What you'll learn

• How a geographical enquiry moves from a question to a conclusion.
• How to collect fieldwork data using observation, measurement and sampling.
• How to process and present fieldwork data using maps, graphs and diagrams.
• How to analyse, conclude and evaluate like a geographer in the exam.

What fieldwork means

Fieldwork is Geography done in the real world. Instead of only learning about rivers, coasts, cities or people from a textbook, you collect evidence in an actual place and use it to answer a geographical question.

For GCSE Geography, your fieldwork must happen outside the classroom and beyond the school grounds, on at least two occasions, in contrasting locations, and must include both a physical geography context and a human geography context. For example, a physical enquiry might study a river such as the River Holford in Somerset, while a human enquiry might investigate environmental quality in a town centre such as Stratford in East London. Your own school may use different places.

The fieldwork enquiry process

A fieldwork enquiry is not just “going somewhere and collecting data”. It is a sequence of decisions: you ask a question, choose methods, collect evidence, process it, analyse it, conclude, and evaluate how good your enquiry was.

The whole process is a cycle, because evaluation helps you improve the enquiry if it were repeated.

Asking a good enquiry question

An enquiry question is the main question your fieldwork is trying to answer. A good one is:

• specific: it names a place, process or group.
• measurable: you can collect data to answer it.
• geographical: it links to patterns, processes, places or people.
• manageable: it can be investigated safely in the time available.

Weak questions are often too vague, such as “Is the town centre good?” Better questions focus on a measurable pattern, such as “How does environmental quality change with distance from the main shopping street?”

Planning data collection

Before collecting data, you need to decide what evidence will answer the enquiry question.

Data can also be quantitative or qualitative. Quantitative data uses numbers, such as beach width in metres or traffic count per minute. Qualitative data uses descriptions, opinions or judgements, such as interview responses or field sketches.

Observation and measurement

Observation means carefully looking, listening and recording what is happening. Examples include land-use mapping, field sketches and environmental quality surveys.

Measurement means collecting numerical data using equipment or a consistent method. Examples include:

• river width, depth and velocity.
• beach gradient and sediment size.
• pedestrian counts and traffic counts.
• questionnaire results converted into categories or totals.

Sampling: choosing where or who to study

You usually cannot measure everything or ask everyone. Instead, you take a sample.

The diagram below compares common sampling strategies used in GCSE fieldwork.

Common sampling strategies

• Random sampling: sites or people are chosen by chance, often using random numbers.
• Systematic sampling: data is collected at regular intervals, such as every 10 metres along a beach transect.
• Stratified sampling: the sample includes different groups or zones in proportion to their importance or size, such as residential, retail and park areas.
• Opportunity sampling: you use people or sites that are easiest to access. This is quick, but it can be biased.

Processing and presenting data

Raw data is the original data you collected. Processed data has been organised or calculated so patterns are easier to see.

You might process data by:

• calculating totals, means, medians, modes or ranges.
• converting results into percentages.
• grouping questionnaire answers into categories.
• coding interview transcripts into themes such as “traffic”, “safety” or “green space”.

Choosing the right presentation method

Different data needs different presentation methods:

• Use a bar chart for categories, such as land use types.
• Use a line graph for change over distance or time, such as river depth downstream.
• Use a scatter graph to test relationships, such as distance from the CBD and environmental quality score.
• Use a located bar chart or proportional symbol map when the location of the data matters.
• Use a transect diagram to show change along a line across a beach, river valley or town centre.
• Use annotated photographs or field sketches to show visual evidence.

Analysing and explaining data

Analysis means describing and explaining what the data shows. You should look for:

• patterns: repeated trends in the data.
• anomalies: results that do not fit the pattern.
• relationships: links between two variables.
• spatial variation: how results change from place to place.

Explanation is where you use your geographical knowledge. For example, a river study might link results to erosion, friction and the Bradshaw model. An urban study might link environmental quality to land use, traffic, regeneration or planning decisions.

Drawing conclusions from evidence

A conclusion is your final answer to the enquiry question. It should not just repeat results; it should make a judgement based on evidence.

A strong conclusion usually:

• directly answers the enquiry question.
• uses specific data, such as scores, counts or measurements.
• mentions whether the evidence supports the original hypothesis.
• refers to anomalies or limitations.
• links to geographical processes or theory.

You may also be asked to use transcripts, which are written records of interviews, questionnaires or conversations. Do not just quote one person and treat it as proof. Instead, look for themes across several responses and compare them with your numerical data.

Evaluating fieldwork

Evaluation means judging how good your enquiry was. This is not the same as saying whether you enjoyed it.

Useful evaluation words include:

• accuracy: how close measurements are to the true value.
• reliability: whether the method would give similar results if repeated.
• validity: whether the method actually measures what it claims to measure.
• representativeness: whether the sample reflects the wider area or population.
• bias: anything that unfairly influences the results.

Good evaluation links a weakness to its likely effect and then suggests a realistic improvement. For example, “Only collecting pedestrian counts at lunchtime may overestimate footfall, so repeating counts in the morning and afternoon would make the results more representative.”