Information Visualization
Card, Mackinlay, and Shneiderman [2]將視覺化(visualization)定義為「為了增強認知，利用電腦支援、互動的資料視覺表現」。他們並指出歸納視覺能增強認知的方式在於
-- 增加可運用的記憶與處理資源
-- 減少資訊的蒐尋
-- 增強樣式的辨認
-- 產生知覺推理(perceptual inference)運作
-- 使用知覺注意機制進行監控
-- 將資訊以可處理的媒介進行編碼
資訊視覺化的價值雖然可以用使用這項技術的計畫成功來判斷，但由於視覺化往往不是這些成功的唯一方法。本研究則提出以知識增加的價值與需要的成本之間的差來計算資訊視覺化的效益。以數學的方式表示如下：
F = nm(W( ΔK) − Cs − kCe) − Ci − nCu.
其中n代表使用這種視覺化方法的使用者人數，m則是他們的平均使用次數，k則是每次探索需要的步驟數目。而每位使用者每次經由視覺化能獲得的知識價值以W( ΔK))表示，每次需要的前置成本為Cs，反覆進行探索時每一步驟花費的成本則需要Ce，並且這個視覺化技術的研究開發成本和每位使用者選擇與獲得這項技術分別為Ci和Cu。根據上面的公式，視覺化技術要獲得最大的效益需要使用的使用者愈多，然後經常使用來獲取高價值的知識，並且在時間與硬體、軟體與精力上的花費盡量少。
They (Card, Mackinlay, and Shneiderman) describe visualization as “the use of computer-supported, interactive visual representations of data to amplify cognition.” [2]
Card, Mackinlay, and Shneiderman [2]將視覺化(visualization)定義為「為了增強認知，利用電腦支援、互動的資料視覺表現」。他們並指出歸納視覺能增強認知的方式在於
-- 增加可運用的記憶與處理資源
-- 減少資訊的蒐尋
-- 增強樣式的辨認
-- 產生知覺推理(perceptual inference)運作
-- 使用知覺注意機制進行監控
-- 將資訊以可處理的媒介進行編碼
根據Ware [26]，前注意處理理論(preattentive processing theory)和格式塔理論(Gestalt theory)是兩種主要的可以解釋視覺如何有效地知覺特徵和形狀的心理學理論。前注意處理理論(preattentive processing theory)解釋能夠有效處理的視覺特徵，資訊視覺化便是根據前注意處理理論選擇資料呈現的視覺編碼，藉以使感興趣的視覺查詢(visual queries)在前注意處理完成。格式塔理論則是提供描述視覺系統使了解圖像的重要原理，包括接近性(proximity)、相似性(similarity)、連續性(continuity)、對稱性(symmetry)、封閉性(closure)以及相對大小(relative size)等。
本研究認為資訊視覺化的最佳應用為極大資訊空間的探索，當人們尚未知道問題何在或是想提出更好、更有意義的問題時，提供檢視資料來進行了解，產生新的發現或者洞悉資料。Lin [8] 認為這種的瀏覽對資料有良好的相關結構，但同時使用者不熟悉資料集合的內容也僅有限地系統的組織方式，他們對相關資訊需求的描述有困難，對資訊的辨認比描述容易，並偏好以較少的認知負荷進行探索等情形下有幫助。因此，資訊視覺化發揮功用的過程如下：使用者提出一個他們感興趣的問題，將資料以正確的表示方式呈現，讓使用者了解這個表示方式，回答問題並且引發許多預期外的發現與問題。資訊視覺化的價值雖然可以用使用這項技術的計畫成功來判斷，但由於視覺化往往不是這些成功的唯一方法。本研究則提出以知識增加的價值與需要的成本之間的差來計算資訊視覺化的效益。以數學的方式表示如下：
F = nm(W( ΔK) − Cs − kCe) − Ci − nCu.
其中n代表使用這種視覺化方法的使用者人數，m則是他們的平均使用次數，k則是每次探索需要的步驟數目。而每位使用者每次經由視覺化能獲得的知識價值以W( ΔK))表示，每次需要的前置成本為Cs，反覆進行探索時每一步驟花費的成本則需要Ce，並且這個視覺化技術的研究開發成本和每位使用者選擇與獲得這項技術分別為Ci和Cu。根據上面的公式，視覺化技術要獲得最大的效益需要使用的使用者愈多，然後經常使用來獲取高價值的知識，並且在時間與硬體、軟體與精力上的花費盡量少。
They (Card, Mackinlay, and Shneiderman) describe visualization as “the use of computer-supported, interactive visual representations of data to amplify cognition.” [2]
InfoVis systems are best applied for exploratory tasks, ones that involve browsing a large information space. Frequently, the person using the InfoVis system may not have a specific goal or question in mind. Instead, the person simply may be examining the data to learn more about it, to make new discoveries, or to gain insight about it. The exploratory process itself may influence the questions and tasks that arise.
InfoVis systems, on the other hand, appear to be most useful when a person simply does not know what questions to ask about the data or when the person wants to ask better, more meaningful questions. InfoVis systems help people to rapidly narrow in from a large space and find parts of the data to study more carefully.
Lin [8] describes a number of conditions in which browsing is useful:
– When there is a good underlying structure so that items close to one another can be inferred to be similar
– When users are unfamiliar with a collection’s contents
– When users have limited understanding of how a system is organized and prefer a less cognitively loaded method of exploration
– When users have difficulty verbalizing the underlying information need
– When information is easier to recognize than describe
Information Visualization is about developing insights from collected data, not about understanding a specific domain.
Information Visualization is still an inductive method in the sense that it is meant at generating new insights and ideas that are the seeds of theories, but it does it by using human perception as a very fast filter: if vision perceives some pattern, there might be a pattern in the data that reveals a structure.
Following that definition, the authors (Card, Mackinlay, and Shneiderman [2]) listed a number of key ways that the visuals can amplify cognition:
– Increasing memory and processing resources available
– Reducing search for information
– Enhancing the recognition of patterns
– Enabling perceptual inference operations
– Using perceptual attention mechanisms for monitoring
– Encoding info in a manipulable medium
According to Ware [26], there are two main psychological theories that explain how vision can be used effectively to perceive features and shapes. At the low level, Preattentive processing theory [19] explains what visual features can be effectively processed. At a higher cognitive level, the Gestalt theory [6] describes some principles used by our brain to understand an image.
Preattentive processing theory explains that some visual features can be perceived very rapidly and accurately by our low-level visual system. ... Information visualization relies on this theory to choose the visual encoding used to display data to allow the most interesting visual queries to be done preattentively.
Gestalt theory explains important principles followed by the visual system when it tries to understand an image. According to Ware [26], it is based on the following principles:
Proximity Things that are close together are perceptually grouped together;
Similarity Similar elements tend to be grouped together;
Continuity Visual elements that are smoothly connected or continuous tend to be grouped;
Symmetry Two symmetrically arranged visual elements are more likely to be perceived as a whole;
Closure A closed contour tends to be seen as an object;
Relative Size Smaller components of a pattern tend to be perceived as objects whereas large ones as a background.
Still, the process of explaining how InfoVis works remains the same: ask a question that interests people, show the right representation, let the audience understand the representation, answer the question and realize how many more unexpected findings and questions arise.
One effective line of argumentation about the value of InfoVis is through reporting the success of projects that used InfoVis techniques. These stories exist but have not been advertised in general scientific publications until recently [16,12,9]. One problem with trying to report on the success of a project is that visualization is rarely the only method used to reach the success.
Visualization can be considered as a technology, a collection of methods, techniques, and tools developed and applied to satisfy a need. Hence, standard technological measures apply: Visualization has to be effective and efficient.
The profit of visualization is defined as the difference between the value of the increase in knowledge and the costs made to obtain this insight.
A schematic model is considered: One visualization method V is used by n users to visualize a data set m times each, where each session takes k explorative steps.
The value of an increase in knowledge (or insight) has to be judged by the user. Users can be satisfied intrinsically by new knowledge, as an enrichment of their understanding of the world. A more pragmatic and operational point of view is to consider if the new knowledge influences decisions, leads to actions, and, hopefully, improves the quality of these. The overall gain now is nm(W( ΔK)), where W( ΔK)) represents the value of the increase in knowledge.
Concerning the costs for the use of (a specific) visualization V , these can be split into various factors. Initial research and development costs Ci have to be made; a user has to make initial costs Cu, because he has to spend time to select and acquire V , and understand how to use it; per session initial costs Cs have to be made, such as conversion of the data; and finally during a session a user makes costs Ce, because he has to spend time to watch and understand the visualization, and interactively explore the data set. The overall profit now is
F = nm(W( ΔK) − Cs − kCe) − Ci − nCu.
In other words, this leads to the obvious insight that a great visualization method is used by many people, who use it routinely to obtain highly valuable knowledge, while having to spend little time and money on hardware, software, and effort.
The costs Ce that have to be made to understand visualizations depend on the prior experience of the users as well as the complexity of the imagery shown.
The costs Cs per session and Cu per user can be reduced by tight integration with applications.
The initial costs Ci for new InfoVis methods and techniques roughly fall into two categories: Research and Development.
Research costs can be high, because it is often hard to improve on the state of the art, and because many experiments (ranging from the development of prototypes to user experiments) are needed. On the other hand, when problems are addressed with many potential usages, these costs are still quite limited.
Development costs can also be high. It takes time and effort to produce software that is stable and useful under all conditions, and that is tightly integrated with its context, but here also one has to take advantage of the large potential market. Development and availability of suitable middleware, for instance as libraries or plug-ins that can easily customized for the problem at hand is an obvious route here.
One effective line of argumentation about the value of InfoVis is through reporting the success of projects that used InfoVis techniques. These stories exist but have not been advertised in general scientific publications until recently [16,12,9]. One problem with trying to report on the success of a project is that visualization is rarely the only method used to reach the success.
Visualization can be considered as a technology, a collection of methods, techniques, and tools developed and applied to satisfy a need. Hence, standard technological measures apply: Visualization has to be effective and efficient.
The profit of visualization is defined as the difference between the value of the increase in knowledge and the costs made to obtain this insight.
A schematic model is considered: One visualization method V is used by n users to visualize a data set m times each, where each session takes k explorative steps.
The value of an increase in knowledge (or insight) has to be judged by the user. Users can be satisfied intrinsically by new knowledge, as an enrichment of their understanding of the world. A more pragmatic and operational point of view is to consider if the new knowledge influences decisions, leads to actions, and, hopefully, improves the quality of these. The overall gain now is nm(W( ΔK)), where W( ΔK)) represents the value of the increase in knowledge.
Concerning the costs for the use of (a specific) visualization V , these can be split into various factors. Initial research and development costs Ci have to be made; a user has to make initial costs Cu, because he has to spend time to select and acquire V , and understand how to use it; per session initial costs Cs have to be made, such as conversion of the data; and finally during a session a user makes costs Ce, because he has to spend time to watch and understand the visualization, and interactively explore the data set. The overall profit now is
F = nm(W( ΔK) − Cs − kCe) − Ci − nCu.
In other words, this leads to the obvious insight that a great visualization method is used by many people, who use it routinely to obtain highly valuable knowledge, while having to spend little time and money on hardware, software, and effort.
The costs Ce that have to be made to understand visualizations depend on the prior experience of the users as well as the complexity of the imagery shown.
The costs Cs per session and Cu per user can be reduced by tight integration with applications.
The initial costs Ci for new InfoVis methods and techniques roughly fall into two categories: Research and Development.
Research costs can be high, because it is often hard to improve on the state of the art, and because many experiments (ranging from the development of prototypes to user experiments) are needed. On the other hand, when problems are addressed with many potential usages, these costs are still quite limited.
Development costs can also be high. It takes time and effort to produce software that is stable and useful under all conditions, and that is tightly integrated with its context, but here also one has to take advantage of the large potential market. Development and availability of suitable middleware, for instance as libraries or plug-ins that can easily customized for the problem at hand is an obvious route here.
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