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Practise 31 exam-style questions on Fieldwork: analysing data and drawing conclusions for AQA GCSE Geography (Paper 3). Write your answer as you would in the exam and get it marked in seconds against the mark scheme, point by point, with an AI tutor on hand if you get stuck.
Here are 4 of the questions in this topic, each with a model answer, an explanation of why it earns the marks and the mark scheme an examiner would use.
Students asked 50 residents of a village how far they travelled to work. Figure 1 shows the results.
State the modal class.
5-10 km
The modal class is the class with the highest frequency, shown by the tallest bar. A common mistake is to give the frequency (14) instead of the class itself.
5-10 km
accept '5 to 10 km' or 'the class from 5 to 10'; the unit is not required; do not accept '14' (the frequency)
Students counted pedestrians for 10 minutes at 14 sites at different distances from the peak land value intersection (PLVI) in Hexley. Figure 1 shows their results. Point B, 320 m from the PLVI, is an anomaly.
Suggest one reason for the anomaly at B.
There could be a bus station or a large supermarket near point B. This would attract many people, so far more pedestrians passed than would normally be expected 320 m from the PLVI.
Anomalies can be real, caused by local features such as transport hubs or shops, or the result of errors in method or timing. The second mark is for explaining how the reason leads to a higher count than expected.
A local attraction near B, e.g. a bus or railway station, supermarket, school, college, market or popular café
max 2; 1 mark for a reason, 1 for development
which draws many people, so the count is much higher than expected for its distance from the PLVI
development of the previous point
The count at B was taken at a busier time than at other sites (e.g. lunchtime, school home time, when a bus arrived) / on a different day
alternative reason
so the counts are not comparable and B is higher for reasons of timing, not location
development of the previous point
A counting or recording error, e.g. people counted twice or a figure written down wrongly
alternative reason; development, e.g. 'so the recorded value is wrong and does not show the real pedestrian flow'
Students measured the air temperature at 10 points along a west-east transect across the town of Marston at 10 pm on a calm, clear evening. The town centre is 4 km along the transect and a large park is 2.5 km along it. Figure 1 shows the results.
Using Figure 1, explain the pattern of air temperature along the transect.
Temperatures are highest in the town centre, 16.8 °C at 4 km, and lowest at the edges of the town, about 12.2 °C, so the town has an urban heat island about 4.6 °C warmer than its surroundings. During the day concrete, brick and tarmac absorb heat and release it slowly at night, and heat is also given off by buildings and traffic. Closely packed buildings shelter the centre from wind, so the warm air is not mixed away on a calm night. The temperature falls to 12.9 °C at the park at 2.5 km because grass and trees store little heat and cool the air through evapotranspiration.
Strong answers use readings from the graph to show the pattern (the peak in the centre and the dip at the park) and explain each part with urban heat island processes. Mentioning why calm, clear conditions matter shows good understanding.
Levels of response (4 marks).
Level 2 (3-4 marks): Clear explanation of the pattern, linking the higher temperatures in the town centre to at least two urban factors and explaining the cooler park, with accurate use of evidence from Figure 1.
Level 1 (1-2 marks): Basic explanation, e.g. 'towns are warmer', with little use of Figure 1 or little explanation of why.
0 marks: nothing worthy of credit.
Indicative content (not a checklist; credit other valid points): temperature is highest in the town centre (16.8 °C at 4 km) and lowest at the edges (12.2 °C at 0 km, 12.4 °C at 8 km), a difference of about 4.6 °C - an urban heat island; buildings, concrete and tarmac absorb heat during the day and release it slowly at night; heat is given off by buildings, traffic and industry; tall, closely spaced buildings trap heat and reduce wind; less vegetation and water, so less cooling by evapotranspiration; drains remove rainwater quickly, so less evaporation; air pollution can trap heat; the dip to 12.9 °C at the park (2.5 km) is because grass and trees do not store heat like concrete and they cool the air by evapotranspiration; the pattern is clearest on calm, clear nights because wind does not mix the air.
Students tested the hypothesis 'Environmental quality improves with distance from the CBD'. They recorded environmental quality scores at 11 sites in each of three zones of a city: the CBD, the inner city and the suburbs. Figure 1 shows the results as a dispersion graph.
Using Figure 1, assess the extent to which the results support the hypothesis.
Overall, the results support the hypothesis. The median environmental quality score rises with distance from the CBD: 14 in the CBD, 17 in the inner city and 23 in the suburbs. Both quartiles also rise from zone to zone, and the means show the same pattern (about 15, 17 and 23).
The evidence is strongest for the suburbs. Their interquartile range runs from 20 to 27, which does not overlap the CBD's interquartile range of 11 to 19 at all, so suburban sites are clearly better. However, the difference between the CBD and the inner city is much smaller: their interquartile ranges (11-19 and 14-22) overlap a lot, so this part of the hypothesis is only weakly supported. There are also individual exceptions: the best CBD site scored 24, higher than the median suburban site, and the worst suburban site scored only 14, the same as the CBD median.
The method also limits the conclusion. Grouping sites into zones hides how quality changes with actual distance within each zone, 11 sites per zone is a small sample, and environmental quality scores are subjective.
In conclusion, the results support the hypothesis to a fairly large extent, because every average rises outwards, but the support is strong only for the contrast between the suburbs and the other two zones.
Dispersion graphs let you compare averages and spread: use medians and quartiles for each zone and comment on the overlap between them. Large overlap means a difference is less convincing, so the best answers judge that support is strong for the suburbs but weak between the CBD and inner city.
Levels of response (6 marks).
Level 3 (5-6 marks): Detailed assessment using statistics from Figure 1 (medians, quartiles, IQR, overlap), comparing all three zones and recognising where the evidence is stronger and weaker; considers limitations; reaches a clear, substantiated judgement.
Level 2 (3-4 marks): Clear comparison of the zones with some use of statistics; some recognition of overlap or limitations; judgement may be implied.
Level 1 (1-2 marks): Basic statements that scores are higher in the suburbs; little use of statistics; no clear judgement.
0 marks: nothing worthy of credit.
Indicative content (not a checklist; credit other valid points): medians rise with distance from the CBD - 14 in the CBD, 17 in the inner city, 23 in the suburbs; quartiles also rise (LQ 11, 14, 20; UQ 19, 22, 27); the suburbs are clearly better - their interquartile range (20-27) does not overlap the CBD's (11-19) at all; the difference between the CBD and the inner city is small - their IQRs (11-19 and 14-22) overlap a lot, so this part of the hypothesis is only weakly supported; some sites break the pattern, e.g. the best CBD site (24) scores higher than the median suburban site (23), and the worst suburban site (14) scores the same as the CBD median; spreads are similar in each zone (ranges 16, 16, 15); means 14.9, 17.4 and 22.9; the data are grouped by zone, so they do not show how quality changes with actual distance within each zone; 11 sites per zone is a small sample, and bipolar scores are subjective; judgement, e.g. the results support the hypothesis overall, strongly for the suburbs but only weakly between the CBD and inner city.
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