1) Analyzing City Population vs. Average Income
Scenario: A government agency wants to study the relationship between a city's population and the average income, with bubble size representing the unemployment rate.
Data Example:
City Population (millions) |
1.2 |
3.5 |
5.0 |
7.8 |
10.2 |
Average Income (USD per year) |
3500 |
4500 |
5000 |
5500 |
6000 |
Unemployment Rate (%) (Bubble Size) |
8 |
6 |
4 |
5 |
3 |
Purpose: The bubble chart helps policymakers understand income distribution across cities and assess the impact of population size on unemployment rates.
Download the
file to analyze trends in a bubble chart.
2) Analyzing Company Revenue vs. Employee Count
Scenario: Investors want to compare different companies based on revenue, workforce size, and profit margins.
Data Example:
Company Revenue (in million USD) |
50 |
120 |
200 |
350 |
500 |
Number of Employees |
300 |
800 |
1500 |
3000 |
5000 |
Profit Margin (%) (Bubble Size) |
10 |
15 |
12 |
18 |
20 |
Purpose: The bubble chart helps investors identify companies with high profitability and efficient workforce utilization.
Download the
file to compare company performance in a bubble chart.
3) Analyzing Temperature vs. Tourism Footfall
Scenario: A travel agency wants to analyze how temperature impacts tourist footfall, with bubble size indicating hotel occupancy rates.
Data Example:
Average Temperature (°C) |
10 |
20 |
25 |
30 |
35 |
Tourist Footfall (in thousands) |
50 |
70 |
100 |
120 |
80 |
Hotel Occupancy Rate (%) (Bubble Size) |
20 |
25 |
30 |
40 |
45 |
Purpose: The bubble chart helps the tourism industry plan promotional campaigns based on temperature trends and hotel occupancy data.
Download the
file to analyze seasonal tourism trends in a bubble chart.