G44: Fieldwork: Data Collection and Presentation
Primary and secondary data, sampling methods (systematic, random, stratified), and data presentation techniques including graphs, maps, and diagrams for geographical fieldwork.
📊 Primary vs Secondary Data
Primary Data: Data collected firsthand by the researcher specifically for their enquiry. It is original, specific to the study, and up-to-date. Secondary Data: Data collected by someone else, previously, for a different purpose. It provides wider context and comparison.
| Primary Data | Secondary Data | |
|---|---|---|
| Definition | Collected by you during fieldwork | Collected by others before your study |
| Examples | Velocity measurements, environmental quality surveys, pedestrian counts, questionnaires | Census data, OS maps, government statistics, newspaper articles, Google Earth imagery |
| Advantages | Specific to your hypothesis; up-to-date; you control the method; reliable if collected carefully | Quick and cheap to access; wider coverage; longer time periods; professional collection methods |
| Disadvantages | Time-consuming to collect; limited sample size; may be affected by weather/day of visit; potential for human error | May be outdated; may not exactly match your needs; accuracy unknown; may contain bias |
Common Primary Data Collection Methods
| Method | Physical Fieldwork | Human Fieldwork |
|---|---|---|
| Measurements | River width (tape measure), depth (ranging pole), velocity (flow meter/timer and float), bedload size (callipers) | Building heights, distance from CBD (tape/GPS), noise levels (decibel meter) |
| Surveys/Counts | Sediment roundness (Power's Scale), vegetation cover (%), angle of slope (clinometer) | Environmental quality survey, land use survey, pedestrian counts, traffic counts |
| Questionnaires | Tourist surveys at coastal sites | Shopper questionnaires, resident interviews about quality of life |
| Field sketches/Photos | River cross-sections, coastal landforms, evidence of erosion/deposition | Street scenes, land use zones, regeneration sites |
🎯 Sampling Methods
Key Concept: Sampling means selecting a subset of the total population or area to study. You cannot measure everything, so you take a representative sample. The sampling method determines how representative and unbiased your data is.
1. Systematic Sampling
Definition: Systematic sampling collects data at regular, predetermined intervals. For example, measuring river characteristics every 500 metres downstream, or surveying every 5th house on a street.
Advantages: Simple to plan and carry out; ensures coverage of the whole study area; easy to replicate; reduces bias in site selection.
Disadvantages: May miss important features between sampling points; if there is a regular pattern in the data (e.g. every 6th house is a corner shop), the sample could be biased.
Example: Systematic Sampling in River Fieldwork
You plan to investigate river characteristics along 6 km of the River Ash. Using systematic sampling, you select a site every 500 metres, giving 12 sites in total. At each site, you measure width, depth, and velocity. This ensures you cover the entire course and can identify changes at regular intervals. However, you might miss interesting features between sites, such as a meander or confluence.
2. Random Sampling
Definition: Random sampling selects points entirely by chance, with no pattern or preference. Each point has an equal probability of being selected. Methods include using a random number generator, drawing grid references from a hat, or throwing a quadrat.
Advantages: Completely unbiased; every location has an equal chance; statistically the most representative method.
Disadvantages: May cluster in some areas and miss others; impractical in the field (may land on inaccessible spots); may not cover the full range of conditions.
3. Stratified Sampling
Definition: Stratified sampling divides the study area into subgroups (strata) based on known characteristics, then samples proportionally from each stratum. This ensures all subgroups are represented.
Advantages: Ensures all relevant groups are included; proportional representation; more representative than random sampling for heterogeneous areas.
Disadvantages: Requires prior knowledge to define strata; more complex to plan and analyse; harder to carry out in the field.
Example: Stratified Sampling in Urban Fieldwork
For a study on environmental quality across a town, you divide the area into land use zones (CBD, inner city, suburbs, rural-urban fringe) based on an OS map. You then allocate a proportional number of survey points to each zone - if the CBD covers 10% of the area, 10% of your surveys are conducted there. This ensures every part of the town is represented.
| Sampling Method | Best Used When... | River Example | Urban Example |
|---|---|---|---|
| Systematic | You want to cover the whole area evenly; looking for trends along a transect | Measure every 500m downstream | Survey every 5th building along a transect from CBD |
| Random | You want to avoid all bias; the area is fairly uniform | Random grid references along the river | Randomly selected postcodes across the town |
| Stratified | The area has distinct subgroups that must all be represented | Sample above and below each tributary junction | Proportional sampling in each land use zone |
📈 Data Presentation Techniques
Graphs
| Graph Type | When to Use | Fieldwork Example |
|---|---|---|
| Line graph | Showing change over distance or time; continuous data | River velocity vs distance downstream; temperature changes over a day |
| Bar chart | Comparing discrete categories | Pedestrian counts at different locations; land use types in each zone |
| Scatter graph | Testing relationship between two variables | River depth vs velocity; distance from CBD vs environmental quality score |
| Pie chart | Showing proportions of a whole | Land use composition in the CBD; reasons visitors chose the area |
| Compound/histogram | Showing frequency distributions; continuous data in ranges | Bedload size distribution; questionnaire score distributions |
| Radar/spider graph | Comparing multiple variables for different sites | Environmental quality profiles for different locations |
Maps
| Map Type | When to Use | Fieldwork Example |
|---|---|---|
| Choropleth map | Showing spatial patterns of density or value | Environmental quality scores by area; deprivation index by ward |
| Isoline map | Showing lines of equal value (like contours) | Noise levels around a road; pedestrian density isolines from CBD |
| Dot map | Showing distribution and quantity of features | Location of surveyed shops; distribution of litter counts |
| Proportional symbol map | Showing quantity at specific locations | Pedestrian counts at different street crossings; velocity at each river site |
| Flow line map | Showing movement between places | Tourist origins; commuter flows into a town |
| Located pie chart | Showing proportions at specific locations | Land use mix at different points along a transect |
Diagrams and Other Techniques
- Cross-sections: Show the shape of a river channel or landform profile
- Field sketches: Annotated drawings of features observed in the field
- Annotated photographs: Photographs with labels identifying key features
- Radar graphs: Multi-variable comparison between sites
- Proportional pie charts on maps: Combine spatial and proportional data
✅ Choosing the Right Presentation Method
Key Principle: The presentation method should clearly show the pattern or relationship you want to demonstrate. The best choice depends on the type of data (continuous, categorical, proportional) and what you want to show (change over distance, comparison between places, distribution across an area).
Decision Framework:
- Showing change along a transect? → Line graph
- Comparing categories? → Bar chart
- Testing a relationship between two variables? → Scatter graph with line of best fit
- Showing proportions? → Pie chart or choropleth map
- Showing spatial patterns? → Choropleth, dot, or proportional symbol map
- Showing a river channel shape? → Cross-section
Example: Presenting River Data
To show how river velocity changes downstream: use a line graph with distance downstream on the x-axis and velocity (m/s) on the y-axis. This clearly shows the trend (increasing velocity) and any anomalies (a decrease at a meander or tributary). To show the river channel shape at each site: draw cross-sections showing width and depth. To compare multiple variables at each site: use a radar graph showing width, depth, velocity, wetted perimeter, and discharge.
❓ Practice Questions
Q1: Explain the difference between primary and secondary data, giving two examples of each from fieldwork.
Q2: Compare systematic and random sampling. When might each be the better choice?
Q3: Explain what stratified sampling is and why it might be used in an urban fieldwork study.
Q4: Suggest the most appropriate presentation method for each of the following: (a) river width at 10 sites downstream; (b) land use proportions in a town centre; (c) the relationship between distance from the CBD and house prices.
Q5: Explain why the choice of sampling method affects the reliability of fieldwork conclusions.
✅ Answers
- Primary data is collected firsthand by the researcher: e.g. river velocity measurements using a flow meter; environmental quality scores recorded on-site. Secondary data was collected by someone else for a different purpose: e.g. census population data; Environment Agency river discharge records. Primary data is specific to the study and up-to-date but time-consuming; secondary data provides wider context and comparison but may be outdated or not perfectly matched to the hypothesis.
- Systematic sampling collects data at regular intervals (e.g. every 500m), while random sampling selects points by chance. Systematic is better when you want to cover the whole area evenly and identify trends along a transect (e.g. river changes downstream). Random is better when you want to eliminate all selection bias and the area is fairly uniform. However, random sampling may cluster or miss areas, while systematic may coincide with a regular pattern in the data.
- Stratified sampling divides the study area into subgroups (strata) based on known characteristics, then samples proportionally from each. In urban fieldwork, this is useful because towns have distinct zones (CBD, inner city, suburbs) with very different characteristics. By sampling proportionally from each zone, you ensure all parts of the town are represented. For example, if the CBD is 15% of the area, 15% of surveys should be conducted there.
- (a) Line graph - shows continuous change along a transect (distance downstream on x-axis, width on y-axis). (b) Pie chart - shows proportions of different land uses making up the whole. (c) Scatter graph - shows the relationship between two continuous variables (distance from CBD and house price) with a line of best fit to identify the trend.
- The sampling method determines how representative the data is of the whole area. A biased sample (e.g. only collecting data near roads because they are easy to access) will give results that don't reflect the true pattern. Systematic sampling ensures even coverage but may miss features between points. Random sampling eliminates bias but may leave gaps. Stratified sampling ensures all groups are represented. If the sampling method is poor, conclusions may be unreliable even if data collection is accurate - the sample simply doesn't represent reality.
🎯 Exam Tips
- Know which sampling method you used and be able to justify why
- Be specific about your data collection methods (e.g. "flow meter at 0.5m depth" not just "measured velocity")
- Presentation method must match the data type and what you want to show
- Always include titles, labels, axes, units, and keys on all presentations
- Common exam question: "Suggest an alternative presentation method and explain why it would be suitable"
- Be able to evaluate your presentation choices - what worked well and what could be improved
- Remember: choropleth maps show density/patterns; scatter graphs show relationships; line graphs show change
📝 Exam Technique
Geography Exam Tips — Fieldwork: Data Collection and Presentation:
1. For Fieldwork: Data Collection and Presentation questions, always name specific case studies with factual detail
2. Use geographical terminology precisely (e.g. specific processes, not vague descriptions)
3. Consider social, economic and environmental perspectives in your evaluations
4. Support your points about Fieldwork: Data Collection and Presentation with data, statistics or named examples
5. For 'assess' or 'evaluate' questions, reach a clear judgement supported by evidence
1. For Fieldwork: Data Collection and Presentation questions, always name specific case studies with factual detail
2. Use geographical terminology precisely (e.g. specific processes, not vague descriptions)
3. Consider social, economic and environmental perspectives in your evaluations
4. Support your points about Fieldwork: Data Collection and Presentation with data, statistics or named examples
5. For 'assess' or 'evaluate' questions, reach a clear judgement supported by evidence
⚠️ Common Errors
Watch Out!
Students often write vague answers without specific geographical evidence. Wrong: Writing generalised statements like 'it causes problems' Correct: Using specific data and named examples, e.g. 'the 2010 Haiti earthquake killed over 200,000 people due to poor building quality'
Students often confuse causes and effects. Wrong: Mixing up what caused the event with what resulted from it Correct: Clearly separate causes (why it happened) from effects (what happened as a result)
Students often describe rather than evaluate. Wrong: Listing strategies without assessing their effectiveness Correct: Weighing up strengths and weaknesses of each approach and reaching a supported judgement
✍️ Model Answer
Full-Mark Response
6 marks: Explain the key factors affecting fieldwork: data collection and presentation.
Fieldwork: Data Collection and Presentation involves multiple interconnected factors that geographers must understand. The key concepts include the processes that create and change fieldwork: data collection and presentation, the impacts on both people and environment, and the strategies used to manage associated challenges. For a comprehensive answer, specific case study evidence should be used throughout, with named examples and data to support each point. Geographical terminology should be used precisely, and the interrelationship between physical and human factors should be demonstrated. Top-level responses evaluate the relative importance of different factors and consider how the situation varies between locations.
Mark scheme: 2 marks for identifying key factors, 2 marks for explaining processes with detail, 2 marks for using specific evidence
📊 AO Deep Dive
Assessment Objective Analysis
AO1 requires knowledge of the key facts and processes related to fieldwork: data collection and presentation. AO2 demands understanding of how and why these processes operate, and their implications. AO3 asks you to analyse, evaluate and make judgements — this is where grade 9 answers stand out by weighing up competing perspectives and reaching supported conclusions. AO4 may involve interpreting maps, graphs or data related to this topic. To move from grade 5 to grade 9: use precise geographical terminology, support every point with specific case study evidence, and always evaluate rather than just describe.
📝 Exam Questions by Topic
🎬 Video Resources
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