Interactive Techniques for Personal Visual Analytics

物联网、在线行为的增加以及联网移动的设备的激增导致了与人们日常生活相关的“大数据”的爆炸式增长。 与此同时，政府、非营利组织和公司都致力于开放数据，提供随时可以访问的数据，任何人都可以自由使用、重新使用和重新分发。 这些变化正在将“大数据”的概念从专家分析师的领域扩展到普通人的领域，但迄今为止，往往有证据表明，在数据的提供与人们是否理解信息或受信息激励之间存在差距。该项目的研究将针对新型个人视觉分析技术和服务的设计和评估，使基于数据的思维更容易为普通人所接受：帮助人们整合和使用外部和个人数据，用于自己的决策，并与他们在日常生活中互动的机构，专业人士，服务和社会组织进行沟通和互动。基于桌面的，以任务为中心的模型的视觉分析和专家的意义，不适用于各种情况下，人们可以使用数据在他们的日常生活中，所以这项研究将探索新的技术PVA，包括移动的，共享和嵌入式应用程序。它将侧重于对PVA特别重要的三个领域：使用环境;数据和视觉框架;参与和动机。与专业数据分析师不同，人们出于不同的目的，在不同的物理，时间和计算情况下进行数据探索。 他们使用适合这些情况的不同设备，例如家中的移动的设备和环境显示器。 他们的目的可能是满足快速浏览数据或更长时间的探索。如何选择和表示的数据提出了框架，感知和情境的限制和适当的背景下的关键问题。这项研究的一个重要方面将涉及以新颖的方式整合不同的数据源（“混搭”）：例如，将个人移动成本和房价与公民密度计划（公民参与）的预测相结合;或者将日常家庭活动记录与家庭能源使用相结合，以通知保护工作。 这项研究将探索可视化和访问技术，灵活的数据混搭在共同的信息工具，如日历和地图。最后，在智力和情感层面上与人们产生共鸣的可视化可以促进参与，兴趣和动机。当可视化的目的是激励（例如健康跟踪）或促进对主题或倡议的兴趣（社交参与的关键问题）时，这一点尤其重要。我们将研究情感可视化技术在沟通和参与方面的设计和实用性。

The Internet of Things, increase in online behavior, and a proliferation of connected mobile devices has resulted in an explosion of “big data” related to people's daily lives. Concurrently governments, non-profit organizations and companies are committed to Open Data, providing ready access data that can be freely used, re-used and redistributed by anyone. These changes are extending the concepts of “big data” from the sphere of the expert analyst to the domain of the ordinary person, but to date there is often evidence of a gap between the provision of data and whether people understand or are motivated by the information. The research in this project will target the design and evaluation of novel personal visual analytics techniques and services for making data-based thinking more accessible to the average person: to help people integrate and use external and personal data both for their own decision making and to communicate and interact with the institutions, professionals, services and social organizations with whom they interact in their daily lives. The desktop-based, task-centric model of visual analytics and expert sense-making does not apply to the various contexts in which people can use data in their daily lives, so this research will explore novel techniques for PVA, including mobile, shared and embedded applications. It will focus on three areas particularly important to PVA: context of use; data and visual framing; and engagement and motivation. Unlike professional data analysts, people undertake data exploration for different purposes and in different physical, temporal and computing situations. They use different devices appropriate to these situations, such as mobile devices and ambient displays in their homes. Their purpose may be met with a quick glimpse of the data or a more prolonged exploration. How the data are chosen and represented presents key questions of framing, perceptual and situational constraints and appropriate context. An important aspect of this research will involve integration of different data sources (“mashups”) in novel ways: for example, combining personal mobility costs and house prices with projections for civic density initiatives (citizen engagement); or combining daily domestic activity records with energy use in the home to inform conservation efforts. This research will explore visualization and access techniques for flexible data mashups in common information tools like calendars and maps. Finally, visualizations that resonate with people on both an intellectual and emotional level promote engagement, interest, and motivation. This is particularly important when the purpose of the visualization is motivational (such as health tracking) or to promote interest in a topic or initiative ( a key issue in social engagement). We will examine the design and utility of affective visualization techniques for both communication and engagement.