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2D and 3D Presentation of Spatial Data: a Systematic Review

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2D and 3D Presentation of Spatial Data: a Systematic Review 2D and 3D Presentation of Spatial Data: A Systematic Review Steve D ubel¨ ∗ Martin Rohlig¨ † Heidrun Schumann ‡ Matthias Trapp § Institute for Computer Science Institute for Computer Science Institute for Computer Science Hasso Plattner Institute University of Rostock, Germany University of Rostock, Germany University of Rostock, Germany University of Potsdam, Germany ABSTRACT Hence, investigations are required, on how these presentation char- The question whether to use 2D or 3D for data visualization is gen- acteristics and influences interact. erally difficult to decide. Two-dimensional and three-dimensional Today, it is assumed that approximately 60-80% of the data avail- visualization techniques exhibit different advantages and disadvan- able can be interpreted as spatial data or geodata [ 28 ]. Thus, it is tages related to various perceptual and technical aspects such as an important category with a strong relevance in visualization and occlusion, clutter, distortion, or scalability. To facilitate problem represents an ideal starting point for such investigations. understanding and comparison of existing visualization techniques Problem Statement. Related research indicates that the choice with regard to these aspects, this report introduces a systematization whether to use 2D or 3D for data visualization depends on various based on presentation characteristics. It enables a categorization factors such as data complexity, display technology, the task, or with respect to combinations of static 2D and 3D presentations of application context. One example refers to the relation of available attributes and their spatial reference. Further, it complements ex- screen-space and the number of items to display. In [ 61 ], a case isting systematizations of data in an effort to formalize a common study focusing on the application of 2D and 3D presentations for terminology and theoretical framework for this problem domain. the visualization of object-oriented systems is presented. Here, the We demonstrate our approach by reviewing different visualization perception of a given presentation is evaluated using the ratio of the techniques of spatial data according to the presented systematization. number of objects perceived and the total number of objects o. For a given display resolution ( 4002 pixels), their research indicates the ex- istence of a boundary value at which 3D presentations exhibit higher Index Terms: H.5.2 [Information Interfaces and Presentation]: context perception ( o > 250) than 2D presentations ( o < 250). User Interfaces—Graphical user interfaces (GUI) H.5.2 [Information Further, researching the effects of 2D and 3D on spatial memory Interfaces and Presentation]: User Interfaces—Theory and methods shows no significant differences [ 15 , 14 ]. Tory et al. conducted a number of experiments of 2D, 3D, and combined visualizations for 1 I NTRODUCTION estimation tasks of relative positioning and orientation as well as Visualization, as a form of visual communication, can be described region selection [ 58 ]. Their results show that 3D can be effective as the process of transforming (non-visual) data into artifacts ac- for approximate navigation and relative positioning, but 2D is more cessible to the human mind. Its major purpose is the effective suitable for precise measurement and interpretation. In general, communication of data, with a strong focus on – but not limited to – combining 2D and 3D achieves a good to superior performance and visual terms and artifacts. State-of-the-art technology enables the increases confidence during problem solving. generation of such image artifacts in real-time using 3D graphics. These examples support the thesis that the question whether to Especially, the increasing computing power and advances in ren- use 2D or 3D for data visualization is difficult to decide. Especially, dering hardware during the last three decades laid the foundations the respective advantages and disadvantages of 2D and 3D presenta- of today’s interactive visualizations of large-scale data sets. With tions, such as occlusion, clutter, distortion, or scalability, have to be these increasing capabilities, the question, whether to use 2D or 3D considered for an effective visualization design. data presentations, is recurrently raised. Visualization designers and Contributions. To facilitate the decision process, common cri- engineers are nowadays confronted with a number of choices for the teria that reflect the characteristics of the individual need to be design, implementation, and integration of visualization techniques. established visualization techniques. While previous work focused Motivation. While various systematizations of the data space on the categorization into 2D or 3D techniques only, this work exist [ 34 ], there are only few differentiations with respect to the introduces a more detailed systematization by distinguishing pre- presentation itself, such as photorealistic and non-photorealistic sentations of attribute space and reference space according to their rendering (PR and NPR) [67 ], static or dynamic presentations [ 6], dimensionality. This allows for a better comparison of existing visu- or the dimension (2D or 3D). However, a presentation-oriented alization techniques. To summarize, this report makes the following systematization is of particular interest because the effectiveness contributions: of communication and thus, human problem solving and decision 1. We introduce a novel systematization of visualization tech- making performance varies enormously (100:1) with different pre- niques for spatial data with respect to static 2D or 3D presenta- sentations [29 ]. Specifically, the suitability of a presentation in- tion of their attribute and reference space displayed on a 2D fluences the speed at which solutions are developed, the number output medium. of errors made, as well as the comprehension and visual working memory capacity [45 ] during this process. According to [ 29 ], a 2. We categorize and discuss exiting visualization techniques fundamental break misconception can be pointed out: more is better . according to this systematization. 3. We present future trends and research steps towards the devel- ∗ e-mail:[email protected] opment of guidelines for 2D and 3D visualization designs. †e-mail:[email protected] ‡e-mail:[email protected] This paper is structured as follows. Section 2 presents and describes §e-mail:[email protected] the systematization as the major contribution of this paper. Section 3 categorizes and discusses existing visualization techniques with respect to the systematization. Subsequently, Section 4 presents enhancements and describes future research directions. Finally, Section 5 concludes this report. 2 S YSTEMATIZATION This section proposes a novel systematization that distinguishes between dimensionality of the presentation of the data values and the presentation of the reference space. First, we introduce termini and definitions for the data and presentation space (Sec. 2.1) and afterwards present the systematization (Sec. 2.2). 2.1 Termini and Definitions Today’s definitions in the field of information visualization vary considerably in literature. Even simple terms, such as ”attribute” or ”data space” are defined differently [66 ]. To avoid possible ambigu- ities, we give a brief description of our understanding of relevant notions. The process of visualization operates on the level of data and on the level of presentation. Extending the visualization reference model of Card [ 12 ], Chi [ 13 ] introduced the concept of the data reference model, which is widely accepted in literature [ 2, 17 , 40 ]. Based on a schematic data flow, this model distinguishes between operations within data space and within presentation space . Data space. The base of every visualization are data. Since data can differ with respect to a number of properties (e.g., structure, dimension, or source), various categorizations have been established. Recently, Kehrer and Hauser [ 34 ] categorized spatio-temporal, multi- variate, multi-modal, multi-run, and multi-model scientific data. The first property refers to the spatio-temporal context of the observed Figure 1: Systematization of visualization techniques based on the data and the second to the dimensionality. The remaining three dimensionality of the attribute space’s and reference space’s presen- refer to specific data sources. They conclude that these properties tation (A and R respectively). For simplicity and clarity, the visual characterize the principle structure of the data, which is crucial to variables of the attribute representations are limited to a single color visualization. (blue) and a single item shape (square). For spatial data, a spatial reference is always present. Within it, observation points are defined, where the observed data values are given. The task-oriented relationship between them are summarized 2.2 Categorization of 2D and 3D Techniques by Andrienko and Andrienko [ 4] in two questions: ”What are the We propose a systematization with respect to combinations of 2D characteristics corresponding to the given reference?” and ”What is and 3D presentations of the attribute space ( A) and the reference the reference corresponding to the given characteristics?” . Accord- space (R). For this purpose we introduce a notation to index a ing to Keller and Keller [ 35 ] this involves a differentiation between particular category of the systematization: !Ai ⊕ R j, with i, j ∈ independent
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