Welcome to Geographical Skills & Fieldwork Investigation

Hello! Welcome to your complete study guide for Geographical Skills and the Fieldwork Enquiry Route for AQA AS Level Geography (7036). Geographical skills are the essential toolkit of every geographer. They allow you to gather evidence from the real world, make sense of patterns, uncover trends, and communicate your findings clearly.

In your AS Level qualification, skills and fieldwork are assessed across both exam components, with a major focus in Component 2 (Paper 7036/2: Human Geography and Geography Fieldwork Investigation), where Section B (40 marks) is dedicated directly to fieldwork and skills. Unlike the full A-Level, there is no separate coursework (NEA) folder to submit; your understanding of fieldwork and data skills is tested entirely through the written exam.

Don't worry if maths, charts, or fieldwork concepts seem tricky right now. We will break down every single technique step by step with clear explanations, worked examples, and handy tips to help you secure top marks!

1. The Fieldwork Enquiry Framework (Specification Section 3.4)

All AQA AS Level geographers must complete a minimum of two days of fieldwork covering both physical and human geographical processes. When you are answering questions in the exam, you need to understand how the enquiry unfolds across six logical stages.

The 6 Stages of the Fieldwork Enquiry Route

  1. Preparation for Fieldwork: Background reading and contextual research; establishing clear aims, hypotheses, and objectives; selecting sampling strategies; and conducting a thorough risk assessment with health and safety procedures.
  2. Data Collection: Gathering primary data in the field (e.g., measuring pebble sizes, conducting footfall counts) and sourcing relevant secondary data (e.g., historical maps, Census records, Environment Agency datasets).
  3. Data Processing and Presentation: Converting raw values into organised tables and selecting appropriate cartographic (maps) or graphical (charts/graphs) techniques.
  4. Data Analysis: Interpreting patterns using descriptive statistics (mean, median, range, standard deviation) and inferential statistics (such as Spearman's rank correlation coefficient).
  5. Conclusions: Linking your results directly back to your original aims, hypotheses, the specific study location, and wider geographical theories.
  6. Evaluation: Critically assessing the accuracy, reliability, and validity of your data collection, presentation, and analysis; identifying limitations; and suggesting meaningful extensions.

Memory Aid: The Enquiry Journey

Think of the fieldwork route as the acronym P-D-P-A-C-E (Prepare, Data collect, Present, Analyse, Conclude, Evaluate). Keep this sequence in mind whenever an exam question asks about a specific stage of your investigation!

Sampling Strategies in the Field

You cannot measure every single pebble on a beach or interview every resident in a town. That is why we use sampling. Understanding which sampling method to use—and its pros and cons—is a vital exam skill.

  • Random Sampling: Every location or individual has an entirely equal chance of being selected.
    How to do it: Use a random number generator to create grid coordinates on a map or select names from an ordered list.
    Advantage: Removes researcher bias completely.
    Disadvantage: Can accidentally miss key sub-areas or create uneven spatial coverage by pure chance.
  • Systematic Sampling: Selecting points or individuals at regular, predetermined spatial or numerical intervals.
    How to do it: Placing a quadrat every \(5\text{ m}\) along a transect line across a sand dune, or surveying every 10th person passing a street corner.
    Advantage: Quick, simple to execute, and ensures even spatial coverage along a line or area.
    Disadvantage: Can introduce bias if there is an underlying regular pattern in the landscape that coincides with your sampling interval.
  • Stratified Sampling: Dividing the total population or landscape into distinct subsets (strata) and sampling proportionally from each stratum.
    How to do it: If an urban ward's population is \(60\%\) aged under 40 and \(40\%\) aged over 40, you ensure that your questionnaire sample matches this exact \(60:40\) ratio.
    Advantage: Highly representative; ensures minority groups or smaller zones are not under-represented.
    Disadvantage: Requires accurate prior secondary data to know the proportions of the strata in advance.
  • Pragmatic / Opportunistic Sampling: Selecting sampling locations based on practical constraints such as physical accessibility, time limits, or safety considerations.
    Advantage: Realistic and safe when terrain is hazardous or time is constrained.
    Disadvantage: Highly subjective; introduces bias and reduces data validity.

Key Takeaway: Fieldwork Enquiry

Fieldwork is a structured process. Whenever evaluating your work, always distinguish clearly between issues with collection (e.g., small sample size, equipment error), presentation (e.g., inappropriate scale, lost detail), and analysis (e.g., confusing correlation with causation).

2. Qualitative vs Quantitative Data Skills (Specification Section 3.5.1)

Geographers use both numerical measurements and lived experiences to understand people and places.

Quantitative Data

Quantitative data consists of numerical measurements and counts. What makes data specifically geographical is that it has geo-located attributes—meaning each numerical value is tied to a specific location in space (e.g., latitude/longitude, a grid reference, or a postcode).

  • Geospatial Technologies: Tools such as Global Positioning Systems (GPS) and Geographical Information Systems (GIS) allow geographers to collect, manipulate, query, and visualise spatial digital data.
  • Big Data and Crowd-Sourced Data: Massive digital datasets gathered from mobile phones, social media check-ins, or smart cards that show high-volume patterns of human movement.
  • Errors in Quantitative Data:
    • Random errors: Unpredictable fluctuations caused by human reading inaccuracies or sudden environmental shifts (e.g., wind gusts during flow meter readings).
    • Systematic errors: Consistent, repeatable errors caused by faulty equipment or poor calibration (e.g., an uncalibrated clinometer that always overestimates slope angles by \(2^\circ\)).

Qualitative Data

Qualitative data consists of non-numerical, descriptive, textual, visual, or artistic information. It helps geographers understand how people perceive, experience, and give meaning to places.

  • Collection Methods: Semi-structured and unstructured interviews, focus groups, and perception studies (e.g., asking residents how safe they feel in different neighbourhoods).
  • Visual and Discursive Sources: Interpreting photographs, local advertisements, poetry, paintings, oral histories, and graffiti.
  • Thematic Coding: A systematic technique where researchers read through qualitative text (such as interview transcripts) and categorise recurring words, phrases, or ideas into distinct themes (e.g., coding mentions of "fear", "community pride", or "loss of heritage").
  • Ethics and Positionality: When researching human communities, geographers must ensure:
    • Informed consent: Participants fully understand the purpose of the study.
    • Anonymity and confidentiality: Personal identities are protected.
    • Positionality: Recognising how the researcher's own background (age, gender, social class) might influence their interactions with participants and interpretation of data.

Common Mistake to Avoid: Never dismiss qualitative data in the exam as "useless because it is subjective". Qualitative sources provide deep insights into place attachment and personal lived experiences that numbers alone cannot reveal!

3. Core & Cartographic Skills (Specification Section 3.5.2.1 & 3.5.2.2)

Core Illustrative Skills

  • Ordnance Survey (OS) Maps: Reading and annotating maps at scales of \(1:25,000\) (where \(4\text{ cm} = 1\text{ km}\)) and \(1:50,000\) (where \(2\text{ cm} = 1\text{ km}\)).
  • Overlays: Placing physical overlays (e.g., tracing paper/acetate) or digital layers in GIS over a base map to compare different datasets (such as overlaying flood risk zones onto population density maps).
  • Field Sketches & Photos: Annotating sketches and ground/aerial/satellite photographs with descriptive and explanatory labels to highlight landforms, land use, or physical processes.
  • Questionnaire Design: Using closed questions (fixed choices), open questions (free-text responses), Likert scales (e.g., strongly agree to strongly disagree), and semantic differential scales (rating between two opposing adjectives, such as noisy ... quiet).

Cartographic Skills (Maps)

Exam questions will often present you with different types of thematic maps or ask you to choose the best map style for a dataset:

1. Spatial Pattern Maps

  • Choropleth Maps: Predefined administrative areas (e.g., local authority districts) are shaded in varying tones of a single colour according to data class intervals (darker shades usually indicate higher densities or percentages).
    Strength: Gives an immediate visual impression of regional spatial variations.
    Limitation: Assumes an entire zone has a uniform value and creates false, abrupt boundaries between areas.
  • Isoline Maps: Continuous lines connecting points of equal numerical value.
    Examples: Contours (equal height), isobars (equal atmospheric pressure), isotherms (equal temperature), and isochrones (equal travel time from a central point).
    Strength: Shows continuous spatial changes without artificial boundaries.
    Limitation: Requires a large number of accurate data collection points to interpolate the lines reliably.
  • Dot Maps: Dots of identical size are placed on a map, where one dot represents a fixed, specified quantity of an attribute (e.g., 1 dot = 100 people).
    Strength: Shows spatial clustering and dispersion accurately.
    Limitation: Hard to count individual dots in densely clustered areas; does not provide exact values.

2. Movement Maps

Make sure you do not confuse these three distinct movement techniques:

  • Flow Lines: Lines that follow the actual route of movement (e.g., along roads, rivers, or railways), where the width/thickness of the line is drawn strictly proportional to the volume of movement.
  • Desire Lines: Direct, straight lines drawn directly from an origin to a destination (ignoring the actual road or path taken) to show the overall direction and volume of movement.
  • Trip Lines: Straight lines connecting individual points of origin to one central node (e.g., mapping where individual shoppers have travelled from to visit a specific retail park to determine its catchment area).

3. Proportional Symbol Maps

Symbols (such as circles, bars, or squares) placed at specific locations on a map, with their size drawn proportional to the total value at that point.

How to calculate the radius of a proportional circle:
Because the area of a circle is proportional to the value (\(\text{Area} \propto \text{Total Value}\)), the radius \(r\) must be proportional to the square root of the total value:

\(r \propto \sqrt{\text{Total Value}}\)

Why this matters: If you double the value, you do not double the radius! Doubling the radius would make the circle's area four times larger, which visually misleads the reader.

Key Takeaway: Cartography

Remember: Flow lines show route-based movement with proportional widths; desire lines show straight-line origin-to-destination connections; trip lines show individual journeys into a single central node.

4. Graphical Skills (Specification Section 3.5.2.3)

Geographers use a wide variety of graphs to present data clearly. Here is a breakdown of the key techniques required by the AQA specification:

Line and Bar Graphs

  • Simple: A single variable plotted over time or across categories.
  • Comparative: Multiple lines or grouped bars plotted on the same axes to compare two or more locations or categories.
  • Compound (Divided / Stacked): Lines or bars stacked on top of each other, where the top line/bar represents the total, and the bands between lines/sections show the sub-components.
  • Divergent: Graphs showing positive and negative deviations from a central baseline or zero axis (e.g., showing percentage population growth above zero and decline below zero, or economic performance).

Scatter Graphs & Lines of Best Fit

Used to plot paired (bivariate) numerical data to identify relationships:

  • Positive correlation: As variable \(X\) increases, variable \(Y\) increases.
  • Negative correlation: As variable \(X\) increases, variable \(Y\) decreases.
  • No correlation: Points are randomly scattered with no discernible pattern.

Pie Charts & Proportional Divided Circles

To construct or interpret a pie chart sector angle:

\(\text{Angle (degrees)} = \left(\frac{\text{Percentage}}{100}\right) \times 360^\circ \quad \text{or} \quad \text{Angle} = \left(\frac{\text{Category Value}}{\text{Total Value}}\right) \times 360^\circ\)

Triangular (Ternary) Graphs

Triangular graphs are used to plot three components that together add up to exactly \(100\%\) (for example: soil composition of sand, silt, and clay; or employment structure divided into primary, secondary, and tertiary sectors).

How to Read a Triangular Graph Step-by-Step:

  1. Find the three axes, each scaled from \(0\%\) to \(100\%\).
  2. Follow the direction of the numbers on each axis.
  3. Read along the grid lines that run parallel to the axis tick marks.
  4. Sanity Check: Always add up your three read values: \(A\% + B\% + C\%\) must equal \(100\%\)! If they do not, you have read along the wrong angled grid line.

Logarithmic Scale Graphs

On a logarithmic scale, the axis increases by orders of magnitude (powers of 10), such as \(1, 10, 100, 1000, 10000\), rather than equal linear steps (\(1, 2, 3, 4\dots\)).

  • Semi-logarithmic graphs: One axis is logarithmic, and the other is standard linear (often used for river discharge or population growth over time).
  • Log-log graphs: Both axes are logarithmic (used when comparing datasets that both span massive numerical ranges, such as flood return periods or river sediment transport).

Dispersion Diagrams

A dispersion diagram displays raw data points plotted as individual dots along a single vertical axis. It allows you to see the degree of spread, clustering, gaps, and outliers instantly without hiding individual data points.

5. Statistical Skills (Specification Section 3.5.2.4)

Statistics help us turn raw numbers into clear, objective conclusions. You need to master both descriptive and inferential statistical techniques.

Measures of Central Tendency

  • Mean (\(\bar{x}\)): The arithmetic average.

    \(\bar{x} = \frac{\sum x}{n}\)

    Where \(\sum x\) is the sum of all values and \(n\) is the number of observations.
  • Median: The middle value when all raw numbers are placed in ascending order.

    \(\text{Position of Median} = \frac{n + 1}{2}\)

    Advantage: Not distorted by extreme values or anomalies (unlike the mean).
  • Mode: The most frequently occurring value in the dataset. Useful for categorical data (e.g., modal land use type).

Measures of Dispersion

  • Range: The simplest measure of spread:

    \(\text{Range} = \text{Maximum Value} - \text{Minimum Value}\)

  • Inter-quartile Range (IQR): The difference between the upper quartile (\(Q_3\)) and the lower quartile (\(Q_1\)), representing the spread of the middle \(50\%\) of the data:

    \(\text{IQR} = Q_3 - Q_1\)

    Advantage: Eliminates the distorting effect of extreme outliers at both ends of the dataset.
  • Standard Deviation (\(s\) or \(\sigma\)): A powerful statistic that measures the average spread of all data points around the arithmetic mean:

    \(s = \sqrt{\frac{\sum (x - \bar{x})^2}{n}} \quad \text{or} \quad s = \sqrt{\frac{\sum x^2}{n} - (\bar{x})^2}\)

    Interpretation: A low standard deviation means data points are clustered closely around the mean (high consistency/uniformity). A high standard deviation indicates a wide spread of data across a broad range.

Inferential Statistics: Spearman's Rank Correlation Coefficient (\(r_s\))

Spearman's rank correlation tests the strength and direction of a statistical relationship between two ranked variables.

Formula:

\(r_s = 1 - \frac{6 \sum d^2}{n(n^2 - 1)}\)

Where:

  • \(d =\) the difference between the ranks of the paired variables for each data item (\(\text{Rank } X - \text{Rank } Y\))
  • \(d^2 =\) the square of that rank difference
  • \(\sum d^2 =\) the sum of all the squared rank differences
  • \(n =\) the total number of paired data observations

How to Calculate Spearman's Rank Step-by-Step:

  1. Set up a table with columns: Variable \(X\), Variable \(Y\), Rank \(X\), Rank \(Y\), Difference \(d\), and \(d^2\).
  2. Rank Variable \(X\) from highest (\(1\)) to lowest. If numbers tie, give them the average rank (e.g., if ranks 3 and 4 are tied, assign both \(3.5\)).
  3. Rank Variable \(Y\) independently from highest (\(1\)) to lowest.
  4. Calculate \(d = \text{Rank } X - \text{Rank } Y\) for every row. (Quick check: \(\sum d\) must equal \(0\)!)
  5. Square each difference to find \(d^2\), and calculate the sum: \(\sum d^2\).
  6. Substitute \(\sum d^2\) and \(n\) into the formula and solve for \(r_s\).

Interpreting the Calculated \(r_s\) Value:

  • \(+1.0 =\) Perfect positive correlation.
  • \(0.0 =\) No correlation whatsoever.
  • \(-1.0 =\) Perfect negative correlation.

Significance Testing (Hypothesis Testing)

Just because your calculated \(r_s\) is close to \(+1\) or \(-1\) does not mean the relationship is real—it could have occurred by pure chance. We use significance testing to find out.

  1. State the Hypotheses:
    • Null Hypothesis (\(H_0\)): "There is no significant relationship between variable \(X\) and variable \(Y\). Any observed pattern is due to chance."
    • Alternative Hypothesis (\(H_1\)): "There is a significant relationship between variable \(X\) and variable \(Y\)."
  2. Determine the Critical Value: Look up the critical value in the statistical table provided in the exam using your sample size \(n\) (or degrees of freedom) at standard significance levels:
    • \(p = 0.05\) (\(95\%\) confidence level): There is only a \(5\%\) probability that the result occurred by chance.
    • \(p = 0.01\) (\(99\%\) confidence level): There is only a \(1\%\) probability that the result occurred by chance.
  3. Apply the Decision Rule:
    • If your calculated value \(\ge\) critical value: You reject the null hypothesis (\(H_0\)) and accept the alternative hypothesis (\(H_1\)) at the chosen confidence level. The relationship is statistically significant!
    • If your calculated value < critical value: You accept the null hypothesis (\(H_0\)) (or fail to reject \(H_0\)). There is no statistically significant relationship.

Vital Exam Warning: Correlation does NOT prove causation! Even if \(r_s\) is statistically significant at \(p = 0.01\), variable \(X\) might not be causing variable \(Y\). The relationship could be influenced by a third, unmeasured variable (confounding variable) or be an accidental correlation.

6. ICT and Geospatial Skills (Specification Section 3.5.2.5)

Modern geographical research relies on digital tools to process and visualise complex spatial patterns:

  • Remote Sensing: Gathering spatial data from a distance using satellites, aircraft, or drones without physical ground contact. Examples include satellite imagery (monitoring urban sprawl or deforestation), thermal imaging (urban heat islands), and LiDAR (light detection and ranging, used for high-precision 3D digital elevation and coastal erosion models).
  • Electronic Databases: Querying, filtering, and analysing large public databases, including Census/Office for National Statistics (ONS) data, British Geological Survey (BGS) borehole and rock data, and Environment Agency river discharge and flood warning records.
  • Geographical Information Systems (GIS): Software such as ArcGIS, QGIS, and Google Earth that links numerical data to spatial locations. GIS allows geographers to:
    • Layer multiple physical and human datasets onto one map interface.
    • Perform spatial queries (e.g., "Find all residential properties located within \(500\text{ m}\) of a river flood zone").
    • Calculate distances, areas, and network routes automatically.

7. Summary of Common Exam Pitfalls

To secure top grades in your AS Level exam, avoid these common mistakes highlighted in AQA examiner reports:

  • 1. Vague Fieldwork Evaluations: Never just write "we should have collected more data." Explain why your sample was biased, how stratified or systematic sampling would resolve the problem, and how this improves data validity.
  • 2. Confusing Movement Maps: Remember that flow lines follow actual transit routes with proportional line widths, whereas desire lines are direct, straight origin-to-destination links.
  • 3. Misreading Triangular Graphs: Always read parallel to the tick mark guidelines and verify that your three component percentages sum to exactly \(100\%\).
  • 4. Stating that Correlation Equals Cause: Never claim a strong Spearman's rank score "proves" one variable caused another. Always mention the possibility of intervening variables.
  • 5. Critical Value Confusion: Remember that to reject the null hypothesis, your calculated \(r_s\) value must be greater than or equal to the critical value found in the table.

Quick Review Quiz

Test yourself on the core concepts covered in this chapter:

  1. What are the 6 stages of the AQA fieldwork enquiry route in order?
  2. Which sampling strategy divides a population into proportional subgroups before sampling?
  3. If an isoline on a weather chart connects points of equal pressure, what is it called?
  4. Why is the radius of a proportional circle calculated using \(\sqrt{\text{Total Value}}\) rather than the raw total value?
  5. What decision do you make regarding the null hypothesis (\(H_0\)) if your calculated Spearman's rank value is greater than the critical value at the \(p = 0.05\) level?

Check your answers against the notes above to ensure you have mastered every section!