Data visualisation: charts and modelling

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課程筆記

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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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

複習筆記

## 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.

字卡

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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