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/L009986/2
• 负责人: Helen Kennedy
• 金额: $ 11.3万
• 依托单位: University of Sheffield
• 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%2F2
• 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.

看到数据的重点是人们如何看待大数据的表示；也就是说，数据可视化。拟议的研究始于以下前提：数据是由数据生成过程中的人类决策构建的。它们从不生，但总是煮熟（Bowker 2005）；它们不仅存在，而且需要生成（Manovich 2011）。但是，通常认为大数据是“仅存在的”，即使某些评论员强调了这种假设的危险，它们通过可视化的表示被视为进入世界的窗户。例如，克劳福德（Crawford，2013年）指出，“地图不是领土”，以警告我们不要将事物的视觉表现视为事物本身。该研究的第二个前提是，公众可以访问大数据的主要方式是通过数据可视化，“利用我们视觉感知能力以扩大认知能力的数据的表示和呈现”（Kirk 2013）。数据可视化（例如它们经常基于的大数据）变得越来越无处不在：David McCandless的十亿美元赚钱，一种动画的可视化，对美国枪支死亡造成的几年和我们感到良好的网站，我们感到非常满意，捕获在线表达的情绪只是三个示例，这是三个示例的示例。如果通过生成的方式构建大数据，并且数据可视化是对大数据的流行访问的主要来源，则需要询问有关数据可视化作用的关键问题。我们需要探索在这些因素上，有效的大数据可视化是否可能是可能的，如果是这样，则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。查看数据解决了这个问题。拟议的研究涉及生成大数据，将其与现有数据相结合，可视化数据，并检查公众对这些可视化的接收，这是数据可视化的主要消费者。通过这些方法，研究将发展对数据可视化的接受的理解，然后将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。有关大数据可视化的问题将通过新媒体学者，数据可视化专家，一名与大型数据的社会科学研究人员和视觉通信学者一起进行的合作研究来解决。我们的实证研究将有关有争议的社会问题和移民的案例研究数据作为案例研究数据，该数据由牛津大学移民观测站（Migobs）持有。 Migobs旨在提供有关英国移民和移民数据的公正，基于证据的分析，以告知媒体，公共和政策辩论；我们的研究将探讨数据可视化是否使实现这一目标成为可能。将有关迁移的现有数据与新生成的数据集相结合，我们将募集现场领域的数据可视化器以产生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来详细介绍这些可视化的接收。为了支持此案例研究，我们还将要求其他消费者在日常生活中与数据可视化以及对他们的反应保持联系。因此，鉴于数据和可视化的构造性，在这种情况下可能意味着什么以及如何衡量有效性，我们将探讨有效的大数据可视化是否可能发生。我们还将具体帮助Migobs清楚地将其数据传达给一系列利益相关者时面临的一些挑战。

项目成果

The Feeling of Numbers: Emotions in Everyday Engagements with Data and Their Visualisation

• DOI: 10.1177/0038038516674675
• 发表时间: 2018-08-01
• 期刊: SOCIOLOGY-THE JOURNAL OF THE BRITISH SOCIOLOGICAL ASSOCIATION
• 影响因子: 2.9
• 作者: Kennedy, Helen;Hill, Rosemary Lucy
• 通讯作者: Hill, Rosemary Lucy

Visual brokerage: Communicating data and research through visualisation.

• DOI: 10.1177/0963662518756853
• 发表时间: 2018-11
• 期刊: Public understanding of science (Bristol, England)
• 作者: Allen WL

Making corpus data visible: visualising text with research intermediaries

• DOI: 10.3366/cor.2017.0128
• 发表时间: 2017-11-01
• 期刊: CORPORA
• 影响因子: 0.5
• 作者: Allen, William

The SAGE Handbook of Online Research Methods

SAGE 在线研究方法手册

• DOI: 10.4135/9781473957992.n18
• 发表时间: 2017
• 作者: Kennedy H

The work that visualisation conventions do

• DOI: 10.1080/1369118x.2016.1153126
• 发表时间: 2016-06-02
• 期刊: INFORMATION COMMUNICATION & SOCIETY
• 影响因子: 4.2
• 作者: Kennedy, Helen;Hill, Rosemary Lucy;Allen, William
• 通讯作者: Allen, William