What you'll learn
Geographical skills and fieldwork form a critical component of OCR GCSE Geography, tested across both written papers and potentially in coursework. This revision guide covers essential map skills, graph and data interpretation techniques, statistical methods, and fieldwork investigation approaches. You'll develop the practical skills examiners expect to see, from reading OS maps to analysing data using appropriate techniques.
Key terms and definitions
Grid reference — A coordinate system using eastings (along the bottom) and northings (up the side) to identify precise locations on a map, given as four-figure (1km squares) or six-figure (100m squares) references.
Contour line — A line on a map connecting points of equal height above sea level, typically shown at regular vertical intervals (e.g. every 10m).
Interquartile range — A measure of dispersion showing the spread of the middle 50% of data, calculated by subtracting the lower quartile (Q1) from the upper quartile (Q3).
Systematic sampling — A fieldwork data collection method where samples are taken at regular intervals (e.g. every 10m or every 5th house), reducing bias whilst remaining practical.
Cross-section — A side-view diagram showing the changing height of the land along a specific line, drawn using contour lines from a map.
Risk assessment — A systematic evaluation of potential hazards during fieldwork, identifying risks and outlining measures to minimise danger to researchers.
Spearman's Rank correlation — A statistical test measuring the strength of relationship between two sets of data, producing values between -1 (perfect negative correlation) and +1 (perfect positive correlation).
Choropleth map — A thematic map using different colours or shading to represent data values across specific areas or regions.
Core concepts
Cartographic skills
OS (Ordnance Survey) maps are fundamental to GCSE Geography. The two main scales you'll encounter are 1:25,000 (4cm = 1km) and 1:50,000 (2cm = 1km). Maps use conventional symbols to represent features — blue for water bodies, green for woodland, black for roads and buildings, brown for contours.
Four-figure grid references identify 1km squares using the eastings followed by northings of the bottom-left corner. For example, 4532 means the square with its bottom-left corner at 45 eastings, 32 northings.
Six-figure grid references pinpoint locations to within 100m by subdividing each square into tenths. The reference 453323 means 45.3 east, 32.3 north. Always read eastings first ('along the corridor') then northings ('up the stairs').
Measuring distance requires careful use of string or paper along routes, accounting for curves, then measuring against the scale bar. Straight-line distances use a ruler directly.
Direction is given using either compass points (N, NE, E, SE, S, SW, W, NW) or three-figure bearings (000° to 360°, measured clockwise from north).
Contour patterns reveal landforms:
- Closely-spaced contours = steep slopes
- Widely-spaced contours = gentle gradients
- V-shapes pointing uphill = valley
- V-shapes pointing downhill = ridge/spur
- Concentric circles = hill or depression (ticks point downward for depressions)
Spot heights (dots with numbers) and triangulation pillars (blue triangles) show precise heights at specific locations.
Graphical skills
Different graph types suit different data:
Line graphs display continuous data over time or distance, ideal for showing trends (e.g. temperature changes throughout the day, river depth along a cross-section). Plot points accurately and join with a line or smooth curve.
Bar charts compare discrete categories using rectangular bars. The height represents the frequency or value. Bars should be equal width with consistent gaps. Compound/stacked bars show subdivisions.
Pie charts illustrate proportions of a whole. Calculate each segment's angle by: (category value ÷ total) × 360°. Label clearly with values and percentages.
Scatter graphs investigate relationships between two variables. Plot the independent variable (x-axis) against the dependent variable (y-axis). Add a line of best fit to show correlation patterns.
Choropleth maps use colour/shading intensity to show spatial variations in data (e.g. population density by region). Group data into classes and create a clear key showing the range each shade represents.
Proportional symbols use circles, bars or pictograms of different sizes to represent quantities at locations. The area should be proportional to the value.
Flow lines show movement between places (e.g. migration, trade), with line thickness proportional to volume.
Data presentation and analysis
Selecting appropriate techniques depends on your data type and research question:
Measures of central tendency:
- Mean = sum of values ÷ number of values (affected by extreme values)
- Median = middle value when arranged in order (better for skewed data)
- Mode = most frequently occurring value
Measures of dispersion:
- Range = highest value - lowest value (simple but affected by outliers)
- Interquartile range = Q3 - Q1 (middle 50% spread, ignores extremes)
Percentages enable comparison between datasets of different sizes: (part ÷ whole) × 100
Ratios express relative proportions, e.g. dependency ratio = (young + old) ÷ working age population
Statistical techniques
Spearman's Rank correlation coefficient (rs) tests relationships between two variables:
- Rank each dataset separately (highest = 1)
- Calculate d (difference between ranks for each pair)
- Square each d value
- Apply formula: rs = 1 - (6Σd² ÷ n(n²-1))
- Interpret: +1 = perfect positive correlation, 0 = no correlation, -1 = perfect negative correlation
- Compare to critical value table to assess statistical significance
Values above ±0.5 generally indicate moderate to strong correlation.
Fieldwork investigation process
OCR requires students to undertake two geographical enquiries: one physical geography, one human geography.
Stage 1: Planning and preparation
Develop a clear enquiry question (e.g. "How does urban land use change with distance from the CBD in Coventry?"). This drives your entire investigation.
Establish hypotheses — testable statements predicting relationships or patterns.
Complete a risk assessment identifying hazards (traffic, weather, difficult terrain, strangers) and control measures (high-visibility clothing, appropriate footwear, working in groups, informing others of location).
Select sampling methods:
- Random — using random number generators to select locations, avoiding bias
- Systematic — regular intervals (every 50m, every 10th person)
- Stratified — dividing area into zones and sampling proportionally from each
Primary data you collect directly through:
- Fieldwork observations
- Questionnaires and surveys
- Environmental quality surveys
- Measurements (velocity, temperature, pedestrian counts)
Secondary data from existing sources:
- Census data
- Weather records
- Maps and aerial photographs
- Published research
Data collection techniques
Physical geography methods:
River studies: measure width using tape measure, depth using ranging pole/metre rule at intervals, velocity using float method or flow meter, discharge calculation (Q = A × V).
Coastal studies: beach profiles using clinometer for angles and tape measure for distance, sediment analysis (size, shape, roundness), wave frequency counts.
Human geography methods:
Land use surveys: recording building functions on base maps using colour codes or symbols.
Environmental quality surveys: scoring locations against criteria (litter, noise, greenery, building condition) using bi-polar scales (-3 to +3) or simple scales (1-5).
Traffic surveys: tally charts recording vehicle types at different times.
Questionnaires: closed questions (yes/no, multiple choice) for quantitative analysis; open questions for qualitative detail. Avoid leading questions, keep concise, consider appropriate sample size.
Data presentation and conclusion
Present data using techniques appropriate to your findings. Include:
- Maps showing study locations
- Graphs/charts illustrating key patterns
- Statistical analysis demonstrating relationships
- Photographs with annotations
Analyse by describing patterns, explaining them using geographical theory, and linking to your original question.
Evaluate by assessing:
- Reliability: Would repetition produce similar results?
- Accuracy: How precise were measurements?
- Limitations: Sample size, time constraints, weather conditions
- Improvements: Larger samples, different times/seasons, additional variables
Worked examples
Example 1: Grid references and distance (4 marks)
Study the OS map extract.
(a) Give the six-figure grid reference for the church in the village of Thornbury. (2 marks)
(b) Calculate the straight-line distance in kilometres from the church to the school at 463287. (2 marks)
Mark scheme answers:
(a) 456284 (2 marks for correct reference; 1 mark if only 4-figure given as 4528)
- Eastings: 45.6
- Northings: 28.4
(b)
- Measure distance on map = 3.4cm (1 mark for measurement)
- Scale 1:50,000 means 2cm = 1km
- Therefore 3.4cm = 1.7km (1 mark for correct calculation and units)
Example 2: Spearman's Rank correlation (6 marks)
A student investigated the relationship between distance from the CBD and house prices. They calculated a Spearman's Rank value of -0.82.
(a) Explain what this value indicates about the relationship. (2 marks)
(b) Suggest why this relationship might exist. (4 marks)
Mark scheme answers:
(a) The value of -0.82 indicates a strong negative correlation (1 mark). This means that as distance from the CBD increases, house prices tend to decrease (1 mark).
(b) Award 1 mark per developed point, maximum 4:
- CBD locations have high accessibility to services/employment (1), making them more desirable for affluent residents/businesses (1)
- Inner city areas often have older, smaller Victorian terraced housing (1) which is less valuable than newer suburban properties (1)
- Outer areas may lack good transport links (1) reducing convenience and therefore value (1)
- However, some suburbs have high land value due to environmental quality (1), showing the relationship may not be uniform (1)
Example 3: Fieldwork evaluation (6 marks)
Evaluate the effectiveness of the data collection methods you used in your human geography fieldwork investigation.
Mark scheme answer:
Level 3 (5-6 marks): Detailed evaluation with specific reference to own investigation; considers multiple aspects of effectiveness; suggests realistic improvements.
"In my investigation of pedestrian flows in Oxford city centre, I used systematic sampling by counting pedestrians every 100m along the High Street. This was effective because it reduced bias and was time-efficient, allowing data collection at six points in two hours (1). However, the method only captured one moment in time (1), so the data may not represent typical patterns if I surveyed during an unusual event (1). To improve reliability, I could repeat counts at the same locations on multiple days and times (1), though this would require more time. My sample size of 20 minutes per location was sufficient to identify clear patterns of higher flows near the central shopping area (1), supporting my hypothesis. The systematic approach meant I could compare like-with-like data (1), though random sampling might have revealed unexpected hotspots of activity."
Common mistakes and how to avoid them
Grid references in wrong order — Always remember "along the corridor, up the stairs" (eastings then northings). Four-figure references use the bottom-left corner of the square, not the centre.
Incorrect graph selection — Line graphs are for continuous data (time, distance), bar charts for discrete categories. Don't join bars together or leave gaps in a line graph's time series.
Forgetting units — Distance answers need km or m, bearings need degrees (°), angles need degrees. Examiners deduct marks for missing or incorrect units.
Poor correlation interpretation — Correlation doesn't prove causation. A Spearman's Rank of +0.7 shows strong positive correlation, but you must explain the geographical reasons why variables are related, not simply describe the statistical result.
Vague fieldwork evaluation — Avoid generic statements like "I could have collected more data." Specify exactly what additional data, why it would help, and acknowledge realistic constraints (time, access, safety).
Ignoring anomalies — When analysing data, identify outliers and suggest geographical explanations rather than dismissing them as errors.
Exam technique for "Geographical Skills and Fieldwork"
Command words matter: 'Describe' means state what you see in the data; 'Explain' requires geographical reasons; 'Suggest' needs plausible ideas supported by evidence; 'Evaluate' demands weighing strengths against weaknesses with judgement.
Use data precisely: Quote specific figures from graphs, maps or tables. Write "pedestrian flow increased from 45 people/hour at 500m to 156 people/hour at the CBD" rather than "pedestrian flow increased."
Show working for calculations: Even if the final answer is wrong, you can gain method marks. Include formulas, substituted values, and units at each stage.
Fieldwork questions reward specificity: Reference your actual investigation location, precise methods, and real findings. Generic answers about "a river" or "a town" score poorly compared to "the River Cherwell at Magdalen Bridge, Oxford."
Quick revision summary
Geographical skills encompass map reading (grid references, distance, direction, contours), data presentation (graphs, maps, charts), and statistical analysis (mean, median, Spearman's Rank). OS maps use scales of 1:25,000 or 1:50,000 with conventional symbols and contour patterns revealing landforms. Fieldwork requires systematic planning: developing enquiry questions, conducting risk assessments, selecting appropriate sampling methods, collecting primary and secondary data, then presenting findings and evaluating methodology. Match graph types to data characteristics and always include units in calculations. Exam success depends on precise use of data, specific fieldwork references, and clear understanding of command words.