Accelerating the Improvement of Visualization Design Methodology

我的研究创造了交互式可视化工具来帮助人们发现数据中的模式。对于这笔拨款，我提出的工作将导致更快，更好的过程，使这些工具。**现代时代的特点是通过获取比以往更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，许多分析问题没有明确说明：人们不知道该问哪些问题。在这种情况下，最好的方法是一个有人类参与的分析过程，利用人类视觉系统强大的模式检测特性。人们使用可视化（vis）工具有很多原因。它们可以成为在开发正式模型之前获得对分析需求更清晰理解的垫脚石。它们可能是针对另一个系统的设计师，帮助他们改进系统的算法，或者是针对另一个系统的最终用户，他们试图确定其行为是否合理，例如，在部署机器学习系统之前，看看它的结果是否值得信赖。除了这些过渡用途之外，可视化工具还设计用于永久使用，即人们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。我自己工作中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。可视化工具也可用于表示；例如，《纽约时报》已经部署了复杂的交互式可视化。**已经提出了许多视觉习惯用法：粗略地说，习惯用法是创造和操纵视觉表征的独特方法，范围从简单的散点图到不同尺度的复杂网络结构描述。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性的指导。我在这里提出的研究将弥补这一差距，为vis设计提供一种通用的方法。这将允许可视化从业者更快地开发更好的工具，从而为依赖可视化进行关键业务决策的许多人提供好处。由于我在其他三个研究领域的经验，我很适合进行拟议的工作，所有这些我都在其他资助下继续推进。一个也是方法学的，但通过提出研究模型来解决vis研究的高层过程。另外两个，我的研究项目的“技术驱动”和“问题驱动”两个分支，涉及构建工具。**我将在我的vis设计方法论的新工作中使用三种主要的评估方法。我将进行(1)受控的定量实验室研究和(2)定性的实地研究，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，它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界和学术界提供HQP的vis专业知识；需求已经超过了供给。在工业、科学、卫生以及个人和公共数据的探索领域，对可视化工具的需求正变得越来越普遍。在加拿大支持基础可视化研究也为衍生公司的创建打开了大门；例如，最近的一家美国vis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.