Narrating Complexity: Exploring Multi-Actor Timelines

几个世纪以来，带有各种视觉设计的“时间线”一直被用来理解按时间顺序发生的不同事件之间的关系，并暗示它们之间的关系。但是我们用来呈现这些时间线的视觉语言受到了两种方式的限制。要么显示的事件数量相对较短，要么时间轴捕获的变量（或信息）有限。因此，随着要显示的数据量的增加，用于“时间轴”信息的视觉显示的传统方法迅速变得不那么有用。尽管如此，与被理解为“时间轴”的数据交互的需要正在增加。在日常生活中，我们中的许多人都熟悉社交媒体上的对话，如Twitter和Facebook，或在政治辩论中，例如，立法议会。我们很容易跟随一个人的谈话，其中包含最少的交流。然而，随着个人人数的增加，交流的次数也在增加，因此，相关的时间表变得越来越复杂，使得很难跟踪可能涉及数百甚至数千行为体的讨论。当事件和行为者网络彼此之间存在复杂和不断演变的关系时，问题就更加复杂。该项目将开发一套互动的可视化分析方法，以更好地理解这些复杂而广泛的时间表，借鉴社交媒体和立法环境的更正式讨论的例子。（例如，欧盟退出法案（英国脱欧立法））。随着越来越多的重点放在“大数据”方法来解决问题，重要的是我们开发新的视觉语言来帮助用户快速准确地查询大型数据集。这个项目的核心是希望开发新的隐喻和惯例，以加强对我们生活的高度联系的世界的理解。

For centuries 'timelines' with various visual designs have been used as a way to understand the relationship between different events happening in chronological sequence and to suggest relationships between them. But our visual language for presenting such timelines has been limited in one of two ways. Either the number of events displayed has been relatively short, or the variables (or information) captured by the timeline have been limited. Traditional approaches to the visual display of 'timeline' information, therefore, rapidly become less useful as the amount of data to be displayed increases.In spite of this, the need to interact with data that is understood as a 'timeline' is increasing. In everyday life, many of us are familiar with following conversations on social media, such as Twitter and Facebook, or in a political debate, e.g., legislative assemblies. It is easy for us to follow a single person conversation that contains minimal exchange. However, as the number of individuals increases so do the communication exchanges, with the result that the associated timelines become increasingly complex, making it very difficult to follow a discussion which may involve hundreds or even thousands of actors. The problem is further compounded when both events and the network of actors exist in complex and evolving relationships to each other. This project will develop a set of interactive, visual analytics approaches to better understand these complicated and extensive timelines, drawing on the example of social media and the more formal discussions of a legislative setting (for example, the European Union Withdrawal Acts (Brexit legislation)).As more emphasis is placed on 'big data' approaches to problems, it is important that we develop new visual languages to help users interrogate large datasets quickly and accurately. At the core of this project is the desire to develop new metaphors and conventions to enhance understanding of the highly connected world in which we live.