📊Graphical Representation: Graphics We Live By (Part IV: Area Charts in MS Excel)

Graphical Representation

An area chart or area graph (see A) is a graphical representation of quantitative data based on a line chart for which the areas between axis and the lines of the series are commonly emphasized with colors, textures, or hatchings (Wikipedia). It resembles a combination between line and bar charts. Each data series results in the formation of a region (aka area), allowing thus to identify the overlapping and do comparisons between the lines within the same visual display. This approach works usually well for two or three data series if the lines don't overlap, though if more data series are added to the chart, the higher are the chances for lines to overlap or for one area to be covered by another (see B). This can easily become more than the chart can handle, even if the data series can be filtered dynamically.

Area Charts

Stacked area charts are a variation of area charts in which the areas are stacked, much like stacked bar charts (see C). Research papers abound with such charts, probably because they allow to stack together multiple data series within a small area, reflecting thus the many variables involved. Such charts allow to track individual as well as intermediary and total aggregated trends.

Stacked Area Charts

Unfortunately, besides the fact that some areas are barely distinguishable or that distant areas can't be compared (especially when one area in between has strong fluctuations), the lack of ticks and/or gridlines (see D) makes it difficult to interpret such charts. Moreover, when the lines are smoothed, it becomes even more difficult to identify the actual points. To address this it makes sense to use markers for data points to show that one works with discrete and not continuous points (see further paragraphs).

In general, it's recommended to reduce the number of data series to 3-5. For example, one can split the data series into 2-3 groups or categories based on series' characteristics (e.g. concentrate on the high values in one chart, respectively the low values in another, or group the low values under an "others" category) which would allow to make better comparisons.

Being able to sort the time series on their average value or other criteria (e.g. showing the areas with minimal variations first) can improve the readability of such charts.

Moreover, areas under curves can easily hide missing data (see F) and occasionally negative values (which is the case of the 8th example), or distort the rate of change when the charts are wider than needed (compare F with C). 

Area Charts Variations

Area charts seem to encode a dimension as area, though that's not necessarily the case. It seems natural to display time series of different granularities (day, month, quarter, year), though one needs to be careful about one important aspect! On a time scale, the more one moves away from the day to weeks and months as time units, the bigger the distance between points is. In the end, all the points in a series are discrete points (not continuous), though the bigger the distance, the more category-like these series become (compare F with C, the charts have the same width).

Using the area under the curve as dimension makes sense when there's continuity or the discrete points are close enough to each other to resemble continuity. Thus, area charts are useful when the number of points is high (and the distance between them becomes neglectable), e.g. showing daily values within a year or the months over several years. 

According to [2], [3] and several other sources, using the area to encode quantitative information is a poor graphical method and this applies to pie charts and area charts altogether. By contrast, for a bar chart (see G) one has either height or width to use for comparisons while the points are always as bars delimited. Scatter plots (see H), even if they might miss the time dimension, they better reflect the dispersion of the points along the lines delimited by encoding the color (compare H with E). 

Alternatives for Area Charts

The more category-like and the fewer data points the data series have, the higher the chances for other graphical representation tools to be able to better represent the data. For example, year or even quarter-based data can be better visualized with Sankey charts (unfortunately, not available as standard Excel visual yet).

Conversely, there are situations in which the area chart isn't supposed to convey specific values but to get a feeling of areas' shape, or its simplicity is more appropriate, situations in which area charts do a good job. In the end, a graphical representation's utility is linked to a chart's purpose (and audience, of course). 

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References:
[1] Wikipedia (2023) Area charts (link)
[2] William S Cleveland (1993) Visualizing Data
[3] Robert L Harris (1996) Information Graphics: A Comprehensive Illustrated Reference

🔭Data Science: Encoding/Decoding (Just the Quotes)

"In comparison with Predicate Calculus encoding is of factual knowledge, semantic nets seem more natural and understandable. This is due to the one-to-one correspondence between nodes and the concepts they denote, to the clustering about a particular node of propositions about a particular thing, and to the visual immediacy of 'interrelationships' between concepts, i.e., their connections via sequences of propositional links." (Lenhart K Schubert, "Extending the Expressive Power of Semantic Networks", Artificial Intelligence 7, 1976)

"The digital-computer field defined computers as machines that manipulated numbers. The great thing was, adherents said, that everything could be encoded into numbers, even instructions. In contrast, scientists in AI [artificial intelligence] saw computers as machines that manipulated symbols. The great thing was, they said, that everything could be encoded into symbols, even numbers." (Allen Newell, "Intellectual Issues in the History of Artificial Intelligence", 1983)

"In order to be easily understood, a display of information must have a logical structure which is appropriate for the user's knowledge and needs, and this structure must be clearly represented visually. In order to indicate structure, it is necessary to be able to emphasize, divide and relate items of information. Visual emphasis can be used to indicate a hierarchical relationship between items of information, as in the case of systems of headings and subheadings for example. Visual separation of items can be used to indicate that they are different in kind or are unrelated functionally, and similarly a visual relationship between items will imply that they are of a similar kind or bear some functional relation to one another. This kind of visual 'coding' helps the reader to appreciate the extent and nature of the relationship between items of information, and to adopt an appropriate scanning strategy." (Linda Reynolds & Doig Simmonds, "Presentation of Data in Science" 4th Ed, 1984)

"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)

"Good engineering is not a matter of creativity or centering or grounding or inspiration or lateral thinking, as useful as those might be, but of decoding the clever, even witty, messages the solution space carves on the corpses of the ideas in which you believed with all your heart, and then building the road to the next message." (Fred Hapgood, "Up the infinite Corridor: MIT and the Technical Imagination", 1993) 

"The unit of coding is the most basic segment, or element, of the raw data or information that can be assessed in a meaningful way regarding the phenomenon." (Richard Boyatzis, "Transforming qualitative information", 1998)

"The acquisition of information is a flow from noise to order - a process converting entropy to redundancy. During this process, the amount of information decreases but is compensated by constant re-coding. In the recoding the amount of information per unit increases by means of a new symbol which represents the total amount of the old. The maturing thus implies information condensation. Simultaneously, the redundance decreases, which render the information more difficult to interpret." (Lars Skyttner, "General Systems Theory: Ideas and Applications", 2001)

"Knowledge is encoded in models. Models are synthetic sets of rules, and pictures, and algorithms providing us with useful representations of the world of our perceptions and of their patterns." (Didier Sornette, "Why Stock Markets Crash - Critical Events in Complex Systems", 2003) 

"By giving numbers a proper shape, by visually encoding them, the graphic has saved you time and energy that you would otherwise waste if you had to use a table that was not designed to aid your mind." (Alberto Cairo, "The Functional Art", 2011) 

"Swarm intelligence (SI) is a branch of computational intelligence that discusses the collective behavior emerging within self-organizing societies of agents. SI was inspired by the observation of the collective behavior in societies in nature such as the movement of birds and fish. The collective behavior of such ecosystems, and their artificial counterpart of SI, is not encoded within the set of rules that determines the movement of each isolated agent, but it emerges through the interaction of multiple agents." (Maximos A Kaliakatsos-Papakostas et al, "Intelligent Music Composition", 2013)

"Bayesian networks provide a more flexible representation for encoding the conditional independence assumptions between the features in a domain. Ideally, the topology of a network should reflect the causal relationships between the entities in a domain. Properly constructed Bayesian networks are relatively powerful models that can capture the interactions between descriptive features in determining a prediction." (John D Kelleher et al, "Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, worked examples, and case studies", 2015)

"Encoding is called redundant when different visual channels are used to represent the same information. Redundant encoding is an efficient trick that helps to understand information from diagrams faster, easier, and more accurately. […] To decode information easier, align it with the reality in perspective of both the physical world and cultural conventions. Some things have particular colors, are larger or heavier than other, or are associated with the specific place. If your encoding is not compatible with these properties, readers may wonder why things do not look like they are expected to. Consequently, their auditory is forced to spend extra efforts decoding." (Vasily Pantyukhin, "Principles of Design Diagramming", 2015)

"To keep accuracy and efficiency of your diagrams appealing to a potential audience, explicitly describe the encoding principles we used. Titles, labels, and legends are the most common ways to define the meaning of the diagram and its elements." (Vasily Pantyukhin, "Principles of Design Diagramming", 2015)

"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)

"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)

"[…] 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)

"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)

"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)

📉Graphical Representation: Decoding (Just the Quotes

"Dot charts are suggested as replacements for bar charts. The replacements allow more effective visual decoding of the quantitative information and can be used for a wider variety of data sets." (William S. Cleveland, "Graphical Methods for Data Presentation: Full Scale Breaks, Dot Charts, and Multibased Logging", The American Statistician Vol. 38" (4) 1984)

"The bar of a bar chart has two aspects that can be used to visually decode quantitative information-size" (length and area) and the relative position of the end of the bar along the common scale. The changing sizes of the bars is an important and imposing visual factor; thus it is important that size encode something meaningful. The sizes of bars encode the magnitudes of deviations from the baseline. If the deviations have no important interpretation, the changing sizes are wasted energy and even have the potential to mislead." (William S. Cleveland, "Graphical Methods for Data Presentation: Full Scale Breaks, Dot Charts, and Multibased Logging", The American Statistician Vol. 38" (4) 1984) 

"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)

"When a graph is constructed, quantitative and categorical information is encoded, chiefly through position, size, symbols, and color. When a person looks at a graph, the information is visually decoded by the person's visual system. A graphical method is successful only if the decoding process is effective. No matter how clever and how technologically impressive the encoding, it is a failure if the decoding process is a failure. Informed decisions about how to encode data can be achieved only through an understanding of the visual decoding process, which is called graphical perception." (William S Cleveland, "The Elements of Graphing Data", 1985)

"A coordinate is a number or value used to locate a point with respect to a reference point, line, or plane. Generally the reference is zero. […] The major function of coordinates is to provide a method for encoding information on charts, graphs, and maps in such a way that viewers can accurately decode the information after the graph or map has been generated. " (Robert L Harris, "Information Graphics: A Comprehensive Illustrated Reference", 1996) 

"Distance and detection also play a role in our ability to decode information from graphs. The closer together objects are, the easier it is to judge attributes that compare them. As distance between objects increases, accuracy of judgment decreases. It is certainly easier to judge the difference in lengths of two bars if they are next to one another than if they are pages apart." (Naomi B Robbins, "Creating More effective Graphs", 2005) 

"One graph is more effective than another if its quantitative information can be decoded more quickly or more easily by most observers. […] This definition of effectiveness assumes that the reason we draw graphs is to communicate information - but there are actually many other reasons to draw graphs." (Naomi B Robbins, "Creating More effective Graphs", 2005)

"[...] visual art, primarily serves the relationship between the designer and the data. [...] it often entails unidirectional encoding of information, meaning that the reader may not be able to decode the visual presentation to understand the underlying information. [...] visual art merely translates the data into a visual form. The designer may intend only to condense it, translate it into a new medium, or make it beautiful; she may not intend for the reader to be able to extract anything from it other than enjoyment." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

📉Graphical Representation: Encoding (Just the Quotes)

"The bar of a bar chart has two aspects that can be used to visually decode quantitative information-size" (length and area) and the relative position of the end of the bar along the common scale. The changing sizes of the bars is an important and imposing visual factor; thus it is important that size encode something meaningful. The sizes of bars encode the magnitudes of deviations from the baseline. If the deviations have no important interpretation, the changing sizes are wasted energy and even have the potential to mislead." (William S. Cleveland, "Graphical Methods for Data Presentation: Full Scale Breaks, Dot Charts, and Multibased Logging", The American Statistician Vol. 38" (4) 1984) 

"When a graph is constructed, quantitative and categorical information is encoded, chiefly through position, size, symbols, and color. When a person looks at a graph, the information is visually decoded by the person's visual system. A graphical method is successful only if the decoding process is effective. No matter how clever and how technologically impressive the encoding, it is a failure if the decoding process is a failure. Informed decisions about how to encode data can be achieved only through an understanding of the visual decoding process, which is called graphical perception." (William S Cleveland, "The Elements of Graphing Data", 1985)

"Using area to encode quantitative information is a poor graphical method. Effects that can be readily perceived in other visualizations are often lost in an encoding by area." (William S Cleveland, "Visualizing Data", 1993)

"A coordinate is a number or value used to locate a point with respect to a reference point, line, or plane. Generally the reference is zero. […] The major function of coordinates is to provide a method for encoding information on charts, graphs, and maps in such a way that viewers can accurately decode the information after the graph or map has been generated. " (Robert L Harris, "Information Graphics: A Comprehensive Illustrated Reference", 1996) 

"[...] the form of a technological object must depend on the tasks it should help with. This is one of the most important principles to remember when dealing with infographics and visualizations: The form should be constrained by the functions of your presentation. There may be more than one form a data set can adopt so that readers can perform operations with it and extract meanings, but the data cannot adopt any form. Choosing visual shapes to encode information should not be based on aesthetics and personal tastes alone." (Alberto Cairo, "The Functional Art", 2011)

"Bear in mind is that the use of color doesn’t always help. Use it sparingly and with a specific purpose in mind. Remember that the reader’s brain is looking for patterns, and will expect both recurrence itself and the absence of expected recurrence to carry meaning. If you’re using color to differentiate categorical data, then you need to let the reader know what the categories are. If the dimension of data you’re encoding isn’t significant enough to your message to be labeled or explained in some way - or if there is no dimension to the data underlying your use of difference colors - then you should limit your use so as not to confuse the reader." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

"By giving numbers a proper shape, by visually encoding them, the graphic has saved you time and energy that you would otherwise waste if you had to use a table that was not designed to aid your mind." (Alberto Cairo, "The Functional Art", 2011)

"To keep accuracy and efficiency of your diagrams appealing to a potential audience, explicitly describe the encoding principles we used. Titles, labels, and legends are the most common ways to define the meaning of the diagram and its elements." (Vasily Pantyukhin, "Principles of Design Diagramming", 2015)

"The first and most important functional quality of color is its suitability to the task. For example, color selection differs depending on whether you want to encode either a categorical variable or a variable with a continuous range of values. The second functional quality of color is stimuli intensity. Pure primary colors and pastel colors have different intensity levels, which allow us to establish various levels of chart reading and evaluate the stimulus intensity of each object on the chart. The final functional quality of color is, in a broad sense, its symbolism." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)

"We must not rush to conclude that we should always select the encoding that ensures a maximum degree of precision, which in practice would result in the exclusive use of dot charts, since those represent the example of 'position in a common scale'. "(Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)

"[…] 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)

"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)

"Too many simultaneous encodings will be overwhelming to the reader; colors must be easily distinguishable, and of a small enough number that the reader can interpret them. " (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"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)

"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)

"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)

"[...] to support a conversation, charts need to provide cohesive and relevant responses to a user's intent. Sometimes the interface needs to respond by changing the visual encoding of existing charts, while in other cases, it is necessary to create a new chart to support the analytical conversation. In addition to appropriate visualization responses, it is critical to help the user understand how the system has interpreted their intent by producing appropriate feedback and allowing them to clarify if necessary." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"A semantic approach to visualization focuses on the interplay between charts, not just the selection of charts themselves. The approach unites the structural content of charts with the context and knowledge of those interacting with the composition. It avoids undue and excessive repetition by instead using referential devices, such as filtering or providing detail-on-demand. A cohesive analytical conversation also builds guardrails to keep users from derailing from the conversation or finding themselves lost without context. Functional aesthetics around color, sequence, style, use of space, alignment, framing, and other visual encodings can affect how users follow the script." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

Charts abstract information. They make it easier to see patterns at a distance, compare, and extrapolate. Icon encodings are graphical elements that are often used to visually represent the semantic meaning of marks for categorical data. Assigning meaningful icons to display elements helps the user perceive and interpret the visualization easier." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)