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Research on information visualization has reached the point where a number of successful point designs have been proposed and a variety of techniques have been discovered. It is now appropriate to describe and analyze portions of the design space so as to understand the differences among designs and to suggest new possibilities. This paper proposes...
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... visualization overloads stock volume, mapping it onto the X axis (actually onto a radial axis from the center of each kiosk, but we approximate here for simplification). The Y and Z locations of the bar are not semantic mappings. This bar is a Length mark, which is an instance of a Retinal Size encoding. Table 7. Internet traffic on earth (See Fig. ...
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... Software yang dapat digunakan yaitu N vision dan Visual Otomatis. Contoh worlds-within-worlds dapat dilihat pada gambar 3.10 berikut: Gambar 3.10: Contoh worlds Within Worlds (Card & Mackinlay, 1997) c. Tree map Tree map adalah metode pengisian layar yang menggunakan partisi hierarkis layar menjadi beberapa wilayah bergantung pada nilai atribut. ...
Buku Ajar Data Mining ini disusun sebagai buku panduan komprehensif yang menjelajahi kompleksitas dan mendalamnya tentang ilmu sistem informasi. Buku ini dapat digunakan oleh pendidik dalam melaksanakan kegiatan pembelajaran di bidang ilmu sistem informasi dan diberbagai bidang Ilmu terkait lainnya. Buku ini dapat digunakan sebagai panduan dan referensi mengajar mata kuliah data mining dan menyesuaikan dengan Rencana Pembelajaran Semester tingkat Perguruan Tinggi masing-masing.
Secara garis besar, buku ajar ini pembahasannya mulai dari data mining and knowledge discovery process, data understanding, knowledge representation, data preprocessing. Buku ini juga membahas materi penting lainnya seperti data mining roles, classification and prediction, cluster analysis, association rules. Selain itu materi mengenai Text Mining dan Feature extraction and selection Method dibahas secara mendalam. Buku ajar ini disusun secara sistematis, ditulis dengan bahasa yang jelas dan mudah dipahami, dan dapat digunakan dalam kegiatan pembelajaran.
... Unlike iconic visual-spatial displays, such as a map, where things being represented already have visible spatial properties, data physicalization often requires the maker to encode numerical information that is not inherently spatial in visual-spatial ways, and requires the viewers to decode the non-spatial information from a spatial representation of number magnitude. Drawing from the information visualization process model (Card and Mackinlay, 1997;, transforming abstract data into visual or tangible representations requires visual mapping. This means assigning visual properties-which can embody spatial or graphical properties (Card and Mackinlay, 1997)-to data variables. ...
... Drawing from the information visualization process model (Card and Mackinlay, 1997;, transforming abstract data into visual or tangible representations requires visual mapping. This means assigning visual properties-which can embody spatial or graphical properties (Card and Mackinlay, 1997)-to data variables. Then, the maker carries out presentation mapping to refine the data display, such as tinkering with the way of presentation to facilitate viewers' perception and understanding . ...
Understanding and effectively using visual representations is important to learning science, technology, engineering, and mathematics (STEM). Various techniques to visualize information, such as two- and three-dimensional graphs, diagrams, and models, not only expand our capacity to work with different types of information but also actively recruit our visual–spatial thinking. Data physicalization is emerging as a beginner-friendly approach to construct information visualization. Mapping intangible data onto tangible artifacts that possess visual, spatial, and physical properties demands an interplay of spatial thinking and hands-on manipulation. Much existing literature has explored using formatted infographics to aid learning and spatial thinking development. However, there is limited insight into how children may leverage their spatial thinking to create information visualizations, particularly tangible ones. This case study documented the data physicalization activities organized in two design classrooms of an international school in Netherlands, with 37 children aged 11–12. Seven themes relevant to spatial thinking were identified from multimodal evidence gathered from the data physicalization artifacts, classroom videos and recordings of children’s making process, and semi-structured interviews with children. Our findings suggested that these children generated various ideas to create visual–spatial forms for data with the materials at hand, such as mapping quantities to tangible materials of different sizes, using spatial ordinal arrangement, and unitizing materials to set visual parameters. Meanwhile, they evaluated and adjusted the visual–spatial properties of these materials according to the numerical data they had, crafting feasibility, and others’ spatial perspectives. What was particularly interesting in our findings was children’s iteration on their visual–spatial understandings of the intangible numerical values and the tangible materials throughout the embodied making processes. Overall, this study illustrated the different types of spatial thinking children applied to create their data physicalizations and offered insights into how embodied experiences accompanying the open-ended visualization challenge allowed children to explore and construct spatial understandings.
... Design spaces have been used in architecture, computer science, and especially in Human-Computer Interaction as a complement to standards and guidelines, to inspire design decisions and innovations (Simon, 1975;Card and Mackinlay, 1997;Shaw, 2012;Haeuslschmid et al., 2016;Halskov et al., 2021). Their primary use is to structure and group designs and parameters according to a set of design dimensions. ...
... Each design option is ideally represented as a point within that space, thus defining the parameters for each of its constituting dimensions (Simon, 1975). While early work focused on fundamental classifications of input devices (Buxton, 1983;Card et al., 1991) and information visualization (Card and Mackinlay, 1997;Chi, 2000), important contributions have also been provided for specific types of interaction, such as mobile phone input (Ballagas et al., 2008), public displays or multimodal interaction (Müller et al., 2010). Since Kern's and Schmidt's design space for the car cockpit (Kern and Schmidt, 2009), further more specific automotive user interface aspects were addressed, such as augmented reality (Tönnis et al., 2009;Haeuslschmid et al., 2016;Wiegand et al., 2019), conversational interaction (Braun et al., 2017), multimodal interaction as well as application contexts like the mobile office (Li et al., 2020). ...
Material Handling Vehicles (loaders, excavators, forklifts, harvesters, etc. ) have seen a strong increase in automation efforts in recent years. The contexts such vehicles operate in are frequently complex and due to the often very specific nature of industrial material handling scenarios, know-how is fragmented and literature is not as numerous as, for example, for passenger vehicle automation. In this paper, we present a contextual design space for automated material handling vehicles (AMHV), that is intended to inform context analysis and design activities across a wide spectrum of material handling use cases. It was developed on the basis of existing context and design spaces for vehicle and machine automation and extended via expert knowledge. The design space consists of separate context and interaction subspaces, that separately capture the situation and each individual point of interaction, respectively. Implications, opportunities, and limitations for the investigation and design of AMHV are discussed.
... Dynamically changing visualizations also have the potential to help build mental models of complex change processes, which static graphics are unable to do (Lowe 2004). Compared to static visualization that is often overloaded with data details and is visually and cognitively overwhelming, interactive visualization can show details on demand, increasing the amount of the variable space that can be examined at a single time and allowing viewers to better perceive how the data patterns are structured (Card and Mackinlay 1997). Therefore, dynamic visualizations have an "overall advantage" over static pictures (Höffler and Leutner 2007) in terms of recognition memory (e.g., Brucker, Scheiter, and Gerjets 2014), problem-solving (e.g., Mayer and Moreno 2002), and procedural knowledge (e.g., Yarden and Yarden 2010). ...
... Since the design space of possible visualization solutions is extensive, the visualization community has worked on theoretical models to formalize design knowledge [7]. Based on Bertin's seminal book Semiology of Graphics [5], many visualization models (e.g., [6,7,9,11,41]) are centered around marks as the basic building blocks of visualization techniques. In general terms, a mark is a geometric object that represents the attributes of a data object by position, color, or other visual features. ...
Both sonification and visualization convey information about data by effectively using our human perceptual system, but their ways to transform the data differ. Over the past 30 years, the sonification community has demanded a holistic perspective on data representation, including audio-visual analysis, several times. A design theory of audio-visual analysis would be a relevant step in this direction. An indispensable foundation for this endeavor is a terminology describing the combined design space. To build a bridge between the domains, we adopt three of the established theoretical constructs from visualization theory for the field of sonification. The three constructs are the spatial substrate, the visual mark, and the visual channel. In our model, we choose time to be the temporal substrate of sonification. Auditory marks are then positioned in time, such as visual marks are positioned in space. Auditory channels are encoded into auditory marks to convey information. The proposed definitions allow discussing visualization and sonification designs as well as multi-modal designs based on a common terminology. While the identified terminology can support audio-visual analytics research, it also provides a new perspective on sonification theory itself.
... In 1997, Card and Mackinlay [23] presented a taxonomy of the information visualization literature oriented to the data flow. This flow data model was subsequently expanded in [22] and consists of a succession of transformations that are represented by nodes, and the direction of data flowing between transformations is indicated by edges between them. ...
Nowadays, the explosive growth of data generated from numerous sources results in ever-increasing volumes of data that are, therefore, difficult to understand, explore and analyze in order to extract information from them. The contribution of visualization to the exploration and understanding of these large datasets is very significant. Various application domains often require different visual representations, although several share the same intermediate steps, transformations, and/or manipulations of such data. These shared aspects lead to the requirement for a consistent and extensible visual analytics model across all application domains. In this context, we introduce the Unified Visual Analytics Model (UVAM), which combines the Unified Visualization Model (UVM) with the specific features of visual analytics. The UVAM is a state model represented as a flow between the many states that the data passes through throughout the process. It includes the user interactions with the data and its intermediate representations and how the user controls the transformations and, subsequently, modifications of the visualizations. This paper illustrates how the model includes various visual analytics processes and describes in detail a UVAM case study.KeywordsVisual Analytics ModelVisualization ModelVisual Analytics PipelineVisualization PipelinesVisualization
... 702 menjelaskan kemajuan pekerjaan kepada klien, telah menggunakan visualisasi BIM sebagai alat untuk memfasilitasi transfer informasi. Visualisasi dilakukan sebagai sarana grafis untuk mengeksplorasi, mengkomunikasikan atau memecahkan masalah logis [4]. Selain itu, sudah ada model kolaborasi, seperti model 3D dengan Revit atau Civil 3D, kemudian dikolaborasikan ke level yang lebih tinggi menggunakan Navisworks untuk memantau jadwal atau langkah kerja. ...
Kajian ini bertujuan untuk mengevaluasi sekaligus mendalami tingkat kematangan implementasi BIM pada suatu organisasi yang mengelola proyek infrastruktur. Pengumpulan data diperoleh dari 19 proyek infrastruktur di salah satu BUMN karya di Indonesia yang memiliki karakteristik berbeda melalui kuesioner self assessment terhadap kondisi penggunaan BIM di masing-masing proyek. Penilaian difokuskan pada rangkaian kompetensi BIM, kapabilitas BIM, dan skala organisasi yang hasilnya berupa grade implementasi BIM. Hasil penelitian menunjukkan bahwa jika hanya satu proyek yang mendapatkan penilaian maksimal atau termasuk dalam kategori kematangan optimis atau tinggi, maka 42,1% proyek berada pada kategori kematangan terintegrasi atau sedang-tinggi dan 52,6% berada pada tingkat kematangan yang dikelola atau sedang. Kesimpulannya, secara organisasi implementasi BIM yang terjadi berada pada level managed atau medium maturity. Diagram visual kapabilitas dan maturitas BIM disajikan dengan banyak celah yang terjadi dalam kumpulan kompetensi proses seperti produk & layanan, gambar, LOI/LOD, desain BIM, spesifikasi jadwal tersemat, 4D, 5D, manajemen operasi & fasilitas, dan kepemimpinan. Temuan ini menjadi acuan bagi pemangku kepentingan untuk menentukan langkah dan strategi perbaikan dan pengembangan ke depan.
... Based on the information visualization process model [2,27], a critical step in transforming abstract data into visual or physical forms of representation is visual mapping, which means assigning "data dimensions to visual variables" to give "an initial visual form" to data [27]. These visual variables can have spatial or graphical properties [2]. ...
... Based on the information visualization process model [2,27], a critical step in transforming abstract data into visual or physical forms of representation is visual mapping, which means assigning "data dimensions to visual variables" to give "an initial visual form" to data [27]. These visual variables can have spatial or graphical properties [2]. Once an initial, abstract form of representation is established, presentation mapping is performed to decide how the physicalization will look like, including applying the visual mapping rule to all data variables or fine-tuning the presentation to facilitate viewers' perception and comprehension [27]. ...
... A total of 23 data physicalization artifacts were created by children either in pairs or individually. We conducted an initial round of thematic analysis [48] of their artifacts and their making processes to identify evidence of using spatial properties to encode data variables, including varying the sizes [2,15,28,35,46], spatial arrangement [2,15,28,35], assigning unit values [19,28], and making meaningful selections or combinations of shapes and materials [14,28,47]. While these properties have been widely employed in existing data physicalization artifacts by experts or non-experts, they have not been explicitly regarded as potential indicators of spatial reasoning. ...
Encoding intangible data variables with visual, spatial, and physical properties demands a high level of spatial reasoning. The ability to reason spatially is widely deemed critical to science, technology, engineering, arts, and mathematics (STEAM) learning. While much research has explored the relationship between learning with vi-sualizations and spatial skills development, little is known about how children use their spatial reasoning in constructing tangible visualizations. This work-in-progress investigates how data physi-calization activities, organized within a Design module in primary classrooms in the Netherlands, provide a window to understanding children's spatial reasoning about data. Based on preliminary analysis, we identify six indicators of children's spatial reasoning as observed in their constructing processes and artifacts. Most children in the study used tangible materials of varied sizes, curated meaningful spatial arrangements, and employed different unitizing methods to encode numerical data with spatial properties. Some children adjusted the sizes, units, or spatial arrangement to refine their tangible visualizations, considered the pros and cons of two-and three-dimensional forms of presentation, and made creative use of spatial shapes. In summary, this case study offers insights into children's use of spatial reasoning in data physicalization creation and practical implications for situating data physicalization activities in formal learning environments.
... Numerous strategies for creating an ingrained data interpretation are proposed by data visualization [8]. A key step toward increasing the availability, transparency, and usability of complex data for effective perception maximization is efficient data visualization [9] [10]. ...
... Design pattern collections are often used in classrooms and for education [1][2][3]18]. Pattern collections have been created for visualization, including Card and Mackinlay [11], Chen [13], He et al. [24], Schulz et al. [45], and Sedig et al. [46]. These collections show the breadth of visualization design options and can support designers in making deliberate choices, e.g., about tasks [45]. ...
This paper introduces design patterns for dashboards to inform dashboard design processes. Despite a growing number of public examples, case studies, and general guidelines there is surprisingly little design guidance for dashboards. Such guidance is necessary to inspire designs and discuss tradeoffs in, e.g., screenspace, interaction, or information shown. Based on a systematic review of 144 dashboards, we report on eight groups of design patterns that provide common solutions in dashboard design. We discuss combinations of these patterns in “dashboard genres” such as
narrative
,
analytical
, or
embedded dashboard
. We ran a 2-week dashboard design workshop with 23 participants of varying expertise working on their own data and dashboards. We discuss the application of patterns for the dashboard design processes, as well as general design tradeoffs and common challenges. Our work complements previous surveys and aims to support dashboard designers and researchers in co-creation, structured design decisions, as well as future user evaluations about dashboard design guidelines. Detailed pattern descriptions and workshop material can be found online:
https://dashboarddesignpatterns.github.io</uri