Data visualisation: charts and modelling

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レッスンノート

What is Data Visualisation?

Data visualisation is the practice of creating graphic or visual representations of data and information.
It helps people explore, understand, and gain insights from data that might be hard to see in raw numbers.
Visualisations can reveal structures, relationships, correlations, patterns, trends, clusters, and outliers.
When designed for the public to convey information engagingly, it is often called an infographic.
Data visualisation uses formats like charts, graphs, geospatial maps, and figures.
It is both an art and a science, combining design, statistics, and computing skills.

Types of Visualisation

Charts and graphs are common for quantitative data (e.g., bar charts, line graphs, pie charts).
Geospatial maps display data based on location.
Information visualisation deals with large, complex datasets, including qualitative and abstract information.
Tools for information visualisation include hierarchical organisations, Sankey diagrams, flowcharts, and timelines.
Narrative visualisation uses visuals to tell a story with data, blending analysis and storytelling.
Emerging technologies like virtual, augmented, and mixed reality can make visualisations more immersive and interactive.

Choosing the Right Chart

The type of chart should match the data and the message you want to convey.
Bar charts are good for comparing categories.
Line graphs show trends over time.
Pie charts show proportions of a whole (but can be hard to read with many slices).
Scatter plots reveal relationships or correlations between two variables.
Always consider your audience and what they need to understand.

Misleading Graphs and How to Spot Them

Misleading graphs can distort data and misinform viewers.
Watch for truncated axes (starting at a value other than zero) that exaggerate differences.
Be cautious of 3D effects or perspective that make some parts look bigger than they are.
Check for inconsistent scales or intervals on axes.
Look out for cherry-picked data that omits important context.
Always examine the source and underlying data to verify accuracy.

Computer Models and Simulations

A computer model is a program that simulates a real-world system or process.
Models use rules and data to represent how something works.
Simulations run models to see how they behave under different conditions.
They allow you to test scenarios without real-world consequences.
Examples include weather forecasting, traffic flow, and population growth models.
Models can help predict outcomes and support decision-making.

Presenting Findings to an Audience

Effective visualisation is well-sourced, contextualised, and simple.
Use clear labels, titles, and supporting text to explain the visuals.
Choose colours and shapes deliberately to enhance understanding, not distract.
Tailor your presentation to the audience's expertise and needs.
Combine visuals with storytelling to make data memorable and persuasive.
Always ensure your data is accurate and up-to-date.

Why Data Visualisation Literacy Matters

Poor or misleading visualisations can spread misinformation and manipulate opinion.
Being able to read and critique visualisations is a key digital literacy skill.
Understanding how visualisations work helps you create effective ones.
Visualisation literacy is as important as textual, mathematical, and visual literacy today.
It empowers you to make informed decisions based on data.

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練習問題

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1.What is the main purpose of data visualization?
Easy
- ATo make data look pretty
- BTo help people understand and interpret data quickly
- CTo replace written reports
- DTo hide errors in data
2.Which type of visualization is best for showing the proportion of parts to a whole?
Easy
- ALine graph
- BPie chart
- CScatter plot
- DSankey diagram
3.Data visualization is only used for quantitative data.
Easy
True or false?
4.Which of the following is an example of a misleading visualization technique?
Medium
- AStarting the y-axis at zero
- BUsing a bar chart with accurate labels
- CTruncating the y-axis to exaggerate differences
- DChoosing a chart type that matches the data
5.In effective data visualization, visual appeal is more important than accuracy of the data.
Medium
True or false?
6.Which of the following are common types of data visualizations? (Select all that apply)
Medium
- ACharts and graphs
- BGeospatial maps
- CFlowcharts
- DSpreadsheets
- ESankey diagrams
7.Data visualization can be used to check data quality and find errors.
Easy
True or false?
8.Match each visualization type to its primary use.
Medium
- Line chart
- Pie chart
- Sankey diagram
- Show trends over time
- Show proportions of a whole
- Show flows or relationships between entities

もっと問題

9.Arrange the steps in the data visualization process from first to last.
Medium
- Collect data
- Clean data
- Choose a chart type
- Create the visualization
- Interpret the visualization
10.Which of the following are examples of information visualization? (Select all that apply)
Medium
- AA map showing locations
- BA flowchart
- CA bar chart of monthly sales
- DA Sankey diagram
- EA table of raw data
11.What is the term for a visualization designed to convey a concise version of information to the public in an engaging manner?
Easy
- AInfographic
- BData dashboard
- CNarrative visualization
- DStatistical graphic
12.Data visualization is only concerned with quantitative data.
Easy
True or false?
13.Which of the following is a characteristic of effective data visualization?
Medium
- AUses many decorative elements to attract attention
- BAccurate, well-sourced, and appropriately contextualized
- CComplex and detailed to show all data
- DUses bright colors to make it visually appealing
14.Which of the following are potential uses of data visualization by data scientists? (Select all that apply)
Medium
- AChecking data quality
- BFinding errors
- CCleaning data
- DMaking data more colorful
- EExploring data structures
15.A dataset shows the number of books read by students in a class over a year. Which chart type would be most appropriate to show the trend of books read over the months?
Easy
- APie chart
- BLine graph
- CBar chart
- DScatter plot
16.A bar chart shows the favourite fruits of 100 students: apples 30, bananas 25, oranges 20, grapes 15, and others 10. If the bar for apples is drawn with a height of 6 cm, what should be the height of the bar for oranges?
Medium
- A4 cm
- B3 cm
- C5 cm
- D2 cm
17.A line graph is suitable for showing the proportion of parts to a whole.
Easy
True or false?
18.Which of the following are potential problems that can make a data visualization misleading? (Select all that apply)
Medium
- AUsing a truncated y-axis
- BChoosing a chart type that does not fit the data
- CIncluding too much detail
- DUsing misleading colour scales
- EAdding a title
19.Match each data set to the most appropriate chart type.
Medium
- Proportion of students in each house
- Temperature change over a day
- Comparison of heights of plants in different soils
- Pie chart
- Line graph
- Bar chart
20.A bar chart shows the number of books read by students in different months. The bar for January is 5 cm tall and represents 25 books. The bar for February is 3.5 cm tall. How many books were read in February?
Medium
- A15 books
- B17.5 books
- C20 books
- D14 books
21.Arrange the steps for creating an effective data visualization in the correct order.
Medium
- Choose the appropriate chart type
- Collect and clean the data
- Present the visualization to the audience
- Interpret the data to find insights
22.A student creates a bar chart to show the number of hours of sunshine each month. The y-axis starts at 10 hours instead of 0. What is the most likely effect on the reader?
Hard
- AThe differences between months will appear larger than they actually are.
- BThe differences between months will appear smaller than they actually are.
- CThe chart will be more accurate because it focuses on the relevant range.
- DThe chart will be easier to read because it uses less space.
23.Which of the following are appropriate uses of a scatter graph? (Select all that apply)
Medium
- ATo show the relationship between two numerical variables
- BTo display the trend of a single variable over time
- CTo compare the proportions of a whole
- DTo identify outliers in a data set
- ETo show the frequency of categories
24.A line graph is always the best choice for showing the proportion of parts to a whole.
Easy
True or false?
25.What is the most likely effect of starting the y-axis at 10?
Medium
- AThe differences between months will appear larger than they actually are.
- BThe differences between months will appear smaller than they actually are.
- CThe chart will be more accurate because it focuses on the relevant range.
- DThe chart will be easier to read because it uses less space.
26.A data scientist uses a visualization to explore a large dataset and identify unusual gaps and missing values. Which type of visualization is most likely being used?
Challenge
- AAn infographic intended for the public
- BA narrative visualization for storytelling
- CA data visualization for exploratory analysis
- DA scientific visualization of physical data
27.In data visualization, visual appeal is more important than accuracy of the data.
Medium
True or false?
28.What is the primary focus of data visualization?
Easy
- APresenting quantitative raw data in schematic form using imagery
- BCreating realistic images of physical objects
- CDesigning interactive video games
- DWriting computer programs
29.Which of the following is an example of a visual format used in data visualization?
Easy
- AGeospatial maps
- BWord processing documents
- CSpreadsheets
- DDatabases
30.Data visualization is only concerned with presenting quantitative data.
Easy
True or false?
31.What is the term for a visualization that uses visual elements to tell a story with data?
Easy
- AInfographic
- BNarrative visualization
- CStatistical graphic
- DScientific visualization
32.Which of the following are listed as visual tools used in information visualization? (Select all that apply)
Easy
- AMaps for location-based data
- BHierarchical organisations of data
- CPie charts for simple proportions
- DSankey diagrams
- EFlowcharts
33.Effective data visualization should be simple and uncluttered.
Easy
True or false?
34.Information visualization deals only with quantitative data.
Easy
True or false?
35.Which of the following is NOT a characteristic of effective data visualization?
Easy
- AWell-sourced and appropriately contextualized
- BSimple and uncluttered
- CUsing many decorative elements to attract attention
- DAccurate and up-to-date data
36.Data visualization is an interdisciplinary field that combines design, statistics, and computing skills.
Easy
True or false?
37.Match each type of data visualization to its primary use.
Medium
- Line graph
- Pie chart
- Scatter graph
- Bar chart
- Show trends over time
- Show proportions of a whole
- Show relationship between two variables
- Compare categories
38.Put the steps for creating an effective data visualization in the correct order.
Medium
- Collect and clean the data
- Choose an appropriate chart type
- Design the visualization with clear labels and colours
- Check the visualization for accuracy and clarity
39.Which of the following are examples of misleading visualization techniques? (Select all that apply)
Medium
- AStarting the y-axis at a value greater than zero
- BUsing a 3D pie chart where the sizes are distorted
- CClearly labeling all axes
- DSelectively choosing a time period that exaggerates a trend
40.Match each visualization type to its main purpose.
Medium
- Infographic
- Narrative visualization
- Scientific visualization
- Visual analytics
- Convey concise information to the public engagingly
- Tell a story with data using a structured narrative
- Render realistic images from physical data to test hypotheses
- Combine statistical analysis and interactive interfaces for decision-making
41.What term is used for a visualization that conveys a concise version of information to the public in an engaging manner?
Easy
42.Fill in the blank: A visualization that combines data analysis, storytelling, and visualization to present information through a structured narrative flow is called ___ visualization.
Easy
A visualization that combines data analysis, storytelling, and visualization to present information through a structured narrative flow is called ____ visualization.
43.What term describes the practice of using visual elements to tell a story with data?
Medium
44.Fill in the blank: Unintentionally poor or intentionally misleading visualizations can function as powerful tools which spread ___.
Easy
Unintentionally poor or intentionally misleading visualizations can function as powerful tools which spread ____.
45.What is the term for the practice of using visual elements to tell a story with data?
Medium
46.Complete the sentence:
Easy
Unintentionally poor or intentionally misleading visualizations can function as powerful tools which spread ____.
47.Name the three main types of visualization mentioned in the source.
Medium
Data visualization, information visualization, and ____ visualization.
48.In a dataset of 50 students, a pie chart shows that 30% prefer blue. How many students prefer blue?
Hard
49.A pie chart shows the distribution of 200 people by favourite sport: football 40%, basketball 25%, tennis 20%, swimming 15%. How many people chose basketball?
Medium
50.Complete the sentence about misleading graphs.
Easy
A misleading graph can be created by starting the y-axis at a value other than ____.
51.A line graph shows the temperature in a city over a week. The y-axis starts at 15°C instead of 0°C. Explain why this could be misleading.
Medium
52.A scatter graph shows the relationship between hours studied and test scores. The line of best fit has equation y = 5x + 40, where y is the test score and x is hours studied. Predict the test score for a student who studies 6 hours.
Hard
53.Explain why it is important to choose the right chart type for your data.
Medium
54.Explain why it is important to consider the target audience when creating a data visualization.
Hard
55.A company claims that sales doubled from 2020 to 2021. The graph they show uses a y-axis starting at 100, not zero. Explain why this graph might be misleading and what the company should do to present the data honestly.
Hard
56.Explain why a line graph with a y-axis starting at 15°C instead of 0°C could be misleading when showing temperature changes over a week.
Hard
57.Explain why starting a bar chart's y-axis at a value greater than zero can be misleading, and describe a situation where it might be acceptable.
Hard
58.In a dataset of 50 students, a pie chart shows that 30% prefer blue. How many students prefer blue?
Hard
- A17 students
- B15 students
- C16 students
- D14 students
59.A pie chart shows the distribution of 200 people by favourite sport: football 40%, basketball 25%, tennis 20%, swimming 15%. How many people chose basketball?
Medium
- A55 people
- B45 people
- C50 people
- D60 people
60.A scatter graph shows the relationship between hours studied and test scores. The line of best fit has equation y = 5x + 40, where y is the test score and x is hours studied. Predict the test score for a student who studies 6 hours.
Hard
- A70 score
- B63 score
- C77 score
- D84 score

復習ノート

## Data Visualisation: Charts and Modelling ### What is Data Visualisation? - Turning raw data into **graphic or visual representations** (charts, graphs, maps, etc.) to help people **explore, understand, and gain insights** quickly. - Helps spot **structures, relationships, correlations, patterns, trends, variations, clusters, outliers**. - When designed for the public to convey a concise message in an engaging way, it's called an **infographic**. ### Types of Visualisation - **Data visualisation**: focuses on **quantitative** (numerical) data using charts, graphs, maps, correlation matrices, percentage gauges. - **Information visualisation**: deals with **large, complex, often abstract** datasets (both quantitative and qualitative). Uses maps, hierarchical diagrams, Sankey diagrams, flowcharts, timelines. - **Narrative visualisation**: combines data with **storytelling** to present information through a structured narrative, helping people identify trends and patterns. - **Scientific visualisation**: renders realistic images from physical/spatial data (e.g., weather models) – different from data/info viz. ### Choosing the Right Chart - **Bar chart**: compare categories. - **Line chart**: show trends over time. - **Pie chart**: show proportions of a whole (use sparingly). - **Scatter plot**: show relationships/correlations between two variables. - **Map**: show geographical patterns. - **Flowchart/Sankey**: show processes or flows. ### Spotting Misleading Graphs - **Truncated axes** (starting at a non-zero value) exaggerate differences. - **3D effects** distort proportions. - **Inappropriate scale** (uneven intervals) misleads. - **Cherry-picked data** (selective time range) hides context. - **Correlation ≠ causation** – just because two things move together doesn't mean one causes the other. ### Computer Models and Simulations - **Models** are simplified representations of real-world systems (e.g., weather, traffic, population). - **Simulations** run models over time to see how they behave under different conditions. - Used to test hypotheses, make predictions, and explore 'what-if' scenarios. ### Presenting Findings - **Know your audience**: tailor complexity to their expertise. - **Keep it simple and uncluttered**: avoid distracting visuals. - **Use supporting text**: explain what the visual shows. - **Ensure accuracy**: data must be well-sourced, accurate, and up-to-date. - **Tell a story**: use narrative to make insights memorable and persuasive. ### Key Principles - Effective visualisation is **well-sourced, contextualised, simple, and aesthetically appealing**. - It is both an **art and a science** – requires design, statistical, and computing skills. - **Visual analytics** combines data analysis, visualisation, and human reasoning to make decisions. - Poor or misleading visualisations can **spread misinformation** – so **data visualisation literacy** is crucial.

Data visualisation: The practice of designing and creating graphic or visual representations of quantitative and qualitative data to help people explore, understand, and gain insights.
Information visualisation: Deals with multiple, large-scale and complicated datasets containing quantitative and qualitative abstract information, aiming to add value to raw data and improve comprehension.
Narrative visualisation: A method that uses visual elements to convey data through a structured narrative flow, blending data analysis, storytelling, and visualization to help identify trends and patterns.
Infographic: A visual representation intended for the public to convey a concise version of information in an engaging manner.
Misleading graph: A graph that unintentionally or intentionally distorts data, often through truncated axes, inappropriate scales, or selective data, to mislead viewers.
Computer model: A simplified representation of a real-world system used to simulate and predict behavior under different conditions.
Simulation: Running a model over time to see how a system behaves, often used for 'what-if' scenarios and predictions.
Data storytelling: Pairing data visualisation with a narrative structure to contextualize analyzed data and communicate insights to convince an audience to make a decision or take action.
Visual analytics: Combines statistical data analysis, data and information visualization, and human analytical reasoning through interactive visual interfaces to help users reach conclusions and make informed decisions.
Data visualisation literacy: The ability to read, interpret, and create effective data visualisations, and to detect misleading ones; an important component of data and information literacy.

フラッシュカード

What is data visualization?

Data visualization is the practice of designing and creating graphic or visual representations of data to help people understand, interpret, and gain insights from the data.

What is the main purpose of data visualization?

The main purpose is to help a target audience visually explore and discover, quickly understand, interpret, and gain important insights into structures, relationships, patterns, trends, and outliers in data.

What are infographics?

Infographics are visualizations intended for the public to convey a concise version of information in an engaging manner.

Name three common types of visual formats used in data visualization.

Charts and graphs, geospatial maps, and figures (also correlation matrices, percentage gauges).

What is the difference between data visualization and information visualization?

Data visualization focuses on presenting quantitative raw data in schematic form, while information visualization deals with large, complicated datasets that may include qualitative and abstract information, aiming to add value and improve comprehension.

Give an example of a visual tool used in information visualization for location-based data.

Maps for location-based data.

What is a Sankey diagram used for?

A Sankey diagram is used to prioritize relationships in data, showing flows and quantities.

What is narrative visualization?

Narrative visualization uses visual elements like charts and graphs to tell a story through data, blending data analysis, storytelling, and visualization to help people identify trends and patterns.

How can emerging technologies like VR and AR enhance information visualization?

They can make information visualization more immersive, intuitive, interactive, and manipulable, enhancing visual perception and cognition.

What is the difference between information visualization and scientific visualization?

Information visualization deals with abstract, non-physical data from databases and systems, while scientific visualization renders realistic images based on physical and spatial scientific data to confirm or reject hypotheses.

List three characteristics of effective data visualization.

Well-sourced, appropriately contextualized, and presented in a simple, uncluttered manner (also accurate, up-to-date, aesthetically appealing, with supporting text).

Why is it important for visualizations to be accompanied by supporting texts?

Supporting texts complement the graphical components to ensure clear, quick, and memorable understanding.

What should effective visualization consider about the target audience?

It should be aware of the needs and expertise level of the target audience.

How do data scientists use data visualization?

They use it to check data quality, find errors, gaps, missing values, clean data, explore structures, and assess outputs of data-driven models.

What is data storytelling?

Data storytelling pairs data and information visualization with a narrative structure to contextualize analyzed data and communicate insights to convince the audience to make a decision or take action.

What is the difference between data storytelling and statistical graphics?

Statistical graphics communicate complex data among researchers for exploratory analysis, where visual appeal and storytelling are less important, unlike data storytelling which aims to engage and persuade.

Name three disciplines that data and information visualization incorporates principles from.

Descriptive statistics, visual communication, graphic design, cognitive science, interactive computer graphics, and human-computer interaction.

Why is data visualization considered both an art and a science?

Because effective visualization requires design skills (art) and statistical and computing skills (science).

What is visual analytics?

Visual analytics combines statistical data analysis, data and information visualization, and human analytical reasoning through interactive visual interfaces to help users gain insights and make informed decisions.

How can poor or misleading visualizations affect people?

They can disseminate misinformation, manipulate public perception, and divert public opinion.

Why is data visualization literacy important?

It is an important component of data and information literacy in the information age, similar to textual, mathematical, and visual literacy.

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