🆕NGD Analytical Styling
Styling the NGD Analytically
The OS NGD is packed full of attributes which give a greater insight into the physical world around us and can help us answer questions and solve problems. The data in the NGD can feel complex, especially when viewed as a list of attributes. Visualising the data, however, can really bring that data to life; allowing us to see those patterns and trends and see spatial relationships between or within datasets.
Whilst the styling you choose to apply will depend on your specific use case and what is of interest to you, we share below some key benefits of styling your data analytically and provide tips and recommendations which might help you get started.
Why style your data analytically?
There are many advantages to styling your data analytically:
Enhanced Data Interpretation
Visual Clarity: Thematic styles (like choropleths, graduated symbols, or heat maps) help users quickly grasp patterns, trends, and anomalies in spatial data.
Focus on Specific Themes: By emphasising a particular variable (e.g., building age, land use, or road speeds), it allows for targeted analysis.
Improved Communication
Storytelling with Maps: Thematic styling helps convey complex spatial narratives in a visually compelling way, making it easier to communicate findings to stakeholders.
Audience-Specific Design: Styles can be tailored to suit technical or non-technical audiences, enhancing accessibility.
Better Decision Support
Spatial Insights: Helps identify areas of concern or opportunity (e.g., high-risk flood zones).
Scenario Analysis: Enables comparison of different scenarios or time periods through consistent styling.
Data Integration and Comparison
Multi-layer Analysis: Supports overlaying different thematic layers (e.g., demographics + infrastructure) to reveal correlations or conflicts.
Standardisation: Analytical styling can follow cartographic standards, ensuring consistency across projects or datasets.
Automation and Scalability
Rule-Based Styling: Tools like QGIS or ArcGIS allow for automated styling based on attribute values, which is efficient for large datasets.
Dynamic Updates: Styles can adapt to real-time data changes, useful in dashboards or live monitoring systems.
Getting Started with styling your data
Sequential data
If the attribute is numerical or could be ordered in some way, such as building age, average speed or building height, we recommend using a sequential colour pallet. Sequential colour styling is a cartographic technique used to visually represent ordered or continuous data on a map using a gradient of colours. It’s especially useful for thematic maps where the data values increase or decrease in a logical sequence—such as elevation, road speeds or residential density.
We naturally associate lighter colours to represent smaller numbers and darker colours to represent larger numbers (this reverses if you’re using a dark basemap) - use this to your advantage to help the audience understand the data.
You can have distinct colour categories e.g. Road Speeds where knowing the exact value of a feature is important, or maybe a continuous colour pallet from low to high where knowing the exact value of an attribute is less important and you want to show a general low to high trend.
When using distinct categories, try to limit the number of categories to 6 or 7. More than this can result in it becoming more difficult to tell the difference in colour between categories. If using a single hue colour ramp makes it hard to distinguish differences between categories, try using a multi-hue colour ramp.
The colour values we have used for these visuals are available our GDV toolkit_ and are also available in our_style libraries_ for QGIS and ArcPro._
For point or line data, you also have the option to vary the size of the point or line. Proportional symbols are a cartographic technique used to represent quantitative data on a map by varying the size of symbols in proportion to the data values they represent. For example, our Pavement Link feature type within NGD Transport Network has a width field which can be used to style the line features so that the symbol represents the width of the feature.
Categorical or Qualitative Data
Categorial or qualitative data is data that you can split into distinct categories, such as, land use types or building types. This data can’t be ordered. For this type of data, we recommend using a Qualitative colour pallet. A qualitative colour pallet uses colours of different hues which have no visual relationship to one another but are seen as being visually equal.
Humans struggle to distinguish more than 12-15 colours reliably and as such we recommend using the guidance below as a guide to help you style your data:
2–6 categories = Ideal range for clear, intuitive maps
7–10 categories = Still manageable, but requires careful colour selection.
11–15 categories = Use with caution; consider adding labels or patterns.
>15 catergories = Consider simplifying, grouping, or using interactive maps.
Fewer than 10 categories
As a general rule, if there are fewer than 10 distinct values, you can give them distinct colour values as there are enough different colours of equal visual weight to do this. In our data, this works well for things like building connectivity, roof material types and construction materials. If you can, try and allocate colours to values that make sense e.g. green for a green roof, yellow for thatch and so on.
We recommend using the categorical colours in our GDV colour pallet.
Options for qualitative/ categorical colour pallets are available via our GDV toolkit and are also available in our style libraries for QGIS and ArcPro.
More than 10 categories
Where there are more than 10 different values, giving them all distinct colours becomes more difficult and can become visually confusing, showing no patterns or trends. This is the case for things like Land Use, Building Description and Land Cover information where there are 15+ different attribute values.
In cases like where you have 15+ categories, we recommend that you group attributes into related categories and give the categories colours or different lightness’s of a single colour.
If we take Building Land Use Tier A as an example, all Commercial Activity could be given a grey hue, Transport could also be given blue hue and some of the Community Services could be given purple hue. This allows similar Land Uses to appear as similar on the map – allowing you to see patterns in the data. As we have varied the lightness of some of the colours across the groups, we can see they’re different but related.
Things to avoid
It’s often tempting to try and give every category a different colour, which can end up leading to visual confusion. When there’s too much going on, it’s hard to see any patterns or trends.
Another solution to lots of categories is to start adding in lots of different pattern fills to help differentiate between features which again can just clutter the visual and make it hard to interpret. Sometimes pattern fills can be useful for data overlays where you want to be able to see the data layer below but use them with care and try not to mix too many patterns on a single image.
Multiple Data Overlays
This is a tricky thing to get right and there’s sadly no ‘one size fits all’ solution, particularly on a static map. In these cases, it is worth considering whether an interactive map may be a better way to display your data. Interactive maps allow users to turn on/off data layers as they need. We recommend only showing a few data layers when the map loads to ensure it’s simple and easy to read and then allow the user to add additional data. An example of an interactive map with multiple data layers is our NGD product viewer.
If you do need to create a static map with multiple data overlays, transparencies and pattern fills can be used to aid readability. You can also use iconography and labelling to help distinguish between features.
The key to showing lots of data on the map is to ensure that similar things appear similar in their styling and different things appear different. It allows the user to in their mind group things into categories.
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