Data presentation: dot maps, kite diagrams, dispersion diagrams and scales

Introduction to Data Presentation

In Geography, collecting data is only half the battle! Once you have your numbers from a windy beach or a busy high street, you need to show them in a way that makes sense. Data presentation is the art of turning raw numbers into a visual story. This is a vital skill for your Independent Investigation (NEA) and is frequently tested in Paper 3.

In this chapter, we will look at four key tools: dot maps, kite diagrams, dispersion diagrams, and the difference between linear and logarithmic scales. Don't worry if these sound technical—by the end of these notes, you'll be able to choose the right one for any data set!

1. Dot Maps: Visualising Density

A dot map uses dots of the same size to represent a specific quantity of a geographical feature. For example, one dot might equal \(100\) people or \(5\) shops.

How they work:

Instead of shading an entire area (like a choropleth map), you place dots exactly where the features are located. If you see a cluster of dots, it means that area has a high density or frequency.

When to use them:

• Showing population distribution (e.g., in Topic 4: Shaping Places).
• Mapping the location of specific services or businesses.
• Showing agricultural land use.

The Pros and Cons:

Advantages: They are very easy to understand and show exactly where things are, rather than averaging them out over a whole region.

Disadvantages: If there are too many dots, they can overlap into a "blob," making it impossible to count them. This is called oversaturation. Also, if your dot value is too high (e.g., \(1\) dot = \(10,000\) people), you might lose the detail of smaller settlements.

Quick Tip: If you are asked to "critique" a dot map in an exam, always look at the scale/key. Is the dot value appropriate? If \(1\) dot represents \(1,000,000\) people, the map will look empty even if millions live there!

2. Kite Diagrams: Tracking Change Over Space

Kite diagrams are specialized charts used to show how things change along a transect (a line across a landscape). They are most common in physical geography fieldwork, such as studying how vegetation changes as you move away from the sea across sand dunes (Topic 2B: Coastal Landscapes).

How to read them:

Imagine a central line for a specific plant species. The "kite" grows wider or narrower around that line.
- The Width: Represents the frequency or percentage cover of that species.
- The X-axis: Represents distance along the transect (e.g., \(0\) to \(100\) metres).
- The Shape: If the kite is wide at \(20m\) but disappears at \(50m\), it tells you exactly where that species thrives.

Key Takeaway:

Kite diagrams are brilliant for comparing multiple species at once. You can see at a glance where one species "takes over" from another as environmental conditions change.

3. Dispersion Diagrams: Spotting the Spread

A dispersion diagram is a simple but powerful way to show the "spread" of a data set. You plot every single piece of data as a dot on a vertical axis.

Why use them?

Unlike a mean (average), which hides the extremes, a dispersion diagram shows you everything!
- Clusters: Where do most of the points sit?
- Range: How far is the highest point from the lowest?
- Anomalies: Are there any "weird" data points far away from the rest?

Fieldwork Example:

If you measured the pebble size at two different beaches, a dispersion diagram would allow you to see if one beach has a "tighter" group of similar-sized pebbles, while the other has a massive variety from tiny sand grains to large rocks.

Note: For more on how to calculate the math behind these spreads (like the Interquartile Range), see the chapter on Descriptive Statistics.

4. Scales: Linear vs. Logarithmic

Choosing the right scale for your graph is like choosing the right lens for a camera—it changes how you see the data.

Linear Scales

This is your "standard" scale. The gaps between numbers are always equal.
Example: \(0, 10, 20, 30, 40...\)
In a linear scale, an increase from \(10\) to \(20\) looks exactly the same as an increase from \(90\) to \(100\). Use this when your data stays within a relatively small range.

Logarithmic Scales

In a logarithmic scale, each main mark on the axis increases by a power of 10.
Example: \(1, 10, 100, 1,000, 10,000...\)
Why on earth would we do this? Sometimes geographical data has a massive range. If you tried to plot river discharge (which might be \(0.5\) in summer but \(5,000\) during a flood) on a linear scale, the small numbers would all be squashed at the bottom. A log scale lets you see the detail in the small numbers while still fitting the huge numbers on the same page.

Common usage:
- The Richter Scale: (Used in Topic 1: Tectonic Hazards). An earthquake of magnitude \(7\) is \(10\) times more powerful than a magnitude \(6\).
- Hjulström Curves: Showing the relationship between river velocity and particle size.

Common Mistake: Don't forget that on a log scale, the distance between \(1\) and \(10\) is the same as the distance between \(10\) and \(100\). Always check the axis labels carefully in Paper 3!

Summary Checklist

Before moving on, make sure you can answer these three questions:

1. Which technique is best for showing a vegetation transect? (Answer: Kite diagram)
2. What is the main risk of using a dot map for a very crowded area? (Answer: Oversaturation/overlapping dots)
3. When should you use a logarithmic scale instead of a linear one? (Answer: When the data has a massive range of values, e.g., \(1\) to \(10,000\))

Ready for more?

Now that you know how to present data, check out the chapter on Descriptive Statistics to learn how to analyse those patterns using the Gini Coefficient and Lorenz Curve!