Seeing Data: are good big data visualisations possible?

看到数据关注的是人们如何看待大数据的表示；也就是数据可视化。拟议的研究始于这样一个前提，即数据是通过在数据生成过程中做出的人为决策来构建的。它们从来不是生的，而是总是煮熟的(Bowker 2005)；它们不仅存在，而且需要产生(Manovich 2011)。但大数据通常被认为是“仅仅存在的”，它们通过可视化的表现被视为了解世界的窗口，尽管一些评论员强调了这种假设的危险。例如，Crawford(2013)指出，地图不是领土，目的是警告我们不要将事物的视觉表示视为事物本身。这项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，即“利用我们的视觉感知能力来放大认知的数据的表示和呈现”(Kirk 2013)。数据可视化，就像它们经常基于的大数据一样，正变得越来越无处不在：大卫·麦克坎德利斯的10亿美元-O-克，美国枪支死亡造成的多年损失的动画可视化，以及捕捉在线表达情绪的网站We Feel Fine，只是广泛传播的数据可视化的三个例子。如果大数据是通过它们的生成方式构建的，如果数据可视化是人们普遍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。考虑到这些因素，我们需要探索有效的大数据可视化是否可能，如果可能，如何衡量有效性。为了回答这些问题，需要对数据可视化的接收有更多的了解。看到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，将该数据可视化，并检查公众对这些可视化的接受情况，而公众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接收的理解，然后与这种可视化的生产者和消费者分享。因此，这项研究旨在促进数据可视化的生产和消费。有关接受大数据可视化的问题将通过一名新媒体学者、一名数据可视化专家、一名从事大规模数据工作的社会科学研究员和一名视觉传播学者开展的合作研究来解决。我们的实证研究以牛津大学移民观察站(MigObs)持有的关于一个有争议的社会问题--移民的数据为例。MigObs旨在为英国的移民和移民数据提供公正的、基于证据的分析，为媒体、公众和政策辩论提供信息；我们的研究将探索数据可视化是否使实现这一目标成为可能。结合现有的迁移数据和新生成的数据集，我们将招聘领域领先的数据可视化人员来生成MigObs数据的可视化。我们将通过与公众视像消费者进行深入的焦点小组讨论，详细审查这些视像的接受情况。为了支持这一案例研究，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此的反应。因此，我们将探讨有效的大数据可视化是否可能，考虑到数据和可视化的构造性，在这种情况下有效性可能意味着什么，以及如何衡量有效性。我们还将具体帮助MigObs解决在将其数据清楚地传达给一系列利益相关者方面面临的一些挑战。

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.