Visual analytics in context: Theory into practice

视觉分析是一门通过交互视觉界面促进分析推理的科学。它是一个不断发展的多学科领域，旨在帮助人们从海量、动态、模棱两可和经常相互冲突的数据中获得见解。视觉分析利用人类分析师独有的视觉智能和认知能力，以及交互式、探索性和可视化软件工具。该项目的主要科学和技术目标是更好地了解视觉分析系统的性质和用途，以及如何将它们有效地应用于处理大型、复杂数据集的各种行业。该项目将沿着从理论到学习再到实践和部署的路径，围绕三个研究方向展开。首先，我们将对与信息可视化和视觉分析相关的人类视觉能力进行基础研究。这些研究的结果将建立与人类应该如何设计和使用这种系统相关的理论，导致使用更有效的可视化来改进设计。接下来，我们将开发一个具有挑战性的学习环境，大学生和行业员工可以通过使用可用的视觉分析工具，从经验丰富的学生导师那里学习视觉分析，以使用真实数据集探索实际问题。最后，我们将应用可视化分析技术和工具来直观地理解财务记录。指导研究问题包括：如何应用可视化分析来支持金融风险分析和决策，如何分析档案数据，以及如何概念化和分析信息保障领域的风险？这个项目带来了几个好处。它将有助于推动加拿大在视觉分析研究、学习和部署方面处于领先地位。该项目涉及一个由高素质的学生和研究人员组成的团队，他们将在这个充满希望和创新的新研究领域接受培训。完成后，我们的行业合作伙伴可以将他们的新知识和程序用于他们的专有数据，很可能使用参与该项目的学生和研究人员获得的独特技能集。

Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces. It is an evolving multidisciplinary field developed to help people derive insights from massive, dynamic, ambiguous, and often conflicting data. Visual analytics takes advantage of the visual intelligence and cognitive capabilities unique to human analysts, coupled with interactive, exploratory, and visualisation software tools. The primary scientific and technical objectives of the project are to better understand the nature and use of visual analytics systems and how they can be effectively applied to various industries working with large, complex date sets. The project will focus on three research directions by following a path from theory, to learning, to practice and deployment. First, we will conduct basic research on human visual capabilities relevant to information visualisation and visual analysis. Results of these investigations will establish theory relevant to how humans should design and use such systems, leading to enhanced designs using more effective visualizations. Next, we will develop a challenging learning environment where university students and industry employees can learn about visual analytics from experienced student mentors by using available visual analytics tools to explore real problems using real data sets. Finally, we will apply visual analytics techniques and tools to make visual sense of financial records. Guiding research questions include: how to apply visual analytics to support financial risk analysis and decision-making, how to analyse archival data, and how to conceptualize and analyse risk in the information assurance domain? Several benefits accrue as a result of this project. It will contribute to the advancement of Canada as a leader in visual analytics research, learning and deployment. The project involves a team of highly qualified students and researchers who will be trained in this promising and innovative new area of research. Upon completion, our industrial partners can use their new knowledge and procedures on their proprietary data, likely employing the unique skill sets acquired by the students and researchers involved in this project.