Accelerating the Improvement of Visualization Design Methodology

我的研究创建了交互式可视化工具，以帮助人们找到数据中的模式。对于这笔赠款，我建议的工作，将导致一个更快，更好的过程，使这些工具。 当今时代的特点是通过访问比以往任何时候都多的数据来做出更好的决策。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，许多分析问题都是不明确的：人们不知道该问哪个问题。在这种情况下，最好的前进路径是一个分析过程，其中有一个人在循环中，利用人类视觉系统强大的模式检测特性。 人们使用可视化工具有很多原因。它们可以作为在开发正式模型之前获得对分析需求的严格理解的垫脚石。它们可能是针对另一个系统的设计者，帮助他们改进该系统的算法，或者是针对另一个系统的最终用户，他们试图决定其行为是否合理，例如在部署之前看看机器学习系统的结果是否值得信赖。除了这些过渡用途，维斯工具也是为永久使用而设计的，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查假设的能力。我自己工作中的一个例子是一种工具，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。维斯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式可视化。 人们已经提出了许多维斯习惯用法：粗略地说，习惯用法是创建和操纵视觉表征的不同方法，从简单的散点图到不同尺度的复杂网络结构。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性的指导。我在这里提出的研究将弥合这一差距，为维斯设计提供一个通用的方法。这将允许维斯从业者更快地开发更好的工具，从而为许多依赖维斯进行关键业务决策的人提供好处。由于我在其他三个研究领域的经验，我有能力进行拟议的工作，所有这些研究我都将继续在其他资金下推进。一个也是方法论，但地址的高层次的过程中，通过提出研究模型。另外两个，我的研究计划中的“技术驱动”和“问题驱动”，涉及构建工具。 我将在我关于维斯设计方法学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，这两种技术在人机交互和认知心理学方面都有着悠久的历史。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关于大量数据集的判断来颠倒实验室研究的重点。 我的研究项目的一个主要重点是为加拿大工业和学术界提供HQP维斯专业知识;需求已经超过了供应。对维斯工具的需求在工业、科学、健康以及个人和公共数据的探索中变得越来越普遍。支持加拿大的基础可视化研究也为创建分拆公司打开了大门;例如，最近美国的一家维斯初创公司（Tableau）是2013年最大的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.