| Course | BU 454 Data Visualization |
|---|---|
| Unit | Unit 1 |
| Paper type | Encoding principles exercise |
| Length | About 910 words, 6 pages |
| Format | APA 7 student paper |
| School | Herzing University |
| Program | BS in Business Analytics |
| Updated | September 2026 |
Free sample paper for BU 454 Unit 1
Position Beats Angle, Length Beats Shade: An Encoding Principles Exercise Drawing Three Comparisons From a Composite Montana Ski Area's Season Three Ways Each
Student Name
Business, Herzing University
BU 454: Data Visualization
Instructor Name
January 14, 2026
Position Beats Angle, Length Beats Shade: An Encoding Principles Exercise Drawing Three Comparisons From a Composite Montana Ski Area's Season Three Ways Each
A Chart as a Set of Encoding Decisions
Bearpaw Ridge, a composite small ski area in western Montana created for this course, recorded 128,400 skier visits over 116 operating days in the 2024 to 2025 season, across nine lifts and five ticket types. Its general manager will be asked three questions at the owners' spring meeting: which lifts carry the most skiers, what share of visits each ticket type accounts for, and how much busier weekends and holidays are than weekdays. This exercise treats each question as a comparison the reader must make and draws it with two or three candidate encodings, that is, visual channels such as position, length, angle, area or color intensity. It then judges which channel lets the eye answer the question quickly and accurately.
The judgments rest on evidence about perception. Cleveland and McGill (1984) found that viewers judge quantities most accurately from where points sit on a shared axis, next from lengths, with angles, areas and color shading judged progressively less accurately. Mackinlay (1986) built that ranking into a method for choosing encodings automatically, and Munzner (2014) extends it to a general rule: the most important attribute should get the most effective channel.
Comparison 1: Rides by Lift
Question: which of the nine lifts carry the most riders per day, and by how much? The data are average daily rides, from 2,850 on the Summit Quad down to 310 on the Upper Ridge surface lift. Three encodings were drawn from exactly the same numbers, with the same title and the same source line, so that only the channel differed.
| Encoding | Channel | What a reader could do | Verdict |
|---|---|---|---|
| Pie chart, one slice per lift | Angle and area | See that Summit Quad is largest; could not rank the middle five lifts or tell Meadow Double (1,420) from Carpet (1,180) | Rejected |
| Heat strip, one shaded cell per lift | Color intensity | Pick out the darkest and lightest; misjudged the middle lifts' order in informal tests with three staff | Rejected |
| Horizontal bars sorted by value | Length along a common baseline | Read the order at once and see that Summit Quad carries about nine times the Upper Ridge lift | Chosen |
The bar chart wins because both the ranking and the ratio are what the question asks, and because sorting by value rather than by lift name puts the answer in reading order from top to bottom; length on a shared baseline supports both. The pie is not wrong about the data, but it asks the eye to compare nine angles, several within a few degrees of each other. The heat strip compresses magnitude into shades that the eye perceives unevenly, so a cell twice as dark does not look twice as much.
Comparison 2: Visits by Ticket Type
Question: what share of visits came from each ticket type? Season passes accounted for 46 percent, online day tickets 28, window day tickets 17, lesson packages 6 and complimentary tickets 3. Part-to-whole questions are the classic case for a pie, so this comparison tests whether the pie earns its place.
A pie with five slices let readers see that season passes are nearly half, since a slice close to a half circle is easy to recognize, but they could not tell whether online tickets were a quarter or a third. A single stacked bar showed the parts in sequence along one length, which made the first segment easy to read and the later segments harder, because they do not share a baseline. A sorted bar chart of the five percentages, with a note that they sum to 100, let readers read each share and compare them directly.
The sorted bars are chosen for the owners' report. The pie is not rejected in principle: with two or three parts, or when the only question is whether one part is more than half, it communicates well. Here the owners want to compare online and window sales, a difference of 11 points that position shows clearly and angle does not.
Comparison 3: Weekdays, Weekends and Holidays
Question: how much busier are weekends and holidays? Average visits were 760 on weekdays, 1,840 on weekend days and 2,450 on holiday days. Three encodings were tried: grouped vertical bars, a dot plot with the three averages on one horizontal scale, and a bubble chart sized by visits.
The bubble chart failed quickly. Readers judge circle area poorly, and the holiday bubble, about three times the weekday value, looked less than twice as large to the staff who saw it. Grouped bars and the dot plot both put values on a common scale and read well. The dot plot is chosen because it uses less ink for the same information and leaves room to add a fourth category, storm days, next season without redrawing.
What the Exercise Shows
Across all three comparisons, the encodings that won put the values that matter on position or length along a shared scale, and the ones that lost relied on angle, area or shade. The program used to draw the charts matters less than these choices; the same decisions can be made in a spreadsheet, a charting library or a business intelligence tool. Munzner (2014) warns that an effective channel spent on an unimportant attribute is wasted, so in each chart the strongest channel carries the comparison the question asks about, and color is kept for a secondary purpose, highlighting the Summit Quad. Bearpaw Ridge, its lifts and its season totals are invented for the course. How the charts were tested is worth recording too: each candidate was shown to three staff members who had not seen the data, with the question printed above it, and the time they took and any wrong answers were noted. The test was informal and small, but it turned the perception research into something observed at this ski area, and in every case it agreed with the ranking of channels.
References
Cleveland, W. S., & McGill, R. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79(387), 531-554. https://doi.org/10.1080/01621459.1984.10478080
Mackinlay, J. (1986). Automating the design of graphical presentations of relational information. ACM Transactions on Graphics, 5(2), 110-141. https://doi.org/10.1145/22949.22950
Munzner, T. (2014). Visualization analysis and design. CRC Press.
Reading the BU 454 Unit 1 instructions
The first BU 454 unit asks students to compare visual encodings for the same data. Typical requirements include choosing several comparisons a reader might need to make, drawing each with more than one channel, such as position, length, angle, area or color, and judging which works best with reference to research on graphical perception. Many instructors ask whether the charting program matters and whether pie charts are always wrong; the answer to both is that the decision matters more than the tool, and pies suit only a few cases. Using a real workplace dashboard is discouraged. Submissions usually combine small charts or descriptions with a short APA 7 analysis for each comparison.
How this BU 454 Unit 1 example is built
The sample opens with the ski area's season and three questions the owners will ask, then explains the perception research behind the judgments. The first comparison tests a pie, a heat strip and sorted bars for rides by lift in a table of channels and verdicts. The second tests whether the pie earns its place for ticket-type shares and explains when a pie would work. The third rejects a bubble chart for weekday and weekend visits and prefers a dot plot. The final section draws the common lesson, describes how three staff members tested each candidate, and notes that the drawing program matters less than the choices themselves.
Where the points sit in the BU 454 Unit 1 rubric
Herzing's rubric for the encoding exercise usually rewards clear comparisons, several genuine alternatives, judgments grounded in perception research, and a coherent summary, along with presentation and APA. Comparisons earn full credit when each states the question the reader must answer. Alternatives score highest when they differ in channel, not merely in styling. Judgments are marked on whether they cite evidence, such as the ranking of position, length, angle and area, and apply it to the specific data. The summary rewards a general principle drawn from the cases. Instructors also credit students who test charts with real readers, even informally, since that turns research findings into observed results for the data in hand.
BU 454 Unit 1 help before you submit
Encoding exercises often lose points by comparing charts that differ only in color or style rather than in the visual channel carrying the data. Another common problem is rejecting pie charts by rule without explaining when they can work. Students frequently judge charts by appearance rather than by how accurately a reader can answer the question. Some forget to sort bars, which weakens the case for length. Others cite perception research without applying it to their own data. Bubble and area charts are often chosen for visual interest despite being hard to read. Finally, reproducing a dashboard from work can expose internal figures; the supplied or composite data demonstrate the same judgment safely.
Get a BU 454 Unit 1 example written to your instructions
Attach the Unit 1 instructions and the rubric posted in your BU 454 classroom, along with any data the assignment supplies. We write a custom example that states each comparison first, draws the same numbers through competing channels, attributes any accuracy claim to its reading and rejects one encoding out loud. First custom sample free, back in 24 to 48 hours.
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BU 454 Unit 1 questions, answered
Does it matter which program I draw the charts in?
Sections differ and the classroom decides, so follow whatever your instructions name. What the criterion actually reads is the encoding decision and whether the drawing supports it: legible labels, a scale a reader can check, and a size that survives being viewed in the submitted document. A neatly drawn spreadsheet chart and output from a statistics package score the same when the reasoning is there.
Is a pie chart simply the wrong answer here?
Not simply, and writing that it always is will cost you the item. A part to whole reading with a handful of slices is something the shape genuinely supports. What it will not carry is a fine ranking, because two slices of similar size cannot be ordered by eye. Say which reading you are asking for, then defend the choice against that.
Can I use a dashboard I built at work as an example?
No, and a screenshot is enough to make it a problem. Anything you built inside an internal reporting tool carries the employer's figures, its naming and often its customers, and a graded submission leaves the company. Build your items from the data the assignment supplies or from figures you construct and label as case material. Your working experience will show in the judgments, which is the part that scores.
Where can I find a free BU 454 Unit 1 sample paper?
This page offers the complete BU 454 Unit 1 encoding principles exercise at no cost, with all three comparisons, the verdict tables and margin notes. Have a different data set? Share the assignment and a matching exercise is written free, usually inside two days.
Is a pie chart always wrong in BU 454 Unit 1?
No. A pie works for two or three parts or for showing that one part is more than half. The sample rejects it for nine lifts and five ticket types because readers could not compare the middle slices accurately.