ENV103 Chap.4 Classifying Data for Thematic Maps
Classifying Data for Thematic Maps
A thematic map rarely shows raw values; it groups them into classes. This chapter explains why classification helps the eye, why about seven classes is a practical ceiling, and how equal interval, quantile and natural breaks place their boundaries differently. It adds fixed intervals, standard deviation classes, ColorBrewer's three scheme types and how to read the histogram in the symbology panel.
A worked example classifies twelve suburbs into four classes by several rules so you can see exactly which values change class, and why a legend should always name its method.
What this chapter covers
- 01
Why classify a data series
- 02
The magical number seven
- 03
Equal interval classification
- 04
Quantile classification
- 05
Natural breaks classification
- 06
Fixed interval and standard deviation
- 07
Sequential, diverging and qualitative colour
Worked example · free
Find which classification splits clusters of canopy values
- +1Equal-interval width = (38 − 1) ÷ 4 = 9.25, so the breaks fall at 10.25, 19.5 and 28.75%.
- +1Those breaks put 9 and 10% below the first break and 11% above it, and leave 21% alone in the third class.
- +1Quantile places three suburbs per class in order: 1, 3, 4 | 6, 9, 10 | 11, 18, 19 | 21, 33, 38.
- +1Quantile splits 10 from 11 and groups 21% with 38%, while natural breaks keeps 1 to 6, 9 to 11, 18 to 21 and 33 to 38 together.
Key terms
- Data classification
- Data classification is the systematic grouping of attribute values into a small number of classes for display.
- Equal interval
- Equal interval classification divides the range of values into classes of equal width.
- Quantile
- Quantile classification assigns the same number of features to each class.
- Natural breaks
- Natural breaks classification places class boundaries at large jumps in the ordered data.
- Sequential scheme
- A sequential colour scheme runs from light to dark to show ordered values from low to high.
- Diverging scheme
- A diverging colour scheme spreads two hues from a neutral midpoint to show values above and below it.
- Data series
- A data series is the list of attribute values for the mapped features, ordered from lowest to highest.
- Standard deviation classes
- Standard deviation classes group values by how many standard deviations they lie above or below the mean.
Classifying Data for Thematic Maps FAQ
How many classes should a choropleth have?
Around five to seven usually works. The lecture draws on Miller's magical number seven: people struggle to separate more than about seven shades at a glance. The Week 3 apps show three classes hiding real variation in Auckland's density and ten classes being too many to tell apart.
When is equal interval a poor choice?
When the data are skewed or unfamiliar, such as population counts, most features can crowd into one or two classes while others sit nearly empty. It works best for familiar measures like temperature, rainfall or percentages, where readers understand evenly spaced class ranges.
Why can quantile maps mislead?
Quantile forces equal numbers of features into each class, so very different values can share a class and near-identical values can be split. On skewed data the top class may span an enormous range, as the Auckland CBD example shows. Adding classes reduces, but does not remove, the effect.
Which colour scheme suits population density?
A sequential scheme, light for low density and dark for high, because the data are ordered from low to high. Diverging schemes need a meaningful midpoint, and qualitative schemes are for unordered categories such as land cover.
Assessment move
Classification is one of the most practical skills in the course, so learn it by doing it by hand. Take any small set of numbers, at least eight values, sort them, and classify them three ways on paper before touching QGIS. For equal interval, practise the width rule until it is automatic: maximum minus minimum, divided by the number of classes. For quantile, practise counting off features in order.
For natural breaks, look for the biggest jumps and then check that each class holds similar values. After each exercise, write one sentence on which map you would trust and why, because that sentence is what the Lab 3 histogram question rewards. Next, open the three Week 3 comparison apps again and answer their questions in writing, especially the one about the Auckland CBD on the quantile map.
Learn the ColorBrewer scheme types with one example each, and connect them to data types: sequential for ordered values, diverging for data with a meaningful midpoint, qualitative for categories. Finally, make it a rule in your own maps to state the classification method and the number of classes in the legend or caption, so a reader can judge the picture for themselves.