Seeing Data: are good big data visualisations possible?

查看数据：良好的大数据可视化可能吗？

• 批准号: AH/L009986/1
• 负责人: Helen Kennedy
• 金额: $ 28.57万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=AH%2FL009986%2F1
• 关键词: Seeing; Data; good; big; data

Seeing Data focuses on how people perceive representations of big data; that is, data visualisations. The proposed research starts from the premise that data are constructed by human decisions made during the data generation process. They are never raw, but always cooked (Bowker 2005); they do not just exist, but need to be generated (Manovich 2011). But big data are often assumed to 'just exist', and their representation through visualisations are taken as windows onto the world, even though some commentators have highlighted the dangers of such assumptions. For example Crawford (2013) states that 'the map is not the territory' in order to warn us against seeing visual representations of things as the things themselves. A second premise of the research is that the main way in which the general public gets to access big data is through data visualisations, 'the representation and presentation of data that exploits our visual perception abilities in order to amplify cognition' (Kirk 2013). Data visualisations, like the big data on which they are often based, are becoming increasingly ubiquitous: David McCandless's billion-dollar-o-gram, an animated visualisation of years lost due to US gun deaths and the website We Feel Fine which captures sentiment expressed online are just three examples of widely circulating data visualisations. If big data are constructed by the ways in which they are generated and if data visualisations are the main source of popular access to big data, then critical questions about the role of data visualisations need to be asked. We need to explore whether, given these factors, effective big data visualisations are ever possible, and if so, how effectiveness might be measured. In order to answer these questions, more understanding of the reception of data visualisations is needed. Seeing Data addresses this issue. The proposed research involves generating big data, combining it with existing data, visualizing that data, and examining the reception of these visualisations amongst the general public, who are the main consumers of data visualisations. Through these methods, the research will develop understanding of the reception of data visualisations, which will then be shared with the producers and consumers of such visualisations. Thus the research aims to enhance both the production and consumption of data visualisations.Questions about the reception of big data visualisations will be addressed through collaborative research carried out by a new media scholar, a data visualisation expert, a social science researcher working with large scale data and a visual communications scholar. Our empirical research takes as a case study data about a contentious social issue, migration, which is held by the Migration Observatory (MigObs) at the University of Oxford. MigObs aims to provide impartial, evidence-based analysis of data on migration and migrants in the UK, to inform media, public and policy debates; our research will explore whether data visualisations make it possible to meet this aim. Combining existing data about migration with newly-generated datasets, we will recruit field-leading data visualizers to produce visualisations of MigObs data. We will examine the reception of these visualisations in detail through in-depth focus group discussions with consumers of visualisations from the general public. To support this case study, we will also ask other consumers to keep diaries of their encounters with data visualisations in their everyday lives and their reactions to them. Thus we will explore whether effective big data visualisations are possible, given the constructedness of data and visualisations, what effectiveness might mean in this context and how effectiveness might be measured. We will also concretely help MigObs address some of the challenges it faces in clearly communicating its data to a range of stakeholders.

Seeing Data 关注人们如何感知大数据的表示；即数据可视化。所提出的研究的前提是数据是由人类在数据生成过程中做出的决策构建的。它们从来都不是生的，而是煮熟的（Bowker 2005）；它们不仅存在，而且需要被生成（Manovich 2011）。但大数据通常被认为“只是存在”，并且它们通过可视化的表示被视为了解世界的窗口，尽管一些评论家强调了这种假设的危险。例如，克劳福德（Crawford，2013）指出“地图不是领土”，以警告我们不要将事物的视觉表征视为事物本身。该研究的第二个前提是，公众获取大数据的主要方式是通过数据可视化，“利用我们的视觉感知能力来增强认知的数据表示和呈现”（Kirk 2013）。数据可视化，就像它们所依据的大数据一样，正变得越来越普遍：大卫·麦坎德利斯的十亿美元-o-gram，一个动画可视化，显示了因美国枪支死亡而损失的岁月，以及捕捉表达的情绪的网站We Feel Fine网上只是广泛流传的数据可视化的三个例子。如果大数据是按照其生成方式构建的，并且如果数据可视化是大众获取大数据的主要来源，那么就需要提出关于数据可视化作用的关键问题。我们需要探索考虑到这些因素，有效的大数据可视化是否可能，如果可以，如何衡量有效性。为了回答这些问题，需要更多地了解数据可视化的接收。 Seeing Data 解决了这个问题。拟议的研究涉及生成大数据，将其与现有数据相结合，将数据可视化，并检查作为数据可视化的主要消费者的公众对这些可视化的接受程度。通过这些方法，研究将加深对数据可视化接收的理解，然后与此类可视化的生产者和消费者共享。因此，该研究旨在增强数据可视化的生产和消费。有关大数据可视化接受的问题将通过新媒体学者、数据可视化专家、大规模工作的社会科学研究人员进行的合作研究来解决。数据和视觉传达学者。我们的实证研究以牛津大学移民观察站 (MigObs) 持有的有关有争议的社会问题移民的数据作为案例研究。 MigObs 旨在对英国的移民和移民数据提供公正、基于证据的分析，为媒体、公众和政策辩论提供信息；我们的研究将探讨数据可视化是否能够实现这一目标。将现有的迁移数据与新生成的数据集相结合，我们将招募领域领先的数据可视化人员来生成 MigObs 数据的可视化。我们将通过与公众的可视化消费者进行深入的焦点小组讨论，详细研究这些可视化的接受情况。为了支持这个案例研究，我们还将要求其他消费者记录他们在日常生活中遇到的数据可视化以及他们对这些数据可视化的反应。因此，我们将探讨考虑到数据和可视化的构造性，有效的大数据可视化是否可能，在这种情况下有效性意味着什么以及如何衡量有效性。我们还将具体帮助 MigObs 解决其在向一系列利益相关者清晰传达数据方​​面所面临的一些挑战。

项目成果

The work that visualisation conventions do

可视化约定所做的工作

• DOI: http://dx.10.1080/1369118x.2016.1153126
• 发表时间: 2016
• 期刊: Information, Communication & Society
• 作者: Kennedy H

Visual brokerage: Communicating data and research through visualisation.

视觉经纪：通过可视化交流数据和研究。

• DOI: http://dx.10.1177/0963662518756853
• 发表时间: 2018
• 期刊: Public understanding of science (Bristol, England)
• 作者: Allen WL

Making corpus data visible: visualising text with research intermediaries

使语料库数据可见：与研究中介一起可视化文本

• DOI: http://dx.10.3366/cor.2017.0128
• 发表时间: 2017
• 期刊: Corpora
• 影响因子: 0.5
• 作者: Allen W