Fieldwork Summary - Human Geography Flashcards
Primary Data Collection Technique: Air Quality using Lichenography
Description: Opportunistic sampling of lichen as close to the centre of each of the 16 sectors within the allocated zone
of Marlow. Lichen species indicates air quality (levels of SO2).
Reason:
Enabled me to find out: Variations in air quality across Marlow, especially with 1) distance from main roads, and 2) distance from the
centre of Marlow. This allowed me to describe spatial variations.
Primary Data Collection Technique (2): Questionnaire Survey
Description: Systematic sampling of pedestrians on Marlow High Street. Aim was to find out how people felt about
traffic levels in the town. This would be correlated with environmental quality data collected on the High Street.
Reason:
Enabled me to find out:
Quantitative data on perceptions of traffic in Marlow High Street. This would reveal whether residents (who live
there and so are familiar with long-term traffic levels) believe traffic is an issue.
How Did I Manage Risk During The Fieldwork?:`
Stranger Danger – conducted fieldwork in pairs or groups of 3 at a minimum.
Traffic Accidents – teachers monitored students + roads were crossed carefully. Students avoided running or
crossing without looking.
Justify The Data Presentation Techniques Used:
Noise Levels – located data.
A grid of 16 sectors was created over all 4 zones of Marlow. The noise data was entered into each
sector, and a colour used to indicate noise levels. This helped reveal spatial patterns in noise levels
across Marlow.
Questionnaire – bar chart of opinions about traffic levels in Marlow. Clearly showed the modal response to the
question about traffic levels.
Describe The Links Identified Between 2 Sets Of Your Data:
Data Sets: Noise and Air Quality
Links: we found that, on average, as noise levels increased, air quality worsened. Possible reason: higher traffic
volumes = more noise & air pollution.
Anomaly (exception): Some areas near main roads were as quiet as areas further away, yet air pollution levels
were higher. Possible reason = short sample time = less reliable (gaps in traffic)
Explain The Limitations Of Your Data Collection Methods:
BIAS: ‘Systematic’ sampling of respondents for questionnaires quickly became ‘opportunistic’ due to lack of time and
non-responses from residents. These choices could have been affected by our preferences. We may have chosen people based on:
Friendliness/ affluence (wealth)/ gender/ age/ speed of walk/ alone or in a group.
This is NOT a random sample – it cannot be used to make general conclusions about the whole population. E.g. We
cannot say for certain how ALL people feel about Marlow’s traffic.
May not match the demographics of Marlow = not representative of the whole.
RESPONSES: People may not have given accurate/ honest answers to questions.
E.g. about income/ age/ reasons for visiting
Another Useful Source Of Data Would Have Been:
INTERVIEWS: With urban planners/ the council. This could have:
Revealed clearer patterns/ explanations of environmental quality differences – e.g., due to waste collection timetables,
economic disparities.
Shown us the long-term trends in environmental quality based on our metrics. E.g., changes in traffic volumes and spatial
patterns across a day/ week.
How Could Data Collection Have Been Improved?:
Questionnaires – SYSTEMATIC SAMPLING. We could have chosen every 5th person to eliminate bias/ our preferences = more
valid conclusion.
Questionnaires – STRATIFIED SAMPLING. 1 respondent could have been chosen from each category – e.g. age group/ gender
– more representative sample of population = more valid conclusion.
Number of Surveys – we could have completed surveys on multiple days and at multiple times to overcome variations in
environmental quality/ traffic based on time of day/ weather/ shops being open/ closed etc.
Assessment Of Effectiveness Of Data Collection Methods In Helping To Answer The Original Question:
Air Quality: While lichenography is a proxy measure of environmental quality (it does not directly measure air quality), lichen are
highly sensitive to air pollution, and so the variations in species did reveal the impact of proximity to roads and changes in air
pollution. We may have misidentified lichen however, causing reliability issues.
Questionnaire: highlighted the perceptions of long-term residents towards traffic, and therefore somewhat mitigated the limitation
that we were only counting traffic on one day at one time. It would have been useful to gain qualitative data here – opinions of
traffic, daily/ weekly patterns.
Small Sample Size: only 10 people were surveyed. This reduces representativeness. We also did not ask how long they have lived
in Marlow or consider how mood etc. may affect their responses.
Only One Survey Day: responses/ results (e.g., noise) may not reflect the average conditions – could be affected by the weather/
time – many people could have been at work/ school.
Noise vs. Air vs. Building upkeep vs. Litter: showed the spatial patterns in these 4 metrics of environmental quality. Enabled
correlations to be revealed. By having 4 metrics, these spatial patterns would be more valid, since similarities in the spatial patterns
of all 4 would reinforce the conclusion that there are variations.
