碍是什么,以及它们是否扭曲了公共机构可能形成的需求图景。
英文摘要
The Urban Big Data Centre aims to promote innovative research methods and the use of big data to improve social, economic and environmental well-being in cities. Traditionally, quantitative urban analysis relied on data designed for research purposes: Census and social surveys, in particular. Their qualities are well understood and the skills needed for extracting knowledge from them widely shared by social researchers. With the arrival of the digital age, we produce an ever increasing volume of data as we go about our daily lives from physical sensors, business and public administrative systems, or social media platforms, for example. These data have the potential to provide valuable insights into urban life but there are many more challenges in extracting useful knowledge from them. Some are technical, arising from the volume and variety of data, and its less structured nature. Some are legal and ethical, concerning data ownership rights and individual privacy rights. Above all, there are important social science issues in the use of big data. We need to shape the questions we ask of these data with an informed perspective on urban problems and contexts, and not have data drive the research. There is a need to ask questions about the data themselves and how they affect the resulting representations of urban life. And there is a need to examine the ways in which these data are taken up by policy makers and used in decision making. UBDC is a research centre which brings together an outstanding multi-disciplinary team to address these complex and varied challenges. We are a unique combination of four capacities: social scientists with expertise from a range of disciplinary backgrounds relevant to urban studies; data scientists with expertise in programming, data management, information retrieval and spatial information systems, as well as in legal issues around big data use; a data infrastructure comprising a substantial data collection and secure data management and analysis systems; and an academic group with strong connections to policy, industry and civil society organisations developed over the course of phase one and wider work. In the second phase, our objectives are to maximise the social and economic benefits of activities from phase one. We will do this in particular through partnerships with industrial and government stakeholders, working together to produce analyses which meet their needs as well as having wider application. We will continue to publish world-leading scientific papers across a range of disciplines. We will work to enhance data collections and develop new methods of analysis. We will conduct research to understand the quality of these new data, how well they represent or misrepresent particular aspects of life, and how they are and could be used by policy makers in practice. Lastly, we will build capacity for researchers and others to work with this kind of data in future. Our work programme comprises four thematic work packages. One focuses on understanding the sustainability, equity and efficiency of urban transport systems and on evaluating the impacts on these of infrastructure investments. There is a particular focus on public transport accessibility as well as active travel and hence health outcomes. The second examines the changing residential structure of cities or patterns of spatial segregation, and their consequences for social equity, with a particular focus on the re-growth of private renting. The third studies how urban systems shape skills development and productivity and, in particular, how the combination of home and school environments combine to shape secondary educational attainment. The fourth explores how big data are being taken up by policy makers. It asks what the barriers are to more effective use of these data but also whether they distort the picture of needs which a public body may form.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1016/j.envsoft.2023.105802
发表时间: 2023-10
期刊: Environ. Model. Softw.
影响因子: --
作者: [Patrycja Antosz;Daniel Birks;B. Edmonds;A. Heppenstall;R. Meyer;J. Gareth Polhill;D. O'Sullivan;Nanda Wijermans]
通讯作者: Patrycja Antosz;Daniel Birks;B. Edmonds;A. Heppenstall;R. Meyer;J. Gareth Polhill;D. O'Sullivan;Nanda Wijermans
The welfare consequences of the suburbanisation of poverty in UK cities: air pollution and school quality
英国城市贫困郊区化的福利后果:空气污染和学校质量
DOI: 10.2478/udi-2019-0003
发表时间: 2019
期刊: Urban Development Issues
影响因子: --
作者: [Bailey N]
通讯作者: Bailey N
Great Britain transport, housing, and employment access datasets for small-area urban area analytics.
英国交通、住房和就业准入数据集,用于小区域城市区域分析。
DOI: 10.1016/j.dib.2019.104616
发表时间: 2019
期刊: Data in brief
影响因子: 1.2
作者: [Anejionu OCD]
通讯作者: Anejionu OCD
Modeling agent decision and behavior in the light of data science and artificial intelligence
根据数据科学和人工智能对代理决策和行为进行建模
DOI: 10.1016/j.envsoft.2023.105713
发表时间: 2023
期刊: Environmental Modelling & Software
影响因子: 4.9
作者: [An L]
通讯作者: An L
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