权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Narrating Complexity: Exploring Multi-Actor Timelines

叙述复杂性：探索多参与者时间线

基本信息

批准号：
EP/V028871/1
负责人：
Alfie Abdul-Rahman
金额：
$ 34.27万
依托单位：
King's College London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2022
资助国家：
英国
起止时间：
2022 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FV028871%2F1
关键词：
Narrating Complexity Exploring Multi Actor

项目摘要

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.

几个世纪以来，具有各种视觉设计的“时间线”一直被用作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的一种方式。但我们呈现此类时间线的视觉语言受到以下两种方式之一的限制。要么显示的事件数量相对较短，要么时间线捕获的变量（或信息）有限。因此，随着要显示的数据量的增加，“时间线”信息的可视化显示的传统方法很快变得不太有用。尽管如此，与被理解为“时间线”的数据交互的需求正在增加。在日常生活中，我们许多人都熟悉关注 Twitter 和 Facebook 等社交媒体上的对话，或立法议会等政治辩论中的对话。我们很容易跟踪包含最少交流的单人对话。然而，随着人数的增加，沟通交流也随之增加，导致相关的时间线变得越来越复杂，使得跟踪可能涉及数百甚至数千参与者的讨论变得非常困难。当事件和参与者网络彼此之间存在复杂且不断变化的关系时，问题会进一步复杂化。该项目将开发一套交互式可视化分析方法，以更好地理解这些复杂而广泛的时间线，借鉴社交媒体的例子和立法环境的更正式讨论（例如，欧盟退出法案（英国脱欧立法））。随着人们越来越重视“大数据”解决问题的方法，我们开发新的可视化语言来帮助用户快速准确地查询大型数据集非常重要。该项目的核心是希望开发新的隐喻和惯例，以增强对我们所生活的高度互联世界的理解。