Accelerating the Improvement of Visualization Design Methodology

我的研究创造了交互式可视化工具，帮助人们找到数据中的模式。对于这笔赠款，我建议开展工作，使制造这些工具的过程更快、更好。现代的特点是承诺通过访问比以往任何时候都更多的数据来更好地做出决策。当人们对数据有明确的问题要问时，他们可以使用统计学和机器学习等领域的纯计算技术。然而，许多分析问题都没有明确说明：人们不知道应该问许多问题中的哪一个。在这种情况下，最好的前进路径是利用人类视觉系统强大的模式检测属性的循环中的人的分析过程。人们出于许多原因使用可视化(VIS)工具。它们可以成为在开发正式模型之前更清楚地了解分析需求的垫脚石。它们可能是针对另一个系统的设计者，以帮助他们改进该系统的算法，或者针对另一个系统的最终用户，他们试图确定其行为是合理的，例如，在部署一个机器学习系统之前，看看它的结果是否值得信赖。除了这些过渡用途外，VIS工具还被设计为永久使用，在这种情况下，人类打算无限期地留在循环中。一个常见的例子是科学发现的探索性分析，其目标是加快和提高人类产生和检验假设的能力。我自己工作的一个例子是一个工具，可以帮助生物学家通过分析DNA序列变异来研究疾病的遗传基础。VIS工具也可以用于演示；例如，《纽约时报》已经部署了复杂的交互可视化。已经提出了许多VIS习语：粗略地说，习语是创建和操作视觉表示的不同方法，从简单的散点图到不同尺度的网络结构的复杂描述。然而，对于如何为他们的领域选择合适的习惯用法，最终用户几乎没有得到一般的指导。我在这里提出的研究将弥补这一差距，为VIS设计提供一个通用的方法学。这将允许VIS实践者更快地开发更好的工具，从而为许多依赖VIS做出关键业务决策的人带来好处。由于我在其他三个研究方面的经验，我能够很好地开展拟议的工作，所有这些研究都在其他资金的资助下继续推进。一种也是方法论的，但通过提出研究模型来解决VIS研究的高层次过程。另外两个部分，我的研究计划的“技术驱动”和“问题驱动”部分，涉及构建工具。我将在我的关于VIS设计方法的新工作中使用三种主要的评估方法。我将进行(1)受控定量实验室研究和(2)定性实地研究，这两项研究都是在人机交互和认知心理学方面具有悠久历史的技术。我还提出了一种新的方法，(3)数据研究，通过从少数人类专家那里收集大量数据集的判断来颠倒实验室研究的重点。我的研究计划的一个主要重点是向加拿大工业界和学术界提供VIS专业知识；需求已经超过了供应。在工业、科学、医疗以及个人和公共数据的探索中，对VIS工具的需求正变得越来越普遍。支持加拿大的基础可视化研究也为创建剥离公司打开了大门；例如，最近一家美国VIS初创公司(Tableau)是2013年规模最大的IPO之一，目前市值为40亿美元。

My research creates interactive visualization tools to help people find patterns in data. For this grant I propose work that will lead to a faster and better process for making these tools.The modern era is characterized by the promise of better decision making through access to more data than ever before. When people have well-defined questions to ask about data, they can use purely computational techniques from fields such as statistics and machine learning. However, many analysis problems are ill specified: people don't know which of many questions to ask. In such cases, the best path forward is an analysis process with a human in the loop, exploiting the powerful pattern detection properties of the human visual system.People use visualization ("vis") tools for many reasons. They can be stepping stones to gaining a crisper understanding of analysis requirements before developing formal models. They might be aimed at the designers of another system to help them refine that system's algorithms, or at end users of another system who are trying to decide its behavior is reasonable, for example to see if the results of a machine learning system are trustworthy before deploying it. In addition to these transitional uses, vis tools are also designed for permanent use, where a human intends to stay in the loop indefinitely. A common case is exploratory analysis for scientific discovery, where the goal is to speed up and improve a human's ability to generate and check hypotheses. An example from my own work is a tool to help biologists studying the genetic basis of disease through analyzing DNA sequence variation. Vis tools can also be used for presentation; for example, the New York Times has deployed sophisticated interactive visualizations.Many vis idioms have been proposed: loosely speaking, idioms are distinct approaches to creating and manipulating visual representations, ranging from simple scatterplots to complex depictions of network structure at different scales. However, end users are offered little general guidance about how to choose an appropriate idiom for their domain. The research I propose here will bridge this gap, offering a general methodology for vis design. This will allow vis practitioners to develop better tools more quickly, thus providing benefits to the many people who rely upon vis for business-critical decisions. I am well positioned to conduct the proposed work because of my experience in three other threads of research, all of which I am continuing to advance under other funding. One is also methodological, but addresses the high-level process of vis research by proposing research models. The other two, the “technique-driven” and “problem-driven” legs of my research program, involve building tools.I will use three main evaluation methods in my new work on methodologies for vis design. I will conduct (1) controlled quantitative lab studies and (2) qualitative field studies, both of which are techniques with a long history in human-computer interaction and cognitive psychology. I have also proposed a new method, (3) data studies, which invert the emphasis of lab studies by collecting judgements about a large number of datasets from a small number of human experts.A major focus of my research program is providing HQP with vis expertise to Canadian industry and academia; the demand already outstrips the supply. The need for vis tools is becoming increasingly pervasive across industry, science, health, and the exploration of personal and public data. Supporting foundational visualization research in Canada also opens the door to the creation of spinoff companies; for example, a recent US vis startup (Tableau) was one of the biggest IPOs of 2013, and now has a $4B market capitalization.