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examBoard: Cambridge
examType: IGCSE
lessonTitle: Data Table Operations
Data tables are everywhere in geography! From population statistics to rainfall measurements, being able to work with tables of data is an essential skill for your iGCSE Geography studies. In this session, we'll explore how to make sense of data tables and perform key calculations that will help you analyse geographical information.
Key Definitions:
Data tables help geographers organise information about places, people and environments. They allow us to compare different locations, track changes over time and identify patterns that might not be obvious just by looking at a map or reading text. Whether you're studying climate change, population growth, or economic development, data tables provide the evidence you need to support geographical arguments.
When approaching a data table, first identify what each row and column represents. The column headings usually tell you what variables are being measured, while rows often represent different locations or time periods. Always check the units of measurement (e.g., °C, km², people per km²) and note any footnotes that explain how the data was collected or what specific terms mean.
To make sense of geographical data, you need to be comfortable with some basic statistical operations. These calculations help you summarise large amounts of data and identify important patterns.
These calculations help you find the "typical" or "average" value in a data set. Different measures are useful in different geographical contexts.
The mean is what most people call the "average" - add up all values and divide by the number of values.
Example: To find the mean annual rainfall across 5 weather stations: (1200mm + 980mm + 1050mm + 1300mm + 870mm) ÷ 5 = 1080mm
The middle value when all data is arranged in order. Useful when there are extreme values that might skew the mean.
Example: For the GDP per capita values $2,500, $3,200, $4,100, $45,000, $48,000, the median is $4,100 (the middle value).
The most frequently occurring value in a data set.
Example: If land use categories in a survey are: residential, commercial, residential, industrial, residential, agricultural - the mode is residential.
Understanding how spread out your data is helps you determine whether values are clustered around the average or widely dispersed.
The difference between the highest and lowest values in a data set. A simple but useful measure of spread.
Example: If temperatures range from 15°C to 32°C, the range is 17°C (32 - 15 = 17).
A large range might indicate significant variation in climate or development levels between regions.
The range of the middle 50% of the data. This ignores extreme values that might be outliers.
To calculate: arrange data in order, find the values a quarter of the way from the bottom (Q1) and three-quarters of the way from the bottom (Q3), then subtract Q1 from Q3.
Particularly useful when comparing development indicators across countries with very different characteristics.
Percentages are crucial in geography as they allow you to compare values of different magnitudes and understand proportional changes over time.
Calculating how much a value has changed over time as a percentage is one of the most common operations you'll perform in geography.
Percentage change = ((New value - Original value) ÷ Original value) × 100
Example: If a city's population grew from 250,000 in 2010 to 320,000 in 2020:
Percentage change = ((320,000 - 250,000) ÷ 250,000) × 100 = (70,000 ÷ 250,000) × 100 = 0.28 × 100 = 28%
The population increased by 28% over the decade.
If your answer is positive, it's an increase. If it's negative, it's a decrease.
For example, if a country's birth rate fell from 28 per 1000 to 22 per 1000:
Percentage change = ((22 - 28) ÷ 28) × 100 = (-6 ÷ 28) × 100 = -21.4%
The birth rate decreased by 21.4%.
Calculating what percentage of a total each category represents helps you understand the composition of geographical phenomena.
Percentage of total = (Value ÷ Total) × 100
Example: Employment sectors in a region:
| Sector | Number of workers | Percentage calculation | Percentage of workforce |
|---|---|---|---|
| Primary | 15,000 | (15,000 ÷ 100,000) × 100 | 15% |
| Secondary | 25,000 | (25,000 ÷ 100,000) × 100 | 25% |
| Tertiary | 45,000 | (45,000 ÷ 100,000) × 100 | 45% |
| Quaternary | 15,000 | (15,000 ÷ 100,000) × 100 | 15% |
| Total | 100,000 | 100% |
This shows the region has a service-dominated economy (tertiary sector) with equal proportions of primary and quaternary employment.
A key skill in geographical data analysis is being able to spot patterns and identify values that don't fit the pattern (anomalies).
When examining a data table, ask yourself:
For example, you might notice that countries with higher GDP per capita tend to have lower birth rates, suggesting a correlation between economic development and demographic change.
Anomalies are values that don't fit the general pattern. They might be:
Anomalies are often the most interesting parts of your data! They might indicate special circumstances, measurement errors, or unique geographical factors worth investigating further.
The table below shows monthly rainfall data for a tropical location:
| Month | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Rainfall (mm) | 25 | 30 | 45 | 120 | 250 | 320 | 380 | 360 | 290 | 150 | 60 | 30 |
Data analysis:
This pattern is typical of a monsoon climate, with a pronounced wet season during summer months when the Inter-Tropical Convergence Zone (ITCZ) moves northward.
Here are some practical strategies to help you work effectively with data tables in your iGCSE Geography exams and coursework:
The mathematical skills you're learning aren't just for passing exams - they're essential tools for understanding real-world geographical issues. When you read about climate change, population growth, economic development, or resource management, you'll encounter data tables that need these exact skills to interpret.
The table below shows birth rates, death rates and natural increase for four countries at different stages of development:
| Country | Birth Rate (per 1000) | Death Rate (per 1000) | Natural Increase (per 1000) |
|---|---|---|---|
| Niger | 46.1 | 11.2 | 34.9 |
| India | 19.3 | 7.3 | 12.0 |
| UK | 11.4 | 9.4 | 2.0 |
| Japan | 7.3 | 10.9 | -3.6 |
This data illustrates the demographic transition model, showing how countries move from high birth and death rates (Niger - Stage 2) through falling birth rates (India - Stage 3) to low birth and death rates (UK - Stage 4) and finally to an ageing population with more deaths than births (Japan - Stage 5).
Note that Natural Increase = Birth Rate - Death Rate and a negative value indicates population decline without migration.
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