5. Tree maps.
A tree map displays hierarchical data using nested squares and rectangles to represent smaller parts of a greater whole, making it great for comparing proportions within categories. For example, you could use a tree graph to visualise business budget by department or website traffic by source.
The size and colour of each rectangle represent different data values, with the size often being proportional. With this in mind, keeping the colour key straightforward and easy to use is important when building out tree maps.
6. Scatter plots.
A scatter plot shows the relationship between two variables using dots plotted on the X and Y axes. It’s great for spotting correlations or trends, such as the link between study time and test scores. Among the different types of graphs, scatter plots are ideal for showing patterns in data and detecting any outliers in your dataset.
A key tip is to add a clear trendline to highlight correlations and detect any major outliers.
7. Histograms.
A histogram is similar to a bar chart, but groups data into ranges and focuses on displaying continuous information. This makes it perfect for showing the distribution and frequency of numerical data, like exam scores or ages. It essentially helps you understand how data spreads across intervals.
When creating your histogram and compiling the relevant data, make sure to choose your intervals carefully for more accurate insights.
8. Heat maps.
A heat map uses colour gradients to represent data intensity across two dimensions, helping you quickly identify patterns or hotspots. It’s ideal for visualising data like website clicks, sales by region, or customer activity. When deciding what graph to use for showing density or concentration, heat maps provide a clear and intuitive view.
Because heat maps often use quite a high volume of data to visualise patterns, it’s a good idea to keep the colour coding clear and consistent.
