Welcome to Geographical Fieldwork Investigation
Fieldwork is at the very heart of geography. It is your opportunity to step out of the classroom, get your hands dirty, and explore how real-world environments operate. Whether you are measuring the speed of a river, recording coastal pebble sizes, or surveying public perceptions in an urban town centre, fieldwork brings geographical theory to life.
Don't worry if fieldwork feels a bit overwhelming at first! This guide breaks down the entire Geographical Enquiry Cycle into simple, step-by-step stages, equipping you with everything you need to master your AQA AS Level Geography (7036) Component 2 exam.
---1. The AS Level Fieldwork Rules & Exam Setup
Before jumping into the methods, let's look at the official rules and how you will be tested:
The Mandatory Rules:
- You must complete a minimum of 2 full days of fieldwork (or the part-day equivalent) outside the classroom and school grounds.
- Your fieldwork must cover both physical geography and human geography processes.
- Your school or college must submit an official Fieldwork Statement by 15 May in the year of your exam to confirm you completed your 2 days.
How It Is Examined (Component 2: 7036/2):
- Fieldwork is assessed in Component 2: Human geography and geography fieldwork investigation.
- The exam lasts 1 hour 30 minutes, has 80 marks in total, and counts for 50% of your total AS Level.
- Section B of this paper is dedicated entirely to Geography fieldwork investigation and geographical skills (worth 40 marks).
- In Section B, you must answer Question 2 (compulsory) and then choose either Question 3 or Question 4. These questions test both unfamiliar fieldwork scenarios and your ability to reflect on your own fieldwork experience.
Top Examiner Warning: Unlike the full A-Level, there is NO coursework / Non-Examined Assessment (NEA) report to write or submit at AS Level! All your fieldwork knowledge is tested directly through the written exam.
Quick Key Takeaway: 2 days of fieldwork (physical + human) \(\rightarrow\) Tested in Paper 2, Section B (40 marks) \(\rightarrow\) No coursework folder submitted!
---2. The Geographical Enquiry Cycle
Every successful geographical investigation follows a logical cycle of six distinct stages:
1. Preparation \(\rightarrow\) 2. Data Collection \(\rightarrow\) 3. Data Presentation \(\rightarrow\) 4. Data Analysis \(\rightarrow\) 5. Conclusions \(\rightarrow\) 6. Evaluation
---3. Stage 1: Preparation & Planning
Aims, Questions, and Hypotheses
Every enquiry starts with a clear focus:
• Aim: The broad goal of what you want to find out (e.g., "To investigate changes in bedload characteristics along the River Ash.").
• Research Question: A focused question guiding your investigation (e.g., "Does pebble roundness increase downstream?").
• Hypothesis: A precise, testable prediction based on geographical theory (e.g., "Pebble size will decrease as distance from the source increases.").
Sampling Strategies
You cannot measure every single pebble on a beach or interview every person in a city. You must select a sample. Choosing the right sampling method is vital to avoid bias.
1. Random Sampling: Every item or location has an equal chance of being selected (e.g., using a random number generator to pick grid coordinates).
Advantage: Completely eliminates researcher bias.
Limitation: Important sub-areas might be missed purely by chance.
2. Systematic Sampling: Data is collected at regular, set intervals (e.g., taking an environmental quality survey every 50 metres along a high street transect, or testing river depth every 0.5 metres across a channel).
Advantage: Simple to organise and guarantees even spatial coverage across a transect.
Limitation: Can introduce bias if the chosen interval accidentally coincides with a hidden environmental pattern.
3. Stratified Sampling: The population or environment is divided into known subgroups (strata), and samples are taken from each subgroup in proportion to their size (e.g., selecting questionnaire respondents to match the exact age demographics of a town from census data).
Advantage: Ensures all key subgroups are fairly represented.
Limitation: Requires accurate background secondary data beforehand.
4. Opportunistic & Pragmatic Sampling: Sampling based on what is readily accessible or practical due to time, safety, or logistical constraints (e.g., surveying people who happen to be sitting on benches, or sampling river sites only where public footpaths meet the bank).
Advantage: Highly practical and saves time in difficult terrain.
Limitation: High risk of sampling bias; results may not represent the entire study area.
Risk Assessment & Ethics
Before stepping into the field, geographers must evaluate risks and ethical responsibilities:
• Health & Safety: Identify hazards (e.g., slipping on wet river boulders, hypothermia, tidal cutoff), assess the severity/likelihood, and establish control measures (e.g., wearing waders/high-grip boots, checking tide timetables, carrying first-aid kits and mobile phones).
• Ethics & Permissions: Respect private property (securing land access permissions), protect participant anonymity in human surveys, and ensure informed consent when recording interviews.
Quick Key Takeaway: Clear hypotheses + appropriate sampling (Random, Systematic, Stratified, Pragmatic) + thorough risk assessments = a solid fieldwork foundation.
---4. Stage 2: Data Collection (Primary & Secondary)
Geographers gather two types of data:
• Primary Data: Fresh data you collect yourself directly in the field (e.g., river flow velocity measurements, beach profiles, pedestrian counts).
• Secondary Data: Information gathered by someone else prior to your study (e.g., UK Census data, historical Ordnance Survey maps, Environment Agency flood risk layers, meteorological records from the Met Office).
Quantitative vs. Qualitative Approaches
Quantitative Data: Numerical data that can be counted, measured, and analysed statistically (e.g., infiltration rates in seconds, traffic tallies, river discharge in \(m^3/s\)). It is objective and easy to graph.
Qualitative Data: Non-numerical, descriptive data that captures human feelings, perceptions, or visual landscapes. Examples include:
• Soundscapes: Recording and mapping ambient sounds (e.g., birdsong vs. traffic rumble) to assess tranquility.
• Perceptual Maps: Asking residents to draw or mark areas they feel are safe, welcoming, or unwelcoming.
• Field Sketches & Annotated Photos: Drawing or capturing a landform/streetscape and labelling key geographical processes.
• Coding & Thematic Analysis: Transcribing unstructured interviews and grouping repeated words or themes into categories to identify community attitudes.
Geographical Skills & Digital Technologies
Modern fieldwork frequently uses digital tools:
• GPS (Global Positioning System): Accurately pinpoints exact latitude and longitude coordinates for survey sites.
• GIS (Geographical Information Systems): Software like ArcGIS, QGIS, or Google Earth allows you to overlay primary field data onto digital basemaps (e.g., overlaying river pebble sizes onto a geology map layer).
• Digital Sensors: Flow meters, digital calipers, and light sensors that record precise measurements instantly.
Quick Key Takeaway: Combine primary and secondary data, balancing quantitative measurements with qualitative insights (perceptions, coding, GIS mapping) for a complete enquiry.
---5. Stage 3: Data Presentation Techniques
Once you have collected your data, you need to present it clearly using cartographical (maps) and graphical (charts) methods.
Cartographical Techniques (Mapping Data)
• Dot Maps: Dots of identical size represent a specific quantity of an item across space.
• Choropleth Maps: Areas are shaded in progressively darker tones to represent graduated densities or percentages (e.g., deprivation levels across census output areas).
• Proportional Symbols: Symbols (like circles or squares) scaled in proportion to the value they represent at a specific point location.
• Flow Line Maps: Lines representing movement between locations, where line thickness indicates the volume of movement (e.g., commuter flows or pedestrian movement).
• Isoline Maps: Continuous lines connecting points of equal value (e.g., contour lines for elevation, isobars for atmospheric pressure, or isochrones for travel time).
Graphical Techniques (Charts & Graphs)
• Bar Charts & Histograms: Bar charts show discrete categories; histograms display continuous numerical data divided into class intervals.
• Scatter Graphs: Plot two numerical variables against each other to identify correlations (e.g., river channel width vs. distance downstream).
• Dispersion Graphs: Show the spread, range, and clustering of a single dataset along a vertical axis.
• Triangular Graphs: Display data composed of three percentage components that always add up to \(100\%\) (e.g., soil texture: % sand, % silt, % clay; or employment: % primary, % secondary, % tertiary).
• Bi-polar Charts & Radial Graphs: Bi-polar charts plot opposing adjectives on a negative-to-positive scale (e.g., \(-3\) noisy to \(+3\) quiet); radial (radar) graphs plot multiple variables spreading out from a central point (great for multi-criteria environmental quality scores).
Quick Key Takeaway: Always select the presentation method that best reveals patterns, trends, and spatial relationships without cluttering the data.
---6. Stage 4: Data Analysis & Statistical Techniques
Data analysis involves describing patterns and applying statistical tests to determine if your results are meaningful or just down to random chance.
Descriptive Statistics
Measures of Central Tendency:
• Mean (\(\bar{x}\)): The arithmetic average: \(\bar{x} = \frac{\sum x}{n}\)
• Median: The middle value when all numbers are placed in ascending order.
• Mode: The most frequently occurring value in the dataset.
Measures of Dispersion:
• Range: Difference between the highest and lowest value.
• Interquartile Range (\(\text{IQR}\)): The spread of the middle \(50\%\) of data: \(\text{IQR} = Q_3 - Q_1\). It ignores extreme outliers.
• Standard Deviation (\(\sigma\)): Measures the average spread of values around the mean. A small standard deviation indicates data is tightly clustered around the mean.
Inferential Statistical Tests
Inferential tests tell us whether a pattern or relationship is statistically significant:
• Spearman's Rank Correlation Coefficient (\(r_s\)): Tests the strength and direction of a non-parametric correlation between two paired variables (e.g., river gradient vs. sediment size). Values range from \(-1\) (perfect negative) to \(+1\) (perfect positive).
• Pearson's Product-Moment Correlation Coefficient (\(r\)): Tests linear correlation for normally distributed (parametric) continuous data.
• Chi-Squared Test (\(\chi^2\)): Tests whether observed frequencies differ significantly from expected frequencies (great for categorical data like retail shop types).
• Mann-Whitney U Test: Tests whether there is a significant difference between the medians of two independent, non-parametric groups (e.g., pebble roundness on two different beaches).
• Student's t-test: Tests whether the means of two normally distributed groups differ significantly from each other.
Quick Key Takeaway: Descriptive statistics summarise the data; inferential statistics prove whether relationships or differences are statistically significant.
---7. Stage 5 & 6: Conclusions & Critical Evaluation
Drawing Valid Conclusions
When concluding your investigation in the exam:
1. Directly answer your original aim and state whether your hypotheses are accepted or rejected based on your data.
2. Support your claims with specific evidence (quote statistical values, means, ranges, and data points).
3. Link back to geographical theory and models (e.g., connect river changes to the Bradshaw Model, coastal sediment sorting to longshore drift, or urban retail patterns to Changing Places / clone-town concepts).
Critical Evaluation (Where the Top Marks Are Won!)
Examiners frequently note that students describe what went wrong rather than providing critical evaluation. Avoid vague statements!
Bad Evaluation vs. Good Evaluation:
Bad (Too Vague): "Human error made our measurements inaccurate, and it rained."
Good (Critical & Specific): "Using an optical flow meter in turbulent shallow riffles caused cavitation bubbles around the impeller, leading to an underestimation of velocity at Sites 1 and 2. Repeating readings across 5 evenly spaced verticals across the transect would improve internal reliability."
Key Areas to Evaluate:
• Accuracy: How close were your field measurements to the true value? (e.g., equipment calibration, parallax error).
• Reliability: If you repeated the investigation under identical conditions, would you get consistent results? (Consider sample sizes, weather anomalies, and time of day).
• Validity: Did your methodology actually measure what you intended to measure? (e.g., did an Environmental Quality Survey genuinely measure regeneration, or just how recently the council emptied the bins?).
• Sample Size & Spatial Distribution: Was your sample large enough? Did your survey points cover the full geographical area evenly?
• Future Improvements & Extensions: Propose realistic modifications (e.g., digital data loggers) or new research questions (e.g., seasonal comparisons).
8. Summary Checklist: Avoiding Common Exam Pitfalls
- Remember the format: No coursework is submitted for AS Level. You must demonstrate your fieldwork knowledge through written responses in Component 2, Section B.
- Don't just describe: In 9-mark or 12-mark questions, evaluate why you chose a specific sampling method, presentation graph, or statistical test, and acknowledge its limitations.
- Avoid generic excuses: Never write "human error" or "we needed more time". Focus on sampling bias, equipment limitations, and environmental variables.
- Always connect to theory: Ensure your conclusions explicitly reference established geographical concepts and models.