Section B: Fieldwork

What you'll learn

• What AQA expects from your two GCSE fieldwork enquiries.
• How to choose a strong enquiry question or hypothesis.
• How to collect, sample, record, present and analyse fieldwork data.
• How to write conclusions and evaluations that are evidence-based, not vague.

Why fieldwork matters

Fieldwork is geography done in the real world. It helps you test geographical ideas using evidence from actual places, rather than only learning about them in a classroom.

For AQA GCSE Geography, you must undertake two geographical enquiries. Each must use primary data, collected outside the classroom and school grounds. Fieldwork must happen on at least two occasions.

Your two enquiries must be in contrasting environments and must show understanding of both physical geography and human geography. At least one enquiry should show how physical and human geography interact, for example how coastal defences affect beach shape and tourism, or how urban surfaces affect runoff and flood risk.

AQA can assess fieldwork in two ways: by asking about your own enquiries, and by giving you unfamiliar fieldwork materials from a different place. For your own work, you must be able to identify the exact titles of your individual enquiries.

Your school or college must also submit a written statement confirming that fieldwork took place. This records the date, location, number of students, main questions investigated, the specification link, and is signed by the Head of Centre. That is mainly a centre responsibility, but it shows how seriously fieldwork is treated.

1. Choosing a suitable enquiry question

A strong enquiry begins with a focused question or hypothesis.

A good fieldwork question should be:

• Geographical: linked to place, space, processes, patterns or people-environment interaction.
• Focused: not too broad for the time and location available.
• Measurable: you can collect data to answer it.
• Linked to theory: based on a concept from physical or human geography.
• Feasible and safe: possible within the site, equipment, time and risk limits.

The geographical theory matters because it gives you an expected pattern. For example, the Bradshaw model suggests that river width, depth, discharge and velocity generally increase downstream, while bedload size and angularity decrease. In an urban enquiry, a land-use model or ideas about environmental quality might predict changes from the central business district to the suburbs.

Choosing locations and managing risk

Your fieldwork location should let you collect the right evidence. If you are studying downstream river changes, you need several sites from upstream to downstream. If you are studying urban change, you may need a transect from the CBD to the rural-urban fringe.

Physical fieldwork risks include tides, waves, unstable cliffs, deep water, steep slopes, poor weather and slippery surfaces. Human fieldwork risks include traffic, crowded pavements, interaction with strangers, personal safety and safeguarding issues. Risks can be reduced by checking weather and tide times, working in groups, wearing suitable clothing, using safe crossing points, keeping away from cliff edges or deep water, and following teacher instructions.

2. Selecting, measuring and recording data

Fieldwork uses evidence. Some evidence is collected by you, and some comes from other sources.

Physical data might include river width, depth, velocity, bedload size, beach profile, sediment size, infiltration rate or noise levels. Human data might include pedestrian counts, traffic counts, land-use surveys, environmental quality scores, questionnaires or photographs of urban features.

Good data collection needs clear recording. Use fieldwork sheets with units, site numbers, grid references or GPS locations, times, weather notes and names of equipment. Repeated measurements often improve reliability.

Sampling methods

A sample is a smaller selection taken from a larger whole. Sampling is the method used to choose what or where to measure. This matters because you usually cannot measure every person, stone, building or point along a river.

Common methods include:

• Random sampling: every possible site or person has an equal chance of being chosen.
• Systematic sampling: data is collected at regular intervals, such as every 100 m.
• Stratified sampling: the sample reflects known groups or zones, such as different land-use types.
• Opportunistic sampling: data is collected from whoever or wherever is easiest to access.

3. Processing and presenting fieldwork data

Processing data means turning raw measurements into something easier to interpret, such as totals, averages, percentages or categories. Presenting data means showing it visually so patterns are clearer.

There are three broad types of presentation:

• Visual methods: annotated photographs, field sketches, labelled diagrams.
• Graphical methods: bar charts, line graphs, scattergraphs, pie charts, divided bar charts.
• Cartographic methods: map-based methods, such as located bar charts, proportional symbols, choropleth maps, isoline maps or flow-line maps.

Choose the method to match the data. A scattergraph is useful for comparing two variables, such as distance downstream and river velocity. A bar chart works well for comparing categories or separate sites. A map is best when location is central to the pattern.

Accurate presentation needs clear titles, labelled axes, units, a suitable scale, a key, and careful plotting. For maps, include location information such as site labels, direction or grid references where relevant.

4. Describing, analysing and explaining results

These three words are not the same.

A strong paragraph usually follows this pattern: overall trend, specific data evidence, anomaly if there is one, then explanation.

An anomaly is a result that does not fit the main pattern. For example, a river site may have unexpectedly high velocity because the channel has been artificially narrowed by a bridge. Do not ignore anomalies; they often help you evaluate the enquiry.

You may also need to establish links between data sets. For example, in urban fieldwork, high pedestrian flow might link to high noise levels and lower environmental quality. In river fieldwork, greater depth might link to higher velocity and larger discharge.

Statistical techniques

Useful fieldwork statistics include:

• Mean: the arithmetic average; useful when there are no extreme anomalies.
• Median: the middle value; useful when data is skewed by outliers.
• Mode: the most common value or category.
• Range: highest value minus lowest value; a simple measure of spread.
• Percentage change: useful for comparing change between sites.

Percentage change is calculated using:

$$percentage\; change=\frac{new\; value-original\; value}{original\; value}\times 100$$

5. Reaching conclusions

A conclusion must answer the original question or hypothesis. It should not introduce a brand-new idea.

A good conclusion says whether the evidence supports, partly supports or does not support the hypothesis. It uses precise evidence from the results, refers back to geographical theory, and mentions any important anomalies.

For example, instead of writing “the river got bigger downstream”, write something like: “The hypothesis was mostly supported because channel width increased from the upstream to downstream sites, although Site 3 was an anomaly, possibly due to channel management near the bridge.” Use your own real place names and figures.

6. Evaluating the enquiry

Evaluation means judging the quality of your investigation. This is where many students lose marks by being too vague.

When evaluating, consider:

• Problems with data collection methods: small sample size, rushed timing, equipment faults, observer bias, weather changes, access restrictions.
• Limitations of the data collected: only one day of data, only one season, too few sites, uneven questionnaire sample, outdated secondary data.
• Other useful data: repeat visits, more sites, different times of day, census data, flood records, Met Office data, land-use planning documents or historic maps.
• Reliability of conclusions: whether the evidence is strong enough to support the final judgement.

Avoid simply saying “human error”. That is too vague. Explain exactly what the error was: misreading a ruler, timing floating objects inconsistently, asking leading questionnaire questions, or using an unclear environmental quality score.