information visualization
資料視覺化(data visualization)希望當將圖形資訊呈現給使用者時，使用者便能夠立即理解，提供較快速而的資料分析機制。目前已經有許多針對資料視覺化技術的分類架構(classification schemes)提出。Keim[13]以進行視覺化的資料類型(data type)、視覺化技術(visualization technique)以及應用的互動/扭曲技術(interaction/distortion technique applied)為分類架構(taxonomy)的三個面向，來將視覺化技術與系統加以歸類(categorize)。Schneiderman [27]的分類學則則是一個成對的系統(a pair wise system)，包含分析的資料類型以及一組分析的任務，任務例如概觀(overview)、放大縮小(zoom)、過濾(filter)、選取詳細訊息(details-on-demand)等等。Chi [4]的分類法根據一個特定的視覺化模型中不同的性質，詳細地說明視覺化技術，包含資料、抽象(abstraction)、轉換和映射(transformation和mapping)、呈現(presentation)以及互動(interaction)。Tory and Möoller [29]根據視覺塑模(visual modeling)的直覺知覺，將科學視覺化(scientific visualization)和資訊視覺化(information visualization)分別定義為連續及離散類型 。Wiss and Carr [34]在探討3-D技術時則提出了一個考慮注意(attention)、抽象化(abstraction)以及互動支持性(interaction affordance)等的認知為基礎的分類法 。這些分類架構大多僅針對視覺化過程(visualization process)中的某一面向，然而還存在許多有待解決的問題，例如：視覺探索場景(visual exploration scene)的基礎構件(building blocks)是什麼？互動機制如何連結到事實？
本研究根據視覺化技術的機制和視覺知覺理論(visual perception theory)，以解釋視覺化場景如何構成以及它們的構成部分如何有助於視覺理解，做為分析模型。本研究認為視覺化是利用空間化(spatialization)的方式讓資料得以空間性的被感知，並以位置(position)、形狀(shape)和顏色(color)等視覺刺激(visual stimuli) 來代表資料項目或特徵，將可以注意到的差異(noticeable differences)極大化(Ware [32])，空間化後的資料才能夠在知覺注意(conscious attention)前確認事物，能夠立即與容易地引發注意，提昇與加速資料理解。Figure 1表現出這樣的分類法概念。
進一步而言，資料的空間化是將資料從難以說明的原始形式轉換為可視的空間形式，空間化的程序有結構展現(structure exposition)、投射(projection)、樣式定位(patterned positioning)以及 再製(reproduction)等。結構展現是指資料可以嵌入階層或關係網絡等內在結構(intrinsic structures)，可以大抵體現出資料的意義，也就是使得相關的資料結構能夠視覺化地覺察，例如樹狀地圖(treemaps) [28] 或力導向圖式布局(force directed graph structures) [8] 都屬於結構展現。樣式定位是根據一個或多個方向、直線、圓，甚至特定模式，依序安排個別資料項目的設定，意圖填滿整個投射空間，有時稱之為密集向素呈現(dense pixel displays)，這樣的空間化技術有像素長條圖(pixel bar charts) [15]，圓餅圖(pie charts)也屬於這類。在投射這類空間化技術的呈現上，資料項目的位置由一個已知或內涵的數學函數來定義，著名的案例有平行座標(parallel coordinates)與星狀座標(star coordinates) [11]。在再製(reproduction)的呈現上，資料的定位是事先已知的，並且從資料蒐集的系統來決定。
在視覺化場景中，位置是前注意知覺(pre-attention perception)的主要部分，而且它和空間化非常有關。位置包括排列(arrangement)、對應(correspondence)、參考(referential)等三種型式，分別由空間化的各種機制所產生。結構展現產生排列，特定的排列能描述結構、階層或其他整體特性(global property)等資訊，資訊可以從局部的個別元素間相互位置或是整體的景觀概況上察覺，例如樹狀地圖表現資料項目間的階層關係。樣式定位會產生位置上的對應，資料項目的位置決定它的對應特性。投射與再製運用函數或明確的參考來源(explicit referewnce)決定資料的位置，提供使用者得以了解。
形狀可以表達的資訊，包含差異(differentiation)、對應(correspondence)、意義(meaning)和關係(relationship)等。差異是指以呈現的形狀區別這些項目，來提供近一步的詮釋。對應則是利用每一種引人注目的形狀對應一種資訊特徵，例如大小比例便是常用的方式。意義是指呈現的形狀本身便帶有意義，這種方式依賴使用者的知識與過去經驗，例如箭頭。關係：線條、外廓或表面可以利用來表現一組資料項目間的關係。
顏色可以傳遞資料項目間的差異和對應。在差異方面，顏色用來表示資料特徵上的相等(或不相等)，對應上則可表現離散資料的類別、層級或連續資料的值大小。
互動模式則有參數的(parametric)、視景轉換(view transformation)、過濾(filtering)、揀選細節(details-on-demand)、變形(distortion)。參數的互動是指變更位置、形狀和顏色等參數。視景轉換以縮放、旋轉、翻轉等，對視景提供物理性接觸。過濾透過顏色或形狀等方式，視覺性地選取一部份的資料。揀選細節是指資料的詳細資訊可以被隨時檢索出，並呈現在場景上。變形提供投射的視覺化結果，讓不同的觀點可以同時被觀察與定義。
These efforts are generically known as (data) Visualization, which provides faster and user-friendlier mechanisms for data analysis, because the user draws on his/her comprehension immediately as graphical information comes up to his/her vision.
Several classification schemes have been proposed for visualization techniques, each one focusing on some aspect of the visualization process.
However, many questions remain unanswered.
What are the building blocks of a visual exploration scene?
How interaction mechanisms relate to these facts?
In this work, we discuss these issues and analytically find answers to them based on the very mechanisms of the visualization techniques and on visual perception theory.
In this paper we discuss the subjective nature of visualization by proposing a discrete model that can better explain how visualization scenes are composed and formed, and how their constituent parts contribute to visual comprehension.
It (Keim [13]) maps visualization techniques within a three dimensional space defined by the following discrete axes: the data type to be visualized (one, two, multi-dimensional, text/web, hierarchies/graphs and algorithm/software), the visualization technique (standard 2D/3D, geometrical, iconic, dense pixel and stacked), and the interaction/distortion technique applied (standard, projection, filtering, zoom, distortion and link & brush).
This taxonomy is suitable to quickly reference and categorize visualization techniques, but it is not adequate to explain their mechanisms.
A simpler taxonomy was earlier presented by Schneiderman [27]. It delineates a pair wise system based on a set of data types to be explored, and on a set of exploratory tasks to be carried out by the analyst. This taxonomy, known as task (overview, zoom, filter, details-on-demand, relate, history and extract) by data type (one, two, three, multidimensional, tree and network) taxonomy, was pioneer in analytically delineating visualization techniques.
Another interesting classification is presented by Chi [4], a quite analytical approach, which details visualization techniques through various properties related to a specific visualization model. The taxonomy embraces data, abstraction, transformation and mapping tasks, presentation and interaction.
Tory and Möoller [29] define Scientific Visualization and Information Visualization, respectively, as continuous ([one, two, three, multi-dimensional] versus [scalar, vector, tense, multi-variate]) and discrete (two, three, multidimensional and graph & tree) classes, according to the intuitive perception of their visual modeling.
Wiss and Carr [34] describe a cognitive based taxonomy that considers attention, abstraction and (interaction) affordance in order to discus 3-D techniques.
Visualization can be understood as data represented visually. That is, it takes advantage of spatialization to allow data to be visually/spatially perceived and it relies on visual stimuli to represent data items or data attributes/ characteristics.
Spatialization of data refers to its transformation from a raw format that is difficult to interpret into a visible spatial format.
In fact, Rohrer et al. [25] state that visualizing the non-visual requires mapping the abstract into a physical form, and Rhyne et al. [23] differentiate Scientific visualization and Information visualization based on whether the spatialization mechanism is given or chosen, respectively.
Semiotic theory is the study of signs and how they convey meaning. According to semiotic theory, the visual process is comprised of two phases, the parallel extraction of low-level properties (called pre-attentive processing) followed by a sequential goal-oriented slower phase.
Pre-attentive processing plays a crucial role in promoting visualization’s major gain, that is, improved and faster data comprehension [30].
The work described by Ware [32] identifies the categories of visual features that are pre-attentively processed. Position (2D position, stereoscopic depth, convex/concave shading), Shape (line orientation, length, width and line collinearity, size, curvature, spatial grouping, added marks, numerosity) and Color (hue, saturation) are considered and, according to Pylyshyn et al [21], specialized areas of the brain exist to process each of them (Figure 2).
Visualizing data demands a maximization of just noticeable differences. To satisfy this need, visualizations rely on pre-attentive stimuli - characteristics inherent to visual/spatial entities. Therefore, the data must first be mapped to the spatial domain (spatialized) in order to be pre-attentively perceived.
In this section we identify a set of procedures for data spatialization: Structure exposition, Projection, Patterned positioning and Reproduction.
Structure exposition: data can embed intrinsic structures, such as hierarchies or relationship networks (graph-like), that embody a considerable part of the data significance.
This class comprises visualization techniques that rely on methods to adjust data presentation so that the underlying data structure can be visually perceived. Examples are the TreeMap technique [28], illustrated in Figure 3(a), and force directed graph layouts [8], such as the one illustrated
in Figure 3(b);
Patterned: this is the simplest positioning procedure, with the set of individual data items arranged sequentially (ordered or not) according to one or more directions, linear, circular or according to specific patterns.
Patterned techniques tend to fully populate the projection area and sometimes are referred to as dense pixel displays. Examples include Pixel Bar Charts [15], showed in Figure 4(a), pie charts (circular disposition), depicted in 4(b) and pixel oriented techniques in general [12].
Projection: stands for a data display modeled by the representation of functional variables. That is, the position of a data item is defined by either a well-known or an implicit mathematical function.
In a projection, the information given is magnitude and not order, as in a patterned spatialization. Examples are Parallel Coordinates (one projection per data dimension), Star Coordinates [11] and conventional graph plots, as illustrated in Figure 5;
Reproduction: data positioning is known beforehand and is determined by the spatialization of the system from where data were collected, as exemplified in Figures 6(a) and 6(b).
In reproduction, the data inherits positioning from its original source.
Position is the primary component for pre-attention perception in visualization scenes and it is strictly related to the spatialization process. So, while spatialization is the cornerstone for enabling visual data analysis (as it maps data to the visual/spatial domain), it also dictates the mechanism for pre-attentive positional perception.
Thus, positional pre-attention occurs in the form of Arrangement, Correspondence and Referential, explained in the following paragraphs. These classes derive, respectively, from spatializations Structure Exposition, Patterned and Projection/ Reproduction.
Structure Exposition → Arrangement: specific arrangements can depict structure, hierarchy or some other global property. Without an explicit referential, information is perceived locally through individual inter-positioning of elements and/or globally through a scene overview. For instance,
TreeMap (Figure 3(a)) presents the hierarchy of the data items, and a graph layout (Figure 3(b)) presents network information.
Patterned → Correspondence: the position of an item, either discrete or continuous, determines its corresponding characteristic without demanding a reference. For example, see Figure 10(b) where each of the four line positions maps one data attribute. Other examples are Parallel Coordinates and Table Lens [22], techniques that define an horizontal sequence for placing data attributes;
Projection → Referential: this is the most obvious relation between spatialization and positional pre-attention. Projections have a supporting function whose intervals define referential scales suited to analogical comprehension.
Reproduction → Referential: the position of an element, discrete or continuous, is given relative to an explicit reference, such as a geographical map (Figure 6(b)), a meaningful shape (Figure 7(a)) or a set of axes (Figure 7(b));
In particular, the Shape stimulus embraces the largest number of possibilities to express information: Differentiation, Correspondence, Meaning and/or Relationship.\
Differentiation: the shape displayed discriminates the items for further interpretation, as in Figures 8(a), 9(a) and 10(a);
Correspondence: discrete (Figure 8(a)) or continuous (Figure 8(b)), each noticeable shape corresponds to one informative feature. Proportion (variable sizing) is the most used variation for this practice;
Meaning: the shape displayed carries meaning, such as an arrow, a face or a complex shape (e.g. text), whose comprehension may depend on user’s knowledge and previous experience, as depicted in Figures 7(b) and 8(b);
Relationship: shapes, such as lines, contours or surfaces, denote the relationship between a set of data items, e.g., in Parallel Coordinates, 3D plots and paths in general, illustrated in Figures 5(a) and 7(a).
Color conveys information by Differentiation and/or Correspondence of data items:
Differentiation: colors have no specific data correspondence, they just depict equality (or inequality) of some data characteristic, as it may be observed in the visualizations depicted in Figures 9(a) and 9(b). The coloring of the items, either discrete or continuous, is data dependent or user input dependent;
Correspondence: discrete or continuous, as observed in Figures 7(a) and (b). In the discrete case each noticeable color maps one informative feature, usually a class, a level, a stratum or some predefined correspondence. In the continuous case, the variation of tones maps a set of continuous data values.
Therefore, we must clarify the role of interaction techniques in the visualization scene. We define two conditions for identifying an interaction technique:
1. An interaction technique must enable a user to define/redefine the visualization by modifying the characteristics of pre-attentive stimuli;
2. An interaction technique, with appropriate adaptations, must be applicable to any visualization technique.
The first condition arises from the direct assumption that interaction techniques alter the state of a computational application. In the case of a visualization scene, its basic components (the pre-attentive stimuli) must be altered.
The second condition arises from the need of having interaction techniques that are well defined, which directs us towards generality. An interaction technique, then, must be applicable to any visualization technique, even if not efficiently.
We identify the following interaction paradigms:
• Parametric: the visualization is redefined, visually (e.g., scrollbar) or textually (e.g., type-in), by modifying position, shape or color parameters.
• View transformation: this interaction adds physical touch to the visualization scene, whose shape (size) and position can be changed through scale, rotation, translation and/or zoom, not necessarily all of them, as in the FastmapDB tool [2];
• Filtering: a user is allowed to visually select a subset of items that, through pre-attentive factors such as color (brushing) and shape (selection contour), will be promptly differentiated for user perception.
• Details-on-demand: detailed information about the data that generated a particular visual entity can be retrieved at any moment and presented in the scene.
• Distortion: allows visualizations to be projected so that different perspectives (positions) can be observed and defined simultaneously.
In proposing this taxonomy we focused on generalizing the rationale of how visualization scenes are engendered and how they are presented to our cognitive system.
Such a general characterization results in a taxonomy that does not rely on specific details on how techniques operate. Rather, it considers their fundamental constituent parts: how they perform spatialization and how they employ pre-attentive stimuli to convey meaning.
Existing taxonomies categorize techniques based on diverse and detailed information on how techniques perform a visual mapping. This diversity and detailing (refer to Section 2) include, e.g., axes arrangement (“stacked techniques”), specific representational patterns (“iconic and pixel-oriented techniques”), predisposition of representativeness (“network and tree techniques”), dimensionality (“2D/3D techniques”) and interaction (“static/dynamic techniques”). Although such approaches can suitably describe the set of available techniques, they lack analytical power because the core constituents of the techniques are diffused within the taxonomical structure.
Our approach results in an extensible taxonomy that can accommodate new techniques as, in fact, any technique will rely on common foundational basis.
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