Scatter Graphs

Purpose of Scatter Graphs

Scatter graphs are used to show the relationship between two different variables. Each point on the graph represents a pair of values, one from each variable. This visual display helps identify if and how the variables are connected.

By plotting data points, scatter graphs allow you to:

• See if there is a correlation (relationship) between the variables
• Determine the type of correlation: positive, negative, or none
• Analyse patterns and trends in data quickly and visually

For example, a scatter graph could show the relationship between rainfall and crop yield in a farming area, helping to understand how rainfall affects agricultural productivity.

For instance, if you have data for average monthly temperature and ice cream sales, you would label the x-axis as "Temperature ($100^\circ\mathrm{C}$)" and the y-axis as "Ice Cream Sales ($\text{\u00a3}$)". Then plot each month's data point according to these values.

Plotting Scatter Graphs

To plot a scatter graph correctly:

• Label the axes clearly with the variables being compared. The independent variable usually goes on the x-axis (horizontal), and the dependent variable on the y-axis (vertical).
• Choose an appropriate scale for each axis so all data points fit well and are spread out for easy interpretation.
• Plot each data point accurately by finding the correct position on the graph where the x and y values meet.

For example, if you are plotting temperature ($100^\circ\mathrm{C}$) on the x-axis and number of visitors to a park on the y-axis, each point shows the visitor number at a specific temperature.

For instance, plotting a single data point: if the temperature is $15^\circ\mathrm{C}$ and ice cream sales are $\text{\u00a3}70$, you would find 15 on the x-axis and 70 on the y-axis, then mark the point where these meet.

Interpreting Scatter Graphs

Once the scatter graph is plotted, you can interpret it by looking for:

• Type of correlation:
  • Positive correlation: As one variable increases, the other also increases. Points slope upwards from left to right.
  • Negative correlation: As one variable increases, the other decreases. Points slope downwards from left to right.
  • No correlation: No clear pattern; points are scattered randomly.
• Strength of correlation: How closely the points fit a line. A strong correlation means points are tightly clustered along a line; a weak correlation means points are more spread out.
• Outliers: Points that do not fit the general pattern. These may indicate unusual data or errors.

For example, a scatter graph showing average hours of sunlight and number of tourists might show a positive correlation if more tourists visit when there is more sunlight. If one point shows very few tourists despite high sunlight, that could be an outlier.

Uses in Geography

Scatter graphs are valuable in geography because they help analyse relationships between environmental and human factors. They are commonly used to:

• Examine environmental data such as rainfall vs. vegetation growth or temperature vs. energy use
• Explore human-environment interactions, for example, population density vs. pollution levels
• Support fieldwork by analysing collected data to identify trends and patterns

For example, during a fieldwork study, students might collect data on soil pH and plant species diversity, then use a scatter graph to see if there is a relationship between soil acidity and biodiversity.