441:
2144:
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
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.
2774:
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".
1613:
1742:
2630:
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.
2352:
2832:
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.
2241:
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.
1164:
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
3168:
2460:
2880:
3796:
1658:
3053:
2406:
1962:
1777:
2571:
3690:
1172:
3735:
2675:
2950:
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,
36:
1673:
3001:
2305:
2188:
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.
6008:
2729:
60:
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."
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."
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.
440:
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,
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
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
2820:
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
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.",
1626:
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.
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
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.
2143:
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.
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.
3631:
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.
5542:
4149:
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
7056:
3756:
3669:: An annual Europe-wide computer graphics conference, held by the European Association for Computer Graphics. Conference is usually held in April or May.
886:
817:
is concerned with visually presenting sets of primarily quantitative raw data in a schematic form. The visual formats used in data visualization include
5258:
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
3571:
1406:
induce the viewer to think about the substance rather than about methodology, graphic design, the technology of graphic production, or something else
5103:
3354:, causes a label to appear that identifies the plot element. It is widely available in many interactive graphics, and is sometimes called mouseover.
1467:
6727:
6380:
Duke University-Christa Kelleher Presentation-Communicating through infographics-visualizing scientific & engineering information-March 6, 2015
4322:
3653:
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
6697:
5641:
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
842:
1853:
Quantitative: Represent measurements, such as the height of a person or the temperature of an environment. Quantitative variables can either be
5653:
997:
have the potential to make information visualization more immersive, intuitive, interactive and easily manipulable and thus enhance the user's
906:
858:
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.
5137:
3903:
Data visualization in that it uses well-established theories of visualization to add or highlight meaning or importance in data presentation.
5617:
4697:
838:
423:
5832:
4581:
Börner, K.; Bueckle, A.; Ginda, M. (2019), "Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments",
7026:
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
7301:
6267:
3621:
687:
4784:
3700:
2516:
3386:. In this line the "Data Visualization: Modern Approaches" (2007) article gives an overview of seven subjects of data visualization:
3241:
6379:
3512:
The most common and simple type of visualisation used for affirming and setting context. For example, a line graph of GDP over time.
7296:
6016:
4056:
3674:
1665:
495:
5330:
5071:
3866:
Creating effective delivery mechanisms for each audience member depending on their role, tasks, locations and access to technology
1699:
6406:
5701:
3900:
Business process improvement in that its goal is to improve and streamline actions and decisions in furtherance of business goals
5020:
3929:, since many of the principles in how to design interactive data visualisation have been developed cross-disciplinary with HCI.
1773:, who saw potential for graphical communication of quantitative data, to generate and develop graphical methods of statistics.
1677:
787:
with the help of static, dynamic or interactive visual items. Typically based on data and information collected from a certain
6291:
6189:
6111:
6092:
6071:
5684:
5594:
4754:
4185:
4051:
3650:: An annual international conference on computer graphics, convened by the ACM SIGGRAPH organization. Conference dates vary.
2030:
7178:
5975:
1493:
Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of sales performance (the
1912:(quantitative and categorical) used to label and assign values to the visual objects. Many graphs are also referred to as
1314:
suggested that an ideal visualization should not only communicate clearly, but stimulate viewer engagement and attention.
4900:
4721:
3657:
2428:
Represents one categorical variable which is divided into slices to illustrate numerical proportion. In a pie chart, the
17:
5395:
2501:
except that the measurement points are ordered (typically by their x-axis value) and joined with straight line segments.
7168:
7111:
6717:
3975:
2022:
For example; comparison of values, such as sales performance for several persons or businesses in a single time period.
743:
75:
as what "may well be the best statistical graphic ever drawn", noting that it captures six variables in two dimensions.
4262:
4125:
7291:
6357:
6249:
6208:
6159:
6133:
6044:
5297:
5014:
4982:
4879:
4652:
4415:
3782:
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.
5848:
5518:
3764:
3127:. This lends itself to intuitive visualizations; for example, the set of all elements that are members of both sets
5049:
3313:
416:
4512:
1512:
Part-to-whole: Categorical subdivisions are measured as a ratio to the whole (i.e., a percentage out of 100%). A
6722:
4922:
Cleveland, W. S.; McGill, R. (1985). "Graphical perception and graphical methods for analyzing scientific data".
4020:
3246:
2711:
1854:
1036:
1516:
or bar chart can show the comparison of ratios, such as the market share represented by competitors in a market.
5420:
4000:
3922:
3760:
1184:
586:
500:
114:
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.
6430:
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.
2773:
2705:
2441:
1816:
1103:
677:
4670:
4482:
6712:
6230:
4770:
3710:
2228:
of a variable. The bins (intervals) must be adjacent, and are often (but not required to be) of equal size.
1707:
1244:
5765:(1999). "Introduction to the special issue on interactive graphical data analysis: What is interaction?".
2290:
Each point on the plot has an associated x and y term that determines its location on the cartesian plane.
1474:
Top panel is a bar chart depicting the flow of occurrences over time (resembles the Sankey diagram in the
7286:
6399:
5096:
3276:
3176:
2609:
2277:
1455:
964:
702:
409:
6641:
3112:
A Venn diagram consists of multiple overlapping closed curves, usually circles, each representing a set.
1904:(e.g., lines, bars, or points). Numerical values are displayed within an area delineated by one or more
5357:
3980:
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."
1264:
1107:
210:
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
6465:
6337:
6308:
6026:
4869:
4046:
3829:
3447:
3293:
2163:
numerical value of second variable (extent in second dimension; like conventional vertical bar chart)
1563:
1330:
1322:
1252:
1080:
460:
104:
72:
48:
5649:
1612:
6490:
6485:
6450:
6415:
6238:
5925:
3912:
3745:
3488:
Used to teach, explain and/or simply concepts. For example, organisation charts and decision trees.
3266:
2938:
2006:
proportional to the values that they represent. The bars can be plotted vertically or horizontally.
1804:
1643:
1575:
1326:
1031:
5130:
3862:
With the above objectives in mind, the actual work of data presentation architecture consists of:
5830:"Milestones in the history of thematic cartography, statistical graphics, and data visualization"
5421:"Milestones in the history of thematic cartography, statistical graphics, and data visualization"
3749:
3027:
898:
485:
5610:
4690:
1741:
1532:
helps visualize key statistics about the distribution, such as median, quartiles, outliers, etc.
7061:
6613:
6500:
6392:
6374:
Milestones in the History of Thematic Cartography, Statistical Graphics, and Data Visualization
5829:
5746:
5715:
5206:
4067:
4041:
3936:
3452:
2934:
2281:
2225:
2217:
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
1087:
736:
672:
119:
6677:
2391:
Discovering bridges (information brokers or boundary spanners) between clusters in the network
1363:
The greatest value of a picture is when it forces us to notice what we never expected to see.
6955:
6768:
6692:
6510:
5719:
4842:
4528:
3825:
3821:
3102:
2090:
1631:
982:
799:) to convey a concise version of known, specific information in a clear and engaging manner (
776:
596:
470:
316:
205:
124:
68:
6264:
3949:, conveying information through styling, typography, position, and other aesthetic concerns.
2397:
Finding outlier actors who do not fit into any cluster or are in the periphery of a network.
1939:
7235:
7126:
6950:
6900:
6808:
6793:
6737:
6702:
6598:
6495:
6470:
5481:
4931:
4590:
4072:
4062:
3995:
3540:
3471:
3390:
3379:
3364:
3324:
3271:
2160:
numerical value of first variable (extent in first dimension; superimposed horizontal bars)
2013:
1991:
1865:
1647:
1341:
1334:
1318:
1311:
1275:
1136:
1091:
1076:
922:
890:
808:
611:
606:
129:
5906:
5543:"NY gets new boot camp for data scientists: It's free but harder to get into than Harvard"
8:
7215:
6970:
6905:
6742:
6603:
6553:
6548:
6384:
3706:
3616:
3037:
of three or more quantitative variables represented on axes starting from the same point.
2660:
2629:
2388:
Finding clusters in the network (e.g. grouping Facebook friends into different clusters).
1240:
818:
788:
505:
387:
336:
4935:
4809:
4594:
2351:
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
7255:
6915:
6910:
6864:
6854:
6788:
6480:
6455:
6445:
6226:
5956:
5926:"Making sense of personal health information: Challenges for information visualization"
5782:
5702:"This Striking Climate Change Visualization Is Now Customizable for Any Place on Earth"
5463:
5455:
5322:
5303:
4955:
4764:
4613:
4542:
4463:
4209:
4036:
3970:
3926:
3906:
3881:
Utilizing appropriate analysis, grouping, visualization, and other presentation formats
3878:
Finding the right data (subject area, historical reach, breadth, level of detail, etc.)
3641:
3637:
Conferences in this field, ranked by significance in data visualization research, are:
3611:
3576:
3557:
1825:
1639:
1216:
1010:
918:
914:
796:
616:
480:
109:
7121:
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).
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:
5467:
5293:
5010:
5004:
4978:
4947:
4875:
4750:
4648:
4618:
4546:
4411:
4316:
4181:
3932:
3916:
3894:
3833:
3824:. Data presentation architecture weds the science of numbers, data and statistics in
3530:
3106:
3095:
3076:
2560:
2343:
Again point can be coded via color, shape and/or size to display additional variables
2010:
1714:
1661:
1621:
1446:, distorting the message, or supporting an erroneous conclusion. According to Tufte,
1443:
1424:
serve a reasonably clear purpose: description, exploration, tabulation, or decoration
1208:
1099:
998:
990:
729:
651:
392:
382:
6347:
5786:
5383:
5307:
4959:
4642:
2831:
2058:
1758:
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.
7260:
7245:
7240:
7220:
7071:
7046:
7010:
7005:
6940:
6874:
6778:
6682:
6672:
6646:
6593:
6520:
6460:
6435:
6376:, An illustrated chronology of innovations by Michael Friendly and Daniel J. Denis.
6177:
5960:
5940:
5825:
5774:
5645:
5447:
5392:
Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems
5285:
4939:
4742:
4741:. Nieuwe Prinsengracht 89 1018 VR Amsterdam Nederland: Amsterdam University Press.
4608:
4598:
4532:
4524:
4490:
4467:
4455:
4403:
4395:
4288:
4221:
4077:
4015:
3626:
3440:
2718:
2448:
2240:
1829:
1770:
1762:
1731:
1651:
1607:
1592:
1260:
1256:
1204:
1188:
1111:
1067:
960:
682:
667:
626:
556:
510:
397:
220:
195:
6687:
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
7265:
7205:
7116:
7041:
6985:
6935:
6578:
6475:
6333:
6304:
6271:
5881:
5855:
5836:
5262:
4943:
4852:
4791:
4674:
4638:
4366:
Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals
4082:
3990:
3985:
3940:
3581:
3302:
2782:
2287:
Points can be coded via color, shape and/or size to display additional variables.
1800:
1686:
1427:
be closely integrated with the statistical and verbal descriptions of a data set.
1307:
1279:
1236:
1152:
1140:
1039:
986:
956:
878:
862:
576:
546:
515:
301:
170:
134:
5289:
4339:
3523:
Data and information visualization insights are being applied in areas such as:
3400:
1163:
7230:
7195:
7136:
7091:
6990:
6975:
6869:
6844:
6798:
6752:
6747:
6583:
6515:
6325:
6256:
5379:
4746:
4210:"Why Is Data Visualization Important? What Is Important in Data Visualization?"
4010:
4005:
3946:
3872:
Determining the required periodicity of data updates (the currency of the data)
3433:
3329:
3261:
3167:
2930:
2865:
2840:
2807:
2379:
2179:
Variables need not be directly related in the way they are in "variwide" charts
1922:
1796:
1785:
1268:
1220:
1128:
1124:
1095:
1072:
948:
902:
717:
646:
326:
215:
165:
5482:"Data visualization: definition, examples, tools, advice [guide 2020]"
4896:
4495:
4407:
4293:
4226:
3656:: An annual international conference on human–computer interaction, hosted by
1713:
The first documented data visualization can be tracked back to 1160 B.C. with
1227:
creation of approaches for conveying abstract information in intuitive ways."
1183:
The field of data and information visualization has emerged "from research in
7280:
7190:
7076:
7000:
6965:
6945:
6849:
6813:
6667:
6651:
6636:
6440:
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
3596:
3562:
3309:
3251:
2525:
2459:
2433:
2360:
1766:
1296:
1207:. It is increasingly applied as a critical component in scientific research,
1196:
1132:
994:
571:
561:
541:
306:
185:
139:
5734:"This scientist just changed how we think about climate change with one GIF"
4603:
1274:
To communicate information clearly and efficiently, data visualization uses
7225:
7185:
7106:
7086:
7051:
7036:
6930:
6925:
6895:
6773:
6300:
6260:
6121:
6059:
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:
4031:
3800:
3795:
3666:
3601:
3383:
3334:
3061:
2656:
2529:
2498:
2249:
2221:
1718:
1657:
1635:
1537:
1395:
1375:
1300:
1176:
968:
882:
870:
866:
707:
531:
490:
296:
276:
266:
230:
225:
200:
190:
180:
149:
64:
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
2879:
1378:
has explained that users of information displays are executing particular
7210:
7158:
7096:
7066:
6995:
6920:
6890:
6828:
6783:
5507:
5382:; Alexander, Jason; Karnik, Abhijit; Kildal, Johan; Subramanian, Sriram;
3567:
3548:
3535:
3281:
3230:
3009:
2816:—with no technical indicia—to communicate intuitively with non-scientists
2683:
2579:
2520:
A log-log chart spanning more than one order of magnitude along both axes
2505:
2128:
1751:
1734:
in Alexandria would serve as reference standards until the 14th century.
1695:
1345:
1340:
In the commercial environment data visualization is often referred to as
1283:
1248:
1212:
1022:
1014:
944:
854:
846:
784:
712:
621:
551:
475:
362:
341:
291:
144:
6373:
5589:. The University of Queensland: Publish on Demand Centre. pp. 4–5.
5041:
4537:
3052:
7081:
6859:
6823:
6818:
6568:
6317:
6232:
Information Visualization – Human-Centered Issues and Perspectives
5778:
4865:
4248:
Ananda Mitra (2018), "Managing and Visualizing Unstructured Big Data",
3312:
since the late 1960s. Examples of the developments can be found on the
3235:
3224:
2813:
2799:
A sequence of colored stripes visually portrays trend of a data series.
2605:
2583:
2468:
2429:
2405:
1820:
1710:(n.d.) can also be considered as visualizing quantitative information.
1550:
1487:
1368:
1232:
1200:
1043:
952:
834:
830:
591:
566:
536:
286:
251:
235:
175:
83:
5611:"Steven Few-Selecting the Right Graph for Your Message-September 2004"
5459:
4566:
3123:, while points outside the boundary represent elements not in the set
1776:
7163:
7131:
6732:
6707:
6588:
6573:
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"
4025:
3960:
3465:
3394:
3219:
2958:
2701:
2414:
2196:
2176:
Pairs of numeric variables, usually color-coded, rendered by category
1995:
1970:
1554:
1546:
1525:
1513:
1506:
1447:
1288:
1063:
1035:, where the goal is to render realistic images based on physical and
1002:
940:
850:
826:
822:
641:
631:
581:
377:
331:
321:
311:
281:
261:
256:
6082:
5424:
5084:
Data source: Advanced Law Enforcement Rapid Response Training Center
3890:
DPA work shares commonalities with several other fields, including:
3734:
2754:
Represents the magnitude of a phenomenon as color in two dimensions.
2674:
2570:
1961:
1585:
1171:
6558:
6152:
Show me the numbers : designing tables and graphs to enlighten
5805:
5451:
5280:
Friendly, Michael (2008). "A Brief History of Data Visualization".
4278:
3965:
3647:
3423:
3351:
2979:
2965:
2906:
2888:
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
1722:
1192:
1148:
1144:
1055:
1018:
1006:
976:
972:
936:
910:
772:
697:
636:
601:
372:
367:
271:
5131:"Steven Few-Tapping the Power of Visual Perception-September 2004"
1356:
7031:
6543:
5377:
5042:"Telling Visual Stories About Data - Congressional Budget Office"
4665:
3418:
3256:
3151:", is represented visually by the area of overlap of the regions
2983:
2969:
2919:
2737:
2638:
2224:. The bins are usually specified as consecutive, non-overlapping
1935:
1808:
1726:
1691:
1529:
874:
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
4306:
4128:. Center for Spatially Integrated Social Science. Archived from
3000:
2949:
2244:
A scatterplot showing negative correlation between two variables
2085:
Areas of non-uniform-width bars represent quantities with areas
1672:
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.
4509:
4135:
3660:
3606:
2916:) indicating variability outside the upper and lower quartiles.
2621:
Example: the visual shows music listened to by a user over time
2304:
2187:
2003:
1999:
768:
6265:"Prefuse: a toolkit for interactive information visualization"
4667:
Illuminating the Path: The R&D Agenda for Visual Analytics
4580:
1885:), with each row of data representing one person (the sampled
1058:, data and information visualization can constitute a part of
6538:
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).
3091:
3072:
3034:
2714:
relationships between activities and current schedule status.
1812:
1703:
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.
5799:
3412:
3406:
3030:
2437:
2166:
category for first and second variables (e.g., color-coded)
1569:
780:
692:
357:
6414:
5976:"A Guide to the Quality of Different Visualization Venues"
2728:
779:
of a large amount of complex quantitative and qualitative
6563:
3481:
These four types of visual communication are as follows;
1215:, financial data analysis, market studies, manufacturing
932:
894:
59:
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
5923:
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.
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
6281:
5275:
5273:
5271:
5120:
4664:James J. Thomas and Kristin A. Cook (Ed.) (2005).
4250:Encyclopedia of Information Science and Technology
4175:
3501:visual discovery (data-driven & exploratory).
3485:idea illustration (conceptual & declarative).
5725:
4381:Statistics: Concepts and Applications for Science
4164:
3679:
2937:without making any assumptions of the underlying
1586:Human perception/cognition and data visualization
7278:
6319:Information Visualization: Perception for design
5693:
5414:
5412:
5351:
5069:
4807:
4734:
4378:
4141:
3493:idea generation (conceptual & exploratory).
2173:Includes most features of basic bar chart, above
2082:Includes most features of basic bar chart, above
5345:
5314:
5268:
4921:
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.
1357:Characteristics of effective graphical displays
6126:The visual display of quantitative information
5674:
5320:
5006:The Visual Display of Quantitative Information
4975:The Visual Display of Quantitative Information
4503:
1392:The Visual Display of Quantitative Information
6400:
5409:
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:
6064:Data Visualization: A Practical Introduction
4998:
4996:
4994:
4321:: CS1 maint: multiple names: authors list (
4247:
4207:
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).
4894:
4445:
4363:
4117:
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
6407:
6393:
5437:
5431:
4680:
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
424:
410:
6345:
6201:Introduction to Information Visualization
6176:
6101:
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"
5640:
5167:"Data Visualization for Human Perception"
5009:. Cheshire, Connecticut: Graphics Press.
4991:
4858:
4612:
4602:
4536:
4494:
4393:
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
5904:
5874:Data Visualization: The State of the Art
5587:A Portable Introduction to Data Analysis
5505:
5418:
5279:
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
4430:
4057:List of countries by economic complexity
3794:
3675:Category:Computer graphics organizations
3166:
3051:
2999:
2948:
2878:
2830:
2772:
2727:
2673:
2628:
2569:
2515:
2458:
2404:
2350:
2303:
2239:
2186:
2142:
2042:· per-person emissions 1990-2018 (along
2029:
1960:
1784:In the second half of the 20th century,
1775:
1740:
1671:
1656:
1611:
1570:Visual perception and data visualization
1466:
1462:
1170:
1162:
1062:, where they are paired with a coherent
58:
5849:"Data Visualization: Modern Approaches"
5508:"A Brief History of Data Visualization"
4798:, Monday Inspiration, January 14, 2008.
4439:
4433:A Framework for Visualizing Information
2850:rotating angle (cycling through months)
2757:There are two categories of heat maps:
1029:) which is different from the field of
44:This article may need to be cleaned up.
14:
7279:
6276:ACM Human Factors in Computing Systems
5973:
5761:
5744:from the original on 6 February 2019.
5731:
5584:
5229:
4203:
4201:
4199:
4197:
4160:from the original on 12 November 2016.
4147:
3301:enables direct actions on a graphical
73:Napoleonic France's invasion of Russia
6388:
6322:. San Francisco, CA: Morgan Kaufmann.
6198:
6120:
6080:
6058:
5900:
5898:
5896:
5894:
5892:
5890:
5865:
5580:
5578:
5161:
5159:
5157:
5002:
4972:
4864:
4832:
4785:"Data Visualization and Infographics"
4628:
4480:
4474:
4334:
4332:
4052:List of information graphics software
3372:
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
6001:
5819:
5699:
5378:Jansen, Yvonne; Dragicevic, Pierre;
5082:from the original on June 22, 2022.
3761:adding citations to reliable sources
3728:
3683:
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
29:
6149:
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
4194:
4123:
3707:create a more balanced presentation
3213:
3115:The points inside a curve labelled
2034:Variable-width bar chart relating:
1540:is typically used for this message.
24:
6084:Fundamentals of Data Visualization
5997:
5887:
5656:from the original on 16 March 2013
5575:
5154:
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
4329:
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:
7313:
7302:Information technology governance
6367:
5907:"Visualizations That Really Work"
5171:The Interaction Design Foundation
3885:
3033:in the form of a two-dimensional
1943:
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
3287:
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.
5967:
5917:
5842:
5793:
5755:
5668:
5634:
5623:from the original on 2014-10-05
5603:
5564:
5553:from the original on 2016-02-15
5535:
5524:from the original on 2016-05-08
5499:
5474:
5398:from the original on 2018-01-13
5371:
5360:from the original on 2018-01-13
5248:
5223:
5188:
5177:from the original on 2015-11-23
5143:from the original on 2014-10-05
5109:from the original on 2014-10-05
5089:
5063:
5052:from the original on 2014-12-04
5034:
5023:from the original on 2013-01-14
4966:
4915:
4888:
4801:
4777:
4728:
4714:
4703:from the original on 2014-10-05
4658:
4574:
4560:
4424:
4387:
4372:
4357:
4311:, Morgan Kaufmann, pp. 6–7
4138:; use archive link for article)
4096:
4028:(1987), graphical data analysis
4021:Grand Tour (data visualisation)
3587:
3518:
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
4300:
4272:
4268:, IGI Global, pp. 175–194
4256:
4241:
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 (
1839:
1694:in Southern France) since the
501:Ontology (information science)
115:Interactive data visualization
13:
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?"
4110:
3845:DPA has two main objectives:
3840:
3319:Common interactions include:
1955:Description / Example usages
1929:
1505:) during a single period. A
1415:make large data sets coherent
1351:
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
7:
5652:. www.visual-literacy.org.
5290:10.1007/978-3-540-33037-0_2
4214:Harvard Data Science Review
3953:
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
1158:
1023:business and financial data
703:Problem structuring methods
10:
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:
1601:
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
7149:
7019:
6883:
6837:
6761:
6660:
6629:
6622:
6529:
6466:Information visualization
6451:Educational visualization
6423:
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 (
4089:
4059:, example of Treemapping
3913:Information architecture
3857:
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
1361:
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
18:Data representation
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:
6626:
6594:Skeletal formula
6461:Geovisualization
6436:Chemical imaging
6409:
6402:
6395:
6386:
6385:
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:
435:
426:
419:
412:
398:Misleading graph
221:Leland Wilkinson
196:David McCandless
97:Major dimensions
79:
78:
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:
6334:Ben Shneiderman
6305:Ben Shneiderman
6294:
6272:Wayback Machine
6211:
6192:
6162:
6136:
6114:
6095:
6074:
6051:
6040:
6034:
6031:
6024:
6017:Further reading
6011:
6007:
6000:
5998:Further reading
5995:
5994:
5984:
5982:
5972:
5968:
5928:
5922:
5918:
5903:
5888:
5882:Wayback Machine
5870:
5866:
5856:Wayback Machine
5847:
5843:
5837:Wayback Machine
5824:
5820:
5811:
5809:
5798:
5794:
5763:Swayne, Deborah
5760:
5756:
5730:
5726:
5698:
5694:
5687:
5673:
5669:
5659:
5657:
5639:
5635:
5626:
5624:
5620:
5613:
5609:
5608:
5604:
5597:
5583:
5576:
5569:
5565:
5556:
5554:
5541:
5540:
5536:
5527:
5525:
5521:
5515:York University
5510:
5504:
5500:
5491:
5489:
5480:
5479:
5475:
5436:
5432:
5417:
5410:
5401:
5399:
5384:Hornbæk, Kasper
5380:Isenberg, Petra
5376:
5372:
5363:
5361:
5350:
5346:
5336:
5334:
5319:
5315:
5300:
5278:
5269:
5263:Wayback Machine
5253:
5249:
5240:
5238:
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:
5068:
5064:
5055:
5053:
5040:
5039:
5035:
5026:
5024:
5017:
5001:
4992:
4985:
4971:
4967:
4920:
4916:
4906:
4904:
4893:
4889:
4882:
4863:
4859:
4853:Wayback Machine
4840:
4833:
4823:
4821:
4806:
4802:
4792:Wayback Machine
4782:
4778:
4762:
4761:
4757:
4733:
4729:
4720:
4719:
4715:
4706:
4704:
4700:
4693:
4689:
4688:
4681:
4675:Wayback Machine
4663:
4659:
4639:Ben Shneiderman
4636:
4629:
4579:
4575:
4565:
4561:
4551:
4549:
4508:
4504:
4479:
4475:
4444:
4440:
4429:
4425:
4418:
4392:
4388:
4377:
4373:
4362:
4358:
4348:
4346:
4338:
4337:
4330:
4314:
4313:
4305:
4301:
4277:
4273:
4261:
4257:
4246:
4242:
4232:
4230:
4206:
4195:
4188:
4174:
4165:
4146:
4142:
4124:Corbett, John.
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:
7305:
7304:
7299:
7294:
7289:
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:
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:
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:
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:
5981:
5977:
5970:
5962:
5958:
5954:
5950:
5946:
5942:
5938:
5934:
5927:
5920:
5912:
5908:
5901:
5899:
5897:
5895:
5893:
5891:
5883:
5879:
5876:
5875:
5868:
5861:
5857:
5853:
5850:
5845:
5838:
5834:
5831:
5827:
5822:
5808:on 2021-01-20
5807:
5803:
5796:
5788:
5784:
5780:
5776:
5772:
5768:
5764:
5758:
5751:
5748:
5743:
5739:
5735:
5728:
5721:
5717:
5711:
5707:
5703:
5696:
5688:
5682:
5678:
5671:
5655:
5651:
5647:
5643:
5637:
5619:
5612:
5606:
5598:
5592:
5588:
5581:
5579:
5572:
5567:
5552:
5548:
5544:
5538:
5520:
5516:
5509:
5502:
5487:
5483:
5477:
5469:
5465:
5461:
5457:
5453:
5449:
5445:
5441:
5434:
5426:
5422:
5415:
5413:
5397:
5394:: 3227–3236.
5393:
5389:
5385:
5381:
5374:
5359:
5355:
5348:
5332:
5328:
5324:
5317:
5309:
5305:
5301:
5299:9783540330370
5295:
5291:
5287:
5283:
5276:
5274:
5272:
5264:
5260:
5257:
5251:
5237:
5233:
5226:
5212:on 2016-01-22
5208:
5204:
5197:
5191:
5176:
5172:
5168:
5162:
5160:
5158:
5139:
5132:
5126:
5124:
5105:
5098:
5092:
5085:
5081:
5077:
5073:
5066:
5051:
5047:
5043:
5037:
5022:
5018:
5016:0-9613921-4-2
5012:
5008:
5007:
4999:
4997:
4995:
4986:
4984:9780318029924
4980:
4976:
4969:
4961:
4957:
4953:
4949:
4945:
4941:
4937:
4933:
4929:
4925:
4918:
4902:
4898:
4891:
4883:
4881:0-201-07616-0
4877:
4873:
4872:
4867:
4861:
4854:
4850:
4847:
4845:
4838:
4836:
4819:
4815:
4811:
4804:
4797:
4793:
4789:
4786:
4780:
4772:
4766:
4758:
4752:
4748:
4744:
4740:
4739:
4731:
4723:
4717:
4699:
4692:
4686:
4684:
4676:
4672:
4669:
4668:
4661:
4654:
4653:1-55860-915-6
4650:
4646:
4645:
4640:
4634:
4632:
4624:
4620:
4615:
4610:
4605:
4600:
4596:
4592:
4588:
4584:
4577:
4570:
4563:
4548:
4544:
4539:
4534:
4530:
4526:
4522:
4518:
4514:
4506:
4497:
4492:
4488:
4484:
4477:
4469:
4465:
4461:
4457:
4453:
4449:
4442:
4434:
4427:
4419:
4417:9783110430295
4413:
4409:
4405:
4401:
4397:
4390:
4382:
4375:
4367:
4360:
4345:
4341:
4335:
4333:
4324:
4318:
4310:
4303:
4295:
4290:
4286:
4282:
4275:
4267:
4259:
4251:
4244:
4228:
4223:
4219:
4215:
4211:
4204:
4202:
4200:
4198:
4189:
4183:
4179:
4172:
4170:
4168:
4159:
4155:
4151:
4144:
4137:
4131:
4127:
4120:
4116:
4099:
4095:
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:
3948:
3945:
3942:
3938:
3934:
3931:
3928:
3924:
3921:
3918:
3914:
3911:
3908:
3905:
3902:
3899:
3896:
3893:
3892:
3891:
3880:
3877:
3874:
3871:
3868:
3865:
3864:
3863:
3851:
3848:
3847:
3846:
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:
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:
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:
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:
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
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:
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:
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:
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:
1599:
1596:
1594:
1583:
1579:
1577:
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:
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:
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:
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:
659:
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:
523:
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:
452:
451:
447:
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:
349:
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:
243:
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:
157:
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:
96:
95:
92:
89:
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.