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FIT3179 Data Visualisation

Test 1 Sample Questions

Below are questions from tests of previous years. Test 1 of this year will not include questions consisting of text only, such as the first three questions below. However, these questions are still useful to understand the tested knowledge.

Some sample questions are multiple choice questions. Test 1 of this year will onlyinclude questions with a single correct answer.

The sample questions mainly focus on marks, channels, dataset types, attribute types and  simple table idioms. However, you should also expect questions about data-ink ratio, chart junk, lying visualisation, storytelling, colour, visual hierarchy, typography, layout, as well as  advanced idioms for table datasets, and idioms for networks and trees.

Answers to the sample questions can be found at the end of this document.

General concepts

1.   Q: Which of the following terms is equivalent to marks in data visualisation?

A) Interaction primitives

B) Data semantic

C) Position and location

D) Colour channels

E) Geometry primitives

2.   Q: Which of the following terms is equivalent to visual channels in data visualisation?

A) Colour channels

B) Data semantic

C) Position and location

D) Retinal variables

E) Geometry primitives

3.   Which of the following terms is equivalent to visual channels in data visualisation?

A) Colour channels

B) Hue, saturation and brightness

C) Position and location

D) Visual variables

E) Point, line, area

Colour channels

4.   Select the main changes in the following colour scale

A) Colour hue

B) Colour saturation

C) Colour luminance

5.   What is the colour channel used in the following visualisation? (hint: check the legend on the right side of the visualisation).

A) Colour hue

B) Colour saturation

C) Colour luminance

Attribute type of a given attribute

6.   Let’s consider a person’s bloodtype (A, AB, B, O, etc.) as an attribute of a medical dataset. This attribute is best described as which one of the following?

A) categorical attribute

B) ordinal attribute

C) quantitative attribute

D) sequential attribute

E) diverging attribute

7.   Let’s think about temperature measurements in Celsius degrees as an attribute of a city dataset. This attribute is best described as which one of the following.

A) categorical attribute

B) ordinal attribute

C) quantitative attribute

D) sequential attribute

E) qualitative attribute

8.   Let’s consider the price range (“below 50 dollars”, “50 to 100 dollars”, “100 to 200 dollars”, “above 200 dollars”) as an attribute of a clothes dataset. This attribute is best described as which one of the following?

A) categorical attribute

B) ordinal attribute

C) quantitative attribute

D) nominal attribute

E) diverging attribute

Given a dataset, select all attribute types.

The table below presents ten rows of a Victorian real estate dataset. The attributes include:

1) latitude

2) longitude

3) price of the property

4) type: house, apartment, townhouse, unit, etc.

5) bedroom: 1, 2, 3, 4, or 5+   (“5+” means 5 or more bedrooms)

6) travel_time_to_MC: Travel time to Melbourne Central by train (in minutes).

7) dist_to_supermarket: Distance to the nearest supermarket (in meters)

8) Suburb

9) Postcode

9. Please select all the categorical (nominal) attributes

Note: Please select all correct answers.

A) lat

B) lng

C) Price

D) Type

E) Bedroom

F) Travel_time_to_MC

G) Dist_to_supermarket

H) Suburb

I) Postcode

10. Select all the ordinal attribute(s):

Note: Please select all correct answers.

A) lat

B) lng

C) Price

D) Type

E) Bedroom

F) Travel_time_to_MC

G) Dist_to_supermarket

H) Suburb

I) Postcode

11. Select all the quantitative attributes.

Note: Please select all correct answers.

A) Price

B) Type

C) Bedroom

D) Travel_time_to_MC

E) Dist_to_supermarket

F) Suburb

G) Postcode

Attribute types vs visual channels

12. Which one of the following attributes is NOT suitable to be encoded with the LENGTH visual channel?

A) The height attribute from a student dataset

B) The age attribute from a customer dataset

C) The nationality attribute from a student dataset

D) The land size attribute from a real estate dataset

13. Which one of the following attributes is NOT suitable to be presented using the COLOUR SATURATION visual channel?

A) The height attribute from a student dataset

B) The citizenship attribute from an employee dataset

C) The price attribute from a property dataset

D) The age attribute from a customer dataset

Channel ranking

14. Let’s think about visualising the property type attribute (house, unit, apartment, etc.) in a real estate dataset. Select the most effective visual channel from below to encode this attribute.

A) Length

B) Shape

C) Area

D) Colour hue

E) Colour saturation

15. Let’s think about visualising a “birth rate” attribute in a country dataset. Select the most effective visual channel from below to encode this attribute.

A) Length

B) Shape

C) Area

D) Colour luminance

E) Colour saturation

16. In visualisation design, “the most important attributes should be encoded with the most effective visual channels in order to be most noticeable, and then decreasingly important attributes can be matched with less effective channels” (unit textbook by Tamara Munzner, 2014, p 101).

Suppose that we use a scatter plot (or bubble chart) to visualise the covid-19 dataset below. Each point represents a country; the colour hue is used to represent “Continent”. The importance ranks of the attributes that we would like to visualise are: “Active (cases)” > “Deaths” > “Confirmed (cases)”.

Which of the following visualisation designs is the most effective?

A) Use x-axis to represent “Active cases”, y-axis to represent “Confirmed cases”, size (area) to represent “Deaths”

B) Use x-axis to represent “Confirmed cases”, y-axis to represent “Active cases”, size (area) to represent “Deaths”

C) Use x-axis to represent “Confirmed cases”, y-axis to represent “Deaths”, size (area) to represent “Active cases”

D) Use x-axis to represent “Active cases”, y-axis to represent “Deaths”, size (area) to represent “Confirmed cases”

Idiom vs attribute types

The table below presents ten rows of a Victorian real estate dataset. The attributes include:

1) latitude

2) longitude

3) price of the property

4) type: house, apartment, townhouse, unit, etc.

5) bedroom: 1, 2, 3, 4, or 5+   (“5+” means 5 or more bedrooms)

6) travel_time_to_MC: Travel time to Melbourne Central by train (in minutes).

7) dist_to_supermarket: Distance to the nearest supermarket (in meters)

8) Suburb

9) Postcode

17. Select a pair of attributes that is suitable to be visualised with a scatter plot.

A) Price and Type

B) Price and Bedroom

C) Price and Dist_to_supermarket

D) Price and Suburb

E) Price and Postcode

18. Select all the attributes that are suitable to be visualisedin a scatterplot matrix.

Note: Please select all correct answers.

A) price

B) type

C) travel_time_to_MC

D) dist_to_supermarket

E) Suburb

F) Postcode

19. Assume we visualise all the properties as dots on a map based on the latitude and longitude attributes. Select all the magnitude channels that are suitable to encode the price attribute.

Note: Please select all correct answers.

A) Area

B) Colour saturation

C) Colour hue

D) Shape

E) Colour luminance

F) motion

Visual channels (easy)

20. Consider the following visualisation. What is the visual channel used to represent the Accessibility Remoteness Index Australia (ARIA+) (2006)? Hint: check the legend on the left-bottom corner.

A) Area

B) Size

C) Position

D) Colour hue

E) Colour saturation

F) Colour luminance

Visual Channels (difficult)

21. The horizon graph idiom is a variation of the line chart idiom, which can visualise time series data with much less space than line charts. The following graphs (Steps 1-4) present how a horizon graph is created.

Consider the final visualisation (Step 4) of this particular horizon graph. Select all the channels used in this visualisation.

Note: Please select all correct answers.

A) Length

B) Position

C) Area

D) Colour hue

E) Colour saturation and/or luminance

F) Shape

Compare bar chart and population pyramid (difficult)

22. Which of the following statements are correct? Select all correct answers.

Note: Please select all correct answers.

A) This graph shows two attributes.

B) Gender is a nominal attribute, which is visualised with the colour hue channel.

C) The age bin (for example, “0–4”) is an ordered attribute that is shown with the position channel.

D) The marks used are lines and there are 20 million males in the age bin “45–49”.

23. Which of the following statements are correct? Select all correct answers.

Note: Please select all correct answers.

A) This graph shows two attributes.

B) Gender is a nominal attribute, which is visualised with both the colour hue channel and the “spatial region” channel.

C) The age bin (for example, “0–4”) is an ordered attribute that is shown with the position channel.

D) The marks used are lines and there are about 20 million people in the age bin “45–49”.

Compare bar chart and connected dot plot (difficult)

24. This visualisation shows a Gender Pay Gap dataset for the USA. Which of the following statements are correct? Select all correct answers.

Note: Please select all correct answers.

A) This chart shows three categorical attributes, and one of these categorical attributes is shown with the colour hue channel.

B) The marks used for the quantitative attribute(s) are points and lines.

C) The salary of male accountants is about $65K higher than the salary of female accountants.

D) The horizontal line marks between the red and blue dots should be removed because they are distracting.

25. Which of the following is not a data attribute type?

A) Qualitative.

B) Ordinal.

C) Ratio.

D) Diverging.

E) Categorical.

26. Let’s think about the size of T-shirts (XS,S, M, L, XL, etc.) as a variable. This variable is best described as which one of the following?

A) categorical attribute

B) ordinal attribute

C) quantitative attribute

D) numeric attribute

E) nominal attribute

27. Which of the following channels is the most effective to encode qualitative data attributes?

A) Length

B) Shape

C) Area

D) Colour hue

E) Colour saturation

28. The bubble chart below presents an overview of different countries in 2019. Each bubble represents a country, and the visual encodings used in the graph include:

* x-axis position: income

* y-axis position: life expectancy

* area size: population

* colour hue: continent

* animation: year

Which of the following list all the attributes that are represented with magnitude channels in this visualisation?

[Source: https://www.gapminder.org/tools/#$chart-type=bubbles]

A) income, life expectancy & population

B) income, life expectancy & continent

C) income & continent

D) population, continent & year

E) income, life expectancy

29. Consider the following bubble chart from Gapminder [link] which compares different countries in a few aspects (population, life expectancy, etc.). Select all the channels used in this visualisation.

(+1 mark per correct channel identified, -1 mark per incorrect channel selected).

A) Length

B) Position

C) 1D Size

D) Colour hue

E) Colour Saturation

F) Colour luminance

G) Area

H) Shape

30. The following boxplot explores factors that may be associated with defaulting on loan repayment. List all the channels used in this visualisation.

A) Length

B) Position

C) Area

D) 2D Size

E) Colour hue

F) Colour Saturation

G) Colour luminance

H) Shape

31. Which of the following statements is not true regarding line charts?

A) The line chart idiom is suitable to show how values develop over time.

B) You can use colours, line width and line dashes in a line chart to make your most important values stick out.

C) Using annotations might make your line chart more interesting to read.

D) A line chart is more effective to show how values differ in different categories compared to a (stacked) bar chart.

E) It is a good idea to show each line in a line chart with a distinctive colour.

32. What is an appropriate idiom for a table dataset with two quantitative value attributes?

A) Scatter plot

B) Bar chart

C) Pie Chart

D) Area Chart

E) Line chart