GEOM90007 Information Visualisation
The Complete Study & Assessment Guide · S2 2026GEOM90007 Overview
- The University of Melbourne
- Semester 2, 2026
- a graduate coursework subject
- 12.5 points
GEOM90007 Information Visualisation teaches how visual thinking, perception, data graphics, cartography and interaction design can turn complex and spatial data into defensible visual evidence. It is taught within University of Melbourne Faculty of Engineering and Information Technology. It is a graduate coursework subject.
- GEOM90007 grading Static information-visualisation assignment 20% · Dynamic information-visualisation assignment 20% · Mid-semester exam 25% · 2 more published item(s)
- GEOM90007 task mode Plan around this operating format: No end-of-semester examination is published in the 2026 assessment table.
- GEOM90007 pressure point The main transfer challenge is this: Matching the representation to both the data and the perceptual task.
What you get in the full GEOM90007 Study & Assessment Guide
- 13 chapters, 35 pages — every assessed topic
- 16 original practice questions, each with a model answer
- 13 worked examples, solved the way GEOM90007 marks
- 26 computed figures, redrawn from the course’s own numbers
- Before-you-submit checklist for the Mid-semester exam (25%) — what markers reward, what costs marks
Free on this page: assessment table, chapter map, one worked example, key terms, FAQ. The rest is in the full study & assessment guide.
How GEOM90007 is assessed
| Component | Weight | Format |
|---|---|---|
| Static information-visualisation assignment | 20% | Individual; Week 5; 27 hours of work, equivalent to 1,500 words |
| Dynamic information-visualisation assignment | 20% | Individual; Week 9; 27 hours of work, equivalent to 1,500 words |
| Mid-semester exam | 25% | 60 minutes in Week 9; current Subject Guide controls permitted materials |
| Group project video presentation | 10% | Ten-minute video presentation in Week 12 |
| Group implementation and report | 25% | Week 12; implementation plus report contribution equivalent to 1,000 words per student |
The 2026 Handbook publishes five items totalling 100%: static individual assignment 20%, dynamic individual assignment 20%, 60-minute mid-semester exam 25%, group video presentation 10%, and group implementation/report 25%. The two group deliverables total 35%.
What other students are asking about GEOM90007
- Where can I find lecture notes or study guides for GEOM90007 at UniMelb?
- The GEOM90007 study guide in AskSia Library covers Information Visualisation in 13 chapters, with the assessment table, a chapter-by-chapter topic map, key terms and a worked example, free to read on this page.
- Is there a final exam in GEOM90007?
- No final exam is listed in the offering this AskSia Library guide is built from; GEOM90007 is assessed by Static information-visualisation assignment 20%, Dynamic information-visualisation assignment 20%, Mid-semester exam 25%, Group project video presentation 10%, Group implementation and report 25%.
- What does GEOM90007 cover on visual thinking, hierarchy and design purpose?
- In AskSia Library's GEOM90007 guide, the Visual Thinking, Hierarchy and Design Purpose chapter explains visual thinking · visual hierarchy · design purpose · state the audience and decision before arranging visual emphasis and reading order.
- What does GEOM90007 cover on data types, descriptive statistics and distribution?
- In AskSia Library's GEOM90007 guide, the Data Types, Descriptive Statistics and Distribution chapter explains data type · distribution · descriptive statistic · match the summary and display to the variable type and analytical comparison.
- What does GEOM90007 cover on sensing, perception and attention?
- In AskSia Library's GEOM90007 guide, the Sensing, Perception and Attention chapter explains visual perception · preattentive feature · attentional limit · use one preattentive feature to support the central comparison and remove competing salience.
Current GEOM90007 dates
| Date | Item | Control |
|---|---|---|
| Week 5 | Static information-visualisation assignment | Individual 20% assignment |
| Week 9 | Dynamic assignment and mid-semester exam | 20% individual assignment and 25% 60-minute exam |
| Week 12 | Group video and implementation/report | The two group deliverables total 35% |
Dates are as published in the current course materials. Confirm exact deadlines and submission settings in the live LMS.
What GEOM90007 covers
Read Visual Questions, Data Evidence and Assessment Products as the foundation, Chart Selection, Comparison and Uncertainty as the main change in method, and Evaluation, Storytelling and Project Synthesis as the final application of the course.
Visual Questions, Data Evidence and Assessment Products
four-item assessment set · visualisation decision chain · source-silence control · build a task ledger without assigning unverified percentages and map each item to the visualisation decisions it assessesDefine four-item assessment set
Visual Questions, Data Evidence and Assessment Products connects four-item assessment set, visualisation decision chain and source-silence control along an information-visualisation pipeline: data type, visual encoding, perception, task fit and evaluation.
- four-item assessment set
- visualisation decision chain
- source-silence control
- Applying four-item assessment set
- Limits of visualisation decision chain and source-silence control
Visual Thinking, Hierarchy and Design Purpose
visual thinking · visual hierarchy · design purpose · state the audience and decision before arranging visual emphasis and reading orderDefine visual thinking
Visual Thinking, Hierarchy and Design Purpose connects visual thinking, visual hierarchy and design purpose along an information-visualisation pipeline: data type, visual encoding, perception, task fit and evaluation.
- visual thinking
- visual hierarchy
- design purpose
- Applying visual thinking
- Limits of visual hierarchy and design purpose
Data Types, Descriptive Statistics and Distribution
data type · distribution · descriptive statistic · match the summary and display to the variable type and analytical comparisonDefine data type
Data Types, Descriptive Statistics and Distribution connects data type, distribution and descriptive statistic along an information-visualisation pipeline: data type, visual encoding, perception, task fit and evaluation.
- data type
- distribution
- descriptive statistic
- Applying data type
- Limits of distribution and descriptive statistic
Sensing, Perception and Attention
visual perception · preattentive feature · attentional limit · use one preattentive feature to support the central comparison and remove competing salienceDefine visual perception
Sensing, Perception and Attention connects visual perception, preattentive feature and attentional limit along an information-visualisation pipeline: data type, visual encoding, perception, task fit and evaluation.
- visual perception
- preattentive feature
- attentional limit
- Applying visual perception
- Limits of preattentive feature and attentional limit
Colour, Light and Accessible Scales
colour channel · sequential colour scale · colour accessibility · select a categorical, sequential or diverging scale from the data semantics and test it without colour aloneDefine colour channel
Colour, Light and Accessible Scales connects colour channel, sequential colour scale and colour accessibility along an information-visualisation pipeline: data type, visual encoding, perception, task fit and evaluation.
- colour channel
- sequential colour scale
- colour accessibility
- Applying colour channel
- Limits of sequential colour scale and colour accessibility
Graphical Encoding and Perceptual Accuracy
graphical encoding · visual mark · perceptual accuracy · rank candidate encodings by the task and preserve the most accurate channel for the main comparisonDefine graphical encoding
Graphical Encoding and Perceptual Accuracy connects graphical encoding, visual mark and perceptual accuracy along an information-visualisation pipeline: data type, visual encoding, perception, task fit and evaluation.
- graphical encoding
- visual mark
- perceptual accuracy
- Applying graphical encoding
- Limits of visual mark and perceptual accuracy
Chart Selection, Comparison and Uncertainty
analytical task · chart grammar · uncertainty representation · choose a chart from the task and data structure, then make uncertainty commensurate with the claimDefine analytical task
Chart Selection, Comparison and Uncertainty connects analytical task, chart grammar and uncertainty representation along an information-visualisation pipeline: data type, visual encoding, perception, task fit and evaluation.
- analytical task
- chart grammar
- uncertainty representation
- Applying analytical task
- Limits of chart grammar and uncertainty representation
Cartography, Projection and Spatial Pattern
map projection · spatial scale · choropleth map · select projection, spatial unit and symbolisation from the question and explain the distortion introducedDefine map projection
Cartography, Projection and Spatial Pattern connects map projection, spatial scale and choropleth map along an information-visualisation pipeline: data type, visual encoding, perception, task fit and evaluation.
- map projection
- spatial scale
- choropleth map
- Applying map projection
- Limits of spatial scale and choropleth map
Interaction, HCI and Coordinated Views
interaction technique · overview first principle · coordinated multiple views · add only the interaction needed for a named analytical task and keep the current state visibleDefine interaction technique
Interaction, HCI and Coordinated Views connects interaction technique, overview first principle and coordinated multiple views along an information-visualisation pipeline: data type, visual encoding, perception, task fit and evaluation.
- interaction technique
- overview first principle
- coordinated multiple views
- Applying interaction technique
- Limits of overview first principle and coordinated multiple views
Tableau Mapping, Filters and Calculations
Tableau data model · filter context · calculated field · build a map or dashboard whose data grain, filters and calculation order match the questionDefine Tableau data model
Tableau Mapping, Filters and Calculations connects Tableau data model, filter context and calculated field along an information-visualisation pipeline: data type, visual encoding, perception, task fit and evaluation.
- Tableau data model
- filter context
- calculated field
- Applying Tableau data model
- Limits of filter context and calculated field
R, ggplot2 and Reproducible Graphics
tidy data · grammar of graphics · reproducible graphic · write a ggplot2 specification whose mappings, transformations and labels expose the analytical choicesDefine tidy data
R, ggplot2 and Reproducible Graphics connects tidy data, grammar of graphics and reproducible graphic along an information-visualisation pipeline: data type, visual encoding, perception, task fit and evaluation.
- tidy data
- grammar of graphics
- reproducible graphic
- Applying tidy data
- Limits of grammar of graphics and reproducible graphic
Shiny and Reactive Visualisation
reactive programming · Shiny application · interaction state · design a minimal reactive path and make defaults, loading states and selected data visibleDefine reactive programming
Shiny and Reactive Visualisation connects reactive programming, Shiny application and interaction state along an information-visualisation pipeline: data type, visual encoding, perception, task fit and evaluation.
- reactive programming
- Shiny application
- interaction state
- Applying reactive programming
- Limits of Shiny application and interaction state
Evaluation, Storytelling and Project Synthesis
visualisation evaluation · data story · design rationale · test the view with representative tasks, revise from observed failures and present the final claim with limitationsDefine visualisation evaluation
Evaluation, Storytelling and Project Synthesis connects visualisation evaluation, data story and design rationale along an information-visualisation pipeline: data type, visual encoding, perception, task fit and evaluation.
- visualisation evaluation
- data story
- design rationale
- Applying visualisation evaluation
- Limits of data story and design rationale
It carries 12.5 points. It is positioned as an information visualisation subject offered online in Semester 2.
The subject couples visual reasoning with practical work in Tableau, R, ggplot2 and Shiny.
It asks students to justify encodings and interactions from the data type, perceptual task and audience rather than treating a polished dashboard as self-validating.
Assessment in GEOM90007 is distributed as follows: Individual static visualisation assignment 20%, individual dynamic visualisation assignment 20%, 60-minute mid-semester exam 25%, group project video presentation 10%, and group implementation/report 25%
The operational assessment conditions matter here.
No end-of-semester examination is published in the 2026 assessment table. The timed component is a 60-minute mid-semester exam worth 25%; its permitted materials remain a live Subject Guide control.
What makes GEOM90007 demanding is concrete: Matching the representation to both the data and the perceptual task.
Students must distinguish exploratory from explanatory goals, choose encodings that preserve comparisons, handle spatial and statistical uncertainty, and evaluate whether interaction clarifies the evidence or merely adds interface complexity.
The current material describes required practical participation in Weeks 1-8 but does not publish a captured numeric grade consequence or a component-level hurdle.
Confirm the current rule in the Subject Guide.
Read Visual Questions, Data Evidence and Assessment Products as the foundation, Chart Selection, Comparison and Uncertainty as the main change in method, and Evaluation, Storytelling and Project Synthesis as the final application of the course.
Worked example · free
Choose an encoding from the viewer's task
- 1Name the comparison task and the temporal and regional data structure.
- 1Choose an aligned-position encoding for the primary value comparison.
- 1Represent uncertainty in the same scale as the estimate.
- 1Define one user test that can falsify the design rationale.
Key terms
- Data visualization
- The representation of data through visual marks, spatial position and graphical attributes to support analysis, communication and decision-making.
- Visual hierarchy
- The ordering of visual elements through position, size, contrast and grouping so viewers perceive intended levels of importance.
- Graphical encoding
- A rule mapping data values to visual properties such as position, length, angle, area, colour or shape.
- Preattentive processing
- Rapid visual processing that makes certain features stand out before deliberate, focused attention is applied.
- Colour scale
- A systematic mapping from data values or categories to colours selected for ordering, separation, accessibility and interpretive purpose.
- Spatial visualisation
- The representation of geographic location, distance, topology and spatial pattern through maps or other position-based visual displays.
- Cartography
- The theory and practice of designing maps that represent spatial relationships through projection, symbolisation, scale and generalisation.
- Interactive visualisation
- A visual representation whose view or parameters respond to user actions such as filtering, selection, zooming or linked highlighting.
- Shiny
- An R framework for building reactive web applications that connect user inputs to analysis, data and visual outputs.
GEOM90007 FAQ
How is GEOM90007 assessed?
Individual static visualisation assignment 20%, individual dynamic visualisation assignment 20%, 60-minute mid-semester exam 25%, group project video presentation 10%, and group implementation/report 25%
What is the GEOM90007 final assessed-task format?
No end-of-semester examination is published in the 2026 assessment table. The timed component is a 60-minute mid-semester exam worth 25%; its permitted materials remain a live Subject Guide control.
Where do students usually lose marks in GEOM90007?
Matching the representation to both the data and the perceptual task. Students must distinguish exploratory from explanatory goals, choose encodings that preserve comparisons, handle spatial and statistical uncertainty, and evaluate whether interaction clarifies the evidence or merely adds interface complexity.
How does ggplot2 and Shiny appear in GEOM90007?
ggplot2 builds graphics by mapping data variables to visual aesthetics and composing layers; Shiny turns R analyses and graphics into reactive interfaces. GEOM90007 uses them to connect visual encoding with reproducible, interactive exploration.
Does GEOM90007 have a hurdle or component-level pass rule?
The current material describes required practical participation in Weeks 1-8 but does not publish a captured numeric grade consequence or a component-level hurdle. Confirm the current rule in the Subject Guide.
Which offering does this GEOM90007 guide cover?
It is aligned to Semester 2, 2026; confirm your enrolled class and timetable in the current institutional system.
Is this GEOM90007 resource an official university guide?
No. It is an independent GEOM90007 study resource; current institutional instructions remain authoritative for assessment operation.
Questions students ask in each GEOM90007 chapter
- What is the main task in Visual Questions, Data Evidence and Assessment Products?
- Build a task ledger without assigning unverified percentages and map each item to the visualisation decisions it assesses. Chapter 01 · Visual Questions, Data Evidence and Assessment Products
- What is the main task in Visual Thinking, Hierarchy and Design Purpose?
- State the audience and decision before arranging visual emphasis and reading order. Chapter 02 · Visual Thinking, Hierarchy and Design Purpose
- What is the main task in Data Types, Descriptive Statistics and Distribution?
- Match the summary and display to the variable type and analytical comparison. Chapter 03 · Data Types, Descriptive Statistics and Distribution
- What is the main task in Sensing, Perception and Attention?
- Use one preattentive feature to support the central comparison and remove competing salience. Chapter 04 · Sensing, Perception and Attention
- What is the main task in Colour, Light and Accessible Scales?
- Select a categorical, sequential or diverging scale from the data semantics and test it without colour alone. Chapter 05 · Colour, Light and Accessible Scales
- What is the main task in Graphical Encoding and Perceptual Accuracy?
- Rank candidate encodings by the task and preserve the most accurate channel for the main comparison. Chapter 06 · Graphical Encoding and Perceptual Accuracy
- What is the main task in Chart Selection, Comparison and Uncertainty?
- Choose a chart from the task and data structure, then make uncertainty commensurate with the claim. Chapter 07 · Chart Selection, Comparison and Uncertainty
- What is the main task in Cartography, Projection and Spatial Pattern?
- Select projection, spatial unit and symbolisation from the question and explain the distortion introduced. Chapter 08 · Cartography, Projection and Spatial Pattern
- What is the main task in Interaction, HCI and Coordinated Views?
- Add only the interaction needed for a named analytical task and keep the current state visible. Chapter 09 · Interaction, HCI and Coordinated Views
- What is the main task in Tableau Mapping, Filters and Calculations?
- Build a map or dashboard whose data grain, filters and calculation order match the question. Chapter 10 · Tableau Mapping, Filters and Calculations
- What is the main task in R, ggplot2 and Reproducible Graphics?
- Write a ggplot2 specification whose mappings, transformations and labels expose the analytical choices. Chapter 11 · R, ggplot2 and Reproducible Graphics
- What is the main task in Shiny and Reactive Visualisation?
- Design a minimal reactive path and make defaults, loading states and selected data visible. Chapter 12 · Shiny and Reactive Visualisation
- What is the main task in Evaluation, Storytelling and Project Synthesis?
- Test the view with representative tasks, revise from observed failures and present the final claim with limitations. Chapter 13 · Evaluation, Storytelling and Project Synthesis
How to prepare for the assessments
Retrieve the course map, practise the recurring method—define the analytical task and data type, select perceptually defensible encodings and interaction, implement the view reproducibly and evaluate comprehension, uncertainty and accessibility—on changed scenarios, and verify every operational assessment detail in the live institutional system.
Your AI Data Science tutor for GEOM90007
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