Map and Fieldwork Skills » Graphical techniques and data presentation

What you'll learn this session

Study time: 30 minutes

Different types of graphs and charts for presenting geographical data
How to select appropriate graphical techniques for different data types
Methods for creating and interpreting line graphs, bar charts, pie charts and scatter graphs
Advanced presentation techniques including proportional symbols and choropleth maps
How to analyse and evaluate the effectiveness of different data presentation methods

Introduction to Graphical Techniques and Data Presentation

Geographers collect lots of data during fieldwork and from other sources. To make sense of all this information, we need to present it in a clear, visual way. Good graphs and charts help us spot patterns, trends and relationships that might be hidden in tables of numbers.

Key Definitions:

Data: Facts and statistics collected for analysis.
Graph: A diagram showing the relationship between two or more variables.
Quantitative data: Numerical information that can be measured.
Qualitative data: Descriptive information based on qualities rather than numbers.
Variable: A factor or characteristic that can change or vary.

📊 Why Use Graphs?

Graphs and charts help us to:

Simplify complex information
Identify patterns and trends quickly
Compare different sets of data
Present findings to others clearly
Support geographical arguments with visual evidence

📝 Choosing the Right Graph

The type of graph you choose depends on:

What type of data you have
What you want to show
How many variables you're comparing
Whether you're showing change over time
Your audience and purpose

Basic Graphical Techniques

Line Graphs

Line graphs are perfect for showing changes over time or continuous data. They're great for spotting trends, patterns and relationships between variables.

💡 Creating Line Graphs

Draw and label both axes (x-axis for time/categories, y-axis for values)
Plot the data points accurately
Connect the points with straight lines
Add a title and legend if using multiple lines
Include the source of your data

Best for: Temperature changes, population growth, river discharge over time

When interpreting line graphs, look for:

Overall trends (increasing, decreasing, fluctuating)
Rate of change (steep or gentle slopes)
Anomalies or unusual patterns
Relationships between different lines if multiple variables are shown

Bar Charts and Histograms

Bar charts use rectangular bars to compare different categories or groups. They're simple but effective for showing discrete data.

📈 Bar Charts

Best for: Comparing discrete categories

Examples: Land use in different areas, employment by sector, rainfall by month

Bars should be the same width with gaps between them.

📊 Histograms

Best for: Showing frequency distributions

Examples: Age structure, building heights, pebble size distribution

Bars touch each other with no gaps and can have different widths.

You can also create:

Compound bar charts: Show subdivisions within each bar
Multiple bar charts: Group bars to compare different sets of data
Divided bar charts: Show proportions of a whole as segments of a bar

Pie Charts

Pie charts show how a whole is divided into parts. They're excellent for showing proportions and percentages.

Creating Pie Charts

Calculate the percentage or proportion of each category
Convert percentages to angles (multiply by 3.6 to get degrees)
Draw a circle and mark the centre
Use a protractor to measure and mark each segment
Label each segment clearly and include a title

Best for: Land use percentages, energy sources, employment sectors

Remember: Pie charts work best when you have relatively few categories (ideally 3-7) and when the parts add up to 100%.

Advanced Graphical Techniques

Scatter Graphs and Correlation

Scatter graphs plot individual data points to show the relationship between two variables. They help identify correlations and patterns.

📈 Types of Correlation

Positive correlation: As one variable increases, so does the other
Negative correlation: As one variable increases, the other decreases
No correlation: No clear pattern between variables
Strong correlation: Points close to a line
Weak correlation: Points scattered more widely

📊 Using Scatter Graphs

Best for: Testing relationships between variables

Examples:

Distance from CBD vs house prices
Temperature vs altitude
Development indicators (e.g., GDP vs literacy rates)

You can add a line of best fit to show the overall trend.

Proportional Symbols and Flow Lines

These techniques allow you to show quantities or movements on maps.

🔳 Proportional Symbols

Symbols (usually circles) sized according to the data they represent.

Best for: Showing quantities at specific locations

Examples: City populations, tourist numbers, earthquake magnitudes

Remember to include a scale to show what the symbol sizes represent.

➡ Flow Lines

Lines with varying thickness to show movement between places.

Best for: Migration patterns, trade flows, traffic volumes

The width of the line is proportional to the quantity being shown.

Arrows indicate direction of movement.

Choropleth Maps

Choropleth maps use different shades or colours to show how a variable changes across an area.

Creating Choropleth Maps

Divide your data into 4-6 categories or classes
Choose a colour scheme (e.g., light to dark shades of one colour)
Shade each area according to its data value
Create a key showing what each shade represents
Add a title and data source

Best for: Population density, unemployment rates, rainfall distribution, development indicators

Limitations: Choropleth maps can be misleading if areas vary greatly in size. They also show abrupt changes at boundaries when in reality, changes may be more gradual.

Practical Tips for Data Presentation

✅ Accuracy

Use appropriate scales
Plot data points carefully
Double-check calculations
Include units of measurement

🔧 Clarity

Label axes clearly
Use a clear title
Include a legend if needed
Don't overcrowd with data

💡 Analysis

Describe what the graph shows
Identify patterns and trends
Suggest reasons for patterns
Link to geographical theories

Case Study Focus: River Study Data Presentation

A group of GCSE Geography students collected data from the River Exe in Devon. They measured river width, depth, velocity and bedload size at five sites moving downstream.

Data presentation methods used:

Line graphs: To show how river depth and width changed downstream
Scatter graph: To test the relationship between distance from source and velocity
Bar chart: To compare average bedload size at each site
Cross-sectional diagrams: To show the changing shape of the river channel

By using multiple graphical techniques, the students could clearly demonstrate the Bradshaw Model of river changes and support their conclusions with visual evidence.

Choosing the Right Technique

When deciding which graphical technique to use, ask yourself:

What type of data do I have? (Continuous, discrete, categorical)
What geographical pattern or relationship am I trying to show?
How many variables do I want to display?
Who is my audience and what will be clearest for them?

Remember, the best data presentation:

Makes complex information easy to understand
Highlights the geographical patterns you want to show
Is accurate and not misleading
Supports your geographical analysis and conclusions