Graphical Representation: Graphics We Live By II (Discount Rates in MS Excel)

Graphical Representation Series

It's difficult, if not impossible, to give general rules on how data visualizations should be built. However, the data professional can use a set of principles, which are less strict than rules, and validate one's work against them. Even then one might need to make concessions and go against the principles or common sense, though such cases should be few, at least in theory. One of such important principles is reflected in Tufte's statement that "if the statistics are boring, then you've got the wrong numbers" [1].

So, the numbers we show should not be boring, but that's the case with most of the numbers we need to show, or we consume in news and other media sources. Unfortunately, in many cases we need to go with the numbers we have and find a way to represent them, ideally by facilitating the reader to make sense of the respective data. This should be our first goal when visualizing data. Secondly, because everybody talks about insights nowadays, one should identify the opportunity for providing views into the data that go beyond the simple visualization, even if this puts more burden on data professional's shoulder. Frankly, from making sense of a set of data and facilitating an 'Aha' moment is a long leap. Thirdly, one should find or use the proper tools and channels for communicating the findings. 

A basic requirement for the data professional to be able to address these goals is to have clear definitions of the numbers, have a good understanding of how the numbers reflect the reality, respectively how the numbers can be put into the broader context. Unfortunately, all these assumptions seem to be a luxury. On the other side, the type of data we work with allows us to address at least the first goal. Upon case, our previous experience can help, though there will be also cases in which we can try to do our best. 

Let's consider a simple set of data retrieved recently from another post - Discount rates (in percentage) per State, in which the values for 5 neighboring States are considered (see the first two columns from diagram A). Without knowing the meaning of data, one could easily create a chart in Excel or any other visualization tool. Because the State has categorical values, probably some visualization tools will suggest using bar and not column charts. Either by own choice or by using the default settings, the data professional might end up with a column chart (see diagram B), which is Ok for some visualizations. 

One can start with a few related questions:

(1) Does it make sense to use a chart to represent 5 values which have small variability (the difference between the first and last value is of only 6%)? 

(2) Does it make sense to use a chart only for the sake of visualizing the data?

(3) Where is the benefit for using a chart as long there's no information conveyed? 

One can see similar examples in the media where non-aggregated values are shown in a chart just for the sake of visualizing the data. Sometimes the authors compensate for the lack of meaning with junk elements, fancy titles or other tricks. Usually, sense-making in a chart takes longer than looking at the values in a table as there are more dimensions or elements to consider. For a table there's the title, headers and the values, nothing more! For a chart one has in addition the axes and some visualization elements that can facilitate or complicate visualization's decoding. Where to add that there are also many tricks to distort the data. 

Tables tend to maximize the amount of digital ink used to represent the data, and minimize the amount used to represent everything else not important to understanding. It's what Tufte calls the data-to-ink ratio (see [1]), a second important principle. This can be translated in (a) removing the border of the chart area, (b) minimizing the number of gridlines shown, (c) minimizing the number of ticks on the axis without leading to information lost, (d) removing redundant information, (e) or information that doesn't help the reader. 

However, the more data is available in the table, the more difficult it becomes to navigate the data. But again, if the chart shows the individual data without any information gained, a table might be still more effective. One shouldn't be afraid to show a table where is the case!

(4) I have a data visualization, what's next?

Ideally, the data professional should try to obtain the maximum of effect with minimum of elements. If this principle aims for the efficiency of design, a fourth related principle aims for the efficiency of effort - one should achieve a good enough visualization with a minimum of effort. Therefore, it's enough maybe if we settle to any of the two above results. 

On the other side, maybe by investing a bit more effort certain aspects can be improved. In this area beginners start playing with the colors, formatting the different elements of the chart. Unfortunately, even if color plays a major role in the encoding and decoding of meaning, is often misused/overused. 

(5) Is there any meaning in the colors used?

In the next examples taken from the web (diagram C and D), the author changed the color of the column with the minimal value to red to contrast it with the other values. Red is usually associated with danger, error, warning, or other similar characteristics with negative impact. The chances are high that the reader will associate the value with a negative connotation, even if red is used also for conveying important information (usually in text). Moreover, the reader will try to interpret the meaning of the other colors. In practice, the color grey has a neutral tone (and calming effect on the mind). Therefore, it's safe to use grey in visualization (see diagram D in contrast with diagram C). Some even advise setting grey as default for the visualization and changing the colors as needed later. 

In these charts, the author signalized in titles that red denotes the lowest value, though it just reduces the confusion. One can meet titles in which several colors are used, reminding of a Christmas tree. Frankly, this type of encoding is not esthetically pleasing, and it can annoy the reader. 

(6) What's in a name?

The titles and, upon case, the subtitles are important elements in communicating what the data reflects. The title should be in general short and succinct in the information it conveys, having the role of introducing, respectively identifying the chart, especially when multiple charts are used. Some charts can also use a subtitle, which can be longer than the title and have more of a storytelling character by highlighting the message and/or the finding in the data. In diagrams C and D the subtitles were considered as tiles, which is not considerably wrong. 

In the media and presentations with influencing character, subtitles help the user understand the message or the main findings, though it's not appropriate for hardcoding the same in dynamic dashboards. Even if a logic was identified to handle the various scenarios, this shifts users' attention, and the chance is high that they'll stop further investigating the visualization. A data professional should present the facts with minimal interference in how the audience and/or users perceive the data. 

As a recommendation, one should aim for clear general titles and avoid transmitting own message in charts. As a principle this can be summarized as "aim for clarity and equidistance".

(7) What about meaning?

Until now we barely considered the meaning of data. Unfortunately, there's no information about what the Discount rate means. It could be "the minimum interest rate set by the US Federal Reserve (and some other national banks) for lending to other banks" or "a rate used for discounting bills of exchange", to use the definitions given by the Oxford dictionary. Searching on the web, the results lead to discount rates for royalty savings, resident tuitions, or retail for discount transactions. Most probably the Discount rates from the data set refer to the latter.

We need a definition of the Discount rate to understand what the values represent when they are ordered. For example, when Texas has a value of 25% (see B), does this value have a negative or a positive impact when compared with other values? It depends on how it's used in the associated formula. The last two charts consider that the minimum value has a negative impact, though without more information the encoding might be wrong! 

Important formulas and definitions should be considered as side information in the visualization, accompanying text or documentation! If further resources are required for understanding the data, then links to the required resources should be provided as well. At least this assures that the reader can acquire the right information without major overhead. 

(8) What do readers look for? 

Frankly, this should have been the first question! Readers have different expectations from data visualizations. First of all, it's the curiosity - how the data look in row and/or aggregated form, or in more advanced form how are they shaped (e.g. statistical characteristics like dispersion, variance, outliers). Secondly, readers look in the first phase to understand mainly whether the "results" are good or bad, even if there are many shades of grey in between. Further on, there must be made distinction between readers who want to learn more about the data, models, and processes behind, respectively readers who just want a confirmation of their expectations, opinions and beliefs (aka bias). And, in the end, there are also people who are not interested in the data and what it tells, where the title and/or subtitle provide enough information. 

Besides this there are further categories of readers segmented by their role in the decision making, the planning and execution of operational, tactical, or strategic activities. Each of these categories has different needs. However, this exceeds the scope of our analysis. 

Returning to our example, one can expect that the average reader will try to identify the smallest and highest Discount rates from the data set, respectively try to compare the values between the different States. Sorting the data and having the values close to each other facilitates the comparison and ranking, otherwise the reader needing to do this by himself/herself. This latter aspect and the fact that bar charts better handle the display of categorical data such as length and number, make from bar charts the tool of choice (see diagram E). So, whenever you see categorical data, consider using a bar chart!

Despite sorting the data, the reader might still need to subtract the various values to identify and compare the differences. The higher the differences between the values, the more complex these operations become. Diagram F is supposed to help in this area, the comparison to the minimal value being shown in orange. Unfortunately, small variances make numbers' display more challenging especially when the visualization tools don't offer display alternatives.

For showing the data from Diagram F were added in the table the third and fourth columns (see diagram A). There's a fifth column which designates the percentage from a percentage (what's the increase in percentages between the current and minimal value). Even if that's mathematically possible, the gain from using such data is neglectable and can create confusion. This opens the door for another principle that applies in other areas as well: "just because you can, it doesn't mean you should!". One should weigh design decisions against common sense or one's intuition on how something can be (mis)used and/or (mis)understood!

The downside of Diagram F is that the comparisons are made only in relation to the minimum value. The variations are small and allow further comparisons. The higher the differences, the more challenging it becomes to make further comparisons. A matrix display (see diagram G) which compares any two values will help if the number of points is manageable. The upper side of the numbers situated on and above the main diagonal were grayed (and can be removed) because they are either nonmeaningful, or the negatives of the numbers found below the diagram. Such diagrams are seldom used, though upon case they prove to be useful.

Choropleth maps (diagram H) are met almost everywhere data have a geographical dimension. Like all the other visuals they have their own advantages (e.g. relative location on the map) and disadvantages (e.g. encoding or displaying data). The diagram shows only the regions with data (remember the data-to-ink ratio principle).

(9) How about the shape of data?

When dealing with numerical data series, it's useful to show aggregated summaries like the average, quartiles, or standard deviation to understand how the data are shaped. Such summaries don't really make sense for our data set given the nature of the numbers (five values with small variance). One can still calculate them and show them in a box plot, though the benefit is neglectable. 

(10) Which chart should be used?

As mentioned above, each chart has advantages and disadvantages. Given the simplicity and the number of data points, any of the above diagrams will do. A table is simple enough despite not using any visualization effects. Also, the bar charts are simple enough to use, with a plus maybe for diagram F which shows a further dimension of the data. The choropleth map adds the geographical dimension, which could be important for some readers. The matrix table is more appropriate for technical readers and involves more effort to understand, at least at first sight, though the learning curve is small. The column charts were considered only for exemplification purposes, though they might work as well. 

In the end one should go with own experience and consider the audience and the communication channels used. One can also choose 2 different diagrams, especially when they are complementary and offer an additional dimension (e.g. diagrams F and H), though the context may dictate whether their use is appropriate or not. The diagrams should be simple to read and understand, but this doesn't mean that one should stick to the standard visuals. The data professional should explore other means of representing the data, a fresh view having the opportunity of catching the reader's attention.

As a closing remark, nowadays data visualization tools allow building such diagrams without much effort. Conversely, it takes more effort to go beyond the basic functionality and provide more value for thyself and the readers. One should be able to evaluate upfront how much time it makes sense to invest. Hopefully, the few methods, principles and recommendations presented here will help further!

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Resources:
[1] Edward R Tufte (1983) "The Visual Display of Quantitative Information"

Data Science: Data Visualization (Just the Quotes)

"No matter how clever the choice of the information, and no matter how technologically impressive the encoding, a visualization fails if the decoding fails. Some display methods lead to efficient, accurate decoding, and others lead to inefficient, inaccurate decoding. It is only through scientific study of visual perception that informed judgments can be made about display methods." (William S Cleveland, "The Elements of Graphing Data", 1985)

"Data that are skewed toward large values occur commonly. Any set of positive measurements is a candidate. Nature just works like that. In fact, if data consisting of positive numbers range over several powers of ten, it is almost a guarantee that they will be skewed. Skewness creates many problems. There are visualization problems. A large fraction of the data are squashed into small regions of graphs, and visual assessment of the data degrades. There are characterization problems. Skewed distributions tend to be more complicated than symmetric ones; for example, there is no unique notion of location and the median and mean measure different aspects of the distribution. There are problems in carrying out probabilistic methods. The distribution of skewed data is not well approximated by the normal, so the many probabilistic methods based on an assumption of a normal distribution cannot be applied." (William S Cleveland, "Visualizing Data", 1993)

"Many of the applications of visualization in this book give the impression that data analysis consists of an orderly progression of exploratory graphs, fitting, and visualization of fits and residuals. Coherence of discussion and limited space necessitate a presentation that appears to imply this. Real life is usually quite different. There are blind alleys. There are mistaken actions. There are effects missed until the very end when some visualization saves the day. And worse, there is the possibility of the nearly unmentionable: missed effects." (William S Cleveland, "Visualizing Data", 1993)

"One important aspect of reality is improvisation; as a result of special structure in a set of data, or the finding of a visualization method, we stray from the standard methods for the data type to exploit the structure or the finding." (William S Cleveland, "Visualizing Data", 1993)

"There are two components to visualizing the structure of statistical data - graphing and fitting. Graphs are needed, of course, because visualization implies a process in which information is encoded on visual displays. Fitting mathematical functions to data is needed too. Just graphing raw data, without fitting them and without graphing the fits and residuals, often leaves important aspects of data undiscovered." (William S Cleveland, "Visualizing Data", 1993)

"Visualization is an approach to data analysis that stresses a penetrating look at the structure of data. No other approach conveys as much information. […] Conclusions spring from data when this information is combined with the prior knowledge of the subject under investigation." (William S Cleveland, "Visualizing Data", 1993)

"Visualization is an effective framework for drawing inferences from data because its revelation of the structure of data can be readily combined with prior knowledge to draw conclusions. By contrast, because of the formalism of probablistic methods, it is typically impossible to incorporate into them the full body of prior information." (William S Cleveland, "Visualizing Data", 1993)

"When visualization tools act as a catalyst to early visual thinking about a relatively unexplored problem, neither the semantics nor the pragmatics of map signs is a dominant factor. On the other hand, syntactics (or how the sign-vehicles, through variation in the visual variables used to construct them, relate logically to one another) are of critical importance." (Alan M MacEachren, "How Maps Work: Representation, Visualization, and Design", 1995)

"The nature of maps and of their use in science and society is in the midst of remarkable change - change that is stimulated by a combination of new scientific and societal needs for geo-referenced information and rapidly evolving technologies that can provide that information in innovative ways. A key issue at the heart of this change is the concept of ‘visualization’." (Alan M MacEachren, "Exploratory cartographic visualization: advancing the agenda", 1997)

"Visualization for large data is an oxymoron - the art is to reduce size before one visualizes. The contradiction (and challenge) is that we may need to visualize first in order to find out how to reduce size." (Peter Huber, "Massive datasets workshop: Four years after", Journal of Computational and Graphical Statistics Vol 8, 1999)

"Visualizations can be used to explore data, to confirm a hypothesis, or to manipulate a viewer. [...] In exploratory visualization the user does not necessarily know what he is looking for. This creates a dynamic scenario in which interaction is critical. [...] In a confirmatory visualization, the user has a hypothesis that needs to be tested. This scenario is more stable and predictable. System parameters are often predetermined." (Usama Fayyad et al, "Information Visualization in Data Mining and Knowledge Discovery", 2002) 

"Dashboards and visualization are cognitive tools that improve your 'span of control' over a lot of business data. These tools help people visually identify trends, patterns and anomalies, reason about what they see and help guide them toward effective decisions. As such, these tools need to leverage people's visual capabilities. With the prevalence of scorecards, dashboards and other visualization tools now widely available for business users to review their data, the issue of visual information design is more important than ever." (Richard Brath & Michael Peters, "Dashboard Design: Why Design is Important," DM Direct, 2004)

"Merely drawing a plot does not constitute visualization. Visualization is about conveying important information to the reader accurately. It should reveal information that is in the data and should not impose structure on the data." (Robert Gentleman, "Bioinformatics and Computational Biology Solutions using R and Bioconductor", 2005)

"Exploratory Data Analysis is more than just a collection of data-analysis techniques; it provides a philosophy of how to dissect a data set. It stresses the power of visualisation and aspects such as what to look for, how to look for it and how to interpret the information it contains. Most EDA techniques are graphical in nature, because the main aim of EDA is to explore data in an open-minded way. Using graphics, rather than calculations, keeps open possibilities of spotting interesting patterns or anomalies that would not be apparent with a calculation (where assumptions and decisions about the nature of the data tend to be made in advance)." (Alan Graham, "Developing Thinking in Statistics", 2006) 

"Data visualization [...] expresses the idea that it involves more than just representing data in a graphical form (instead of using a table). The information behind the data should also be revealed in a good display; the graphic should aid readers or viewers in seeing the structure in the data. The term data visualization is related to the new field of information visualization. This includes visualization of all kinds of information, not just of data, and is closely associated with research by computer scientists." (Antony Unwin et al, "Introduction" [in "Handbook of Data Visualization"], 2008) 

"The main goal of data visualization is its ability to visualize data, communicating information clearly and effectively. 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 dataset by communicating its key aspects in a more intuitive way. Yet designers often tend to discard the balance between design and function, creating gorgeous data visualizations which fail to serve its main purpose - communicate information." (Vitaly Friedman, "Data Visualization and Infographics", Smashing Magazine, 2008)

"With the ever increasing amount of empirical information that scientists from all disciplines are dealing with, there exists a great need for robust, scalable and easy to use clustering techniques for data abstraction, dimensionality reduction or visualization to cope with and manage this avalanche of data."  (Jörg Reichardt, "Structure in Complex Networks", 2009)

"So what is the difference between a chart or graph and a visualization? […] a chart or graph is a clean and simple atomic piece; bar charts contain a short story about the data being presented. A visualization, on the other hand, seems to contain much more ʻchart junkʼ, with many sometimes complex graphics or several layers of charts and graphs. A visualization seems to be the super-set for all sorts of data-driven design." (Brian Suda, "A Practical Guide to Designing with Data", 2010)

"The goal of visualization is to aid our understanding of data by leveraging the human visual system’s highly tuned ability to see patterns, spot trends, and identify outliers." (J Heer et al, "A tour through the visualization zoo",Queue 8, 2010) 

"All graphics present data and allow a certain degree of exploration of those same data. Some graphics are almost all presentation, so they allow just a limited amount of exploration; hence we can say they are more infographics than visualization, whereas others are mostly about letting readers play with what is being shown, tilting more to the visualization side of our linear scale. But every infographic and every visualization has a presentation and an exploration component: they present, but they also facilitate the analysis of what they show, to different degrees." (Alberto Cairo, "The Functional Art", 2011)

"In data visualization, the number one rule of thumb to bear is mind is: Function first, suave second." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

"The first and main goal of any graphic and visualization is to be a tool for your eyes and brain to perceive what lies beyond their natural reach." (Alberto Cairo, "The Functional Art", 2011)

"Thinking of graphics as art leads many to put bells and whistles over substance and to confound infographics with mere illustrations." (Alberto Cairo, "The Functional Art", 2011)

"[...] the terms data visualization and information visualization (casually, data viz and info viz) are useful for referring to any visual representation of data that is: (•) algorithmically drawn (may have custom touches but is largely rendered with the help of computerized methods); (•) easy to regenerate with different data (the same form may be repurposed to represent different datasets with similar dimensions or characteristics); (•) often aesthetically barren (data is not decorated); and (•) relatively data-rich (large volumes of data are welcome and viable, in contrast to infographics)." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

"Good infographic design is about storytelling by combining data visualization design and graphic design." (Randy Krum, "Good Infographics: Effective Communication with Data Visualization and Design", 2013)

"Good visualization is a winding process that requires statistics and design knowledge. Without the former, the visualization becomes an exercise only in illustration and aesthetics, and without the latter, one of only analyses. On their own, these are fine skills, but they make for incomplete data graphics. Having skills in both provides you with the luxury - which is growing into a necessity - to jump back and forth between data exploration and storytelling." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"The biggest thing to know is that data visualization is hard. Really difficult to pull off well. It requires harmonization of several skills sets and ways of thinking: conceptual, analytic, statistical, graphic design, programmatic, interface-design, story-telling, journalism - plus a bit of 'gut feel'. The end result is often simple and beautiful, but the process itself is usually challenging and messy." (David McCandless, 2013)

"Visualization can be appreciated purely from an aesthetic point of view, but it’s most interesting when it’s about data that’s worth looking at. That’s why you start with data, explore it, and then show results rather than start with a visual and try to squeeze a dataset into it. It’s like trying to use a hammer to bang in a bunch of screws. […] Aesthetics isn’t just a shiny veneer that you slap on at the last minute. It represents the thought you put into a visualization, which is tightly coupled with clarity and affects interpretation." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"Visualization is what happens when you make the jump from raw data to bar graphs, line charts, and dot plots. […] In its most basic form, visualization is simply mapping data to geometry and color. It works because your brain is wired to find patterns, and you can switch back and forth between the visual and the numbers it represents. This is the important bit. You must make sure that the essence of the data isn’t lost in that back and forth between visual and the value it represents because if you can’t map back to the data, the visualization is just a bunch of shapes." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"What is good visualization? It is a representation of data that helps you see what you otherwise would have been blind to if you looked only at the naked source. It enables you to see trends, patterns, and outliers that tell you about yourself and what surrounds you. The best visualization evokes that moment of bliss when seeing something for the first time, knowing that what you see has been right in front of you, just slightly hidden. Sometimes it is a simple bar graph, and other times the visualization is complex because the data requires it." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"Just because data is visualized doesn’t necessarily mean that it is accurate, complete, or indicative of the right course of action. Exhibiting a healthy skepticism is almost always a good thing." (Phil Simon, "The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions", 2014)

"To be sure, data doesn’t always need to be visualized, and many data visualizations just plain suck. Look around you. It’s not hard to find truly awful representations of information. Some work in concept but fail because they are too busy; they confuse people more than they convey information [...]. Visualization for the sake of visualization is unlikely to produce desired results - and this goes double in an era of Big Data. Bad is still bad, even and especially at a larger scale." (Phil Simon, "The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions", 2014)

"We are all becoming more comfortable with data. Data visualization is no longer just something we have to do at work. Increasingly, we want to do it as consumers and as citizens. Put simply, visualizing helps us understand what’s going on in our lives - and how to solve problems." (Phil Simon, "The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions", 2014)

"Creating effective visualizations is hard. Not because a dataset requires an exotic and bespoke visual representation - for many problems, standard statistical charts will suffice. And not because creating a visualization requires coding expertise in an unfamiliar programming language [...]. Rather, creating effective visualizations is difficult because the problems that are best addressed by visualization are often complex and ill-formed. The task of figuring out what attributes of a dataset are important is often conflated with figuring out what type of visualization to use. Picking a chart type to represent specific attributes in a dataset is comparatively easy. Deciding on which data attributes will help answer a question, however, is a complex, poorly defined, and user-driven process that can require several rounds of visualization and exploration to resolve." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"[…] no single visualization is ever quite able to show all of the important aspects of our data at once - there just are not enough visual encoding channels. […] designing effective visualizations to make sense of data is not an art - it is a systematic and repeatable process." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

 "[…] the data itself can lead to new questions too. In exploratory data analysis (EDA), for example, the data analyst discovers new questions based on the data. The process of looking at the data to address some of these questions generates incidental visualizations - odd patterns, outliers, or surprising correlations that are worth looking into further." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"The field of [data] visualization takes on that goal more broadly: rather than attempting to identify a single metric, the analyst instead tries to look more holistically across the data to get a usable, actionable answer. Arriving at that answer might involve exploring multiple attributes, and using a number of views that allow the ideas to come together. Thus, operationalization in the context of visualization is the process of identifying tasks to be performed over the dataset that are a reasonable approximation of the high-level question of interest." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"Apart from the technical challenge of working with the data itself, visualization in big data is different because showing the individual observations is just not an option. But visualization is essential here: for analysis to work well, we have to be assured that patterns and errors in the data have been spotted and understood. That is only possible by visualization with big data, because nobody can look over the data in a table or spreadsheet." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"As a first principle, any visualization should convey its information quickly and easily, and with minimal scope for misunderstanding. Unnecessary visual clutter makes more work for the reader’s brain to do, slows down the understanding (at which point they may give up) and may even allow some incorrect interpretations to creep in." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"Data storytelling can be defined as a structured approach for communicating data insights using narrative elements and explanatory visuals." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Data storytelling involves the skillful combination of three key elements: data, narrative, and visuals. Data is the primary building block of every data story. It may sound simple, but a data story should always find its origin in data, and data should serve as the foundation for the narrative and visual elements of your story." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"In addition to managing how the data is visualized to reduce noise, you can also decrease the visual interference by minimizing the extraneous cognitive load. In these cases, the nonrelevant information and design elements surrounding the data can cause extraneous noise. Poor design or display decisions by the data storyteller can inadvertently interfere with the communication of the intended signal. This form of noise can occur at both a macro and micro level." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"One very common problem in data visualization is that encoding numerical variables to area is incredibly popular, but readers can’t translate it back very well." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"There is often no one 'best' visualization, because it depends on context, what your audience already knows, how numerate or scientifically trained they are, what formats and conventions are regarded as standard in the particular field you’re working in, the medium you can use, and so on. It’s also partly scientific and partly artistic, so you get to express your own design style in it, which is what makes it so fascinating." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019) 

"When visuals are applied to data, they can enlighten the audience to insights that they wouldn’t see without charts or graphs. Many interesting patterns and outliers in the data would remain hidden in the rows and columns of data tables without the help of data visualizations. They connect with our visual nature as human beings and impart knowledge that couldn’t be obtained as easily using other approaches that involve just words or numbers." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"While visuals are an essential part of data storytelling, data visualizations can serve a variety of purposes from analysis to communication to even art. Most data charts are designed to disseminate information in a visual manner. Only a subset of data compositions is focused on presenting specific insights as opposed to just general information. When most data compositions combine both visualizations and text, it can be difficult to discern whether a particular scenario falls into the realm of data storytelling or not." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Another problem is that while data visualizations may appear to be objective, the designer has a great deal of control over the message a graphic conveys. Even using accurate data, a designer can manipulate how those data make us feel. She can create the illusion of a correlation where none exists, or make a small difference between groups look big." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"As presenters of data visualizations, often we just want our audience to understand something about their environment – a trend, a pattern, a breakdown, a way in which things have been progressing. If we ask ourselves what we want our audience to do with that information, we might have a hard time coming up with a clear answer sometimes. We might just want them to know something." (Ben Jones, "Avoiding Data Pitfalls: How to Steer Clear of Common Blunders When Working with Data and Presenting Analysis and Visualizations", 2020)

"Data visualizations are either used (1) to help people complete a task, or (2) to give them a general awareness of the way things are, or (3) to enable them to explore the topic for themselves."  (Ben Jones, "Avoiding Data Pitfalls: How to Steer Clear of Common Blunders When Working with Data and Presenting Analysis and Visualizations", 2020)

"Much of the data visualization that bombards us today is decoration at best, and distraction or even disinformation at worst. The decorative function is surprisingly common, perhaps because the data visualization teams of many media organizations are part of the art departments. They are led by people whose skills and experience are not in statistics but in illustration or graphic design. The emphasis is on the visualization, not on the data. It is, above all, a picture." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"A data visualization, or dashboard, is great for summarizing or describing what has gone on in the past, but if people don’t know how to progress beyond looking just backwards on what has happened, then they cannot diagnose and find the ‘why’ behind it." (Jordan Morrow, "Be Data Literate: The data literacy skills everyone needs to succeed", 2021)

"Data literacy is for the masses, and data visualization is powerful to simplify what could be very complicated." (Jordan Morrow, "Be Data Literate: The data literacy skills everyone needs to succeed", 2021)

"Understanding the entire data ecosystem, from the production of a data point to its consumption in a dashboard or a visualization, provides the ability to invoke action, which is more valuable than the mere sum of its parts." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)

"Data visualization is a simplified approach to studying data." (Jordan Morrow, "Be Data Literate: The data literacy skills everyone needs to succeed", 2021)

"Data visualization is a mix of science and art. Sometimes we want to be closer to the science side of the spectrum - in other words, use visualizations that allow readers to more accurately perceive the absolute values of data and make comparisons. Other times we may want to be closer to the art side of the spectrum and create visuals that engage and excite the reader, even if they do not permit the most accurate comparisons." (Jonathan Schwabish, "Better Data Visualizations: A guide for scholars, researchers, and wonks", 2021)

"I agree that data visualizations should be visually appealing, driving and utilizing the appeal and power for individuals to utilize it effectively, but sometimes this can take too much time, taking it away from more valuable uses in data. Plus, if the data visualization is not moving the needle of a business goal or objective, how effective is that visualization?" (Jordan Morrow, "Be Data Literate: The data literacy skills everyone needs to succeed", 2021)

"Data becomes more useful once it’s transformed into a data visualization or used in a data story. Data storytelling is the ability to effectively communicate insights from a dataset using narratives and visualizations. It can be used to put data insights into context and inspire action from your audience. Color can be very helpful when you are trying to make information stand out within your data visualizations." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Data visualization is the practice of taking insights found in data analysis and turning them into numbers, graphs, charts, and other visual concepts to make them easier to grasp, understand, learn from, and utilize.[...] The visualization of data can be thought of as both a science and an art in that the way it is displayed is often as important to its understanding as the actual information that is being displayed." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Visualizations can remove the background noise from enormous sets of data so that only the most important points stand out to the intended audience. This is particularly important in the era of big data. The more data there is, the more chance for noise and outliers to interfere with the core concepts of the data set." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"The best approach is to build visualizations in the most digestible form, fitted to how that executive thinks. You will have to interact with executives, show them different visualizations, and see how they react in order to learn which forms work best for them. Be ready to fail often and learn fast, particularly with visualizations." (John Lucker)

"Visualisation is fundamentally limited by the number of pixels you can pump to a screen. If you have big data, you have way more data than pixels, so you have to summarise your data. Statistics gives you lots of really good tools for this." (Hadley Wickham)

"We often think of visualization as a design and programming task, but the process starts further back with the data. You have to understand the data - its trends and patterns, along with its flaws and imperfections - and the rest follows." (Nathan Yau)

Data Science: Self-Organizing Map (Definitions)

"A clustering neural net, with topological structure among cluster units." (Laurene V Fausett, "Fundamentals of Neural Networks: Architectures, Algorithms, and Applications", 1994)

"A self organizing map is a form of Kohonen network that arranges its clusters in a (usually) two-dimensional grid so that the codebook vectors (the cluster centers) that are close to each other on the grid are also close in the k-dimensional feature space. The converse is not necessarily true, as codebook vectors that are close in feature-space might not be close on the grid. The map is similar in concept to the maps produced by descriptive techniques such as multi-dimensional scaling (MDS)." (William J Raynor Jr., "The International Dictionary of Artificial Intelligence", 1999)

"result of a nonparametric regression process that is mainly used to represent high-dimensional, nonlinearly related data items in an illustrative, often two-dimensional display, and to perform unsupervised classification and clustering." (Teuvo Kohonen, "Self-Organizing Maps" 3rd Ed., 2001)

"a method of organizing and displaying textual information according to the frequency of occurrence of text and the relationship of text from one document to another." (William H Inmon, "Building the Data Warehouse", 2005)

"A type of unsupervised neural network used to group similar cases in a sample. SOMs are unsupervised (see supervised network) in that they do not require a known dependent variable. They are typically used for exploratory analysis and to reduce dimensionality as an aid to interpretation of complex data. SOMs are similar in purpose to Ic-means clustering and factor analysis." (David Scarborough & Mark J Somers, "Neural Networks in Organizational Research: Applying Pattern Recognition to the Analysis of Organizational Behavior", 2006)

"A method to learn to cluster input vectors according to how they are naturally grouped in the input space. In its simplest form, the map consists of a regular grid of units and the units learn to represent statistical data described by model vectors. Each map unit contains a vector used to represent the data. During the training process, the model vectors are changed gradually and then the map forms an ordered non-linear regression of the model vectors into the data space." (Atiq Islam et al, "CNS Tumor Prediction Using Gene Expression Data Part II", Encyclopedia of Artificial Intelligence, 2009)

"A neural-network method that reduces the dimensions of data while preserving the topological properties of the input data. SOM is suitable for visualizing high-dimensional data such as microarray data." (Emmanuel Udoh & Salim Bhuiyan, "C-MICRA: A Tool for Clustering Microarray Data", 2009)

"A neural network unsupervised method of vector quantization widely used in classification. Self-Organizing Maps are a much appreciated for their topology preservation property and their associated data representation system. These two additive properties come from a pre-defined organization of the network that is at the same time a support for the topology learning and its representation. (Patrick Rousset & Jean-Francois Giret, "A Longitudinal Analysis of Labour Market Data with SOM" Encyclopedia of Artificial Intelligence, 2009)

"A simulated neural network based on a grid of artificial neurons by means of prototype vectors. In an unsupervised training the prototype vectors are adapted to match input vectors in a training set. After completing this training the SOM provides a generalized K-means clustering as well as topological order of neurons." (Laurence Mukankusi et al, "Relationships between Wireless Technology Investment and Organizational Performance", 2009)

"A subtype of artificial neural network. It is trained using unsupervised learning to produce low dimensional representation of the training samples while preserving the topological properties of the input space." (Soledad Delgado et al, "Growing Self-Organizing Maps for Data Analysis", 2009)

"An unsupervised neural network providing a topology-preserving mapping from a high-dimensional input space onto a two-dimensional output space." (Thomas Lidy & Andreas Rauber, "Music Information Retrieval", 2009)

"Category of algorithms based on artificial neural networks that searches, by means of self-organization, to create a map of characteristics that represents the involved samples in a determined problem." (Paulo E Ambrósio, "Artificial Intelligence in Computer-Aided Diagnosis", 2009)

"Self-organizing maps (SOMs) are a data visualization technique which reduce the dimensions of data through the use of self-organizing neural networks." (Lluís Formiga & Francesc Alías, "GTM User Modeling for aIGA Weight Tuning in TTS Synthesis", Encyclopedia of Artificial Intelligence, 2009)

"SOFM [self-organizing feature map] is a data mining method used for unsupervised learning. The architecture consists of an input layer and an output layer. By adjusting the weights of the connections between input and output layer nodes, this method identifies clusters in the data." (Indranil Bose, "Data Mining in Tourism", 2009)

"The self-organizing map is a subtype of artificial neural networks. It is trained using unsupervised learning to produce low dimensional representation of the training samples while preserving the topological properties of the input space. The self-organizing map is a single layer feed-forward network where the output syntaxes are arranged in low dimensional (usually 2D or 3D) grid. Each input is connected to all output neurons. Attached to every neuron there is a weight vector with the same dimensionality as the input vectors. The number of input dimensions is usually a lot higher than the output grid dimension. SOMs are mainly used for dimensionality reduction rather than expansion." (Larbi Esmahi et al, "Adaptive Neuro-Fuzzy Systems", Encyclopedia of Artificial Intelligence, 2009)

"A type of neural network that uses unsupervised learning to produce two-dimensional representations of an input space." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The Self-organizing map is a non-parametric and non-linear neural network that explores data using unsupervised learning. The SOM can produce output that maps multidimensional data onto a two-dimensional topological map. Moreover, since the SOM requires little a priori knowledge of the data, it is an extremely useful tool for exploratory analyses. Thus, the SOM is an ideal visualization tool for analyzing complex time-series data." (Peter Sarlin, "Visualizing Indicators of Debt Crises in a Lower Dimension: A Self-Organizing Maps Approach", 2012)

"SOMs or Kohonen networks have a grid topology, with unequal grid weights. The topology of the grid provides a low dimensional visualization of the data distribution." (Siddhartha Bhattacharjee et al, "Quantum Backpropagation Neural Network Approach for Modeling of Phenol Adsorption from Aqueous Solution by Orange Peel Ash", 2013)

"An unsupervised neural network widely used in exploratory data analysis and to visualize multivariate object relationships." (Manuel Martín-Merino, "Semi-Supervised Dimension Reduction Techniques to Discover Term Relationships", 2015)

"ANN used for visualizing low-dimensional views of high-dimensional data." (Pablo Escandell-Montero et al, "Artificial Neural Networks in Physical Therapy", 2015)

"Is a unsupervised learning ANN, which means that no human intervention is needed during the learning and that little needs to be known about the characteristics of the input data." (Nuno Pombo et al, "Machine Learning Approaches to Automated Medical Decision Support Systems", 2015)

"A kind of artificial neural network which attempts to mimic brain functions to provide learning and pattern recognition techniques. SOM have the ability to extract patterns from large datasets without explicitly understanding the underlying relationships. They transform nonlinear relations among high dimensional data into simple geometric connections among their image points on a low-dimensional display." (Felix Lopez-Iturriaga & Iván Pastor-Sanz, "Using Self Organizing Maps for Banking Oversight: The Case of Spanish Savings Banks", 2016)

"Neural network which simulated some cerebral functions in elaborating visual information. It is usually used to classify a large amount of data." (Gaetano B Ronsivalle & Arianna Boldi, "Artificial Intelligence Applied: Six Actual Projects in Big Organizations", 2019)

"Classification technique based on unsupervised-learning artificial neural networks allowing to group data into clusters." Julián Sierra-Pérez & Joham Alvarez-Montoya, "Strain Field Pattern Recognition for Structural Health Monitoring Applications", 2020)

"It is a type of artificial neural network (ANN) trained using unsupervised learning for dimensionality reduction by discretized representation of the input space of the training samples called as map." (Dinesh Bhatia et al, "A Novel Artificial Intelligence Technique for Analysis of Real-Time Electro-Cardiogram Signal for the Prediction of Early Cardiac Ailment Onset", 2020)

"Being a particular type of ANNs, the Self Organizing Map is a simple mapping from inputs: attributes directly to outputs: clusters by the algorithm of unsupervised learning. SOM is a clustering and visualization technique in exploratory data analysis." (Yuh-Wen Chen, "Social Network Analysis: Self-Organizing Map and WINGS by Multiple-Criteria Decision Making", 2021)

Strategic Management: Roadmap (Definitions)

"An abstracted plan for business or technology change, typically operating across multiple disciplines over multiple years." (David Lyle & John G Schmidt, "Lean Integration", 2010)

"Techniques that capture market trends, product launches, technology development, and competence building over time in a multilayer, consistent framework." (Gina C O'Connor & V K Narayanan, "Encyclopedia of Technology and Innovation Management", 2010)

"Defines the actions required to move from current to future (target) state. Similar to a high-level project plan." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

[portfolio roadmap:] "A document that provides the high-level strategic direction and portfolio information in a chronological fashion for portfolio management and ensures dependencies within the portfolio are established and evaluated." (Project Management Institute, "The Standard for Portfolio Management" 3rd Ed., 2012)

"Forward-looking plans intended to be taken by the security program over the foreseeable future." (Mark Rhodes-Ousley, "Information Security: The Complete Reference" 2nd Ed., 2013)

"Within the context of business analytics, a defined set of staged initiatives that deliver tactical returns while moving the team toward strategic outcomes." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"High-level action plan for change that will involve several facets of the enterprise (business, organization, technical)." (Gilbert Raymond & Philippe Desfray, "Modeling Enterprise Architecture with TOGAF", 2014)

"An action plan that matches the organization's business goals with specific technology solutions in order to help meet those goals." (David K Pham, "From Business Strategy to Information Technology Roadmap", 2016)

"The Roadmap is a schedule of events and Milestones that communicate planned Solution deliverables over a timeline. It includes commitments for the planned, upcoming Program Increment (PI) and offers visibility into the deliverables forecasted for the next few PIs." (Dean Leffingwell, "SAFe 4.5 Reference Guide: Scaled Agile Framework for Lean Enterprises" 2nd Ed., 2018)

"A product roadmap is a visual summary of a product’s direction to facilitate communication with customers, prospects, partners, and internal stakeholders." (Pendo) [source]

"A Roadmap is a plan to progress toward a set of defined goals. Depending on the purpose of the Roadmap, it may be either high-level or detailed. In terms of Enterprise Architecture, roadmaps are usually developed as abstracted plans for business or technology changes, typically operating across multiple disciplines over multiple years." (Orbus Software)

"A roadmap is a strategic plan that defines a goal or desired outcome and includes the major steps or milestones needed to reach it." (ProductPlan) [source]

Knowledge Management: Concept Map (Definitions)

"Concept maps are built of nodes connected by connectors, which have written descriptions called linking phrases instead of polarity of strength. Concept maps can be used to describe conceptual structures and relations in them and the concept maps suit also aggregation and preservation of knowledge" (Hannu Kivijärvi et al, "A Support System for the Strategic Scenario Process", 2008) 

"A hierarchal picture of a mental map of knowledge." (Gregory MacKinnon, "Concept Mapping as a Mediator of Constructivist Learning", 2009)

"A tool that assists learners in the understanding of the relationships of the main idea and its attributes, also used in brainstorming and planning." (Diane L Judd, "Constructing Technology Integrated Activities that Engage Elementary Students in Learning", 2009)

"Concept maps are graphical knowledge representations that are composed to two components: (1) Nodes: represent the concepts, and (2) Links: connect concepts using a relationship." (Faisal Ahmad et al, "New Roles of Digital Libraries", 2009)

"A concept map is a diagram that depicts concepts and their hierarchical relationships." (Wan Ng & Ria Hanewald, "Concept Maps as a Tool for Promoting Online Collaborative Learning in Virtual Teams with Pre-Service Teachers", 2010)

"A diagram that facilitates organization, presentation, processing and acquisition of knowledge by showing relationships among concepts as node-link networks. Ideas in a concept map are represented as nodes and connected to other ideas/nodes through link labels." (Olusola O Adesope & John C Nesbit, "A Systematic Review of Research on Collaborative Learning with Concept Maps", 2010)

"A visual construct composed of encircled concepts (nodes) that are meaningfully inter-connected by descriptive concept links either directly, by branch-points (hierarchies), or indirectly by cross-links (comparisons). The construction of a concept map can serve as a tool for enhancing communication, either between an author and a student for a reading task, or between two or more students engaged in problem solving. (Dawndra Meers-Scott, "Teaching Critical Thinking and Team Based Concept Mapping", 2010)

"Are graphical ways of working with ideas and presenting information. They reveal patterns and relationships and help students to clarify their thinking, and to process, organize and prioritize. The visual representation of information through word webs or diagrams enables learners to see how the ideas are connected and understand how to group or organize information effectively." (Robert Z Zheng & Laura B Dahl, "Using Concept Maps to Enhance Students' Prior Knowledge in Complex Learning", 2010)

"Concept maps are hierarchical trees, in which concepts are connected with labelled, graphical links, most general at the top." (Alexandra Okada, "Eliciting Thinking Skills with Inquiry Maps in CLE", 2010)

"One powerful knowledge presentation format, devised by Novak, to visualize conceptual knowledge as graphs in which the nodes represent the concepts, and the links between the nodes are the relationships between these concepts." (Diana Pérez-Marín et al, "Adaptive Computer Assisted Assessment", 2010)

"A form of visualization showing relationships among concepts as arrows between labeled boxes, usually in a downward branching hierarchy." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A graphical depiction of relationships ideas, principals, and activities leading to one major theme." (Carol A Brown, "Using Logic Models for Program Planning in K20 Education", 2013)

"A diagram that presents the relationships between concepts." (Gwo-Jen Hwang, "Mobile Technology-Enhanced Learning", 2015)

"A graphical two-dimensional display of knowledge. Concepts, usually presented within boxes or circles, are connected by directed arcs that encode, as linking phrases, the relationships between the pairs of concepts." (Anna Ursyn, "Visualization as Communication with Graphic Representation", 2015)

"A graphical tool for representing knowledge structure in a form of a graph whose nodes represent concepts, while arcs between nodes correspond to interrelations between them." (Yigal Rosen & Maryam Mosharraf, "Evidence-Centered Concept Map in Computer-Based Assessment of Critical Thinking", 2016) 

"Is a directed graph that shows the relationship between the concepts. It is used to organize and structure knowledge." (Anal Acharya & Devadatta Sinha, "A Web-Based Collaborative Learning System Using Concept Maps: Architecture and Evaluation", 2016)

"A graphic depiction of brainstorming, which starts with a central concept and then includes all related ideas." (Carolyn W Hitchens et al, "Studying Abroad to Inform Teaching in a Diverse Society", 2017)

"A graphic visualization of the connections between ideas in which concepts (drawn as nodes or boxes) are linked by explanatory phrases (on arrows) to form a network of propositions that depict the quality of the mapper’s understanding" (Ian M Kinchin, "Pedagogic Frailty and the Ecology of Teaching at University: A Case of Conceptual Exaptation", 2019)

"A diagram in which related concepts are linked to each other." (Steven Courchesne &Stacy M Cohen, "Using Technology to Promote Student Ownership of Retrieval Practice", 2020)

Knowledge Management: Cognitive Map (Definitions)

"A cognitive map is a specific way of representing a person's assertions about some limited domain, such as a policy problem. It is designed to capture the structure of the person's causal assertions and to generate the consequences that follow front this structure." (Robert M Axelrod, "Structure of Decision: The cognitive maps of political elites", 1976)

"A cognitive map is the representation of thinking about a problem that follows from the process of mapping." (Colin Eden, "Analyzing cognitive maps to help structure issues or problems", 2002)

"A mental representation of a portion of the physical environment and the relative locations of points within it." (Andrew M Colman, "A Dictionary of Psychology" 3rd Ed, 2008)

"A mental model (or map) of the external environment which may be constructed following exploratory behaviour." (Michael Allaby, "A Dictionary of Zoology" 3rd Ed., 2009)

"An FCM [Fuzzy Cognitive Map] is a directed graph with concepts like policies, events etc. as nodes and causalities as edges. It represents causal relationship between concepts." (Florentin Smarandache &  W B Vasantha Kandasamy, "Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps", 2014)

"A conceptual tool that provides a representation of particular natural or social environments in the form of a model." (Evangelos C Papakitsos et al, "The Challenges of Work-Based Learning via Systemic Modelling in the European Union", 2020)

"A representation of the conceptualization that the subject constructs of the system in which he evolves. The set of cognitive representations that emerge make it possible to understand his actions, the links between the factors structuring the cognitive patterns dictating his behaviors." (Henda E Karray & Souhaila Kammoun, "Strategic Orientation of the Managers of a Tunisian Family Group Before and After the Revolution", 2020)

"A cognitive map is a type of mental representation which serves an individual to acquire, code, store, recall, and decode information about the relative locations and attributes of phenomena in their everyday or metaphorical spatial environment." (Wikipedia) [source]

Process Management: Process Map (Definitions)

"The Process Chart is a device for visualizing a process as a means of improving it. Every detail of a process is more or less affected by every other detail; therefore the entire process must be presented in such form that it can be visualized all at once before any changes are made in any of its subdivisions. In any subdivision of the process under examination, any changes made without due consideration of all the decisions and all the motions that precede and follow that subdivision will often be found unsuited to the ultimate plan of operation." (Frank B Gilbreth & Lillian M Gilbreth, "Process Charts", 1921) 

"A method used to examine the effectiveness of the approach currently used in completing a task." (Dale Furtwengler, "Ten Minute Guide to Performance Appraisals", 2000)

"A type of flowchart depicting the steps in a process, identifying its inputs outputs, and often assigning responsibility for each step and the key measures." (Clyde M Creveling, "Six Sigma for Technical Processes: An Overview for R Executives, Technical Leaders, and Engineering Managers", 2006)

"A type of flow chart depicting the steps in a process, its inputs/outputs, and often identification of responsibility for each step and the key measures." (Lynne Hambleton, "Treasure Chest of Six Sigma Growth Methods, Tools, and Best Practices", 2007)

"A kind of data flow diagram used in business process engineering to represent the tasks performed in an enterprise and the links between them." (David C Hay, "Data Model Patterns: A Metadata Map", 2010)

"A high-level flowchart diagram illustrating the various activities, tasks, decisions, and relationships of participant functions, departments, or groups associated with the execution of a process. It is used as a starting point to be critical of the efficiency and effectiveness of business process execution." (Carl F Lehmann, "Strategy and Business Process Management", 2012)

"A workflow diagram is a graphic depiction of the steps, sequence of steps, and flow control that constitute a process using standard symbols and conventions." (Meredith Zozus, "The Data Book: Collection and Management of Research Data", 2017)

Graphical Representation: Maps (Just the Quotes)

"Two important characteristics of maps should be noticed. A map is not the territory it represents, but, if correct, it has a similar structure to the territory, which accounts for its usefulness. If the map could be ideally correct, it would include, in a reduced scale, the map of the map; the map of the map, of the map [...]" (Alfred Korzybski, "Science and Sanity: An Introduction to Non-Aristotelian Systems and General Semantics", 1933)

"The first of the principles governing symbols is this: The symbol is NOT the thing symbolized; the word is NOT the thing; the map is NOT the territory it stands for." (Samuel I Hayakawa, "Language in Thought and Action", 1949)

"A fundamental value in the scientific outlook is concern with the best available map of reality. The scientist will always seek a description of events which enables him to predict most by assuming least. He thus already prefers a particular form of behavior. If moralities are systems of preferences, here is at least one point at which science cannot be said to be completely without preferences. Science prefers good maps." (Anatol Rapoport, "Science and the goals of man: a study in semantic orientation", 1950)

"No map contains all the information about the territory it represents. [...] Furthermore, the scale of the map makes a big difference. The smaller the scale the less features will be shown." (Anatol Rapoport, "Science and the goals of man: a study in semantic orientation", 1950)

"Good design looks right. It is simple (clear and uncomplicated). Good design is also elegant, and does not look contrived. A map should be aesthetically pleasing, thought provoking, and communicative."  (Arthur H Robinson, "Elements of Cartography", 1953)

"The design process involves a series of operations. In map design, it is convenient to break this sequence into three stages. In the first stage, you draw heavily on imagination and creativity. You think of various graphic possibilities, consider alternative ways." (Arthur H Robinson, "Elements of Cartography", 1953)

"The types of graphics used in operating a business fall into three main categories: diagrams, maps, and charts. Diagrams, such as organization diagrams, flow diagrams, and networks, are usually intended to graphically portray how an activity should be, or is being, accomplished, and who is responsible for that accomplishment. Maps such as route maps, location maps, and density maps, illustrate where an activity is, or should be, taking place, and what exists there. [...] Charts such as line charts, column charts, and surface charts, are normally constructed to show the businessman how much and when. Charts have the ability to graphically display the past, present, and anticipated future of an activity. They can be plotted so as to indicate the current direction that is being followed in relationship to what should be followed. They can indicate problems and potential problems, hopefully in time for constructive corrective action to be taken." (Robert D Carlsen & Donald L Vest, "Encyclopedia of Business Charts", 1977)

"Maps containing marks that indicate a variety of features at specific locations are easy to produce and often revealing for the reader. You can use dots, numbers, and shapes, with or without keys. The basic map must always be simple and devoid of unnecessary detail. There should be no ambiguity about what happens where." (Bruce Robertson, "How to Draw Charts & Diagrams", 1988)

"Maps used as charts do not need fine cartographic detail. Their purpose is to express ideas, explain relationships, or store data for consultation. Keep your maps simple. Edit out irrelevant detail. Without distortion, try to present the facts as the main feature of your map, which should serve only as a springboard for the idea you're trying to put across." (Bruce Robertson, "How to Draw Charts & Diagrams", 1988)

"The prevailing style of management must undergo transformation. A system cannot understand itself. The transformation requires a view from outside. The aim [...] is to provide an outside view - a lens - that I call a system of profound knowledge. It provides a map of theory by which to understand the organizations that we work in." (Dr. W. Edwards Deming, "The New Economics for Industry, Government, Education", 1994)

"Maps, due to their melding of scientific and artistic approaches, always involve complex interaction between the denotative and the connotative meanings of signs they contain." (Alan MacEachren, "How Maps Work: Representation, Visualization, and Design", 1995)

"The fact that map is a fuzzy and radial, rather than a precisely defined, category is important because what a viewer interprets a display to be will influence her expectations about the display and how she interacts with it." (Alan MacEachren, "How Maps Work: Representation, Visualization, and Design", 1995)

"The representational nature of maps, however, is often ignored - what we see when looking at a map is not the word, but an abstract representation that we find convenient to use in place of the world. When we build these abstract representations we are not revealing knowledge as much as are creating it." (Alan MacEachren, "How Maps Work: Representation, Visualization, and Design", 1995)

"Understanding how maps work and why maps work (or do not work) as representations in their own right and as prompts to further representations, and what it means for a map to work, are critical issues as we embark on a visual information age." (Alan MacEachren, "How Maps Work: Representation, Visualization, and Design", 1995)

"Eliciting and mapping the participant's mental models, while necessary, is far from sufficient [...] the result of the elicitation and mapping process is never more than a set of causal attributions, initial hypotheses about the structure of a system, which must then be tested. Simulation is the only practical way to test these models. The complexity of the cognitive maps produced in an elicitation workshop vastly exceeds our capacity to understand their implications. Qualitative maps are simply too ambiguous and too difficult to simulate mentally to provide much useful information on the adequacy of the model structure or guidance about the future development of the system or the effects of policies." (John D Sterman, "Learning in and about complex systems", Systems Thinking Vol. 3 2003)

"A road plan can show the exact location, elevation, and dimensions of any part of the structure. The map corresponds to the structure, but it's not the same as the structure. Software, on the other hand, is just a codification of the behaviors that the programmers and users want to take place. The map is the same as the structure. […] This means that software can only be described accurately at the level of individual instructions. […] A map or a blueprint for a piece of software must greatly simplify the representation in order to be comprehensible. But by doing so, it becomes inaccurate and ultimately incorrect. This is an important realization: any architecture, design, or diagram we create for software is essentially inadequate. If we represent every detail, then we're merely duplicating the software in another form, and we're wasting our time and effort." (George Stepanek, "Software Project Secrets: Why Software Projects Fail", 2005) 

"A map does not just chart, it unlocks and formulates meaning; it forms bridges between here and there, between disparate ideas that we did not know were previously connected." (Reif Larsen, "The Selected Works of T S Spivet", 2009)

"Graphics, charts, and maps aren’t just tools to be seen, but to be read and scrutinized. The first goal of an infographic is not to be beautiful just for the sake of eye appeal, but, above all, to be understandable first, and beautiful after that; or to be beautiful thanks to its exquisite functionality." (Alberto Cairo, "The Functional Art", 2011)

"The utility of mapping as a form of data visualization isn’t in accuracy or precision, but rather the map’s capacity to help us make and organize hypothesis about the world of ideas and things. hypothesis-making through the map isn’t strictly inductive or deductive, although it can use the thought process of either, but it is often based on general observations." (Winifred E Newman, "Data Visualization for Design Thinking: Applied Mapping", 2017)

"Maps also have the disadvantage that they consume the most powerful encoding channels in the visualization toolbox - position and size - on an aspect that is held constant. This leaves less effective encoding channels like color for showing the dimension of interest." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"Maps are a type of chart that can convey relationships about space and relationships between objects that we relate to in the real world. Their effectiveness as a communication medium is strongly influenced by a host of factors: the nature of spatial data, the form and structure of representation, their intended purpose, the experience of the audience, and the context in the time and space in which the map is viewed. In other words, maps are a ubiquitous representation of spatial information that we can understand and relate to." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

More quotes on 'Maps' on "The Web of Knowledge".

Graphical Representation: Heatmaps (Definitions)

"A chart where one set of values is represented by areas of rectangles, and other sets of values are represented by colors. [...] the size of a rectangle reflects its importance, and color conveys the speed of change." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A type of map presentation where the intensity of color for each polygon corresponds to the related analytical data. For example, low values in a range appear as blue (cold) and high values as red (hot)." (Microsoft, "SQL Server 2012 Glossary", 2012)

"A heat map is a graphical representation where the data values are mapped to color intensities. The name heat map refers to the popular color-encoding where high values are encoded to hotter colors such as reds and yellows and smaller values are encoded to greens and blues. However, a heat map can have any color encoding that is convenient." (Ira Greenberg et al, "Processing: Creative Coding and Generative Art in Processing 2", 2013)

"A color-coded matrix generated by stakeholders voting on risk level by color (e.g., red being highest)." (Robert F Smallwood, "Information Governance: Concepts, Strategies, and Best Practices", 2014)

"Heatmap is a visualization that displays the expression values of the features (genes, exons, etc.) using a color scale. Features are typically arranged in columns (samples) and rows (features) as in the original data matrix. Each feature-sample pair is represented with a small rectangle that is colored according to its expression. Often both samples and features are hierarchically clustered before constructing the heatmap, and clustering is represented with a tree on the left and top from the colored data matrix." (Eija Korpelainen et al, "RNA-seq Data Analysis: A Practical Approach", 2014)

"[Heatmap is] a special kind of tile map, which is a two-dimensional graphical representation of data having values displayed using colors instead of numbers, text, or markers (data  points). It provides an easy way to understand and analyze complex/huge data sets. It applies blurring of markers and shading (dark or light) on the basis of the total amount of overlap."  (Yuvraj Gupta, "Kibana Essentials", 2015)

"Heatmaps are two-dimensional graphical representations of data where the values of a variable are shown as colors." (Agnieszka Bojkon, "Informative or Misleading? Heatmaps Deconstructed", [in "Human-Computer Interaction: New Trends, 13th International Conference"] 2009) 

"A heat map is a graphical representation of a table of data. The individual values are arranged in a table/matrix and represented by colors. Use grayscale or gradient for coloring. Sorting of the variables changes the color pattern." (Kristen Sosulski, "Data Visualization Made Simple: Insights into Becoming Visual", 2019)

"A heat map is a thematic map in which areas are shaded or patterned in proportion to the measurement of the statistical variable being displayed on the map, such as population density or per capita income. Heat maps provide an easy way to visualize how a measurement varies across a geographic area or show the level of variability within a region." (Sankar N. Nair & E S Gopi,  "Deep Learning Techniques for Crime Hotspot Detection", 2020)

"A heatmap is a visualization where values contained in a matrix are represented as colors or color saturation. Heatmaps are great for visualizing multivariate data (data in which analysis is based on more than two variables per observation), where categorical variables are placed in the rows and columns and a numerical or categorical variable is represented as colors or color saturation." (Mario Döbler & Tim Großmann, "The Data Visualization Workshop", 2nd Ed., 2020)

"Heatmap is another representational way in which the frequencies of the various parameters of the data set is represented in different colors, much like an image captured by a thermal imaging camera in which the graph consists of varying temperatures and the temperatures are differentiated according to the colors." (Shreyans Pathak & Shashwat Pathak, "Data Visualization Techniques, Model and Taxonomy", 2020)

"A heatmap is a plot that shows the magnitude of a phenomenon as color in two dimensions. The color variation may be by hue or intensity." (Swapnil Saurav, "Python Apps on Visual Studio Code", 2024)

Graphical Representation: Data Flow Diagram (Definitions)

"A diagram that shows the data flows in an organization, including sources of data, where data are stored, and processes that transform data." (Jan L Harrington, "Relational Database Dessign: Clearly Explained" 2nd Ed., 2002)

"A diagram of the data flow from sources through processes and files to users. A source or user is represented by a square; a data file is represented by rectangles with missing righthand edges; a process is represented by a circle or rounded rectangle; and a data flow is represented by an arrow." (Jens Mende, "Data Flow Diagram Use to Plan Empirical Research Projects", 2009)

"A diagram used in functional analysis which specifies the functions of the system, the inputs/outputs from/to external (user) entities, and the data being retrieved from or updating data stores. There are well-defined rules for specifying correct DFDs, as well as for creating hierarchies of interrelated DFDs." (Peretz Shoval & Judith Kabeli, "Functional and Object-Oriented Methodology for Analysis and Design", 2009)

[Control Data Flow Graph (CDFG):] " Represents the control flow and the data dependencies in a program." (Alexander Dreweke et al, "Text Mining in Program Code", 2009)

"A graphic method for documenting the flow of data within an organization." (Jan L Harrington, "Relational Database Design and Implementation: Clearly explained" 3rd Ed., 2009)

"A graphic representation of the interactions between different processes in an organization in terms of data flow communications among them. This may be a physical data flow diagram that describes processes and flows in terms of the mechanisms involved, a logical data flow diagram that is without any representation of the mechansm, or an essential data flow diagram that is a logical data flow diagram organized in terms of the processes that respond to each external event." (David C Hay, "Data Model Patterns: A Metadata Map", 2010)

"Data-flow diagrams (DFDs) are system models that show a functional perspective where each transformation represents a single function or process. DFDs are used to show how data flows through a sequence of processing steps." (Ian Sommerville, "Software Engineering" 9th Ed., 2011)

"A model of the system that shows the system’s processes, the data that flow between them (hence the name), and the data stores used by the processes. The data flow diagram shows the system as a network of processes, and is thought to be the most easily recognized of all the analysis models." (James Robertson et al, "Complete Systems Analysis: The Workbook, the Textbook, the Answers", 2013)

"A picture of the movement of data between external entities and the processes and data stores within a system." (Jeffrey A Hoffer et al, "Modern Systems Analysis and Design" 7th Ed., 2014)

"A schematic indicating the direction of the movement of data" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"A Data Flow Diagram (DFD) is a graphical representation of the 'flow' of data through an information system, modeling its process aspects. Often it is a preliminary step used to create an overview of the system that can later be elaborated." (Henrikvon Scheel et al, "Process Concept Evolution", 2015)

"Data flow maps are tools that graphically represent the results of a comprehensive data assessment to illustrate what information comes into an organization, for what purposes that information is used, and who has access to that information." (James R Kalyvas & Michael R Overly, "Big Data: A Business and Legal Guide", 2015)

"A graphical representation of the logical or conceptual movement of data within an existing or planned system." (George Tillmann, "Usage-Driven Database Design: From Logical Data Modeling through Physical Schmea Definition", 2017)

"a visual depiction using standard symbols and conventions of the sources of, movement of, operations on, and storage of data." (Meredith Zozus, "The Data Book: Collection and Management of Research Data", 2017)

"A data-flow diagram is a way of representing a flow of data through a process or a system (usually an information system). The DFD also provides information about the outputs and inputs of each entity and the process itself." (Wikipedia) [source]

"A graphical representation of the sequence and possible changes of the state of data objects, where the state of an object is any of: creation, usage, or destruction." (IQBBA)

Graphical Representation: Mind Map (Definitions)

"A visual note-taking process that pares thoughts to key words and pictures illustrating the relationships among concepts." (Ruth C Clark & Chopeta Lyons, "Graphics for Learning", 2004)

"A mind map consists of a central concept which acts as a headline for the map and the branches that represent the aspects of the main concept. A mind map allows summarizing and decomposition of the key aspects of a complex problem or issue." (Hannu Kivijärvi et al, "A Support System for the Strategic Scenario Process", 2008) 

"A mind map is a diagram uses intuition to depict words, ideas or other items in branches around a central key word or idea." (Wan Ng & Ria Hanewald, "Concept Maps as a Tool for Promoting Online Collaborative Learning in Virtual Teams with Pre-Service Teachers", 2010)

[mind mapping:] "A process that brainstorms ideas, words, tasks or other elements and arranges them in groups around a central notion."  (Wan Ng & Ria Hanewald, "Concept Maps as a Tool for Promoting Online Collaborative Learning in Virtual Teams with Pre-Service Teachers", 2010)

[mind-mapping:] "A technique that uses multiple levels of detail for a texture. This technique selects from among the different sizes of an image available, or possibly combines the two nearest sized matches to produce the final fragments used for texturing." (Graham Sellers et al, "OpenGL SuperBible: Comprehensive Tutorial and Reference" 5th Ed., 2010)

"Refers to a technique for the graphical representation of information items, enabling visualization. A mindmap has a radial structure: it is constructed by starting from a central information item, around which other information items are organized like rays from a star, except that each ray can in turn be subdivided in a plurality of finer rays, and so on. The 'rays' are linear, going from an upstream point to downstream, more secondary points, and so on." (Humbert Lesca & Nicolas Lesca, "Weak Signals for Strategic Intelligence: Anticipation Tool for Managers", 2011)

"Powerful techniques you can utilize to increase your comprehension of written materials." (Jeffrey Magee, "The Managerial Leadership Bible", 2015)

[mind-mapping:] "A technique used to consolidate ideas created through individual brainstorming sessions into a single map to reflect commonality and differences in understanding and to generate new ideas." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK Guide)", 2017)

[mind mapping:] "A method to brainstorm thoughts while showing relationships of the parts to the whole." (Errick D Farmer et al, "Digital Course Redesign to Increase Student Engagement and Success", 2019)

"A diagram used to represent words, ideas, tasks, or other items linked to and arranged around a central keyword or idea. Mind maps are  used to generate, visualize, structure, and classify ideas, and as an aid in study, organization, problem solving, decision making, and writing." (Software Quality Assurance)

DBMS: Hash Joins (Definitions)

"A sophisticated join algorithm that builds an interim structure to derive result sets." (Microsoft Corporation, "SQL Server 7.0 System Administration Training Kit", 1999)

"A method for producing a joined table. Given two input tables Table1 and Table2, processing is as follows: (a) For each row in Table1, produce a hash. Assign the hash to a hash bucket. (b) For each row in Table2, produce a hash. Check if the hash is already in the hash bucket. If it is: there's a join. If it is not: there's no join." (Peter Gulutzan & Trudy Pelzer, "SQL Performance Tuning", 2002)

"An efficient method of searching two tables to be joined when they have very low selectivity (i.e., very few matching values). Common values are matched in fast memory, then the rest of the data record is obtained using hashing mechanisms to access the disk only once for each record." (Sam Lightstone et al, "Physical Database Design: The Database Professional’s Guide to Exploiting Indexes, Views, Storage, and More", 2007)

"A method for joining large data sets. The database uses the smaller of two data sets to build a hash table on the join key in memory. It then scans the larger data set, probing the hash table to find the joined rows." (Oracle, "Database SQL Tuning Guide Glossary", 2013)

"The hash join is based on a hash function that provides access to items in the joining data structure in constant time. A hash function maps arbitrary inputs to fixed length keys, even though the inputs might have variable lengths. The joining data structure for the hash join is a so-called hash map, which implements an associative array that maps keys to values." (Hasso Plattner, "A Course in In-Memory Data Management: The Inner Mechanics of In-Memory Databases" 2nd Ed., 2014)

 "A join in which the database uses the smaller of two tables or data sources to build a hash table in memory. The database scans the larger table, probing the hash table for the addresses of the matching rows in the smaller table." (Oracle, "Oracle Database Concepts")

Robert Grant - Collected Quotes

"A map by itself requires little explanation, but once data are superimposed, readers will probably need labels on the maps, and legends explaining encodings like the color of markers." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"A recurring theme in machine learning is combining predictions across multiple models. There are techniques called bagging and boosting which seek to tweak the data and fit many estimates to it. Averaging across these can give a better prediction than any one model on its own. But here a serious problem arises: it is then very hard to explain what the model is (often referred to as a 'black box'). It is now a mixture of many, perhaps a thousand or more, models." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"Any fool can fit a statistical model, given the data and some software. The real challenge is to decide whether it actually fits the data adequately. It might be the best that can be obtained, but still not good enough to use." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"Apart from the technical challenge of working with the data itself, visualization in big data is different because showing the individual observations is just not an option. But visualization is essential here: for analysis to work well, we have to be assured that patterns and errors in the data have been spotted and understood. That is only possible by visualization with big data, because nobody can look over the data in a table or spreadsheet." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"As a first principle, any visualization should convey its information quickly and easily, and with minimal scope for misunderstanding. Unnecessary visual clutter makes more work for the reader’s brain to do, slows down the understanding (at which point they may give up) and may even allow some incorrect interpretations to creep in." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"Cross-validation is a useful tool for finding optimal predictive models, and it also works well in visualization. The concept is simple: split the data at random into a 'training' and a 'test' set, fit the model to the training data, then see how well it predicts the test data. As the model gets more complex, it will always fit the training data better and better. It will also start off getting better results on the test data, but there comes a point where the test data predictions start going wrong." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"Dashboards are collections of several linked visualizations all in one place. The idea is very popular as part of business intelligence: having current data on activity summarized and presented all inone place. One danger of cramming a lot of disparate information into one place is that you will quickly hit information overload. Interactivity and small multiples are definitely worth considering as ways of simplifying the information a reader has to digest in a dashboard. As with so many other visualizations, layering the detail for different readers is valuable." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"Decision trees show the breakdown of the data by one variable then another in a very intuitive way, though they are generally just diagrams that don’t actually encode data visually." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"Estimates based on data are often uncertain. If the data were intended to tell us something about a wider population (like a poll of voting intentions before an election), or about the future, then we need to acknowledge that uncertainty. This is a double challenge for data visualization: it has to be calculated in some meaningful way and then shown on top of the data or statistics without making it all too cluttered." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"One very common problem in data visualization is that encoding numerical variables to area is incredibly popular, but readers can’t translate it back very well." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"The relevance to data visualization is that we are always conveying a message to some extent, and in the case of associations between variables, that message is sometimes a step removed from the data itself. If you are making visualizations, be careful not to impose your own interpretation too much when showing associations. If you are reading them, don’t assume that the message accompanying the data is as sound and scientifically based as the data themselves." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"The term 'infographics' is used for eye-catching diagrams which get a simple message across. They are very popular in advertising and can convey an impression of scientific, reliable information, but they are not the same thing as data visualization. An infographic will typically only convey a few numbers, and not use visual presentations to allow the reader to make comparisons of their own." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"There is often no one 'best' visualization, because it depends on context, what your audience already knows, how numerate or scientifically trained they are, what formats and conventions are regarded as standard in the particular field you’re working in, the medium you can use, and so on. It’s also partly scientific and partly artistic, so you get to express your own design style in it, which is what makes it so fascinating." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"With skewed data, quantiles will reflect the skew, while adding standard deviations assumes symmetry in the distribution and can be misleading." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"Random forests are essentially an ensemble of trees. They use many short trees, fitted to multiple samples of the data, and the predictions are averaged for each observation. This helps to get around a problem that trees, and many other machine learning techniques, are not guaranteed to find optimal models, in the way that linear regression is. They do a very challenging job of fitting non-linear predictions over many variables, even sometimes when there are more variables than there are observations. To do that, they have to employ 'greedy algorithms', which find a reasonably good model but not necessarily the very best model possible." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)