Presenting and Analysing Fieldwork Data Revision Notes | GCSE AQA Higher Geography

1. Why Presenting and Analysing Data Is the Heart of Fieldwork

You have collected your fieldwork data. Now comes the part that actually earns marks on Paper 3: turning raw numbers into geographical understanding. The way you present data shapes what patterns become visible, and the way you analyse it determines whether your enquiry answers its original question.

Paper 3 will ask you about your own individual enquiry — including its title, your chosen presentation methods, and how you interpreted your results. This lesson builds the skills to handle all three confidently.

2. Selecting Appropriate Presentation Methods

Choosing a presentation method is not a free choice — it must match your data type and your enquiry question. A method that suits continuous numerical data (like river velocity at different points) is different from one that suits categorical data (like land use types in a CBD).

Qualitative data — such as photographs, field sketches, or environmental quality scores — tells you about character and appearance rather than precise measurements. It is often displayed using annotated images, bipolar surveys, or written descriptions rather than graphs.

The key justification question is: does this method make the pattern in my data visible and straightforward to interpret? If your enquiry asks whether pedestrian footfall decreases with distance from the town centre, a scatter graph directly tests that relationship — a pie chart would not.

3. Describing Fieldwork Data — What 'Describe' Actually Means

When an exam question says 'describe', it wants you to state what the data shows — the pattern, the trend, the range, and any anomalies. It does not want reasons or explanations at this stage. Think of it as narrating what you see, not why you see it.

• State the overall trend: 'Pedestrian counts generally decrease with distance from the town centre.'
• Quote specific data values to support the trend: 'Counts fell from 320 per minute at Site 1 to 47 per minute at Site 6.'
• Identify the range: 'Values ranged from 47 to 320 per minute across the six sites.'
• Note any anomaly: 'Site 4 recorded a higher count than expected at 210 per minute, breaking the overall pattern.'

Quoting specific data values is what separates a Level 2 description from a Level 1 one. Vague statements like 'the numbers go down' do not demonstrate that you have actually read the data — precise figures do.

4. Analysing and Explaining Fieldwork Data

Analysis goes one step further than description. Once you have stated what the data shows, you need to explain why — linking your results back to geographical processes and concepts. This is where your enquiry becomes genuinely geographical rather than just a list of numbers.

The structure to use is: describe the pattern → link to a geographical process → explain the mechanism. Each step builds on the last, creating a causal chain that examiners reward.

Worked example: How to Analyse a Fieldwork Result

Your data shows that pedestrian counts were highest near the town centre and decreased with distance. Analyse this result.

1. Pedestrian counts decreased from 320 per minute at Site 1 (town centre) to 47 per minute at Site 6 (edge of CBD). — Always open with a precise description that quotes values. This earns the first marks and sets up your explanation.
2. This pattern reflects the concept of land use zoning and the bid-rent theory — land values and footfall both decline with distance from the central business district (CBD). — Name the relevant geographical idea. This signals to the examiner that you understand the theoretical context of your enquiry.
3. As distance from the CBD increases, the concentration of retail and commercial functions decreases. Fewer shops and services mean fewer people are drawn to those locations, reducing pedestrian flow. — Use connective language — 'because', 'as a result', 'this means' — to build the causal chain. Avoid listing facts without linking them.
4. Site 4 recorded a higher count than expected (210 per minute). This may reflect the presence of a large supermarket or transport hub at that location, which generates its own footfall independent of CBD proximity. — Anomalies are not mistakes — they are opportunities to demonstrate deeper analysis. Suggest a geographical reason for the deviation.

Answer: Pedestrian counts decreased from 320 per minute at Site 1 to 47 per minute at Site 6, consistent with the pattern predicted by bid-rent theory. As distance from the CBD increases, retail density falls, reducing the number of people drawn to those areas. Site 4 is an anomaly (210 per minute), likely explained by a large attractor such as a supermarket generating independent footfall.

Notice how the analysis above never just restates the data — every sentence either describes, explains, or accounts for a deviation. That three-part rhythm (pattern → process → anomaly) is what moves a response into the higher mark bands.

5. Reliability, Limitations, and Improvements

No fieldwork is perfect. Paper 3 questions regularly ask you to evaluate the reliability of your data and suggest how the enquiry could be improved. This is not an invitation to criticise your own work carelessly — it is a chance to show geographical thinking about evidence quality.

When evaluating reliability, work through four areas: sample size, sampling strategy, timing and conditions, and equipment accuracy. Each one can introduce error or bias that affects how far your conclusions can be trusted.

• Sample size: a small sample (e.g. five sites) may not represent the full range of conditions along a river or street. A larger sample reduces the influence of individual anomalies.
• Sampling strategy: systematic sampling at regular intervals avoids the unconscious bias of choosing 'convenient' sites, but may miss important features between sample points.
• Timing and conditions: data collected on one day may not reflect typical conditions — weather, time of day, and season all affect results such as pedestrian counts or river discharge.
• Equipment accuracy: measuring river velocity with a flow meter introduces less human error than timing a float over a set distance, but both have limitations that should be acknowledged.

An improvement suggestion is only useful if it is specific and linked to the limitation it addresses. 'Collect more data' is too vague. 'Increase the number of sample sites from five to fifteen at equal intervals along the transect to improve spatial coverage' is the kind of precision that earns marks.

6. Exam Technique for Paper 3 Fieldwork Questions

Paper 3 fieldwork questions are built around your own enquiry, so you need to know your enquiry title, your data collection methods, your presentation choices, and your conclusions. The examiner cannot give you marks for vague references — specificity is everything.

The command words used in fieldwork questions follow the same rules as the rest of the paper. 'Describe' wants pattern and data. 'Explain' wants a causal chain. 'Evaluate' or 'assess' want a balanced judgement — strengths and limitations — with a conclusion. Mixing these up is one of the most common ways students lose marks.

Worked example: How to Answer: 'Justify Your Choice of Presentation Method'

Justify why you chose a scatter graph to present your fieldwork data on the relationship between distance from the CBD and environmental quality scores. [4 marks]

1. I used a scatter graph to present environmental quality scores (recorded on a bipolar survey scale) against distance from the CBD in metres. — Always name the method and describe the data it displays. This confirms you are answering about your actual enquiry.
2. A scatter graph is appropriate because both variables are numerical and continuous, allowing me to plot each site as a point and assess whether a correlation exists between the two variables. — Link the method to the data type. Scatter graphs are designed to show relationships between two numerical variables — this is the core justification.
3. My enquiry question asked whether environmental quality decreases with distance from the CBD. A scatter graph with a line of best fit directly tests this by showing the direction and strength of any correlation. — The best justifications connect the method to the specific enquiry question, not just to the data type in general.
4. However, a scatter graph cannot show the spatial distribution of scores across the study area. A choropleth map would have been needed to display which specific locations had the lowest environmental quality. — Acknowledging a limitation shows evaluative thinking and is expected in a 'justify' or 'evaluate' response at this level.

Answer: A scatter graph was chosen because both distance and environmental quality score are numerical variables, making it possible to plot each site and assess correlation. This directly addressed the enquiry question about whether quality decreases with distance from the CBD. A line of best fit confirmed a negative correlation. A limitation is that the scatter graph does not show the spatial pattern of scores — a choropleth map would have been needed for that purpose.