Graphical Skills » Rose Charts and Scatter Graphs

What you'll learn this session

Study time: 30 minutes

How to create and interpret rose charts (wind roses)
How to construct and analyse scatter graphs
When to use each type of graph in geographical studies
How to identify patterns and relationships in data
Real-world applications of these graphical techniques in geography

Introduction to Graphical Skills: Rose Charts and Scatter Graphs

Graphical skills are essential tools for geographers to present, analyse and interpret data. In this session, we'll explore two important graphical techniques that help us understand patterns and relationships in geographical data: rose charts and scatter graphs.

Key Definitions:

Rose Chart (Wind Rose): A circular diagram showing the frequency and strength of phenomena from different directions, commonly used to display wind patterns.
Scatter Graph: A diagram that uses coordinates to display values for two variables, showing how they relate to each other and whether there is correlation between them.

📑 Why Graphical Skills Matter

Graphical skills allow geographers to:

Visualise complex data in an accessible format
Identify patterns and relationships that might not be obvious in raw data
Support geographical arguments with clear visual evidence
Communicate findings effectively to different audiences

🔬 Exam Skills Focus

In your iGCSE Geography exam, you may be asked to:

Interpret data presented in rose charts or scatter graphs
Construct your own graphs from given data
Describe patterns shown in graphs
Suggest explanations for relationships shown

Rose Charts (Wind Roses)

Rose charts, also known as wind roses, are circular diagrams that show the frequency of occurrences by direction. They're particularly useful in geography for displaying wind patterns, but can also be used for other directional data.

Understanding Rose Charts

A rose chart consists of:

Spokes or petals: Extending from the centre in different directions (usually showing compass directions)
Length of spokes: Represents the frequency or percentage of occurrences from that direction
Different colours or patterns: Often used to show different intensities (e.g., wind speeds)

🌬 Wind Direction

The direction shown on a wind rose indicates where the wind is coming FROM, not where it's going to. For example, a northerly wind comes from the north.

📊 Data Representation

The length of each 'petal' shows the percentage of time that wind blows from that direction. Longer petals = more frequent winds from that direction.

🎨 Colour Coding

Different colours or shading within each petal typically represent different wind speeds, with a key explaining what each colour means.

How to Construct a Rose Chart

Follow these steps to create your own rose chart:

Draw a circle with compass directions (N, NE, E, SE, S, SW, W, NW)
Collect your directional data and calculate percentages for each direction
Draw 'petals' extending from the centre in each direction, with length proportional to the frequency
If showing intensity (e.g., wind speed), use different colours or patterns within each petal
Add a title, key/legend and source of data

Case Study Focus: Wind Patterns in the UK

The UK experiences prevailing south-westerly winds due to the influence of the North Atlantic Drift. A rose chart of annual wind patterns in London would show longer petals pointing from the southwest, indicating that winds most frequently come from this direction. The chart would also show that winter months have stronger winds (shown by darker colours within the petals) compared to summer months.

Interpreting Rose Charts

When analysing a rose chart, ask yourself:

Which direction has the highest frequency? (longest petals)
Are there any directions with very little or no occurrence? (short or no petals)
What patterns can you see in terms of intensity? (colour variations)
How might these patterns relate to geographical factors? (e.g., local topography, global wind patterns)

Scatter Graphs

Scatter graphs (also called scatter plots or scattergrams) show the relationship between two variables. Each point represents a pair of values, with one variable on the x-axis and the other on the y-axis.

Understanding Scatter Graphs

Scatter graphs help us identify if there's a relationship (correlation) between two variables. They're particularly useful in geography for exploring relationships like:

Development indicators (e.g., GDP per capita vs life expectancy)
Climate data (e.g., temperature vs altitude)
Urban studies (e.g., distance from city centre vs house prices)
Environmental relationships (e.g., rainfall vs vegetation density)

📈 Types of Correlation

Positive correlation: As one variable increases, the other tends to increase too. Points form a pattern from bottom-left to top-right.

Negative correlation: As one variable increases, the other tends to decrease. Points form a pattern from top-left to bottom-right.

No correlation: No clear relationship between variables. Points appear randomly scattered.

💡 Strength of Correlation

Strong correlation: Points closely follow a clear pattern (almost forming a line).

Moderate correlation: General trend visible, but with some scatter.

Weak correlation: Slight pattern, but lots of scatter.

Remember: Correlation does not necessarily mean causation!

How to Construct a Scatter Graph

Follow these steps to create your own scatter graph:

Draw x and y axes on graph paper
Label the axes with the variables and units
Choose appropriate scales for both axes
Plot each pair of data as a point on the graph
Add a title and source of data
If appropriate, draw a line of best fit through the points to show the general trend

Line of Best Fit

A line of best fit (trend line) helps to show the relationship between variables more clearly. To draw one:

The line should pass through or close to as many points as possible
There should be roughly equal numbers of points above and below the line
Draw the line with a ruler
The steeper the line, the stronger the relationship

Case Study Focus: Development Indicators

A scatter graph plotting GDP per capita against life expectancy for different countries shows a positive correlation. Countries with higher GDP per capita (like Norway and Switzerland) generally have higher life expectancy, while countries with lower GDP per capita (like Niger and Chad) tend to have lower life expectancy. However, some countries don't fit the pattern perfectly - these 'outliers' are interesting to investigate. For example, the USA has very high GDP but lower life expectancy than might be expected, while Cuba has relatively high life expectancy despite lower GDP.

Interpreting Scatter Graphs

When analysing a scatter graph, consider:

Is there a positive, negative, or no correlation?
How strong is the correlation?
Are there any outliers (points that don't fit the general pattern)?
What might explain the relationship shown?
Could other factors be influencing the relationship?

Choosing the Right Graph

Knowing when to use each type of graph is an important geographical skill:

🌹 When to Use Rose Charts

Use rose charts when:

Showing directional data (e.g., wind direction, ocean currents)
Displaying frequency by direction
Comparing patterns across different time periods
Analysing coastal processes like longshore drift

📌 When to Use Scatter Graphs

Use scatter graphs when:

Investigating relationships between two variables
Testing geographical theories or models
Comparing development indicators
Exploring environmental relationships

Common Mistakes to Avoid

Watch out for these common errors when working with these graphs:

Rose charts: Confusing the direction (remember it shows where phenomena come FROM), using inconsistent scales, or not including a key for colours/patterns
Scatter graphs: Choosing inappropriate scales, forcing a line of best fit when there's no correlation, or claiming causation when only correlation is shown

Applying Your Skills

Being able to create and interpret these graphs is valuable for:

Fieldwork: Collecting and analysing your own data
Exam questions: Interpreting data presented in different formats
Geographical investigations: Testing hypotheses and theories
Real-world applications: Understanding weather forecasts, development patterns and environmental relationships

Remember, the key to mastering these graphical skills is practice. Try creating your own graphs with real geographical data and analyse examples in textbooks and online resources.

🧠 Test Your Knowledge!