Data and information visualization

441: 1054:

elements used deliberately in a meaningful and non-distracting manner. The visuals are accompanied by supporting texts (labels and titles). These verbal and graphical components complement each other to ensure clear, quick and memorable understanding. Effective information visualization is aware of the needs and concerns and the level of expertise of the target audience, deliberately guiding them to the intended conclusion. Such effective visualization can be used not only for conveying specialized, complex, big data-driven ideas to a wider group of non-technical audience in a visually appealing, engaging and accessible manner, but also to domain experts and executives for making decisions, monitoring performance, generating new ideas and stimulating research. In addition, data scientists, data analysts and data mining specialists use data visualization to check the quality of data, find errors, unusual gaps and missing values in data, clean data, explore the structures and features of data and assess outputs of data-driven models. In

2144: 1468: 2517: 2031: 3368:: connects elements selected in one plot with elements in another plot. The simplest kind of linking, one-to-one, where both plots show different projections of the same data, and a point in one plot corresponds to exactly one point in the other. When using area plots, brushing any part of an area has the same effect as brushing it all and is equivalent to selecting all cases in the corresponding category. Even when some plot elements represent more than one case, the underlying linking rule still links one case in one plot to the same case in other plots. Linking can also be by categorical variable, such as by a subject id, so that all data values corresponding to that subject are highlighted, in all the visible plots. 1303:. According to Vitaly Friedman (2008) the "main goal of data visualization is to communicate information clearly and effectively through graphical means. It doesn't mean that data visualization needs to look boring to be functional or extremely sophisticated to look beautiful. To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex data set by communicating its key aspects in a more intuitive way. Yet designers often fail to achieve a balance between form and function, creating gorgeous data visualizations which fail to serve their main purpose — to communicate information". 2774: 1613: 1591:

problem solving. Human visual processing is efficient in detecting changes and making comparisons between quantities, sizes, shapes and variations in lightness. When properties of symbolic data are mapped to visual properties, humans can browse through large amounts of data efficiently. It is estimated that 2/3 of the brain's neurons can be involved in visual processing. Proper visualization provides a different approach to show potential connections, relationships, etc. which are not as obvious in non-visualized quantitative data. Visualization can become a means of

1271:, etc.). Among these approaches, information visualization, or visual data analysis, is the most reliant on the cognitive skills of human analysts, and allows the discovery of unstructured actionable insights that are limited only by human imagination and creativity. The analyst does not have to learn any sophisticated methods to be able to interpret the visualizations of the data. Information visualization is also a hypothesis generation scheme, which can be, and is typically followed by more analytical or formal analysis, such as statistical hypothesis testing. 1742: 1738:

apparently was meant to represent a plot of the inclinations of the planetary orbits as a function of the time. For this purpose, the zone of the zodiac was represented on a plane with a horizontal line divided into thirty parts as the time or longitudinal axis. The vertical axis designates the width of the zodiac. The horizontal scale appears to have been chosen for each planet individually for the periods cannot be reconciled. The accompanying text refers only to the amplitudes. The curves are apparently not related in time.

2630: 1836:

How can computing, design, and design thinking help maximize research results? What methodologies are most effective for leveraging knowledge from these fields? By encoding relational information with appropriate visual and interactive characteristics to help interrogate, and ultimately gain new insight into data, the program develops new interdisciplinary approaches to complex science problems, combining design thinking and the latest methods from computing, user-centered design, interaction design and 3D graphics.

2352: 931:, on the other hand, deals with multiple, large-scale and complicated datasets which contain quantitative (numerical) data as well as qualitative (non-numerical, i.e. verbal or graphical) and primarily abstract information and its goal is to add value to raw data, improve the viewers' comprehension, reinforce their cognition and help them derive insights and make decisions as they navigate and interact with the computer-supported graphical display. Visual tools used in information visualization include 2832: 2241: 1164: 3168: 2460: 2880: 3796: 1658: 3053: 1721:, which is a type of data visualization that presents and communicates specific data and information through a geographical illustration designed to show a particular theme connected with a specific geographic area. Earliest documented forms of data visualization were various thematic maps from different cultures and ideograms and hieroglyphs that provided and allowed interpretation of information illustrated. For example, 2406: 1962: 2571: 1777: 3478:

over the past ten years or a conceptual idea like how a specific organisation is structured. Once this question is answered one can then focus on whether they are trying to communicate information (declarative visualisation) or trying to figure something out (exploratory visualisation). Scott Berinato combines these questions to give four types of visual communication that each have their own goals.

3690: 3735: 2675: 1172: 2950: 36: 3837:

skill set but exists as a separate field of expertise. Often confused with data visualization, data presentation architecture is a much broader skill set that includes determining what data on what schedule and in what exact format is to be presented, not just the best way to present data that has already been chosen. Data visualization skills are one element of DPA."

3001: 1673: 2305: 2188: 440: 6008: 791:, these visualizations are intended for a broader audience to help them visually explore and discover, quickly understand, interpret and gain important insights into otherwise difficult-to-identify structures, relationships, correlations, local and global patterns, trends, variations, constancy, clusters, outliers and unusual groupings within data ( 1439:

of the army at points in time), while the temperature axis suggests a cause of the change in army size. This multivariate display on a two-dimensional surface tells a story that can be grasped immediately while identifying the source data to build credibility. Tufte wrote in 1983 that: "It may well be the best statistical graphic ever drawn."

2729: 60: 1754:, covering an entire wall in his observatory). Particularly important were the development of triangulation and other methods to determine mapping locations accurately. Very early, the measure of time led scholars to develop innovative way of visualizing the data (e.g. Lorenz Codomann in 1596, Johannes Temporarius in 1596). 3360:: maps the data onto the window, and changes in the area of the. mapping function help us learn different things from the same plot. Scaling is commonly used to zoom in on crowded regions of a scatterplot, and it can also be used to change the aspect ratio of a plot, to reveal different features of the data. 3943:: Visual journalism is concerned with all types of graphic facilitation of the telling of news stories, and data-driven and data journalism are not necessarily told with data visualisation. Nevertheless, the field of journalism is at the forefront in developing new data visualisations to communicate data. 1578:". For example, it may require significant time and effort ("attentive processing") to identify the number of times the digit "5" appears in a series of numbers; but if that digit is different in size, orientation, or color, instances of the digit can be noted quickly through pre-attentive processing. 3477:

Within The Harvard Business Review, Scott Berinato developed a framework to approach data visualisation. To start thinking visually, users must consider two questions; 1) What you have and 2) what you're doing. The first step is identifying what data you want visualised. It is data-driven like profit

1920:

Eppler and Lengler have developed the "Periodic Table of Visualization Methods," an interactive chart displaying various data visualization methods. It includes six types of data visualization methods: data, information, concept, strategy, metaphor and compound. In "Visualization Analysis and Design"

1791:

John Tukey and Edward Tufte pushed the bounds of data visualization; Tukey with his new statistical approach of exploratory data analysis and Tufte with his book "The Visual Display of Quantitative Information" paved the way for refining data visualization techniques for more than statisticians. With

1835:

Beginning with the symposium "Data to Discovery" in 2013, ArtCenter College of Design, Caltech and JPL in Pasadena have run an annual program on interactive data visualization. The program asks: How can interactive data visualization help scientists and engineers explore their data more effectively?

1118:

Research into how people read and misread various types of visualizations is helping to determine what types and features of visualizations are most understandable and effective in conveying information. On the other hand, unintentionally poor or intentionally misleading and deceptive visualizations

1849:

Categorical: Represent groups of objects with a particular characteristic. Categorical variables can either be nominal or ordinal. Nominal variables for example gender have no order between them and are thus nominal. Ordinal variables are categories with an order, for sample recording the age group

1689:

are engaged in a project that attempts to provide a comprehensive history of visualization. Contrary to general belief, data visualization is not a modern development. Since prehistory, stellar data, or information such as location of stars were visualized on the walls of caves (such as those found

1438:

For example, the Minard diagram shows the losses suffered by Napoleon's army in the 1812–1813 period. Six variables are plotted: the size of the army, its location on a two-dimensional surface (x and y), time, the direction of movement, and temperature. The line width illustrates a comparison (size

3332:

to control a paintbrush, directly changing the color or glyph of elements of a plot. The paintbrush is sometimes a pointer and sometimes works by drawing an outline of sorts around points; the outline is sometimes irregularly shaped, like a lasso. Brushing is most commonly used when multiple plots

1729:

provided a visualization of information regarding Late Bronze Age era trades in the Mediterranean. The idea of coordinates was used by ancient Egyptian surveyors in laying out towns, earthly and heavenly positions were located by something akin to latitude and longitude at least by 200 BC, and the

1053:

Effective data visualization is properly sourced, contextualized, simple and uncluttered. The underlying data is accurate and up-to-date to make sure that insights are reliable. Graphical items are well-chosen for the given datasets and aesthetically appealing, with shapes, colors and other visual

1450:

refers to the extraneous interior decoration of the graphic that does not enhance the message or gratuitous three-dimensional or perspective effects. Needlessly separating the explanatory key from the image itself, requiring the eye to travel back and forth from the image to the key, is a form of

1226:

Data and information visualization presumes that "visual representations and interaction techniques take advantage of the human eye's broad bandwidth pathway into the mind to allow users to see, explore, and understand large amounts of information at once. Information visualization focused on the

3836:

in order to provide business intelligence solutions with the data scope, delivery timing, format and visualizations that will most effectively support and drive operational, tactical and strategic behaviour toward understood business (or organizational) goals. DPA is neither an IT nor a business

3040:

The relative position and angle of the axes is typically uninformative, but various heuristics, such as algorithms that plot data as the maximal total area, can be applied to sort the variables (axes) into relative positions that reveal distinct correlations, trade-offs, and a multitude of other

1590:

Almost all data visualizations are created for human consumption. Knowledge of human perception and cognition is necessary when designing intuitive visualizations. Cognition refers to processes in human beings like perception, attention, learning, memory, thought, concept formation, reading, and

1286:

and other tools. Numerical data may be encoded using dots, lines, or bars, to visually communicate a quantitative message. Effective visualization helps users analyze and reason about data and evidence. It makes complex data more accessible, understandable, and usable, but can also be reductive.

4102:

The first formal, recorded, public usages of the term data presentation architecture were at the three formal Microsoft Office 2007 Launch events in Dec, Jan and Feb of 2007–08 in Edmonton, Calgary and Vancouver (Canada) in a presentation by Kelly Lautt describing a business intelligence system

1581:

Compelling graphics take advantage of pre-attentive processing and attributes and the relative strength of these attributes. For example, since humans can more easily process differences in line length than surface area, it may be more effective to use a bar chart (which takes advantage of line

1291:, and the design principle of the graphic (i.e., showing comparisons or showing causality) follows the task. Tables are generally used where users will look up a specific measurement, while charts of various types are used to show patterns or relationships in the data for one or more variables. 3337:

can be a transient operation in which points in the active plot only retain their new characteristics. At the same time, they are enclosed or intersected by the brush, or it can be a persistent operation, so that points retain their new appearance after the brush has been moved away. Transient

1737:

The invention of paper and parchment allowed further development of visualizations throughout history. Figure shows a graph from the 10th or possibly 11th century that is intended to be an illustration of the planetary movement, used in an appendix of a textbook in monastery schools. The graph

1925:

writes "Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively." Munzner agues that visualization "is suitable when there is a need to augment human capabilities rather than replace people with computational

1114:

marries statistical data analysis, data and information visualization and human analytical reasoning through interactive visual interfaces to help human users reach conclusions, gain actionable insights and make informed decisions which are otherwise difficult for computers to do.

1458:

summarized several best practices for graphical displays in a June 2014 presentation. These included: a) Knowing your audience; b) Designing graphics that can stand alone outside the report's context; and c) Designing graphics that communicate the key messages in the report.

1684:

There is no comprehensive 'history' of data visualization. There are no accounts that span the entire development of visual thinking and the visual representation of data, and which collate the contributions of disparate disciplines. Michael Friendly and Daniel J Denis of

1624:, which "has from its beginning been used to study scientific problems. However, in its early days the lack of graphics power often limited its usefulness. The recent emphasis on visualization started in 1987 with the special issue of Computer Graphics on Visualization in 1294:

Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines, or bars) contained in graphics. The goal is to communicate information clearly and efficiently to users. It is one of the steps in

2231:

For example, determining frequency of annual stock market percentage returns within particular ranges (bins) such as 0–10%, 11–20%, etc. The height of the bar represents the number of observations (years) with a return % in the range represented by the respective

3852:

To use data to provide knowledge in the most effective manner possible (provide relevant, timely and complete data to each audience member in a clear and understandable manner that conveys important meaning, is actionable and can affect understanding, behavior and

4103:

designed to improve service quality in a pulp and paper company. The term was further used and recorded in public usage on December 16, 2009 in a Microsoft Canada presentation on the value of merging Business Intelligence with corporate collaboration processes.

1561:

Analysts reviewing a set of data may consider whether some or all of the messages and graphic types above are applicable to their task and audience. The process of trial and error to identify meaningful relationships and messages in the data is part of

1519:

Deviation: Categorical subdivisions are compared against a reference, such as a comparison of actual vs. budget expenses for several departments of a business for a given time period. A bar chart can show comparison of the actual versus the reference

4510:

O'Donoghue, Seán I.; Baldi, Benedetta Frida; Clark, Susan J.; Darling, Aaron E.; Hogan, James M.; Kaur, Sandeep; Maier-Hein, Lena; McCarthy, Davis J.; Moore, William J.; Stenau, Esther; Swedlow, Jason R.; Vuong, Jenny; Procter, James B. (2018-07-20).

5749:

took these monthly temperature data and plotted them in the form of a spiral, so that for each year, there are twelve points, one for each month, around the center of a circle – with warmer temperatures farther outward and colder temperatures nearer

1230:

Data analysis is an indispensable part of all applied research and problem solving in industry. The most fundamental data analysis approaches are visualization (histograms, scatter plots, surface plots, tree maps, parallel coordinate plots, etc.),

2925:

The two boxes graphed on top of each other represent the middle 50% of the data, with the line separating the two boxes identifying the median data value and the top and bottom edges of the boxes represent the 75th and 25th percentile data points

1398:

defines 'graphical displays' and principles for effective graphical display in the following passage: "Excellence in statistical graphics consists of complex ideas communicated with clarity, precision, and efficiency. Graphical displays should:

1386:

of the information graphic should support the analytical task. As William Cleveland and Robert McGill show, different graphical elements accomplish this more or less effectively. For example, dot plots and bar charts outperform pie charts.

3344:: Persistent brushing is useful when we want to group the points into clusters and then proceed to use other operations, such as the tour, to compare the groups. It is becoming common terminology to call the persistent operation painting, 2820: 1535:

Correlation: Comparison between observations represented by two variables (X,Y) to determine if they tend to move in the same or opposite directions. For example, plotting unemployment (X) and inflation (Y) for a sample of months. A

1844:

Data visualization involves specific terminology, some of which is derived from statistics. For example, author Stephen Few defines two types of data, which are used in combination to support a meaningful analysis or visualization:

1823:

help to make the visualization of quantitative data a possibility. Private schools have also developed programs to meet the demand for learning data visualization and associated programming libraries, including free programs like

1857:. Continuous variables capture the idea that measurements can always be made more precisely. While discrete variables have only a finite number of possibilities, such as a count of some outcomes or an age measured in whole years. 1876:

contains quantitative data organized into rows and columns with categorical labels. It is primarily used to look up specific values. In the example above, the table might have categorical column labels representing the name (a

1792:

the progression of technology came the progression of data visualization; starting with hand-drawn visualizations and evolving into more technical applications – including interactive designs leading to software visualization.

5741: 5174: 2147:

Projected (1) frequency and (2) intensity of extreme "10-year heat waves" are connected in pairs of horizontal and vertical bars, respectively. Bars are distinguished by (3) color-coded primary category (degree of global

1070:

to contextualize the analyzed data and communicate the insights gained from analyzing the data clearly and memorably with the goal of convincing the audience into making a decision or taking an action in order to create

2143: 1749:

By the 16th century, techniques and instruments for precise observation and measurement of physical quantities, and geographic and celestial position were well-developed (for example, a "wall quadrant" constructed by

4263:

Bhuvanendra Putchala; Lasya Sreevidya Kanala; Devi Prasanna Donepudi; Hari Kishan Kondaveeti (2023), "Applications of Big Data Analytics in Healthcare Informatics", in Narasimha Rao Vajjhala; Philip Eappen (eds.),

1523:

Frequency distribution: Shows the number of observations of a particular variable for given interval, such as the number of years in which the stock market return is between intervals such as 0–10%, 11–20%, etc. A

2870:

Dependent variable is progressively plotted along a continuous "spiral" determined as a function of (a) constantly rotating angle (twelve months per revolution) and (b) evolving color (color changes over passing

1110:. Since effective visualization requires design skills, statistical skills and computing skills, it is argued by authors such as Gershon and Page that it is both an art and a science. The neighboring field of 3377:

There are different approaches on the scope of data visualization. One common focus is on information presentation, such as Friedman (2008). Friendly (2008) presumes two main parts of data visualization:

1482:

Author Stephen Few described eight types of quantitative messages that users may attempt to understand or communicate from a set of data and the associated graphs used to help communicate the message:

5550: 4157: 1769:'s work on statistics and probability theory laid the groundwork for what we now conceptualize as data. According to the Interaction Design Foundation, these developments allowed and helped William 1337:. In the new millennium, data visualization has become an active area of research, teaching and development. According to Post et al. (2002), it has united scientific and information visualization. 1478:

original). Middle panel is a bubble chart that separately quantifies discrete outcomes. Bottom panel is an exploded pie chart showing relative shares of categories, and shares within categories.

1811:, Cornerstone and more allow for data visualization in the field of statistics. Other data visualization applications, more focused and unique to individuals, programming languages such as 1467: 4817: 1676:

Tree Map of Benin Exports (2009) by product category. The Product Exports Treemaps are one of the most recent applications of these kind of visualizations, developed by the Harvard-MIT

4279:

Olshannikova, Ekaterina; Ometov, Aleksandr; Koucheryavy, Yevgeny; Ollson, Thomas (2015), "Visualizing Big Data with augmented and virtual reality: challenges and research agenda.",

3350:: which could also be called labeling or label brushing, is another plot manipulation that can be linked. Bringing the cursor near a point or edge in a scatterplot, or a bar in a 5733: 5166: 2019:

Some bar graphs present bars clustered in groups of more than one, showing the values of more than one measured variable. These clustered groups can be differentiated using color.

3631: 4787: 3333:

are visible and some linking mechanism exists between the plots. There are several different conceptual models for brushing and a number of common linking mechanisms. Brushing

1079:, where complex statistical data are communicated graphically in an accurate and precise manner among researchers and analysts with statistical expertise to help them perform 3756: 3202:

Replace a correlation matrix by a diagram where the "remarkable" correlations are represented by a solid line (positive correlation), or a dotted line (negative correlation).

5079: 5709: 3504:

Used to spot trends and make sense of data. This type of visual is more common with large and complex data where the dataset is somewhat unknown and the task is open-ended.

1574:

A human can distinguish differences in line length, shape, orientation, distances, and color (hue) readily without significant processing effort; these are referred to as "

3044:

For example, comparing attributes/skills (e.g., communication, analytical, IT skills) learnt across different university degrees (e.g., mathematics, economics, psychology)

3849:

To use data to provide knowledge in the most efficient manner possible (minimize noise, complexity, and unnecessary data or detail given each audience's needs and roles)

2941:, thus are useful for getting an initial understanding of a data set. For example, comparing the distribution of ages between a group of people (e.g., male and females). 3919:

and therefore excludes both analysis (in the statistical/data sense) and direct transformation of the actual content (data, for DPA) into new entities and combinations.

2516: 4322: 3653: 1543:

Nominal comparison: Comparing categorical subdivisions in no particular order, such as the sales volume by product code. A bar chart may be used for this comparison.

1717:

which accurately illustrates the distribution of geological resources and provides information about quarrying of those resources. Such maps can be categorized as

2760:

cluster heat map: where magnitudes are laid out into a matrix of fixed cell size whose rows and columns are categorical data. For example, the graph to the right.

1765:

developed analytic geometry and two-dimensional coordinate system which heavily influenced the practical methods of displaying and calculating values. Fermat and

2763:

spatial heat map: where no matrix of fixed cell size for example a heat-map. For example, a heat map showing population densities displayed on a geographical map

2615:

Unlike a traditional stacked area chart in which the layers are stacked on top of an axis, in a streamgraph the layers are positioned to minimize their "wiggle".

1598:

Studies have shown individuals used on average 19% less cognitive resources, and 4.5% better able to recall details when comparing data visualization with text.

5851: 3813:) is a skill-set that seeks to identify, locate, manipulate, format and present data in such a way as to optimally communicate meaning and proper knowledge. 2340:

Similar to the 2-dimensional scatter plot above, the 3-dimensional scatter plot visualizes the relationship between typically 3 variables from a set of data.

423: 5542: 4149: 2030: 1553:: Comparison of a variable across a map or layout, such as the unemployment rate by state or the number of persons on the various floors of a building. A 7301: 3669:: An annual Europe-wide computer graphics conference, held by the European Association for Computer Graphics. Conference is usually held in April or May. 3621: 817:

is concerned with visually presenting sets of primarily quantitative raw data in a schematic form. The visual formats used in data visualization include

687: 5258: 3241: 2220:

of numerical data. Divide the entire range of values into a series of intervals and then count how many values fall into each interval this is called

1406:

induce the viewer to think about the substance rather than about methodology, graphic design, the technology of graphic production, or something else

7296: 5103: 4056: 3674: 3354:, causes a label to appear that identifies the plot element. It is widely available in many interactive graphics, and is sometimes called mouseover. 1665: 495: 6380:

Duke University-Christa Kelleher Presentation-Communicating through infographics-visualizing scientific & engineering information-March 6, 2015

2394:

Determining the most influential nodes in the network (e.g. A company wants to target a small group of people on Twitter for a marketing campaign).

1005:. In data and information visualization, the goal is to graphically present and explore abstract, non-physical and non-spatial data collected from 6406: 3644:: An annual international conference on scientific visualization, information visualization, and visual analytics. Conference is held in October. 1486:

Time-series: A single variable is captured over a period of time, such as the unemployment rate or temperature measures over a 10-year period. A

1853:

Quantitative: Represent measurements, such as the height of a person or the temperature of an environment. Quantitative variables can either be

5653: 1677: 997:

have the potential to make information visualization more immersive, intuitive, interactive and easily manipulable and thus enhance the user's

1861:

The distinction between quantitative and categorical variables is important because the two types require different methods of visualization.

1083:

or to convey the results of such analyses, where visual appeal, capturing attention to a certain issue and storytelling are not as important.

6291: 6189: 6111: 6092: 6071: 5684: 5594: 5137: 4754: 4185: 4051: 3903:

Data visualization in that it uses well-established theories of visualization to add or highlight meaning or importance in data presentation.

7178: 5617: 4697: 5832: 4581:

Börner, K.; Bueckle, A.; Ginda, M. (2019), "Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments",

5195: 1086:

The field of data and information visualization is of interdisciplinary nature as it incorporates principles found in the disciplines of

5877: 5872: 4848: 3820:

is attributed to Kelly Lautt: "Data Presentation Architecture (DPA) is a rarely applied skill set critical for the success and value of

3657: 6267: 6016: 2773: 7168: 7111: 6717: 4784: 3975: 1699: 743: 3700: 3386:. In this line the "Data Visualization: Modern Approaches" (2007) article gives an overview of seven subjects of data visualization: 7291: 6379: 6357: 6249: 6208: 6159: 6133: 6044: 5297: 5014: 4982: 4879: 4652: 4415: 3782: 3512:

The most common and simple type of visualisation used for affirming and setting context. For example, a line graph of GDP over time.

5330: 5071: 3866:

Creating effective delivery mechanisms for each audience member depending on their role, tasks, locations and access to technology

5701: 3900:

Business process improvement in that its goal is to improve and streamline actions and decisions in furtherance of business goals

3313: 416: 6722: 5020: 4020: 3929:, since many of the principles in how to design interactive data visualisation have been developed cross-disciplinary with HCI. 3246: 2711: 1854: 1773:, who saw potential for graphical communication of quantitative data, to generate and develop graphical methods of statistics. 1036: 787:

with the help of static, dynamic or interactive visual items. Typically based on data and information collected from a certain

3922: 3710: 1184: 4000: 3760: 586: 500: 114: 3650:: An annual international conference on computer graphics, convened by the ACM SIGGRAPH organization. Conference dates vary. 677: 5975: 1612: 1493:

Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of sales performance (the

6430: 2705: 2441: 1912:(quantitative and categorical) used to label and assign values to the visual objects. Many graphs are also referred to as 1816: 1314:

suggested that an ideal visualization should not only communicate clearly, but stimulate viewer engagement and attention.

1103: 4900: 4721: 2428:

Represents one categorical variable which is divided into slices to illustrate numerical proportion. In a pie chart, the

6712: 5395: 4770: 2501:

except that the measurement points are ordered (typically by their x-axis value) and joined with straight line segments.

1707: 1244: 6026: 2022:

For example; comparison of values, such as sales performance for several persons or businesses in a single time period.

75:

as what "may well be the best statistical graphic ever drawn", noting that it captures six variables in two dimensions.

7286: 6399: 4125: 3276: 3176: 2609: 2277: 1741: 1455: 964: 702: 409: 6641: 3745: 3159:. In Venn diagrams, the curves are overlapped in every possible way, showing all possible relations between the sets. 2618:

Streamgraphs display data with only positive values, and are not able to represent both negative and positive values.

2016:. One axis of the chart shows the specific categories being compared, and the other axis represents a measured value. 1626: 5848: 5518: 3127:. This lends itself to intuitive visualizations; for example, the set of all elements that are members of both sets 5049: 3980: 1264: 1107: 210: 4512: 3764: 3749: 1512:

Part-to-whole: Categorical subdivisions are measured as a ratio to the whole (i.e., a percentage out of 100%). A

6465: 4922:

Cleveland, W. S.; McGill, R. (1985). "Graphical perception and graphical methods for analyzing scientific data".

4869: 4046: 3829: 3447: 3293: 1563: 1330: 1322: 1252: 1080: 460: 104: 72: 48: 1516:

or bar chart can show the comparison of ratios, such as the market share represented by competitors in a market.

6490: 6485: 6450: 6415: 6238: 5420: 3912: 3266: 2938: 1804: 1643: 1575: 1326: 1031: 5801: 2992:

For example, outlying the actions to undertake if a lamp is not working, as shown in the diagram to the right.

1451:"administrative debris." The ratio of "data to ink" should be maximized, erasing non-data ink where feasible. 5255: 3828:

from data and making it usable, relevant and actionable with the arts of data visualization, communications,

2989:

The flowchart shows the steps as boxes of various kinds, and their order by connecting the boxes with arrows.

4670: 4482: 3027: 2629: 898: 485: 6230: 2351: 2228:

of a variable. The bins (intervals) must be adjacent, and are often (but not required to be) of equal size.

7061: 6677: 6613: 6500: 6392: 5765:(1999). "Introduction to the special issue on interactive graphical data analysis: What is interaction?". 5746: 5715: 4067: 4041: 3936: 3452: 2934: 2290:

Each point on the plot has an associated x and y term that determines its location on the cartesian plane.

2281: 2225: 2217: 1474:

Top panel is a bar chart depicting the flow of occurrences over time (resembles the Sankey diagram in the

1087: 736: 672: 119: 5096: 3706: 6955: 6768: 6692: 6510: 5719: 3825: 3821: 3112:

A Venn diagram consists of multiple overlapping closed curves, usually circles, each representing a set.

3102: 2090: 1904:(e.g., lines, bars, or points). Numerical values are displayed within an area delineated by one or more 1631: 982: 776: 596: 470: 316: 205: 124: 68: 7056: 5357: 3496:

Used to discover, innovate and solve problems. For example, a whiteboard after a brainstorming session.

2494:

Represents information as a series of data points called 'markers' connected by straight line segments.

1788:

used quantitative graphs to represent information "intuitively, clearly, accurately, and efficiently".

1435:

data. Indeed, graphics can be more precise and revealing than conventional statistical computations."

886: 4446:

Gershon, Nahum; Page, Ward (1 August 2001). "What storytelling can do for information visualization".

3875:

Determining the right timing for data presentation (when and how often the user needs to see the data)

3869:

Defining important meaning (relevant knowledge) that is needed by each audience member in each context

2831: 7235: 7126: 6950: 6900: 6808: 6793: 6737: 6702: 6598: 6495: 6470: 6337: 6308: 4931: 4590: 4072: 4062: 3995: 3571: 3540: 3471: 3390: 3379: 3364: 3324: 3271: 2163:

numerical value of second variable (extent in second dimension; like conventional vertical bar chart)

2013: 1991: 1865: 1647: 1341: 1334: 1318: 1311: 1275: 1136: 1091: 1076: 922: 890: 808: 611: 606: 129: 6727: 5649: 2240: 7215: 6970: 6905: 6742: 6697: 6603: 6553: 6548: 5925: 5641: 3616: 3488:

Used to teach, explain and/or simply concepts. For example, organisation charts and decision trees.

2660: 2006:

proportional to the values that they represent. The bars can be plotted vertically or horizontally.

1240: 842: 818: 788: 505: 387: 336: 5130: 3862:

With the above objectives in mind, the actual work of data presentation architecture consists of:

906: 858: 17: 7255: 6915: 6910: 6864: 6854: 6788: 6480: 6455: 6445: 6226: 5956: 5830:"Milestones in the history of thematic cartography, statistical graphics, and data visualization" 5782: 5463: 5455: 5421:"Milestones in the history of thematic cartography, statistical graphics, and data visualization" 5303: 4955: 4764: 4542: 4463: 4036: 3970: 3926: 3906: 3641: 3611: 3576: 3557: 1825: 1639: 1216: 1010: 918: 914: 796: 616: 480: 109: 7121: 5610: 4690: 1532:

helps visualize key statistics about the distribution, such as median, quartiles, outliers, etc.

1163: 838: 7026: 6374:

Milestones in the History of Thematic Cartography, Statistical Graphics, and Data Visualization

5829: 5206: 3167: 1582:

length to show comparison) rather than pie charts (which use surface area to show comparison).

1131:

toward a certain agenda. Thus data visualization literacy has become an important component of

7250: 7200: 7173: 7101: 6980: 6960: 6803: 6608: 6505: 6353: 6329: 6287: 6245: 6214: 6204: 6185: 6165: 6155: 6129: 6107: 6088: 6067: 5948: 5762: 5680: 5590: 5383: 5293: 5010: 4978: 4947: 4875: 4750: 4648: 4618: 4411: 4316: 4181: 3932: 3916: 3894: 3833: 3530: 3106: 3095: 3076: 2560: 2391:

Discovering bridges (information brokers or boundary spanners) between clusters in the network

2010: 1758: 1714: 1661: 1621: 1443: 1363:

The greatest value of a picture is when it forces us to notice what we never expected to see.

1208: 1099: 998: 990: 729: 651: 392: 382: 4842: 4528: 799:) to convey a concise version of known, specific information in a clear and engaging manner ( 7260: 7245: 7240: 7220: 7071: 7046: 7010: 7005: 6940: 6874: 6778: 6687: 6682: 6672: 6646: 6593: 6520: 6460: 6435: 6264: 6177: 5940: 5825: 5774: 5645: 5447: 5285: 4939: 4742: 4608: 4598: 4532: 4524: 4490: 4455: 4403: 4288: 4221: 4077: 4015: 3949:, conveying information through styling, typography, position, and other aesthetic concerns. 3626: 3440: 2718: 2459: 2448: 2397:

Finding outlier actors who do not fit into any cluster or are in the periphery of a network.

1829: 1770: 1762: 1731: 1651: 1592: 1260: 1256: 1204: 1188: 1111: 1067: 960: 682: 667: 626: 556: 510: 397: 220: 195: 5481: 2160:

numerical value of first variable (extent in first dimension; superimposed horizontal bars)

7265: 7205: 7116: 7041: 6985: 6935: 6578: 6475: 6333: 6304: 6271: 5906: 5881: 5855: 5836: 5543:"NY gets new boot camp for data scientists: It's free but harder to get into than Harvard" 5262: 4852: 4791: 4674: 4638: 4082: 3990: 3985: 3940: 3795: 3581: 3302: 2782: 1800: 1686: 1657: 1307: 1279: 1236: 1152: 1140: 1039: 986: 956: 878: 862: 576: 546: 515: 301: 170: 134: 6384: 3037:

of three or more quantitative variables represented on axes starting from the same point.

2388:

Finding clusters in the network (e.g. grouping Facebook friends into different clusters).

4935: 4809: 4594: 2879: 1421:

reveal the data at several levels of detail, from a broad overview to the fine structure

1287:

Users may have particular analytical tasks, such as making comparisons or understanding

1175:

Partial map of the Internet early 2005 represented as a graph, each line represents two

7230: 7195: 7136: 7091: 6990: 6975: 6869: 6844: 6798: 6752: 6747: 6583: 6515: 6325: 6256: 5926:"Making sense of personal health information: Challenges for information visualization" 5702:"This Striking Climate Change Visualization Is Now Customizable for Any Place on Earth" 5379: 5322: 4613: 4209: 4010: 4005: 3946: 3881:

Utilizing appropriate analysis, grouping, visualization, and other presentation formats

3878:

Finding the right data (subject area, historical reach, breadth, level of detail, etc.)

3637:

Conferences in this field, ranked by significance in data visualization research, are:

3433: 3329: 3261: 2930: 2865: 2840: 2807: 2379: 1922: 1796: 1785: 1268: 1220: 1128: 1124: 1095: 1072: 948: 902: 717: 646: 326: 215: 165: 3052: 2556:

Represents data as lines or series of points spanning large ranges on one or both axes

2293:

Scatter plots are often used to highlight the correlation between variables (x and y).

7280: 7190: 7076: 7000: 6965: 6945: 6849: 6813: 6667: 6651: 6636: 6440: 5467: 5004: 4546: 3824:. Data presentation architecture weds the science of numbers, data and statistics in 3596: 3562: 3309: 3251: 2525: 2433: 2360: 2343:

Again point can be coded via color, shape and/or size to display additional variables

1766: 1446:, distorting the message, or supporting an erroneous conclusion. According to Tufte, 1424:

serve a reasonably clear purpose: description, exploration, tabulation, or decoration

1296: 1196: 1132: 994: 571: 561: 541: 306: 185: 139: 6347: 5786: 5307: 4959: 4642: 2405: 2058: 1630:. Since then there have been several conferences and workshops, co-sponsored by the 1167:

Data visualization is one of the steps in analyzing data and presenting it to users.

7225: 7185: 7106: 7086: 7051: 7036: 6930: 6925: 6895: 6773: 6376:, An illustrated chronology of innovations by Michael Friendly and Daniel J. Denis. 6300: 6260: 6121: 6059: 5960: 5392:

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

4741:. Nieuwe Prinsengracht 89 1018 VR Amsterdam Nederland: Amsterdam University Press. 4467: 4395: 4031: 3800: 3666: 3601: 3383: 3334: 3061: 2656: 2529: 2498: 2249: 2221: 1776: 1718: 1635: 1607: 1537: 1395: 1375: 1300: 1176: 968: 882: 870: 866: 707: 531: 490: 296: 276: 266: 230: 225: 200: 190: 180: 149: 64: 4569:

Misinformed by Visualization: What Do We Learn From Misinformative Visualizations?

1900:

is primarily used to show relationships among data and portrays values encoded as

4943: 4366:

Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals

2287:

Points can be coded via color, shape and/or size to display additional variables.

1427:

be closely integrated with the statistical and verbal descriptions of a data set.

7210: 7158: 7096: 7066: 6995: 6920: 6890: 6828: 6783: 5289: 4339: 3734: 3567: 3548: 3535: 3523:

Data and information visualization insights are being applied in areas such as:

3400: 3281: 3230: 3009: 2683: 2674: 2579: 2570: 2505: 2128: 1961: 1939: 1751: 1695: 1345: 1283: 1248: 1212: 1171: 1022: 1014: 944: 854: 846: 784: 712: 621: 551: 475: 362: 341: 291: 144: 4746: 4210:"Why Is Data Visualization Important? What Is Important in Data Visualization?" 3872:

Determining the required periodicity of data updates (the currency of the data)

2179:

Variables need not be directly related in the way they are in "variwide" charts

7081: 6859: 6823: 6818: 6568: 5482:"Data visualization: definition, examples, tools, advice [guide 2020]" 4896: 4865: 4495: 4407: 4293: 4226: 3656:: An annual international conference on human–computer interaction, hosted by 3235: 3224: 2813: 2605: 2583: 2468: 2429: 1820: 1713:

The first documented data visualization can be tracked back to 1160 B.C. with

1550: 1487: 1368: 1232: 1227:

creation of approaches for conveying abstract information in intuitive ways."

1200: 1183:

The field of data and information visualization has emerged "from research in

1043: 952: 834: 830: 591: 566: 536: 286: 251: 235: 175: 83: 6218: 6169: 6143: 5944: 5387: 5231: 4691:"Stephen Few-Perceptual Edge-Selecting the Right Graph for Your Message-2004" 4567:

Leo Yu-Ho Lo; Ayush Gupta; Kento Shigyo; Aoyu Wu; Enrico Bertini; Huamin Qu,

4178:

Storytelling with Data: A Data Visualization Guide for Business Professionals

1207:. It is increasingly applied as a critical component in scientific research, 7163: 7131: 6732: 6707: 6588: 6573: 5734:"This scientist just changed how we think about climate change with one GIF" 4603: 4025: 3960: 3465: 3394: 3219: 2958: 2701: 2414: 2196: 1995: 1970: 1554: 1546: 1525: 1513: 1506: 1447: 1288: 1274:

To communicate information clearly and efficiently, data visualization uses

1063: 1002: 940: 850: 826: 822: 641: 631: 581: 377: 331: 321: 311: 281: 261: 256: 5952: 5679:. A.K. Peters visualization series. Boca Raton London New York: CRC Press. 5438:

Funkhouser, Howard Gray (January 1936). "A Note on a Tenth Century Graph".

4951: 4736: 4622: 4129: 3000: 2949: 1672: 4459: 3897:

in determining business goals, collecting requirements, mapping processes.

3443:

perspective, Frits H. Post in 2002 categorized the field into sub-fields:

3338:

brushing is usually chosen for linked brushing, as we have just described.

2905:

A method for graphically depicting groups of numerical data through their

2304: 2187: 1378:

has explained that users of information displays are executing particular

6558: 5507: 5382:; Alexander, Jason; Karnik, Abhijit; Kildal, Johan; Subramanian, Sriram; 3965: 3647: 3423: 3351: 2979: 2965: 2906: 2888: 2816:—with no technical indicia—to communicate intuitively with non-scientists 2520:

A log-log chart spanning more than one order of magnitude along both axes

1734:

in Alexandria would serve as reference standards until the 14th century.

1722: 1340:

In the commercial environment data visualization is often referred to as

1192: 1148: 1144: 1055: 1018: 1006: 976: 972: 936: 910: 772: 697: 636: 601: 372: 367: 271: 6373: 5589:. The University of Queensland: Publish on Demand Centre. pp. 4–5. 5041: 4537: 7031: 6543: 6317: 6232:

Information Visualization – Human-Centered Issues and Perspectives

5778: 4248:

Ananda Mitra (2018), "Managing and Visualizing Unstructured Big Data",

3418: 3312:

since the late 1960s. Examples of the developments can be found on the

3256: 2983: 2969: 2919: 2799:

A sequence of colored stripes visually portrays trend of a data series.

2737: 2638: 1935: 1808: 1726: 1710:(n.d.) can also be considered as visualizing quantitative information. 1691: 1529: 874: 5611:"Steven Few-Selecting the Right Graph for Your Message-September 2004" 5459: 3123:, while points outside the boundary represent elements not in the set 6282:

Post, Frits H.; Nielson, Gregory M.; Bonneau, Georges-Pierre (2003).

5871:

Frits H. Post, Gregory M. Nielson and Georges-Pierre Bonneau (2002).

4841:

Frits H. Post, Gregory M. Nielson and Georges-Pierre Bonneau (2002).

4150:"Data is Beautiful: 7 Data Visualization Tools for Digital Marketers" 3660: 3606: 2176:

Pairs of numeric variables, usually color-coded, rendered by category

2003: 1999: 1035:, where the goal is to render realistic images based on physical and 768: 6082: 5424: 5084:

Data source: Advanced Law Enforcement Rapid Response Training Center

3890:

DPA work shares commonalities with several other fields, including:

2754:

Represents the magnitude of a phenomenon as color in two dimensions.

6152:

Show me the numbers : designing tables and graphs to enlighten

5805: 5451: 5280:

Friendly, Michael (2008). "A Brief History of Data Visualization".

2728: 2504:

Often used to visualize a trend in data over intervals of time – a

2447:

For example, as shown in the graph to the right, the proportion of

1730:

map projection of a spherical Earth into latitude and longitude by

6538: 5131:"Steven Few-Tapping the Power of Visual Perception-September 2004" 3794: 3166: 3091: 3072: 3051: 3034: 2999: 2948: 2878: 2830: 2772: 2727: 2673: 2628: 2569: 2515: 2458: 2404: 2350: 2303: 2239: 2186: 2142: 2029: 1960: 1812: 1775: 1740: 1703: 1671: 1656: 1611: 1466: 1162: 59: 58: 5042:"Telling Visual Stories About Data - Congressional Budget Office" 4665: 3151:", is represented visually by the area of overlap of the regions 2224:. The bins are usually specified as consecutive, non-overlapping 6310:

The Craft of Information Visualization: Readings and Reflections

6104:

Effective Data Visualization: The Right Chart for the Right Data

4644:

The Craft of Information Visualization: Readings and Reflections

4128:. Center for Spatially Integrated Social Science. Archived from 3412: 3406: 3030: 2437: 2244:

A scatterplot showing negative correlation between two variables

2085:

Areas of non-uniform-width bars represent quantities with areas

780: 692: 357: 6388: 6242:

Information Visualization: Design for Interaction (2nd Edition)

5800:

American Statistics Association, Statistical Graphics Section.

5353: 4977:(2nd ed.). Cheshire, Connecticut, US: Graphics Press LLC. 4135: 2916:) indicating variability outside the upper and lower quartiles. 2621:

Example: the visual shows music listened to by a user over time

6563: 6265:"Prefuse: a toolkit for interactive information visualization" 6001: 4667:

Illuminating the Path: The R&D Agenda for Visual Analytics

3728: 3683: 1885:), with each row of data representing one person (the sampled 1058:, data and information visualization can constitute a part of 932: 894: 29: 5570: 4897:"Tech@State: Data Visualization - Keynote by Dr Edward Tufte" 3592:

Notable academic and industry laboratories in the field are:

3509:

everyday data-visualisation (data-driven & declarative).

2714:

relationships between activities and current schedule status.

6339:

Readings in Information Visualization: Using Vision to Think

4309:

Readings in Information Visualization: Using Vision to Think

1509:

may be used to show the comparison across the sales persons.

2166:

category for first and second variables (e.g., color-coded)

5976:"A Guide to the Quality of Different Visualization Venues" 779:

of a large amount of complex quantitative and qualitative

3481:

These four types of visual communication are as follows;

1943: 1215:, financial data analysis, market studies, manufacturing 6235:. Volume 4950 of LNCS State-of-the-Art Survey, Springer. 4735:

Engebretsen, Martin; Helen, Kennedy, eds. (2020-04-16).

4483:"Why scientists need to be better at data visualization" 4266:

Health Informatics and Patient Safety in Times of Crisis

2912:

Box plots may also have lines extending from the boxes (

1528:, a type of bar chart, may be used for this analysis. A 771:

and creating easy-to-communicate and easy-to-understand

5924:

Faisal, Sarah; Blandford, Ann; Potts, Henry WW (2013).

5388:"Opportunities and challenges for data physicalization" 5354:"List of Physical Visualizations and Related Artefacts" 4808:

Viegas, Fernanda; Wattenberg, Martin (April 19, 2011).

4126:"Charles Joseph Minard: Mapping Napoleon's March, 1861" 3909:

explores more nuanced ways of visualising complex data.

4435:, Springer Science & Business Media, p. xxiii 3654:

Conference on Human Factors in Computing Systems (CHI)

5125: 5123: 3632:

University of Maryland Human-Computer Interaction Lab

3308:

Interactive data visualization has been a pursuit of

1418:

encourage the eye to compare different pieces of data

5097:"Stephen Few-Perceptual Edge-Graph Selection Matrix" 4722:"10 Examples of Interactive Map Data Visualizations" 4171: 4169: 4167: 3305:

to change elements and link between multiple plots.

2860:

Portrays a single dependent variable—prototypically

2559:

One or both axes are represented using a non-linear

1348:

are another very common form of data visualization.

7149: 7019: 6883: 6837: 6761: 6660: 6629: 6622: 6529: 6423: 5070:Buchanan, Larry; Letherby, Lauren (June 22, 2022). 4677:. National Visualization and Analytics Center. p.30 3094:relations between a finite collection of different 3075:relations between a finite collection of different 1638:". They have been devoted to the general topics of 1123:) can function as powerful tools which disseminate 5275: 5273: 5271: 4664:James J. Thomas and Kristin A. Cook (Ed.) (2005). 4250:Encyclopedia of Information Science and Technology 3501:visual discovery (data-driven & exploratory). 3485:idea illustration (conceptual & declarative). 4381:Statistics: Concepts and Applications for Science 2937:without making any assumptions of the underlying 1586:Human perception/cognition and data visualization 6319:Information Visualization: Perception for design 5414: 5412: 3493:idea generation (conceptual & exploratory). 2173:Includes most features of basic bar chart, above 2082:Includes most features of basic bar chart, above 4583:Proceedings of the National Academy of Sciences 4383:, Jones & Bartlett Learning, pp. 35–36 2666:For example, disk space by location / file type 2508:– thus the line is often drawn chronologically. 2066:category (size/count/extent in first dimension) 1620:The modern study of visualization started with 1490:may be used to demonstrate the trend over time. 1361: 1357:Characteristics of effective graphical displays 6126:The visual display of quantitative information 5006:The Visual Display of Quantitative Information 4975:The Visual Display of Quantitative Information 1392:The Visual Display of Quantitative Information 6400: 4685: 4683: 3915:, but information architecture's focus is on 3703:to certain ideas, incidents, or controversies 3663:. Conference is usually held in April or May. 2986:or a step-by-step approach to solving a task. 2819:Can be "stacked" to represent plural series ( 1698:era. Physical artefacts such as Mesopotamian 921:, etc., which sometimes can be combined in a 737: 417: 8: 6064:Data Visualization: A Practical Introduction 4998: 4996: 4994: 4321:: CS1 maint: multiple names: authors list ( 4176:Nussbaumer Knaflic, Cole (2 November 2015). 2154:Orthogonal (orthogonal composite) bar chart 1442:Not applying these principles may result in 1027:presentational and exploratory visualization 5352:Dragicevic, Pierre; Jansen, Yvonne (2012). 3763:. Unsourced material may be challenged and 2131:(also known as Marimekko, or Mekko, charts) 1654:published the first presentation graphics. 1075:. This can be contrasted with the field of 6626: 6407: 6393: 6385: 3713:this issue before removing this message. 3622:Scientific Computing and Imaging Institute 3432:All these subjects are closely related to 2802:Portrays a single variable—prototypically 1127:, manipulate public perception and divert 744: 730: 688:List of concept- and mind-mapping software 435: 424: 410: 78: 6201:Introduction to Information Visualization 6066:. Princeton: Princeton University Press. 6045:Learn how and when to remove this message 5675:Munzner, Tamara; Maguire, Eamonn (2015). 5650:"Periodic Table of Visualization Methods" 5167:"Data Visualization for Human Perception" 5009:. Cheshire, Connecticut: Graphics Press. 4612: 4602: 4536: 4494: 4307:Card, Mackinlay, and Shneiderman (1999), 4292: 4225: 3783:Learn how and when to remove this message 3242:Information visualization reference model 2933:: they display variation in samples of a 1472:The same dataset plotted in three charts: 1409:avoid distorting what the data has to say 1317:Data visualization is closely related to 1179:, and some delay between those two nodes. 795:). When intended for the general public ( 6284:Data Visualization: The State of the Art 5874:Data Visualization: The State of the Art 5587:A Portable Introduction to Data Analysis 4844:Data Visualization: The State of the Art 4837: 4835: 4633: 4631: 4529:10.1146/annurev-biodatasci-080917-013424 4517:Annual Review of Biomedical Data Science 4057:List of countries by economic complexity 3675:Category:Computer graphics organizations 1784:In the second half of the 20th century, 1570:Visual perception and data visualization 1170: 1062:, where they are paired with a coherent 5849:"Data Visualization: Modern Approaches" 5508:"A Brief History of Data Visualization" 4798:, Monday Inspiration, January 14, 2008. 4433:A Framework for Visualizing Information 4116: 4095: 2850:rotating angle (cycling through months) 2757:There are two categories of heat maps: 1029:) which is different from the field of 659: 523: 452: 447: 349: 243: 157: 96: 89: 44:This article may need to be cleaned up. 6276:ACM Human Factors in Computing Systems 5744:from the original on 6 February 2019. 4762: 4314: 4203: 4201: 4199: 4197: 4160:from the original on 12 November 2016. 3301:enables direct actions on a graphical 73:Napoleonic France's invasion of Russia 6322:. San Francisco, CA: Morgan Kaufmann. 5900: 5898: 5896: 5894: 5892: 5890: 5580: 5578: 5161: 5159: 5157: 4785:"Data Visualization and Infographics" 4334: 4332: 4052:List of information graphics software 2216:An approximate representation of the 2069:size/count/extent in second dimension 2059:Variable-width ("variwide") bar chart 1940:Infographic § Data visualization 1757:French philosopher and mathematician 1412:present many numbers in a small space 7: 5378:Jansen, Yvonne; Dragicevic, Pierre; 5082:from the original on June 22, 2022. 3761:adding citations to reliable sources 2922:may be plotted as individual points. 2847:radial distance (dependent variable) 2432:of each slice (and consequently its 2280:to display values for typically two 2009:A bar graph shows comparisons among 5974:Kosara, Robert (11 November 2013). 5712:from the original on June 26, 2019. 5677:Visualization analysis & design 5333:from the original on 6 January 2018 5284:. Springer-Verlag. pp. 15–56. 5072:"Who Stops a 'Bad Guy With a Gun'?" 4368:, John Wiley & Sons, p. 16 3707:create a more balanced presentation 3115:The points inside a curve labelled 2034:Variable-width bar chart relating: 1540:is typically used for this message. 6084:Fundamentals of Data Visualization 5656:from the original on 16 March 2013 4903:from the original on 29 March 2017 4513:"Visualization of Biomedical Data" 4481:Mason, Betsy (November 12, 2019). 4400:Handbook of Digital Public History 3976:Color coding in data visualization 2710:Modern Gantt charts also show the 1678:Observatory of Economic Complexity 1646:, and more specific areas such as 1616:Selected milestones and inventions 757:Data and information visualization 91:Data and information visualization 25: 18:Color coding in data visualization 7302:Information technology governance 5907:"Visualizations That Really Work" 5171:The Interaction Design Foundation 3033:in the form of a two-dimensional 6225:Andreas Kerren, John T. Stasko, 6144:Adaptive Semantics Visualization 6006: 5321:Whitehouse, D. (9 August 2000). 5232:"Can images stop data overload?" 4846:. Research paper TU delft, 2002. 4820:from the original on May 6, 2011 4396:"Data Visualization for History" 4208:Antony Unwin (31 January 2020). 3826:discovering valuable information 3733: 3688: 3436:and information representation. 3314:American Statistical Association 3109:as regions inside closed curves. 2072:size/count/extent as area of bar 1965:Bar chart of tips by day of week 1642:, information visualization and 1382:such as making comparisons. The 1090:(as early as the 18th century), 439: 34: 7297:Statistical charts and diagrams 6154:(2 ed.). Analytics Press. 5623:from the original on 2014-10-05 5553:from the original on 2016-02-15 5524:from the original on 2016-05-08 5398:from the original on 2018-01-13 5360:from the original on 2018-01-13 5177:from the original on 2015-11-23 5143:from the original on 2014-10-05 5109:from the original on 2014-10-05 5052:from the original on 2014-12-04 5023:from the original on 2013-01-14 4703:from the original on 2014-10-05 4311:, Morgan Kaufmann, pp. 6–7 4138:; use archive link for article) 4028:(1987), graphical data analysis 4021:Grand Tour (data visualisation) 2612:, resulting in a flowing shape. 1706:(2600 BC) and Marshall Islands 6346:Cleveland, William S. (1993). 6128:(2 ed.). Graphics Press. 5571:Interactive Data Visualization 5282:Handbook of Data Visualization 4268:, IGI Global, pp. 175–194 4148:Shewan, Dan (5 October 2016). 4001:Data Presentation Architecture 3818:data presentation architecture 3807:Data presentation architecture 3680:Data presentation architecture 3299:Interactive data visualization 3205:Points can be coded via color. 3143:and read "the intersection of 3119:represent elements of the set 2444:to the quantity it represents. 2105:· horizontal-axis quantities ( 2042:· per-person emissions (along 1694:in Southern France) since the 501:Ontology (information science) 115:Interactive data visualization 1: 6431:Biological data visualization 6342:, Morgan Kaufmann Publishers. 6102:Evergreen, Stephanie (2016). 6029:and help improve the section. 5905:Berinato, Scott (June 2016). 5732:Mooney, Chris (11 May 2016). 5700:Kahn, Brian (June 17, 2019). 5323:"Ice Age star map discovered" 5205:. SFU lecture. Archived from 4895:techatstate (7 August 2013). 4738:Data Visualization in Society 4340:"What is data visualization?" 3845:DPA has two main objectives: 3319:Common interactions include: 1955:Description / Example usages 1505:) during a single period. A 1415:make large data sets coherent 1104:interactive computer graphics 27:Visual representation of data 5230:Graham, Fiona (2012-04-17). 4944:10.1126/science.229.4716.828 4810:"How To Make Data Look Sexy" 3105:as points in the plane, and 3019:value assigned to attributes 2663:figures, usually rectangles. 2098:· vertical-axis quantities ( 1143:akin to the roles played by 1121:misinformative visualization 965:entity-relationship diagrams 5652:. www.visual-literacy.org. 5290:10.1007/978-3-540-33037-0_2 4214:Harvard Data Science Review 3673:For further examples, see: 3277:Problem solving environment 3177:Iconography of correlations 3171:Iconography of correlations 2655:Is a method for displaying 2608:that is displaced around a 2191:Histogram of housing prices 2048:· total emissions (area as 1608:Infographics § History 1501:, with each sales person a 1456:Congressional Budget Office 1023:business and financial data 703:Problem structuring methods 7318: 6471:Mathematical visualization 6229:, and Chris North (2008). 5933:Health Informatics Journal 5506:Friendly, Michael (2006). 5486:Market research consulting 5419:Friendly, Michael (2001). 4747:10.5117/9789463722902_ch02 4394:Grandjean, Martin (2022). 4252:(4th ed.), IGI Global 3981:Computational visualistics 3799:A data visualization from 3439:On the other hand, from a 3291: 3199:Exploratory data analysis. 1933: 1926:decision-making methods." 1662:Product Space Localization 1605: 1557:is a typical graphic used. 1185:human–computer interaction 1108:human-computer interaction 1048:confirmatory visualization 821:, charts and graphs (e.g. 807:), it is typically called 6466:Information visualization 6451:Educational visualization 6146:Eurographics Association. 5714:Developed in May 2018 by 4973:Tufte, Edward R. (1983). 4871:Exploratory Data Analysis 4637:Benjamin B. Bederson and 4496:10.1146/knowable-110919-1 4448:Communications of the ACM 4408:10.1515/9783110430295-024 4379:David C. LeBlanc (2004), 4294:10.1186/s40537-015-0031-2 4227:10.1162/99608f92.8ae4d525 4180:. John Wiley & Sons. 4047:List of graphical methods 3830:organizational psychology 3448:Information visualization 3294:Interactive visualization 2451:native speakers worldwide 1954: 1946: 1564:exploratory data analysis 1331:exploratory data analysis 1323:information visualization 1081:exploratory data analysis 929:Information visualization 805:explanatory visualization 793:exploratory visualization 678:Entity–relationship model 461:Business decision mapping 244:Information graphic types 105:Exploratory data analysis 49:Information visualization 7292:Visualization (graphics) 6642:Charles-René de Fourcroy 6491:Scientific visualization 6418:of technical information 6199:Mazza, Riccardo (2009). 6081:Wilke, Claus O. (2018). 5945:10.1177/1460458212465213 5767:Computational Statistics 5585:Bulmer, Michael (2013). 5265:. Accessed Jan 19, 2010. 5256:History of Visualization 4769:: CS1 maint: location ( 4059:, example of Treemapping 3913:Information architecture 3267:Multidimensional scaling 2939:statistical distribution 1951: 1948: 1644:scientific visualization 1576:pre-attentive attributes 1497:) by sales persons (the 1327:scientific visualization 1032:scientific visualization 899:proportional symbol maps 46:It has been merged from 6244:, Prentice Hall, 2007, 5911:Harvard Business Review 5802:"Video Lending Library" 4783:Vitaly Friedman (2008) 4604:10.1073/pnas.1807180116 4136:CSISS website has moved 3816:Historically, the term 3545:Financial data analysis 3461:Multiresolution methods 3316:video lending library. 2841:Animated spiral graphic 2835:Animated spiral graphic 2296:Also called "dot plots" 1868:are tables and graphs. 1664:, intended to show the 1503:categorical subdivision 486:Knowledge visualization 63:Statistician professor 7062:Christopher R. Johnson 6614:Technical illustration 6501:Software visualization 6286:. New York: Springer. 6184:. New York: Springer. 5254:G. Scott Owen (1999). 5003:Tufte, Edward (1983). 4068:Software visualization 4042:Information management 3937:data-driven journalism 3803: 3453:Interaction techniques 3172: 3101:These diagrams depict 3057: 3005: 2954: 2935:statistical population 2884: 2836: 2778: 2733: 2679: 2634: 2575: 2521: 2464: 2410: 2356: 2309: 2245: 2192: 2149: 2053: 1966: 1855:continuous or discrete 1828:or paid programs like 1781: 1746: 1681: 1669: 1617: 1479: 1365: 1180: 1168: 1088:descriptive statistics 673:Diagrammatic reasoning 496:Morphological analysis 125:Inferential statistics 120:Descriptive statistics 76: 6956:Lawrence J. Rosenblum 6769:Edward Walter Maunder 6693:Charles Joseph Minard 6511:User interface design 6486:Product visualization 6150:Few, Stephen (2012). 5720:University of Reading 4460:10.1145/381641.381653 3822:Business Intelligence 3798: 3328:: works by using the 3292:Further information: 3170: 3055: 3041:comparative measures. 3003: 2952: 2882: 2862:temperature over time 2853:color (passing years) 2834: 2804:temperature over time 2776: 2731: 2717:For example, used in 2677: 2632: 2573: 2519: 2462: 2408: 2354: 2307: 2278:Cartesian coordinates 2243: 2190: 2146: 2033: 1964: 1908:. These axes provide 1883:quantitative variable 1864:Two primary types of 1779: 1744: 1675: 1660: 1632:IEEE Computer Society 1615: 1470: 1463:Quantitative messages 1174: 1166: 1042:to confirm or reject 983:Emerging technologies 891:box-and-whisker plots 767:) is the practice of 698:Ontology (philosophy) 597:Layered graph drawing 471:Graphic communication 317:Stem-and-leaf display 206:Alexander Osterwalder 69:Charles Joseph Minard 62: 7236:Scientific modelling 7211:Information graphics 6951:Clifford A. Pickover 6901:William S. Cleveland 6809:Henry Norris Russell 6794:Howard G. Funkhouser 6738:Florence Nightingale 6703:Francis Amasa Walker 6599:Statistical graphics 6521:Volume visualization 6496:Social visualization 6142:Kawa Nazemi (2014). 4911:– via YouTube. 4364:Brent Dykes (2019), 4073:Statistical analysis 4063:Patent visualisation 3996:Data physicalization 3757:improve this section 3541:Information graphics 3472:Volume visualization 3458:Modelling techniques 3384:thematic cartography 3380:statistical graphics 3272:Parallel coordinates 2889:Box and Whisker Plot 2883:Box and whisker plot 2747:categorical variable 2532:(non-linear) charts 2089:that are respective 2036:· population (along 1891:category subdivision 1879:qualitative variable 1866:information displays 1719:thematic cartography 1648:volume visualization 1627:Scientific Computing 1335:statistical graphics 1319:information graphics 1312:Martin M. Wattenberg 1284:information graphics 1276:statistical graphics 1137:information literacy 1102:and, more recently, 1092:visual communication 1077:statistical graphics 915:correlation matrices 809:information graphics 612:Organizational chart 607:Object-role modeling 524:Node–link approaches 130:Statistical graphics 82:Part of a series on 7216:Information science 7179:in computer science 6971:Sheelagh Carpendale 6906:George G. Robertson 6743:Karl Wilhelm Pohlke 6678:André-Michel Guerry 6554:Graph of a function 6549:Engineering drawing 6316:Colin Ware (2000). 6182:Grammar of Graphics 5738:The Washington Post 5517:. Springer-Verlag. 4936:1985Sci...229..828C 4595:2019PNAS..116.1857B 4281:Journal of Big Data 3617:Panopticon Software 3527:Scientific research 2704:that illustrates a 2093:of related pairs of 1850:someone falls into. 1780:Playfair TimeSeries 1745:Planetary movements 1666:Economic Complexity 1011:information systems 789:domain of expertise 506:Schema (psychology) 448:Information mapping 388:Regression analysis 71:'s 1869 graphic of 7287:Data visualization 7256:Volume cartography 7020:Early 21st century 6916:Catherine Plaisant 6911:Bruce H. McCormick 6865:Mary Eleanor Spear 6855:Arthur H. Robinson 6789:Arthur Lyon Bowley 6762:Early 20th century 6609:Technical drawings 6481:Molecular graphics 6456:Flow visualization 6446:Data visualization 6313:. Morgan Kaufmann. 6270:2007-06-12 at the 6227:Jean-Daniel Fekete 5880:2009-10-07 at the 5854:2008-07-22 at the 5835:2008-09-11 at the 5779:10.1007/PL00022700 5261:2012-10-08 at the 5076:The New York Times 4874:. Addison-Wesley. 4851:2009-10-07 at the 4790:2008-07-22 at the 4673:2008-09-29 at the 4647:, Morgan Kaufmann 4154:Business2Community 4037:Information design 3971:Climate change art 3927:interaction design 3907:Digital humanities 3804: 3642:IEEE Visualization 3612:Microsoft Research 3577:Digital Humanities 3558:production control 3428:Tools and services 3373:Other perspectives 3173: 3058: 3006: 2955: 2885: 2837: 2779: 2734: 2680: 2635: 2604:A type of stacked 2576: 2522: 2465: 2411: 2357: 2313:Scatter plot (3D) 2310: 2284:for a set of data. 2246: 2193: 2150: 2054: 2052:product of values) 1967: 1952:Visual dimensions 1826:The Data Incubator 1782: 1747: 1682: 1670: 1668:of a given economy 1640:data visualization 1618: 1480: 1253:association mining 1217:production control 1181: 1169: 887:distribution plots 815:Data visualization 797:mass communication 617:Pathfinder network 481:Information design 466:Data visualization 110:Information design 77: 7274: 7273: 7251:Visual perception 7201:Graphic organizer 7174:Computer graphics 7145: 7144: 7127:Martin Wattenberg 7102:Hanspeter Pfister 7057:Martin Krzywinski 6981:Jock D. Mackinlay 6961:Thomas A. DeFanti 6884:Late 20th century 6804:Ejnar Hertzsprung 6506:Technical drawing 6330:Jock D. Mackinlay 6293:978-1-4613-5430-7 6191:978-1-4419-2033-1 6178:Wilkinson, Leland 6113:978-1-5063-0305-5 6094:978-1-4920-3108-6 6073:978-0-691-18161-5 6055: 6054: 6047: 5686:978-1-4665-0891-0 5646:Eppler, Martin. J 5596:978-1-921723-10-0 4756:978-90-485-4313-7 4487:Knowable Magazine 4431:E.H. Chi (2013), 4187:978-1-119-00225-3 3933:Visual journalism 3917:unstructured data 3895:Business analysis 3834:change management 3793: 3792: 3785: 3727: 3726: 3705:. Please help to 3697:This section may 3531:Digital libraries 3455:and architectures 3211: 3210: 2561:logarithmic scale 1887:experimental unit 1715:Turin Papyrus Map 1622:computer graphics 1444:misleading graphs 1390:In his 1983 book 1209:digital libraries 1100:cognitive science 1060:data storytelling 999:visual perception 961:semantic networks 754: 753: 453:Topics and fields 434: 433: 393:Statistical model 383:Visual perception 158:Important figures 57: 56: 16:(Redirected from 7309: 7261:Volume rendering 7246:Visual analytics 7241:Spatial analysis 7221:Misleading graph 7072:David McCandless 7047:Gordon Kindlmann 7011:Alfred Inselberg 7006:Leland Wilkinson 6941:Michael Friendly 6875:Howard T. Fisher 6838:Mid 20th century 6779:W. E. B. Du Bois 6683:William Playfair 6673:Adolphe Quetelet 6647:Joseph Priestley 6630:Pre-19th century 6627: 6594:Skeletal formula 6461:Geovisualization 6436:Chemical imaging 6409: 6402: 6395: 6386: 6363: 6352:. Hobart Press. 6349:Visualizing Data 6297: 6222: 6195: 6173: 6139: 6122:Tufte, Edward R. 6117: 6098: 6077: 6050: 6043: 6039: 6036: 6030: 6025:Please read the 6021:may need cleanup 6010: 6009: 6002: 5991: 5990: 5988: 5986: 5971: 5965: 5964: 5930: 5921: 5915: 5914: 5902: 5885: 5869: 5863: 5862:, August 2, 2007 5846: 5840: 5826:Michael Friendly 5823: 5817: 5816: 5814: 5813: 5804:. Archived from 5797: 5791: 5790: 5759: 5753: 5752: 5729: 5723: 5713: 5697: 5691: 5690: 5672: 5666: 5665: 5663: 5661: 5638: 5632: 5631: 5629: 5628: 5622: 5615: 5607: 5601: 5600: 5582: 5573: 5568: 5562: 5561: 5559: 5558: 5539: 5533: 5532: 5530: 5529: 5523: 5512: 5503: 5497: 5496: 5494: 5493: 5478: 5472: 5471: 5435: 5429: 5428: 5423:. Archived from 5416: 5407: 5406: 5404: 5403: 5375: 5369: 5368: 5366: 5365: 5349: 5343: 5342: 5340: 5338: 5318: 5312: 5311: 5277: 5266: 5252: 5246: 5245: 5243: 5242: 5227: 5221: 5220: 5218: 5217: 5211: 5200: 5192: 5186: 5185: 5183: 5182: 5163: 5152: 5151: 5149: 5148: 5142: 5135: 5127: 5118: 5117: 5115: 5114: 5108: 5101: 5093: 5087: 5086: 5067: 5061: 5060: 5058: 5057: 5038: 5032: 5031: 5029: 5028: 5000: 4989: 4988: 4970: 4964: 4963: 4930:(4716): 828–33. 4919: 4913: 4912: 4910: 4908: 4892: 4886: 4885: 4862: 4856: 4839: 4830: 4829: 4827: 4825: 4805: 4799: 4781: 4775: 4774: 4768: 4760: 4732: 4726: 4725: 4718: 4712: 4711: 4709: 4708: 4702: 4695: 4687: 4678: 4662: 4656: 4635: 4626: 4625: 4616: 4606: 4589:(6): 1857–1864, 4578: 4572: 4571: 4564: 4558: 4557: 4555: 4553: 4540: 4507: 4501: 4500: 4498: 4478: 4472: 4471: 4443: 4437: 4436: 4428: 4422: 4421: 4391: 4385: 4384: 4376: 4370: 4369: 4361: 4355: 4354: 4352: 4350: 4336: 4327: 4326: 4320: 4312: 4304: 4298: 4297: 4296: 4276: 4270: 4269: 4260: 4254: 4253: 4245: 4239: 4238: 4236: 4234: 4229: 4205: 4192: 4191: 4173: 4162: 4161: 4145: 4139: 4133: 4132:on 19 June 2003. 4121: 4104: 4100: 4078:Visual analytics 4016:Geovisualization 3788: 3781: 3777: 3774: 3768: 3737: 3729: 3722: 3719: 3692: 3691: 3684: 3627:Tableau Software 3441:computer science 3238:(classification) 3214:Other techniques 2719:project planning 2706:project schedule 2355:Network analysis 1992:categorical data 1944: 1830:General Assembly 1763:Pierre de Fermat 1732:Claudius Ptolemy 1702:(5500 BC), Inca 1652:William Playfair 1593:data exploration 1384:design principle 1380:analytical tasks 1371: 1257:machine learning 1205:business methods 1189:computer science 1112:visual analytics 957:network diagrams 863:waterfall charts 746: 739: 732: 683:Geovisualization 668:Design rationale 627:Semantic network 557:Conceptual graph 511:Visual analytics 443: 436: 426: 419: 412: 398:Misleading graph 221:Leland Wilkinson 196:David McCandless 97:Major dimensions 79: 38: 37: 30: 21: 7317: 7316: 7312: 7311: 7310: 7308: 7307: 7306: 7277: 7276: 7275: 7270: 7266:Information art 7206:Imaging science 7151: 7141: 7122:Fernanda Viégas 7117:Moritz Stefaner 7042:Jessica Hullman 7015: 6986:Alan MacEachren 6936:Ben Shneiderman 6879: 6833: 6757: 6656: 6618: 6531: 6525: 6476:Medical imaging 6419: 6413: 6370: 6360: 6345: 6334:Ben Shneiderman 6305:Ben Shneiderman 6294: 6281: 6272:Wayback Machine 6211: 6198: 6192: 6176: 6162: 6149: 6136: 6120: 6114: 6101: 6095: 6080: 6074: 6058: 6051: 6040: 6034: 6031: 6024: 6017:Further reading 6011: 6007: 6000: 5998:Further reading 5995: 5994: 5984: 5982: 5973: 5972: 5968: 5928: 5923: 5922: 5918: 5904: 5903: 5888: 5882:Wayback Machine 5870: 5866: 5856:Wayback Machine 5847: 5843: 5837:Wayback Machine 5824: 5820: 5811: 5809: 5799: 5798: 5794: 5763:Swayne, Deborah 5761: 5760: 5756: 5731: 5730: 5726: 5699: 5698: 5694: 5687: 5674: 5673: 5669: 5659: 5657: 5640: 5639: 5635: 5626: 5624: 5620: 5613: 5609: 5608: 5604: 5597: 5584: 5583: 5576: 5569: 5565: 5556: 5554: 5541: 5540: 5536: 5527: 5525: 5521: 5515:York University 5510: 5505: 5504: 5500: 5491: 5489: 5480: 5479: 5475: 5437: 5436: 5432: 5418: 5417: 5410: 5401: 5399: 5384:Hornbæk, Kasper 5380:Isenberg, Petra 5377: 5376: 5372: 5363: 5361: 5351: 5350: 5346: 5336: 5334: 5320: 5319: 5315: 5300: 5279: 5278: 5269: 5263:Wayback Machine 5253: 5249: 5240: 5238: 5229: 5228: 5224: 5215: 5213: 5209: 5198: 5196:"Visualization" 5194: 5193: 5189: 5180: 5178: 5165: 5164: 5155: 5146: 5144: 5140: 5133: 5129: 5128: 5121: 5112: 5110: 5106: 5099: 5095: 5094: 5090: 5069: 5068: 5064: 5055: 5053: 5040: 5039: 5035: 5026: 5024: 5017: 5002: 5001: 4992: 4985: 4972: 4971: 4967: 4921: 4920: 4916: 4906: 4904: 4894: 4893: 4889: 4882: 4864: 4863: 4859: 4853:Wayback Machine 4840: 4833: 4823: 4821: 4807: 4806: 4802: 4792:Wayback Machine 4782: 4778: 4761: 4757: 4734: 4733: 4729: 4720: 4719: 4715: 4706: 4704: 4700: 4693: 4689: 4688: 4681: 4675:Wayback Machine 4663: 4659: 4639:Ben Shneiderman 4636: 4629: 4580: 4579: 4575: 4566: 4565: 4561: 4551: 4549: 4509: 4508: 4504: 4480: 4479: 4475: 4445: 4444: 4440: 4430: 4429: 4425: 4418: 4393: 4392: 4388: 4378: 4377: 4373: 4363: 4362: 4358: 4348: 4346: 4338: 4337: 4330: 4313: 4306: 4305: 4301: 4278: 4277: 4273: 4262: 4261: 4257: 4247: 4246: 4242: 4232: 4230: 4207: 4206: 4195: 4188: 4175: 4174: 4165: 4147: 4146: 4142: 4124:Corbett, John. 4123: 4122: 4118: 4113: 4108: 4107: 4101: 4097: 4092: 4087: 4083:Warming stripes 3991:Data management 3986:Information art 3956: 3941:data journalism 3888: 3860: 3843: 3789: 3778: 3772: 3769: 3754: 3738: 3723: 3717: 3714: 3693: 3689: 3682: 3607:Google Research 3590: 3572:Policy Modeling 3521: 3375: 3296: 3290: 3231:Concept Mapping 3216: 2114:Arithmetically: 2047: 2041: 2035: 1942: 1932: 1842: 1687:York University 1610: 1604: 1588: 1572: 1465: 1373: 1367: 1359: 1354: 1308:Fernanda Viegas 1237:hypothesis test 1161: 1153:visual literacy 1141:information age 1040:scientific data 949:Sankey diagrams 903:choropleth maps 777:representations 750: 577:Hyperbolic tree 547:Concept lattice 516:Visual language 430: 342:Marimekko chart 171:Ben Shneiderman 53: 39: 35: 28: 23: 22: 15: 12: 11: 5: 7315: 7313: 7305: 7304: 7299: 7294: 7289: 7279: 7278: 7272: 7271: 7269: 7268: 7263: 7258: 7253: 7248: 7243: 7238: 7233: 7231:Patent drawing 7228: 7223: 7218: 7213: 7208: 7203: 7198: 7196:Graphic design 7193: 7188: 7183: 7182: 7181: 7171: 7166: 7161: 7155: 7153: 7147: 7146: 7143: 7142: 7140: 7139: 7137:Hadley Wickham 7134: 7129: 7124: 7119: 7114: 7109: 7104: 7099: 7094: 7092:Tamara Munzner 7089: 7084: 7079: 7074: 7069: 7064: 7059: 7054: 7049: 7044: 7039: 7034: 7029: 7023: 7021: 7017: 7016: 7014: 7013: 7008: 7003: 6998: 6993: 6991:David Goodsell 6988: 6983: 6978: 6976:Cynthia Brewer 6973: 6968: 6963: 6958: 6953: 6948: 6943: 6938: 6933: 6928: 6923: 6918: 6913: 6908: 6903: 6898: 6893: 6887: 6885: 6881: 6880: 6878: 6877: 6872: 6870:Edgar Anderson 6867: 6862: 6857: 6852: 6847: 6845:Jacques Bertin 6841: 6839: 6835: 6834: 6832: 6831: 6826: 6821: 6816: 6811: 6806: 6801: 6799:John B. Peddle 6796: 6791: 6786: 6781: 6776: 6771: 6765: 6763: 6759: 6758: 6756: 6755: 6753:Francis Galton 6750: 6748:Toussaint Loua 6745: 6740: 6735: 6730: 6728:Georg von Mayr 6725: 6720: 6718:Matthew Sankey 6715: 6710: 6705: 6700: 6695: 6690: 6685: 6680: 6675: 6670: 6664: 6662: 6658: 6657: 6655: 6654: 6649: 6644: 6639: 6633: 6631: 6624: 6620: 6619: 6617: 6616: 6611: 6606: 6601: 6596: 6591: 6586: 6584:Sankey diagram 6581: 6576: 6571: 6566: 6561: 6556: 6551: 6546: 6541: 6535: 6533: 6527: 6526: 6524: 6523: 6518: 6516:Visual culture 6513: 6508: 6503: 6498: 6493: 6488: 6483: 6478: 6473: 6468: 6463: 6458: 6453: 6448: 6443: 6438: 6433: 6427: 6425: 6421: 6420: 6414: 6412: 6411: 6404: 6397: 6389: 6383: 6382: 6377: 6369: 6368:External links 6366: 6365: 6364: 6358: 6343: 6326:Stuart K. Card 6323: 6314: 6298: 6292: 6279: 6257:Stuart K. Card 6255:Jeffrey Heer, 6253: 6239:Spence, Robert 6236: 6223: 6209: 6196: 6190: 6174: 6160: 6147: 6140: 6134: 6118: 6112: 6099: 6093: 6078: 6072: 6053: 6052: 6014: 6012: 6005: 5999: 5996: 5993: 5992: 5966: 5939:(3): 198–217. 5916: 5886: 5864: 5841: 5818: 5792: 5754: 5724: 5692: 5685: 5667: 5642:Lengler, Ralph 5633: 5602: 5595: 5574: 5563: 5534: 5498: 5473: 5452:10.1086/368425 5430: 5427:on 2014-04-14. 5408: 5370: 5344: 5313: 5298: 5267: 5247: 5222: 5187: 5153: 5119: 5088: 5062: 5033: 5015: 4990: 4983: 4965: 4914: 4887: 4880: 4857: 4831: 4800: 4776: 4755: 4727: 4713: 4679: 4657: 4627: 4573: 4559: 4523:(1): 275–304. 4502: 4473: 4438: 4423: 4416: 4386: 4371: 4356: 4328: 4299: 4271: 4255: 4240: 4193: 4186: 4163: 4140: 4115: 4114: 4112: 4109: 4106: 4105: 4094: 4093: 4091: 4088: 4086: 4085: 4080: 4075: 4070: 4065: 4060: 4054: 4049: 4044: 4039: 4034: 4029: 4023: 4018: 4013: 4011:Data warehouse 4008: 4006:Data profiling 4003: 3998: 3993: 3988: 3983: 3978: 3973: 3968: 3963: 3957: 3955: 3952: 3951: 3950: 3947:Graphic design 3944: 3930: 3920: 3910: 3904: 3901: 3898: 3887: 3886:Related fields 3884: 3883: 3882: 3879: 3876: 3873: 3870: 3867: 3859: 3856: 3855: 3854: 3850: 3842: 3839: 3791: 3790: 3741: 3739: 3732: 3725: 3724: 3709:. Discuss and 3696: 3694: 3687: 3681: 3678: 3671: 3670: 3664: 3651: 3645: 3635: 3634: 3629: 3624: 3619: 3614: 3609: 3604: 3599: 3597:Adobe Research 3589: 3586: 3585: 3584: 3579: 3574: 3565: 3560: 3556:Manufacturing 3554: 3553:Market studies 3551: 3546: 3543: 3538: 3533: 3528: 3520: 3517: 3516: 3515: 3514: 3513: 3507: 3506: 3505: 3499: 3498: 3497: 3491: 3490: 3489: 3475: 3474: 3469: 3468:and techniques 3464:Visualization 3462: 3459: 3456: 3450: 3434:graphic design 3430: 3429: 3426: 3421: 3415: 3409: 3403: 3397: 3374: 3371: 3370: 3369: 3361: 3355: 3348:Identification 3345: 3339: 3289: 3286: 3285: 3284: 3279: 3274: 3269: 3264: 3262:HyperbolicTree 3259: 3254: 3249: 3244: 3239: 3233: 3228: 3222: 3215: 3212: 3209: 3208: 3207: 3206: 3203: 3200: 3195: 3194: 3193: 3190: 3187: 3184: 3179: 3174: 3163: 3162: 3161: 3160: 3113: 3110: 3099: 3082: 3081: 3080: 3064: 3059: 3048: 3047: 3046: 3045: 3042: 3038: 3022: 3021: 3020: 3017: 3012: 3007: 2996: 2995: 2994: 2993: 2990: 2987: 2974: 2973: 2972: 2961: 2956: 2945: 2944: 2943: 2942: 2931:non-parametric 2929:Box plots are 2927: 2923: 2917: 2910: 2901: 2900: 2899: 2896: 2891: 2886: 2875: 2874: 2873: 2872: 2868: 2866:global warming 2856: 2855: 2854: 2851: 2848: 2843: 2838: 2827: 2826: 2825: 2824: 2817: 2810: 2808:global warming 2800: 2795: 2794: 2793: 2790: 2785: 2783:Stripe graphic 2780: 2777:Stripe graphic 2769: 2768: 2767: 2766: 2765: 2764: 2761: 2755: 2750: 2749: 2748: 2745: 2740: 2735: 2724: 2723: 2722: 2721: 2715: 2708: 2696: 2695: 2694: 2691: 2686: 2681: 2670: 2669: 2668: 2667: 2664: 2651: 2650: 2649: 2646: 2641: 2636: 2625: 2624: 2623: 2622: 2619: 2616: 2613: 2600: 2599: 2598: 2595: 2592: 2587: 2577: 2566: 2565: 2564: 2563: 2557: 2552: 2551: 2550: 2547: 2544: 2541: 2538: 2533: 2523: 2512: 2511: 2510: 2509: 2502: 2495: 2490: 2489: 2488: 2485: 2482: 2479: 2476: 2471: 2466: 2455: 2454: 2453: 2452: 2445: 2424: 2423: 2422: 2417: 2412: 2401: 2400: 2399: 2398: 2395: 2392: 2389: 2384: 2383: 2382: 2380:spatialization 2377: 2374: 2373:ties thickness 2371: 2368: 2363: 2358: 2347: 2346: 2345: 2344: 2341: 2336: 2335: 2334: 2331: 2328: 2325: 2322: 2319: 2314: 2311: 2300: 2299: 2298: 2297: 2294: 2291: 2288: 2285: 2272: 2271: 2270: 2267: 2264: 2261: 2258: 2253: 2247: 2236: 2235: 2234: 2233: 2229: 2212: 2211: 2210: 2207: 2204: 2199: 2194: 2183: 2182: 2181: 2180: 2177: 2174: 2169: 2168: 2167: 2164: 2161: 2156: 2151: 2139: 2138: 2135: 2134: 2133: 2132: 2124: 2123: 2116: 2115: 2111: 2110: 2103: 2095: 2094: 2083: 2078: 2077: 2076: 2073: 2070: 2067: 2062: 2055: 2026: 2025: 2024: 2023: 2020: 2017: 2007: 1986: 1985: 1984: 1981: 1978: 1973: 1968: 1957: 1956: 1953: 1950: 1947: 1931: 1928: 1923:Tamara Munzner 1918: 1917: 1902:visual objects 1894: 1859: 1858: 1851: 1841: 1838: 1795:Programs like 1786:Jacques Bertin 1759:René Descartes 1603: 1600: 1587: 1584: 1571: 1568: 1559: 1558: 1544: 1541: 1533: 1521: 1517: 1510: 1491: 1476:New York Times 1464: 1461: 1429: 1428: 1425: 1422: 1419: 1416: 1413: 1410: 1407: 1404: 1360: 1358: 1355: 1353: 1350: 1269:decision trees 1265:classification 1221:drug discovery 1160: 1157: 1129:public opinion 1125:misinformation 1096:graphic design 1073:business value 893:), geospatial 843:pyramid charts 801:presentational 752: 751: 749: 748: 741: 734: 726: 723: 722: 721: 720: 718:Wicked problem 715: 710: 705: 700: 695: 690: 685: 680: 675: 670: 662: 661: 657: 656: 655: 654: 649: 647:Tree structure 644: 639: 634: 629: 624: 619: 614: 609: 604: 599: 594: 589: 584: 579: 574: 569: 564: 559: 554: 549: 544: 539: 534: 526: 525: 521: 520: 519: 518: 513: 508: 503: 498: 493: 488: 483: 478: 473: 468: 463: 455: 454: 450: 449: 445: 444: 432: 431: 429: 428: 421: 414: 406: 403: 402: 401: 400: 395: 390: 385: 380: 375: 370: 365: 360: 352: 351: 350:Related topics 347: 346: 345: 344: 339: 334: 329: 327:Small multiple 324: 319: 314: 309: 304: 302:Stripe graphic 299: 294: 289: 284: 279: 274: 269: 264: 259: 254: 246: 245: 241: 240: 239: 238: 233: 228: 223: 218: 216:Hadley Wickham 213: 208: 203: 198: 193: 188: 183: 178: 173: 168: 166:Tamara Munzner 160: 159: 155: 154: 153: 152: 147: 142: 137: 132: 127: 122: 117: 112: 107: 99: 98: 94: 93: 87: 86: 55: 54: 42: 40: 33: 26: 24: 14: 13: 10: 9: 6: 4: 3: 2: 7314: 7303: 7300: 7298: 7295: 7293: 7290: 7288: 7285: 7284: 7282: 7267: 7264: 7262: 7259: 7257: 7254: 7252: 7249: 7247: 7244: 7242: 7239: 7237: 7234: 7232: 7229: 7227: 7224: 7222: 7219: 7217: 7214: 7212: 7209: 7207: 7204: 7202: 7199: 7197: 7194: 7192: 7191:Graph drawing 7189: 7187: 7184: 7180: 7177: 7176: 7175: 7172: 7170: 7167: 7165: 7162: 7160: 7157: 7156: 7154: 7148: 7138: 7135: 7133: 7130: 7128: 7125: 7123: 7120: 7118: 7115: 7113: 7112:Claudio Silva 7110: 7108: 7105: 7103: 7100: 7098: 7095: 7093: 7090: 7088: 7085: 7083: 7080: 7078: 7077:Mauro Martino 7075: 7073: 7070: 7068: 7065: 7063: 7060: 7058: 7055: 7053: 7050: 7048: 7045: 7043: 7040: 7038: 7035: 7033: 7030: 7028: 7025: 7024: 7022: 7018: 7012: 7009: 7007: 7004: 7002: 7001:Michael Maltz 6999: 6997: 6994: 6992: 6989: 6987: 6984: 6982: 6979: 6977: 6974: 6972: 6969: 6967: 6966:George Furnas 6964: 6962: 6959: 6957: 6954: 6952: 6949: 6947: 6946:Howard Wainer 6944: 6942: 6939: 6937: 6934: 6932: 6929: 6927: 6924: 6922: 6919: 6917: 6914: 6912: 6909: 6907: 6904: 6902: 6899: 6897: 6894: 6892: 6889: 6888: 6886: 6882: 6876: 6873: 6871: 6868: 6866: 6863: 6861: 6858: 6856: 6853: 6851: 6850:Rudolf Modley 6848: 6846: 6843: 6842: 6840: 6836: 6830: 6827: 6825: 6822: 6820: 6817: 6815: 6814:Max O. Lorenz 6812: 6810: 6807: 6805: 6802: 6800: 6797: 6795: 6792: 6790: 6787: 6785: 6782: 6780: 6777: 6775: 6772: 6770: 6767: 6766: 6764: 6760: 6754: 6751: 6749: 6746: 6744: 6741: 6739: 6736: 6734: 6731: 6729: 6726: 6724: 6723:Charles Booth 6721: 6719: 6716: 6714: 6711: 6709: 6706: 6704: 6701: 6699: 6698:Luigi Perozzo 6696: 6694: 6691: 6689: 6688:August Kekulé 6686: 6684: 6681: 6679: 6676: 6674: 6671: 6669: 6668:Charles Dupin 6666: 6665: 6663: 6659: 6653: 6652:Gaspard Monge 6650: 6648: 6645: 6643: 6640: 6638: 6637:Edmond Halley 6635: 6634: 6632: 6628: 6625: 6621: 6615: 6612: 6610: 6607: 6605: 6602: 6600: 6597: 6595: 6592: 6590: 6587: 6585: 6582: 6580: 6577: 6575: 6572: 6570: 6567: 6565: 6562: 6560: 6557: 6555: 6552: 6550: 6547: 6545: 6542: 6540: 6537: 6536: 6534: 6528: 6522: 6519: 6517: 6514: 6512: 6509: 6507: 6504: 6502: 6499: 6497: 6494: 6492: 6489: 6487: 6484: 6482: 6479: 6477: 6474: 6472: 6469: 6467: 6464: 6462: 6459: 6457: 6454: 6452: 6449: 6447: 6444: 6442: 6441:Crime mapping 6439: 6437: 6434: 6432: 6429: 6428: 6426: 6422: 6417: 6416:Visualization 6410: 6405: 6403: 6398: 6396: 6391: 6390: 6387: 6381: 6378: 6375: 6372: 6371: 6367: 6361: 6359:0-9634884-0-6 6355: 6351: 6350: 6344: 6341: 6340: 6335: 6331: 6327: 6324: 6321: 6320: 6315: 6312: 6311: 6306: 6302: 6299: 6295: 6289: 6285: 6280: 6277: 6273: 6269: 6266: 6262: 6258: 6254: 6251: 6250:0-13-206550-9 6247: 6243: 6240: 6237: 6234: 6233: 6228: 6224: 6220: 6216: 6212: 6210:9781848002180 6206: 6202: 6197: 6193: 6187: 6183: 6179: 6175: 6171: 6167: 6163: 6161:9780970601971 6157: 6153: 6148: 6145: 6141: 6137: 6135:9780961392147 6131: 6127: 6123: 6119: 6115: 6109: 6105: 6100: 6096: 6090: 6086: 6085: 6079: 6075: 6069: 6065: 6061: 6060:Healy, Kieran 6057: 6056: 6049: 6046: 6038: 6028: 6027:editing guide 6022: 6018: 6013: 6004: 6003: 5997: 5981: 5977: 5970: 5967: 5962: 5958: 5954: 5950: 5946: 5942: 5938: 5934: 5927: 5920: 5917: 5912: 5908: 5901: 5899: 5897: 5895: 5893: 5891: 5887: 5883: 5879: 5876: 5875: 5868: 5865: 5861: 5857: 5853: 5850: 5845: 5842: 5838: 5834: 5831: 5827: 5822: 5819: 5808:on 2021-01-20 5807: 5803: 5796: 5793: 5788: 5784: 5780: 5776: 5772: 5768: 5764: 5758: 5755: 5751: 5748: 5743: 5739: 5735: 5728: 5725: 5721: 5717: 5711: 5707: 5703: 5696: 5693: 5688: 5682: 5678: 5671: 5668: 5655: 5651: 5647: 5643: 5637: 5634: 5619: 5612: 5606: 5603: 5598: 5592: 5588: 5581: 5579: 5575: 5572: 5567: 5564: 5552: 5548: 5544: 5538: 5535: 5520: 5516: 5509: 5502: 5499: 5487: 5483: 5477: 5474: 5469: 5465: 5461: 5457: 5453: 5449: 5445: 5441: 5434: 5431: 5426: 5422: 5415: 5413: 5409: 5397: 5394:: 3227–3236. 5393: 5389: 5385: 5381: 5374: 5371: 5359: 5355: 5348: 5345: 5332: 5328: 5324: 5317: 5314: 5309: 5305: 5301: 5299:9783540330370 5295: 5291: 5287: 5283: 5276: 5274: 5272: 5268: 5264: 5260: 5257: 5251: 5248: 5237: 5233: 5226: 5223: 5212:on 2016-01-22 5208: 5204: 5197: 5191: 5188: 5176: 5172: 5168: 5162: 5160: 5158: 5154: 5139: 5132: 5126: 5124: 5120: 5105: 5098: 5092: 5089: 5085: 5081: 5077: 5073: 5066: 5063: 5051: 5047: 5043: 5037: 5034: 5022: 5018: 5016:0-9613921-4-2 5012: 5008: 5007: 4999: 4997: 4995: 4991: 4986: 4984:9780318029924 4980: 4976: 4969: 4966: 4961: 4957: 4953: 4949: 4945: 4941: 4937: 4933: 4929: 4925: 4918: 4915: 4902: 4898: 4891: 4888: 4883: 4881:0-201-07616-0 4877: 4873: 4872: 4867: 4861: 4858: 4854: 4850: 4847: 4845: 4838: 4836: 4832: 4819: 4815: 4811: 4804: 4801: 4797: 4793: 4789: 4786: 4780: 4777: 4772: 4766: 4758: 4752: 4748: 4744: 4740: 4739: 4731: 4728: 4723: 4717: 4714: 4699: 4692: 4686: 4684: 4680: 4676: 4672: 4669: 4668: 4661: 4658: 4654: 4653:1-55860-915-6 4650: 4646: 4645: 4640: 4634: 4632: 4628: 4624: 4620: 4615: 4610: 4605: 4600: 4596: 4592: 4588: 4584: 4577: 4574: 4570: 4563: 4560: 4548: 4544: 4539: 4534: 4530: 4526: 4522: 4518: 4514: 4506: 4503: 4497: 4492: 4488: 4484: 4477: 4474: 4469: 4465: 4461: 4457: 4453: 4449: 4442: 4439: 4434: 4427: 4424: 4419: 4417:9783110430295 4413: 4409: 4405: 4401: 4397: 4390: 4387: 4382: 4375: 4372: 4367: 4360: 4357: 4345: 4341: 4335: 4333: 4329: 4324: 4318: 4310: 4303: 4300: 4295: 4290: 4286: 4282: 4275: 4272: 4267: 4259: 4256: 4251: 4244: 4241: 4228: 4223: 4219: 4215: 4211: 4204: 4202: 4200: 4198: 4194: 4189: 4183: 4179: 4172: 4170: 4168: 4164: 4159: 4155: 4151: 4144: 4141: 4137: 4131: 4127: 4120: 4117: 4110: 4099: 4096: 4089: 4084: 4081: 4079: 4076: 4074: 4071: 4069: 4066: 4064: 4061: 4058: 4055: 4053: 4050: 4048: 4045: 4043: 4040: 4038: 4035: 4033: 4030: 4027: 4024: 4022: 4019: 4017: 4014: 4012: 4009: 4007: 4004: 4002: 3999: 3997: 3994: 3992: 3989: 3987: 3984: 3982: 3979: 3977: 3974: 3972: 3969: 3967: 3964: 3962: 3959: 3958: 3953: 3948: 3945: 3942: 3938: 3934: 3931: 3928: 3924: 3921: 3918: 3914: 3911: 3908: 3905: 3902: 3899: 3896: 3893: 3892: 3891: 3885: 3880: 3877: 3874: 3871: 3868: 3865: 3864: 3863: 3857: 3851: 3848: 3847: 3846: 3840: 3838: 3835: 3831: 3827: 3823: 3819: 3814: 3812: 3808: 3802: 3797: 3787: 3784: 3776: 3766: 3762: 3758: 3752: 3751: 3747: 3742:This section 3740: 3736: 3731: 3730: 3721: 3718:February 2021 3712: 3708: 3704: 3702: 3695: 3686: 3685: 3679: 3677: 3676: 3668: 3665: 3662: 3659: 3655: 3652: 3649: 3646: 3643: 3640: 3639: 3638: 3633: 3630: 3628: 3625: 3623: 3620: 3618: 3615: 3613: 3610: 3608: 3605: 3603: 3600: 3598: 3595: 3594: 3593: 3587: 3583: 3580: 3578: 3575: 3573: 3569: 3566: 3564: 3563:Crime mapping 3561: 3559: 3555: 3552: 3550: 3547: 3544: 3542: 3539: 3537: 3534: 3532: 3529: 3526: 3525: 3524: 3518: 3511: 3510: 3508: 3503: 3502: 3500: 3495: 3494: 3492: 3487: 3486: 3484: 3483: 3482: 3479: 3473: 3470: 3467: 3463: 3460: 3457: 3454: 3451: 3449: 3446: 3445: 3444: 3442: 3437: 3435: 3427: 3425: 3422: 3420: 3416: 3414: 3410: 3408: 3404: 3402: 3398: 3396: 3392: 3389: 3388: 3387: 3385: 3381: 3372: 3367: 3366: 3362: 3359: 3356: 3353: 3349: 3346: 3343: 3340: 3336: 3331: 3327: 3326: 3322: 3321: 3320: 3317: 3315: 3311: 3310:statisticians 3306: 3304: 3300: 3295: 3288:Interactivity 3287: 3283: 3280: 3278: 3275: 3273: 3270: 3268: 3265: 3263: 3260: 3258: 3255: 3253: 3252:Graph drawing 3250: 3248: 3245: 3243: 3240: 3237: 3234: 3232: 3229: 3226: 3223: 3221: 3218: 3217: 3213: 3204: 3201: 3198: 3197: 3196: 3191: 3188: 3185: 3182: 3181: 3180: 3178: 3175: 3169: 3165: 3164: 3158: 3154: 3150: 3146: 3142: 3138: 3134: 3130: 3126: 3122: 3118: 3114: 3111: 3108: 3104: 3100: 3097: 3093: 3089: 3085: 3084: 3083: 3078: 3074: 3070: 3067: 3066: 3065: 3063: 3060: 3054: 3050: 3049: 3043: 3039: 3036: 3032: 3029: 3025: 3024: 3023: 3018: 3015: 3014: 3013: 3011: 3008: 3002: 2998: 2997: 2991: 2988: 2985: 2981: 2978:Represents a 2977: 2976: 2975: 2971: 2967: 2964: 2963: 2962: 2960: 2957: 2951: 2947: 2946: 2940: 2936: 2932: 2928: 2926:respectively. 2924: 2921: 2918: 2915: 2911: 2908: 2904: 2903: 2902: 2897: 2894: 2893: 2892: 2890: 2887: 2881: 2877: 2876: 2869: 2867: 2863: 2859: 2858: 2857: 2852: 2849: 2846: 2845: 2844: 2842: 2839: 2833: 2829: 2828: 2822: 2818: 2815: 2812:Deliberately 2811: 2809: 2805: 2801: 2798: 2797: 2796: 2791: 2788: 2787: 2786: 2784: 2781: 2775: 2771: 2770: 2762: 2759: 2758: 2756: 2753: 2752: 2751: 2746: 2743: 2742: 2741: 2739: 2736: 2730: 2726: 2725: 2720: 2716: 2713: 2709: 2707: 2703: 2699: 2698: 2697: 2692: 2689: 2688: 2687: 2685: 2682: 2676: 2672: 2671: 2665: 2662: 2658: 2654: 2653: 2652: 2647: 2644: 2643: 2642: 2640: 2637: 2631: 2627: 2626: 2620: 2617: 2614: 2611: 2607: 2603: 2602: 2601: 2596: 2593: 2590: 2589: 2588: 2585: 2581: 2578: 2572: 2568: 2567: 2562: 2558: 2555: 2554: 2553: 2548: 2545: 2542: 2539: 2536: 2535: 2534: 2531: 2527: 2524: 2518: 2514: 2513: 2507: 2503: 2500: 2497:Similar to a 2496: 2493: 2492: 2491: 2486: 2483: 2480: 2477: 2474: 2473: 2472: 2470: 2467: 2461: 2457: 2456: 2450: 2446: 2443: 2439: 2435: 2434:central angle 2431: 2427: 2426: 2425: 2420: 2419: 2418: 2416: 2413: 2407: 2403: 2402: 2396: 2393: 2390: 2387: 2386: 2385: 2381: 2378: 2375: 2372: 2369: 2366: 2365: 2364: 2362: 2359: 2353: 2349: 2348: 2342: 2339: 2338: 2337: 2332: 2329: 2326: 2323: 2320: 2317: 2316: 2315: 2312: 2306: 2302: 2301: 2295: 2292: 2289: 2286: 2283: 2279: 2275: 2274: 2273: 2268: 2265: 2262: 2259: 2256: 2255: 2254: 2251: 2248: 2242: 2238: 2237: 2230: 2227: 2223: 2219: 2215: 2214: 2213: 2208: 2205: 2202: 2201: 2200: 2198: 2195: 2189: 2185: 2184: 2178: 2175: 2172: 2171: 2170: 2165: 2162: 2159: 2158: 2157: 2155: 2152: 2145: 2141: 2140: 2137: 2136: 2130: 2126: 2125: 2121: 2118: 2117: 2113: 2112: 2108: 2104: 2101: 2097: 2096: 2092: 2088: 2084: 2081: 2080: 2079: 2074: 2071: 2068: 2065: 2064: 2063: 2061: 2060: 2056: 2051: 2045: 2039: 2032: 2028: 2027: 2021: 2018: 2015: 2012: 2008: 2005: 2001: 1997: 1993: 1989: 1988: 1987: 1982: 1979: 1976: 1975: 1974: 1972: 1969: 1963: 1959: 1958: 1945: 1941: 1937: 1929: 1927: 1924: 1915: 1911: 1907: 1903: 1899: 1895: 1892: 1888: 1884: 1881:) and age (a 1880: 1875: 1871: 1870: 1869: 1867: 1862: 1856: 1852: 1848: 1847: 1846: 1839: 1837: 1833: 1831: 1827: 1822: 1818: 1814: 1810: 1806: 1802: 1798: 1793: 1789: 1787: 1778: 1774: 1772: 1768: 1767:Blaise Pascal 1764: 1760: 1755: 1753: 1743: 1739: 1735: 1733: 1728: 1724: 1720: 1716: 1711: 1709: 1705: 1701: 1697: 1693: 1688: 1679: 1674: 1667: 1663: 1659: 1655: 1653: 1649: 1645: 1641: 1637: 1633: 1629: 1628: 1623: 1614: 1609: 1601: 1599: 1596: 1594: 1585: 1583: 1579: 1577: 1569: 1567: 1565: 1556: 1552: 1548: 1545: 1542: 1539: 1534: 1531: 1527: 1522: 1518: 1515: 1511: 1508: 1504: 1500: 1496: 1492: 1489: 1485: 1484: 1483: 1477: 1473: 1469: 1462: 1460: 1457: 1452: 1449: 1445: 1440: 1436: 1434: 1426: 1423: 1420: 1417: 1414: 1411: 1408: 1405: 1403:show the data 1402: 1401: 1400: 1397: 1393: 1388: 1385: 1381: 1377: 1372: 1370: 1364: 1356: 1351: 1349: 1347: 1343: 1338: 1336: 1332: 1328: 1324: 1320: 1315: 1313: 1309: 1304: 1302: 1298: 1297:data analysis 1292: 1290: 1285: 1281: 1277: 1272: 1270: 1266: 1262: 1258: 1255:, etc.), and 1254: 1250: 1246: 1242: 1238: 1234: 1228: 1224: 1222: 1218: 1214: 1210: 1206: 1202: 1198: 1197:visual design 1194: 1190: 1186: 1178: 1173: 1165: 1158: 1156: 1155:in the past. 1154: 1150: 1146: 1142: 1138: 1134: 1130: 1126: 1122: 1116: 1113: 1109: 1105: 1101: 1097: 1093: 1089: 1084: 1082: 1078: 1074: 1069: 1066:structure or 1065: 1061: 1057: 1051: 1049: 1045: 1041: 1038: 1034: 1033: 1028: 1024: 1020: 1016: 1012: 1008: 1004: 1000: 996: 995:mixed reality 992: 988: 984: 980: 978: 974: 970: 969:venn diagrams 966: 962: 958: 954: 950: 946: 942: 938: 934: 930: 926: 924: 920: 917:, percentage 916: 912: 908: 907:isopleth maps 904: 900: 896: 892: 888: 884: 883:scatter plots 880: 876: 872: 871:bullet graphs 868: 867:funnel charts 864: 860: 859:cohort charts 856: 852: 848: 844: 840: 836: 832: 828: 824: 820: 816: 812: 810: 806: 802: 798: 794: 790: 786: 782: 778: 774: 770: 766: 762: 758: 747: 742: 740: 735: 733: 728: 727: 725: 724: 719: 716: 714: 711: 709: 706: 704: 701: 699: 696: 694: 691: 689: 686: 684: 681: 679: 676: 674: 671: 669: 666: 665: 664: 663: 658: 653: 650: 648: 645: 643: 640: 638: 635: 633: 630: 628: 625: 623: 620: 618: 615: 613: 610: 608: 605: 603: 600: 598: 595: 593: 590: 588: 585: 583: 580: 578: 575: 573: 572:Graph drawing 570: 568: 565: 563: 562:Decision tree 560: 558: 555: 553: 550: 548: 545: 543: 542:Cognitive map 540: 538: 535: 533: 530: 529: 528: 527: 522: 517: 514: 512: 509: 507: 504: 502: 499: 497: 494: 492: 489: 487: 484: 482: 479: 477: 474: 472: 469: 467: 464: 462: 459: 458: 457: 456: 451: 446: 442: 438: 437: 427: 422: 420: 415: 413: 408: 407: 405: 404: 399: 396: 394: 391: 389: 386: 384: 381: 379: 376: 374: 371: 369: 366: 364: 361: 359: 356: 355: 354: 353: 348: 343: 340: 338: 335: 333: 330: 328: 325: 323: 320: 318: 315: 313: 310: 308: 307:Control chart 305: 303: 300: 298: 295: 293: 290: 288: 285: 283: 280: 278: 275: 273: 270: 268: 265: 263: 260: 258: 255: 253: 250: 249: 248: 247: 242: 237: 234: 232: 229: 227: 224: 222: 219: 217: 214: 212: 209: 207: 204: 202: 199: 197: 194: 192: 189: 187: 186:Simon Wardley 184: 182: 179: 177: 174: 172: 169: 167: 164: 163: 162: 161: 156: 151: 148: 146: 143: 141: 140:Data analysis 138: 136: 133: 131: 128: 126: 123: 121: 118: 116: 113: 111: 108: 106: 103: 102: 101: 100: 95: 92: 88: 85: 81: 80: 74: 70: 66: 61: 51: 50: 45: 41: 32: 31: 19: 7226:Neuroimaging 7186:CPK coloring 7169:Color coding 7107:Hans Rosling 7087:Miriah Meyer 7052:Aaron Koblin 7037:Jeffrey Heer 6931:Edward Tufte 6926:Pat Hanrahan 6896:Nigel Holmes 6774:Otto Neurath 6713:Oliver Byrne 6661:19th century 6348: 6338: 6318: 6309: 6301:Ben Bederson 6283: 6275: 6261:James Landay 6241: 6231: 6203:. Springer. 6200: 6181: 6151: 6125: 6103: 6087:. O'Reilly. 6083: 6063: 6041: 6032: 6020: 5983:. Retrieved 5979: 5969: 5936: 5932: 5919: 5910: 5873: 5867: 5859: 5844: 5821: 5810:. Retrieved 5806:the original 5795: 5770: 5766: 5757: 5745: 5737: 5727: 5705: 5695: 5676: 5670: 5658:. Retrieved 5636: 5625:. Retrieved 5605: 5586: 5566: 5555:. Retrieved 5547:Venture Beat 5546: 5537: 5526:. Retrieved 5514: 5501: 5490:. Retrieved 5488:. 2020-12-09 5485: 5476: 5443: 5439: 5433: 5425:the original 5400:. Retrieved 5391: 5373: 5362:. Retrieved 5347: 5335:. Retrieved 5326: 5316: 5281: 5250: 5239:. Retrieved 5235: 5225: 5214:. Retrieved 5207:the original 5202: 5190: 5179:. Retrieved 5170: 5145:. Retrieved 5111:. Retrieved 5091: 5083: 5075: 5065: 5054:. Retrieved 5045: 5036: 5025:. Retrieved 5005: 4974: 4968: 4927: 4923: 4917: 4905:. Retrieved 4890: 4870: 4860: 4843: 4822:. Retrieved 4813: 4803: 4795: 4779: 4737: 4730: 4716: 4705:. Retrieved 4666: 4660: 4643: 4586: 4582: 4576: 4568: 4562: 4550:. Retrieved 4538:10453/125943 4520: 4516: 4505: 4486: 4476: 4454:(8): 31–37. 4451: 4447: 4441: 4432: 4426: 4399: 4389: 4380: 4374: 4365: 4359: 4347:. Retrieved 4343: 4308: 4302: 4284: 4280: 4274: 4265: 4258: 4249: 4243: 4231:. Retrieved 4217: 4213: 4177: 4153: 4143: 4130:the original 4119: 4098: 4032:Infographics 3889: 3861: 3844: 3817: 3815: 3810: 3806: 3805: 3801:social media 3779: 3770: 3755:Please help 3743: 3715: 3701:undue weight 3698: 3672: 3667:Eurographics 3648:ACM SIGGRAPH 3636: 3602:IBM Research 3591: 3588:Organization 3522: 3519:Applications 3480: 3476: 3438: 3431: 3376: 3363: 3357: 3347: 3341: 3335:scatterplots 3323: 3318: 3307: 3298: 3297: 3156: 3152: 3148: 3144: 3140: 3136: 3132: 3128: 3124: 3120: 3116: 3087: 3068: 3062:Venn diagram 3056:Venn diagram 3028:multivariate 2913: 2861: 2803: 2657:hierarchical 2610:central axis 2543:symbol/glyph 2499:scatter plot 2481:symbol/glyph 2442:proportional 2308:Scatter plot 2263:symbol/glyph 2250:Scatter plot 2218:distribution 2206:count/length 2153: 2129:Mosaic plots 2122:for each bar 2119: 2106: 2099: 2086: 2057: 2049: 2043: 2037: 1977:length/count 1919: 1913: 1909: 1905: 1901: 1897: 1890: 1886: 1882: 1878: 1873: 1863: 1860: 1843: 1834: 1794: 1790: 1783: 1756: 1748: 1736: 1712: 1708:stick charts 1692:Lascaux Cave 1683: 1636:ACM SIGGRAPH 1625: 1619: 1597: 1589: 1580: 1573: 1560: 1538:scatter plot 1502: 1498: 1494: 1481: 1475: 1471: 1453: 1441: 1437: 1432: 1430: 1396:Edward Tufte 1391: 1389: 1383: 1379: 1376:Edward Tufte 1374: 1366: 1362: 1346:Infographics 1339: 1316: 1305: 1301:data science 1293: 1273: 1229: 1225: 1182: 1177:IP addresses 1149:mathematical 1120: 1117: 1085: 1059: 1052: 1047: 1030: 1026: 1015:file systems 981: 945:infographics 928: 927: 913:), figures, 855:spectrograms 847:donut charts 814: 813: 804: 800: 792: 765:info viz/vis 764: 761:data viz/vis 760: 756: 755: 708:Semantic Web 532:Argument map 491:Mental model 476:Infographics 465: 297:Bubble chart 277:Pareto chart 267:Scatter plot 231:Jeffrey Heer 226:Mike Bostock 201:Kim Albrecht 191:Hans Rosling 181:Edward Tufte 150:Data science 90: 65:Edward Tufte 47: 43: 7159:Cartography 7097:Ade Olufeko 7067:Manuel Lima 6996:Kwan-Liu Ma 6921:Stuart Card 6891:Borden Dent 6829:Erwin Raisz 6784:Henry Gantt 5446:: 260–262. 5046:www.cbo.gov 4907:29 November 4866:Tukey, John 4402:: 291–300. 3568:eGovernance 3549:Health care 3536:Data mining 3417:Displaying 3411:Displaying 3405:Displaying 3401:connections 3399:Displaying 3282:Treemapping 3227:(phylogeny) 3189:dotted line 3010:Radar chart 3004:Radar chart 2864:to portray 2806:to portray 2693:time (flow) 2684:Gantt chart 2678:Gantt chart 2659:data using 2597:time (flow) 2580:Streamgraph 2574:Streamgraph 2549:connections 2506:time series 2370:nodes color 2252:(dot plot) 2127:Instances: 2046:axis), and 1996:rectangular 1840:Terminology 1752:Tycho Brahe 1725:tablets of 1700:clay tokens 1696:Pleistocene 1650:. In 1786, 1249:data mining 1213:data mining 953:flow charts 839:cone charts 835:area charts 831:line charts 785:information 713:Treemapping 622:Radial tree 552:Concept map 363:Information 145:Infographic 7281:Categories 7082:John Maeda 6860:John Tukey 6824:Harry Beck 6819:Fritz Kahn 6569:Photograph 6035:April 2022 6019:" section 5812:2021-02-17 5773:(1): 1–6. 5747:Ed Hawkins 5716:Ed Hawkins 5627:2014-09-08 5557:2016-02-21 5528:2015-11-22 5492:2020-12-09 5402:2018-01-12 5364:2018-01-12 5337:20 January 5241:2020-07-30 5216:2015-11-22 5181:2015-11-23 5147:2014-10-08 5113:2014-09-08 5056:2014-11-27 5027:2019-08-10 4707:2014-09-08 4111:References 3853:decisions) 3841:Objectives 3773:March 2022 3466:algorithms 3247:Grand tour 3236:Dendrogram 3186:Solid line 3135:, denoted 3016:attributes 2814:minimalist 2789:x position 2712:dependency 2606:area chart 2584:area chart 2540:y position 2537:x position 2478:y position 2475:x position 2469:Line chart 2463:Line chart 2430:arc length 2376:ties color 2367:nodes size 2324:position z 2321:position y 2318:position x 2260:y position 2257:x position 2203:bin limits 2014:categories 1998:bars with 1934:See also: 1930:Techniques 1821:JavaScript 1606:See also: 1551:geospatial 1547:Geographic 1488:line chart 1369:John Tukey 1352:Principles 1342:dashboards 1261:clustering 1241:regression 1233:statistics 1201:psychology 1044:hypotheses 941:animations 851:histograms 827:bar charts 823:pie charts 775:or visual 592:Issue tree 567:Dendrogram 537:Cladistics 287:Area chart 252:Line chart 236:Ihab Ilyas 211:Ed Hawkins 176:John Tukey 84:Statistics 67:described 7164:Chartjunk 7132:Bang Wong 7027:Polo Chau 6733:John Snow 6708:John Venn 6589:Schematic 6574:Pictogram 6278:CHI 2005. 6219:458726890 6170:795009632 5980:eagereyes 5913:: 92–100. 5468:144492131 4765:cite book 4547:199591321 4026:imc FAMOS 3961:Analytics 3744:does not 3424:Mind maps 3395:resources 3225:Cladogram 3220:Cartogram 3090:possible 3071:possible 3026:Displays 2959:Flowchart 2953:Flowchart 2907:quartiles 2702:bar chart 2582:(type of 2415:Pie chart 2409:Pie chart 2282:variables 2226:intervals 2197:Histogram 2148:warming). 2120:(A/X)*X=A 1990:Presents 1971:Bar chart 1555:cartogram 1526:histogram 1514:pie chart 1507:bar chart 1448:chartjunk 1431:Graphics 1289:causality 1259:methods ( 1247:, etc.), 1068:storyline 1064:narrative 1019:documents 1007:databases 1003:cognition 991:augmented 977:mind maps 973:timelines 937:tree maps 935:(such as 923:dashboard 911:heat maps 897:(such as 873:, etc.), 769:designing 642:Topic map 632:Sociogram 587:Issue map 582:Hypertext 378:Chartjunk 332:Sparkline 322:Cartogram 312:Run chart 282:Pie chart 262:Histogram 257:Bar chart 7150:Related 6559:Ideogram 6336:(1999). 6307:(2003). 6268:Archived 6263:(2005). 6180:(2012). 6124:(2015). 6106:. Sage. 6062:(2019). 5953:23981395 5878:Archived 5860:Graphics 5852:Archived 5833:Archived 5828:(2008). 5787:86788346 5742:Archived 5710:Archived 5660:15 March 5654:Archived 5618:Archived 5551:Archived 5519:Archived 5396:Archived 5386:(2015). 5358:Archived 5331:Archived 5327:BBC News 5308:62626937 5259:Archived 5236:BBC News 5175:Archived 5138:Archived 5104:Archived 5080:Archived 5050:Archived 5021:Archived 4960:16342041 4952:17777913 4901:Archived 4868:(1977). 4849:Archived 4818:Archived 4796:Graphics 4788:Archived 4698:Archived 4671:Archived 4641:(2003). 4623:30718386 4349:27 March 4317:citation 4233:27 March 4158:Archived 3966:Big data 3954:See also 3582:Data Art 3419:websites 3391:Articles 3352:barchart 3342:Painting 3325:Brushing 3103:elements 2980:workflow 2966:workflow 2920:Outliers 2914:whiskers 2738:Heat map 2732:Heat map 2700:Type of 2526:Semi-log 2091:products 2011:discrete 1980:category 1771:Playfair 1723:Linear B 1499:category 1306:Indeed, 1193:graphics 1159:Overview 1056:business 1025:, etc. ( 875:diagrams 660:See also 637:Timeline 602:Mind map 373:Database 368:Big data 292:Tree map 272:Box plot 7032:Ben Fry 6544:Diagram 5985:7 April 5961:3825148 5750:inward. 5706:Gizmodo 4932:Bibcode 4924:Science 4614:6369751 4591:Bibcode 4552:25 June 4468:7666107 3765:removed 3750:sources 3711:resolve 3365:Linking 3358:Scaling 3257:Heatmap 3183:No axis 3092:logical 3073:logical 2984:process 2970:process 2821:example 2639:Treemap 2633:Treemap 2530:log-log 2449:English 2361:Network 2222:binning 2040:axis), 2004:lengths 2000:heights 1936:Diagram 1809:Minitab 1727:Mycenae 1602:History 1530:boxplot 1520:amount. 1495:measure 1145:textual 1139:in the 1037:spatial 987:virtual 979:, etc. 773:graphic 7152:topics 6623:People 6530:Image 6424:Fields 6356: 6290: 6274:. In: 6248: 6217: 6207: 6188: 6168: 6158: 6132: 6110: 6091: 6070: 6015:This " 5959: 5951: 5858:. in: 5785: 5683: 5593: 5466: 5460:301609 5458: 5440:Osiris 5306: 5296: 5013: 4981: 4958: 4950: 4878: 4824:May 7, 4753: 4651: 4621: 4611: 4545: 4466: 4414: 4287:(22), 4184: 3661:SIGCHI 3393:& 3382:, and 3086:Shows 2898:y axis 2895:x axis 2871:years) 2661:nested 2440:), is 2330:symbol 1914:charts 1910:scales 1817:Python 1704:quipus 1433:reveal 1219:, and 1203:, and 919:gauges 881:(e.g. 819:tables 652:ZigZag 6604:Table 6539:Chart 6532:types 5957:S2CID 5929:(PDF) 5783:S2CID 5621:(PDF) 5614:(PDF) 5522:(PDF) 5511:(PDF) 5464:S2CID 5456:JSTOR 5304:S2CID 5210:(PDF) 5199:(PDF) 5141:(PDF) 5134:(PDF) 5107:(PDF) 5100:(PDF) 4956:S2CID 4701:(PDF) 4694:(PDF) 4543:S2CID 4464:S2CID 4220:(1). 4090:Notes 3858:Scope 3699:lend 3330:mouse 3192:color 3035:chart 2792:color 2744:color 2690:color 2648:color 2594:color 2591:width 2546:color 2484:color 2421:color 2327:color 2276:Uses 2266:color 2209:color 2102:) and 2075:color 1994:with 1983:color 1949:Name 1898:graph 1874:table 1280:plots 985:like 879:plots 337:Table 6579:Plot 6354:ISBN 6332:and 6303:and 6288:ISBN 6246:ISBN 6215:OCLC 6205:ISBN 6186:ISBN 6166:OCLC 6156:ISBN 6130:ISBN 6108:ISBN 6089:ISBN 6068:ISBN 5987:2017 5949:PMID 5681:ISBN 5662:2013 5591:ISBN 5339:2018 5294:ISBN 5011:ISBN 4979:ISBN 4948:PMID 4909:2016 4876:ISBN 4826:2017 4794:in: 4771:link 4751:ISBN 4649:ISBN 4619:PMID 4554:2021 4412:ISBN 4351:2023 4323:link 4235:2023 4182:ISBN 3935:and 3925:and 3832:and 3748:any 3746:cite 3570:and 3413:news 3407:data 3303:plot 3155:and 3147:and 3131:and 3107:sets 3096:sets 3077:sets 3031:data 2645:size 2487:size 2438:area 2436:and 2333:size 2269:size 2232:bin. 1938:and 1906:axes 1819:and 1801:SOFA 1761:and 1634:and 1454:The 1333:and 1310:and 1151:and 1135:and 1133:data 1106:and 1050:). 1001:and 993:and 933:maps 909:and 895:maps 811:. 783:and 781:data 693:Olog 358:Data 135:Plot 6564:Map 5941:doi 5775:doi 5448:doi 5286:doi 5203:SFU 4940:doi 4928:229 4814:CNN 4743:doi 4609:PMC 4599:doi 4587:116 4533:hdl 4525:doi 4491:doi 4456:doi 4404:doi 4344:IBM 4289:doi 4222:doi 3939:or 3923:HCI 3811:DPA 3759:by 3658:ACM 3088:all 3069:all 2968:or 2528:or 2100:A/X 2050:x*y 2002:or 1889:or 1797:SAS 1690:in 1549:or 1299:or 1245:PCA 1223:". 939:), 925:. 803:or 763:or 7283:: 6328:, 6259:, 6213:. 6164:. 5978:. 5955:. 5947:. 5937:19 5935:. 5931:. 5909:. 5889:^ 5781:. 5771:14 5769:. 5740:. 5736:. 5718:, 5708:. 5704:. 5648:. 5644:; 5616:. 5577:^ 5549:. 5545:. 5513:. 5484:. 5462:. 5454:. 5442:. 5411:^ 5390:. 5356:. 5329:. 5325:. 5302:. 5292:. 5270:^ 5234:. 5201:. 5173:. 5169:. 5156:^ 5136:. 5122:^ 5102:. 5078:. 5074:. 5048:. 5044:. 5019:. 4993:^ 4954:. 4946:. 4938:. 4926:. 4899:. 4834:^ 4816:. 4812:. 4767:}} 4763:{{ 4749:. 4696:. 4682:^ 4630:^ 4617:, 4607:, 4597:, 4585:, 4541:. 4531:. 4519:. 4515:. 4489:. 4485:. 4462:. 4452:44 4450:. 4410:. 4398:. 4342:. 4331:^ 4319:}} 4315:{{ 4283:, 4216:. 4212:. 4196:^ 4166:^ 4156:. 4152:. 3139:∩ 2982:, 2586:) 2109:). 1896:A 1893:). 1872:A 1832:. 1815:, 1813:D3 1807:, 1803:, 1799:, 1595:. 1566:. 1394:, 1344:. 1329:, 1325:, 1321:, 1282:, 1278:, 1267:, 1263:, 1243:, 1239:, 1211:, 1199:, 1195:, 1191:, 1187:, 1147:, 1098:, 1094:, 1021:, 1017:, 1013:, 1009:, 989:, 975:, 971:, 967:, 963:, 959:, 955:, 951:, 947:, 943:, 905:, 901:, 889:, 885:, 877:, 869:, 865:, 861:, 857:, 853:, 849:, 845:, 841:, 837:, 833:, 829:, 825:, 6408:e 6401:t 6394:v 6362:. 6296:. 6252:. 6221:. 6194:. 6172:. 6138:. 6116:. 6097:. 6076:. 6048:) 6042:( 6037:) 6033:( 6023:. 5989:. 5963:. 5943:: 5884:. 5839:. 5815:. 5789:. 5777:: 5722:. 5689:. 5664:. 5630:. 5599:. 5560:. 5531:. 5495:. 5470:. 5450:: 5444:1 5405:. 5367:. 5341:. 5310:. 5288:: 5244:. 5219:. 5184:. 5150:. 5116:. 5059:. 5030:. 4987:. 4962:. 4942:: 4934:: 4884:. 4855:. 4828:. 4773:) 4759:. 4745:: 4724:. 4710:. 4655:. 4601:: 4593:: 4556:. 4535:: 4527:: 4521:1 4499:. 4493:: 4470:. 4458:: 4420:. 4406:: 4353:. 4325:) 4291:: 4285:2 4237:. 4224:: 4218:2 4190:. 4134:( 3809:( 3786:) 3780:( 3775:) 3771:( 3767:. 3753:. 3720:) 3716:( 3157:T 3153:S 3149:T 3145:S 3141:T 3137:S 3133:T 3129:S 3125:S 3121:S 3117:S 3098:. 3079:. 2909:. 2823:) 2107:X 2087:A 2044:y 2038:x 1916:. 1805:R 1680:. 1251:( 1235:( 1119:( 1046:( 759:( 745:e 738:t 731:v 425:e 418:t 411:v 52:. 20:)

Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.

Knowledge (XXG)

Data and information visualization

Index