Overview
Open Point projects generate data — survey responses, participation counts, sentiment scores, geographic distributions, trend lines over time. How you present that data shapes how decision-makers understand it, and how communities trust it. This guide sets out the principles that underpin every chart, table, and map in the platform.
Why data visualisation matters in engagement
A community consultation might receive thousands of survey responses. A stakeholder report might synthesise months of feedback across a dozen engagement activities. Without clear visualisation, that volume of data is inaccessible — not just to the public, but to the government teams making decisions from it.
Good data visualisation in engagement work does three things:
- It builds trust. When people can see that their feedback has been counted and represented accurately — that their "strongly oppose" isn't lumped in with "somewhat oppose" — they trust the process more. Honest, clear charts are a transparency mechanism, not just a design choice.
- It supports better decisions. Planners, engineers, policy advisors, and elected officials read reports quickly. A well-designed chart communicates a finding in seconds. A poorly designed one obscures it, or worse, misleads.
- It extends accessibility. Some people find numerical data easier to engage with as a visual; others rely on the data table behind it. Our charts should serve both. Designing for accessibility from the start — not as a retrofit — is the only way to meet that standard reliably.
Open Point is used by government agencies whose audiences span age, language background, digital literacy, and disability. The visualisation system has to work in that context. That means not chasing complexity, not defaulting to visual novelty, and always asking: who needs to read this, and can they?
Guiding principles
01
Accuracy first
The chart must represent the data faithfully. Truncated axes, cherry-picked date ranges, and proportional distortions — even unintentional ones — mislead readers. Always start quantitative axes at zero unless there is a documented, explicit reason not to, and label that reason clearly in the chart. If the data has caveats (small sample size, self-reported responses, incomplete data period), show them. Accuracy is non-negotiable.
02
Accessibility always
Colour cannot be the only means of communicating information. Every chart must have a text alternative (a title, a data table, or an accessible description). Labels, patterns, and shapes must support colour distinctions. Contrast between adjacent chart elements must meet WCAG 1.4.11 (3:1 minimum for non-text). Visualisations that only work for sighted users with full colour vision are not finished.
03
Clarity over complexity
One chart should answer one question. If you find yourself stacking three data series onto a dual-axis chart with a secondary pie inset, the problem is the question, not the design. Break it apart. Use multiple simpler charts over one complicated one. Remove any element — gridline, legend entry, annotation — that doesn't help the reader understand the answer. Complexity is a cost the reader pays; spend it carefully.
04
Brand consistency
Charts are part of the Open Point product experience. The colour palette, type scale, spacing, and border radii in charts come from the same token system as every other component. A chart exported from an Open Point report should be recognisably part of the same visual language as the rest of the interface. Use the defined categorical, sequential, and diverging palettes. Don't introduce colours outside the token system.
Why it matters
Accessibility in charts isn't optional
WCAG 2.1 Success Criterion 1.4.11 (Non-text Contrast) requires that UI components and graphical objects achieve at least 3:1 contrast against adjacent colours. This applies directly to charts: bars, lines, pie segments, and map regions all need to be distinguishable. For people with low vision or colour vision deficiency, a chart that only uses colour to differentiate categories may be completely unreadable.
For Open Point specifically: many of our customers are government agencies required to meet WCAG 2.1 AA under legislation (the Australian Government's Digital Service Standard, various state accessibility policies). A chart that fails accessibility can put a project's compliance at risk. Build it right from the start.
When to use charts vs tables
Charts and tables are not interchangeable. Charts are best for showing patterns, distributions, and relationships. Tables are best for precise values, lookup tasks, and providing the accessible data layer behind a chart. In many cases, both belong on the same page — the chart for the story, the table for the record.
Use a chart when
- You want to show a trend, pattern, or distribution at a glance
- The relationship between values is more important than the exact values
- You're comparing a small number of categories (2–7) across one dimension
- The reader needs to quickly answer "which is bigger?" or "is this going up or down?"
- Visualising geographic distribution or spatial patterns
Use a table when
- Readers need to look up or compare specific values precisely
- You have more than 8–10 categories — charts with many series become unreadable
- The data includes multiple dimensions that can't be collapsed into one visual encoding
- You're presenting raw counts alongside percentages and need both visible
- The data will be downloaded or copied into another system
When to use both
A survey result showing sentiment across five themes is well served by a bar chart (pattern at a glance) paired with a data table (exact counts and percentages for the record). The chart goes in the body of the report; the table is either directly below it or linked as a data download. This is also the correct accessibility approach: charts must have a programmatic or visual data table equivalent available.
Do
- Provide a data table or data download alongside every chart in a published report
- Use the chart title to state the finding, not just describe the data ("Support was highest in the 25–44 age group" rather than "Support by age group")
- Include sample size and data collection period in chart captions
Don't
- Use a chart when there are only 1–2 data points — state it in prose instead
- Use a pie chart for more than 5–6 segments — switch to a bar chart
- Use a chart without a title and label — an unlabelled axis is an inaccessible one
How Open Point colour ramps map to charts
The Open Point colour system is built on eight ramps — green, teal, blue, purple, orange, red, yellow, and neutral — each with ten steps from 50 (lightest) to 900 (darkest). Chart colour usage draws from three modes: categorical for unrelated series, sequential for quantity scales, and diverging for scales that move away from a neutral midpoint.
Categorical: distinguishing unrelated series
Categorical palettes represent data categories that have no inherent order or relationship — "Community A vs Community B", "Survey A vs Survey B", "Themes across a project". Use distinct ramps in the defined order below. All categorical series should use the 400-step value of each ramp, which sits in the perceptually mid-range and maintains reasonable contrast against the neutral-50 chart background.
This is the mathematically optimal 6-colour combination from the Orbit token system — every entry passes 3:1 against the chart background and the sequence covers all six available hue families. The green anchor uses green-700 (dark olive) rather than green-400 (lime) because the lighter step fails contrast. Red is excluded — reserved for error states and inaccessible alongside green for colour blind users. Always pair colour with a direct label or distinct shape — hue alone is never sufficient. See the Colour in charts page for the full accessibility breakdown.
Sequential: showing magnitude on a single scale
Sequential palettes use a single ramp stepping from light to dark to encode quantity, density, or intensity. They work well for choropleth maps, heatmaps, and any visualisation where the reader needs to answer "how much?" rather than "which category?".
Green (positive quantity — e.g. participation rate)
low → high steps 100–700 --op-color-green-*
Neutral (neutral quantity — e.g. response count)
low → high steps 100–700 --op-color-neutral-*
Diverging: showing values that move in two directions
Diverging palettes encode data that has a meaningful midpoint — sentiment (support vs oppose), change from baseline (above vs below), or agreement scales (strongly agree to strongly disagree). Teal anchors the positive extreme, red anchors the negative extreme, and neutral-50 sits at the midpoint.
Positive extreme: --op-color-teal-60 •
Midpoint: --op-color-neutral-95 •
Negative extreme: --op-color-red-60
The teal-to-red diverging palette was chosen specifically because it is more accessible for people with red-green colour vision deficiency than the common red-to-green convention. Teal (blue-green) and red remain distinguishable to most people with deuteranopia or protanopia. For full detail on this decision, see Colour in charts — colour blindness.
Where to go next
Chart types
Standards & guidance
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