Cartographic Design, Visualization, and Map Production
This domain tests whether a map communicates correctly: classification choices, visual variables, colour logic, layout elements and production settings. Questions typically show a described map or dataset and ask which design choice misleads the reader.
Visual variables and hierarchy
Position is the most accurate variable for reading values, followed by size and value (lightness); colour hue is poor for ordering numbers but good for categories. Figure-ground separates the subject from the background: a muted basemap and saturated foreground. Visual hierarchy should make the title, the primary theme and the legend readable in that order.
Classification of quantitative data
- Equal interval splits the value range into equal steps; it is easy to read and hides skewed distributions.
- Quantile puts equal counts in each class; it highlights rank but can put similar values in different classes.
- Natural breaks (Jenks) minimises within-class variance; it is data-driven and hard to compare across maps.
- Standard deviation shows distance from the mean; manual classes support policy thresholds. A skewed income dataset with a few extreme values will look washed out under equal interval - quantile or natural breaks reveal the pattern, at the cost of legibility.
Colour and production
Sequential schemes suit ordered data (light to dark of one hue); diverging schemes suit data around a meaningful midpoint; qualitative schemes suit unordered categories. Avoid rainbow ramps for ordered data and red-green pairs for accessibility. Always include title, legend, scale bar, north arrow, data source, author, date and a projection or coordinate-system note.
Worked example
A map of median household income by census tract uses a red-green diverging ramp centred on the mean, with eight quantile classes. Three problems follow. Red-green is inaccessible to colour-blind readers, so use a colour-blind-safe diverging pair such as blue-orange. Centring on the mean is defensible only if the mean is meaningful; a median split or a policy threshold may be better. Eight quantile classes over 200 tracts produce roughly 25 tracts per class, which is legible, but the legend must state that classes are quantiles so readers do not read them as equal value ranges. Reasoning: fix accessibility, justify the midpoint, and label the classification method.
Traps that cost marks
- A legend whose class breaks do not match the rendered classes.
- Using a sequential ramp for categorical data, or hue for ordered data.
- Omitting the source and projection note, which are required elements, not decoration.
- Exporting at screen resolution when print at 300 dpi is required.