Executive Summary CH3
Edited by Ben. Turn to ContentPage
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目录
Visual representations and interaction technologies provide the mechanism for allowing the user to see and understand large volumes of information at once. The human mind can understand complex information received through visual channels. Visual analytics builds upon this ability to facilitate the analytical reasoning process.
Scientific principles for depicting information must provide the basis for visual representations, and principles are needed for new interaction approaches to support analytical techniques. Together, these foundations provide the basis for new visual paradigms that can scale to support analytical reasoning in many situations.
Visual design theory is more mature than interaction theory, so investments in the further development of interaction theory should take priority. Interaction theory must take into account the time constraints associated with varying levels of urgency in an analytic task. The application of visual representations and interactions must necessarily be adapted to fit the needs of the task at hand. The issues of scale also profoundly affect the design of visual representations and interactions and must be considered explicitly in the design of new visual representation and interaction techniques.
Creating effective visual representations is a labor-intensive process that requires a solid understanding of the visualization pipeline, characteristics of the data to be displayed, and the tasks to be performed. Currently, most visualization software is written with incomplete knowledge of at least some of this information. Generally, it is not possible for the analyst, who has the best understanding of the data and task, to construct new tools. We need new methods for constructing visually based systems that simplify the development process and result in better-targeted applications.
The panel makes several high-level recommendations aimed at addressing these challenges.
Recommendation
Create a science of visual representations based on cognitive and perceptual principles that can be deployed through engineered, reusable components. Visual representation principles must address all types of data, address scale and information complexity, enable knowledge discovery through information synthesis, and facilitate analytical reasoning.
Visual representations and interaction techniques provide the analyst and the first responder with their understanding of developing situations so that they may take action. A science of visual representations has been developed to support scientific applications, but different visual representations are needed to address the diverse data types that are relevant to homeland security missions. These data must be combined and presented to the user in a way that allows the user to understand their meaning, regardless of the data type or format of the original data. The goal is to expose all relevant data in a way that facilitates the reasoning process to enable action.
Recommendation
Develop a new suite of visual paradigms that support the analytical reasoning process.
These visualizations must:
Facilitate understanding of massive and continually growing collections of data of multiple types
Provide frameworks for analysis of spatial and temporal data
Support understanding of uncertain, incomplete, and often misleading information
Provide user- and task-adaptable, guided representations that enable full situate on awareness while supporting development of detailed actions
Support multiple levels of data and information abstraction
Facilitate knowledge discovery through information synthesis, which is the integration of data based on their meaning rather than the original data type.
No one visual paradigm can address all possible tasks and situations. Therefore, we recommend developing a suite of visual paradigms that address multiple situations ranging from vulnerability analysis to real-time monitoring to emergency response support. The scale of data, especially in the forms of sensor, text, and imagery, is rapidly growing. Data are continually growing and changing, and visual representations must help analysts understand the changing nature of their data and the situations they represent. Likewise, many data are associated with a particular place and time. Representing these spatial and temporal qualities is necessary to provide analytical understanding. Furthermore, the visualization process is complicated by the need to support understanding of missing, conflicting, and deceptive information in an analytic discourse that is guided by the individual’s knowledge and his or her task.
Recommendation
Develop a new science of interactions that supports the analytical reasoning process. This interaction science must provide a taxonomy of interaction techniques ranging from the low-level interactions to more complex interaction techniques and must address the challenge to scale across different types of display environments and tasks.
Interaction is the fuel for analytic discourse. Although the fundamental principles of interaction have been around for more than a decade, they do not address the needs for higher-order interaction techniques, such as task-directed or hypothesis-guided discourse, to support the analysis process. A new scientific theory and practice are critical to address the complexity of homeland security needs for analysis, prevention, and response. These interaction techniques must adapt to the particular dimensions of the analytical situation, ranging from longer-term analytical assessments to urgent and highly stressful emergency response support tasks. These interactions must be adaptable for use in platforms ranging from the large displays in emergency management control rooms to field-deployable handheld devices in the hands of first responders. This is high priority for initial investments.
视觉表达和交互技术为用户提供了一种机制，使用户能够一次看到和理解大量的信息。人类的思维可以理解通过视觉渠道接收到的复杂信息。视觉分析建立在这种能力的基础上，以促进分析推理过程。
科学原理必须为视觉表达提供基础，并且需要原则来指导新的交互方法，以支持分析技术。这些基础一起为新的视觉范式提供了基础，这些范式可以扩展到支持许多情况下的分析推理。
视觉设计理论比交互理论更成熟，因此在进一步发展交互理论方面的投资应该优先考虑。交互理论必须考虑到与分析任务中不同紧迫性水平相关的时间约束。视觉表达和交互的应用必须根据任务的需要进行调整。规模问题也深刻（profoundly）影响了视觉表达和交互的设计，必须在设计新的视觉表达和交互技术时明确（explicitly）考虑。
创建有效的视觉表达是一个劳动密集型（labor-intensive）的过程，需要对可视化管道、要显示的数据的特征和要执行的任务有扎实的理解。目前，大多数可视化软件是在至少部分信息不完整的情况下编写的。通常，分析师无法构建新工具，他们对数据和任务有最好的理解。我们需要构建基于视觉的系统的新方法，这些方法简化了开发过程，并产生了更好地针对应用程序。
该小组提出了几项高层次的建议，旨在解决这些挑战。
建议
创建基于认知和感知原则的可部署、可重用组件的视觉表达科学。视觉表达原则必须涵盖所有类型的数据，解决规模和信息复杂性问题，通过信息综合实现知识发现，并促进分析推理。
视觉表达和交互技术为分析师和第一响应者（responder ）提供了对发展中情况的理解，以便他们采取行动。已经发展了一门支持科学应用的视觉表达科学，但是需要不同的视觉表达来处理与国土安全任务相关的多种数据类型。这些数据必须以一种方式结合起来并呈现给用户，使用户能够理解它们的含义，而不管数据类型或原始数据格式如何。目标是以一种促进推理过程并实现行动的方式展示所有相关数据。
建议
开发一套支持分析推理过程的新视觉范式。
这些可视化必须：
促进对大量并不断增长的多种类型数据的理解
提供空间和时间数据分析框架
支持对不确定、不完整和往往具有误导性信息的理解
提供用户和任务可适应、引导式表达，能够实现全面情况感知，同时支持详细行动的制定
支持多层次的数据和信息抽象
通过信息综合促进知识发现，信息综合是基于数据的含义而不是原始数据类型对数据进行整合的过程。
没有一种视觉范式可以解决所有可能的任务和情况。因此，我们建议开发一套视觉范式，以应对从脆弱性分析到实时监测到应急响应支持等多种情况。尤其是传感器、文本和图像形式的数据的规模正在迅速增长。数据不断增长和变化，视觉表达必须帮助分析师理解他们的数据和所代表的情况的变化性。同样，许多数据与特定的地点和时间相关。表示这些空间和时间特征对于提供分析理解是必要的。此外，可视化过程还受到需要支持在分析话语中理解缺失、冲突和欺骗性信息的复杂性的影响，这种分析话语是由个人的知识和他或她的任务指导的。
建议
开发一门支持分析推理过程的交互科学。这门交互科学必须提供一个交互技术的分类法，从低级别的交互到更复杂的交互技术，并且必须解决在不同类型的显示环境和任务之间进行扩展的挑战。
交互是分析话语的燃料。尽管基本原理已经存在了十多年，但它们并不能满足高阶交互技术（如任务导向或假设引导话语）来支持分析过程的需求。一门新的科学理论和实践对于解决国土安全对分析、预防和响应支持方面的复杂需求至关重要。这些交互技术必须适应分析情境的特定维度，从长期的分析评估到紧急且高压力的应急响应支持任务。这些交互必须能够适应各种平台，从应急管理控制室中的大型显示器到第一响应者手中可现场部署的手持设备（handheld devices）。这是最初投资的高优先级。