Fieldwork Presentation and Evaluation Skills

🔍 Why Presentation Matters — What Examiners Actually Reward

At GCSE, your fieldwork assessment is not just testing whether you collected data. It is explicitly testing whether you can present, interpret, and evaluate that data. These are assessed separately from collection, and each has its own mark tariff.

Raw data in a table earns almost nothing on its own. The examiner has to do all the cognitive work themselves — spotting the trend, noticing the outlier, seeing the pattern. When you present data well, the pattern becomes immediately visible. The examiner can see in two seconds what you are trying to show. That is what earns marks.

The key insight is this: GCSE fieldwork marks are explicitly awarded for choosing the right technique AND explaining why it suits your data type. It is not enough to draw a scatter graph. You need to be able to say: "I used a scatter graph because I was testing the relationship between two continuous variables — pebble size and distance from the cliff — and a scatter graph preserves all individual data points while making any correlation immediately visible." That sentence is worth more than a perfectly drawn graph with no justification.

The Three Levels of Presentation Skill

🗂️ The Decision Framework — Matching Data Type to Technique

The most important skill in this topic is choosing the right technique for the right data. Every presentation method is designed to show a particular type of data or relationship. Use the wrong technique and you obscure the pattern. Use the right one and the pattern speaks for itself.

Ask yourself two questions before choosing any technique: What type of data do I have? (continuous, discrete, categorical, locational?) and What am I trying to show? (a trend, a comparison, a spatial distribution, a relationship between variables?)

Quantitative Data (Numbers)

Qualitative and Locational Data

📈 Bar Charts and Line Graphs — When, How, and Common Errors

Bar Charts

Bar charts are for discrete, categorical data — data where each category is separate and distinct (land use types, days of the week, named fieldwork sites). The height of each bar represents the value for that category. Because the categories are discrete, the bars should not touch.

Drawing a bar chart correctly:

Variations on the bar chart:

• Compound (stacked) bar chart — each bar is divided into stacked segments showing sub-categories. Useful when you want to show both total and the breakdown of that total (e.g. total traffic broken down by vehicle type at each site).
• Deviation bar chart — bars extend both above and below a zero line. Used for bi-polar data (e.g. environmental quality scores that can be positive or negative).
• Comparative bar chart — two or more bars side by side for each category. Allows direct comparison between groups (e.g. comparing pedestrian counts in summer vs winter at the same sites).

Line Graphs

Line graphs are for continuous data over time or distance — data where values are measured at regular intervals along a continuous scale (river depth at different distances from source, temperature across months, traffic counts across hours of the day). The line connecting points implies that the values vary continuously between your measurement points — which is why you should never use a line graph for discrete categorical data (e.g. do not connect "crime in London" to "crime in Birmingham" with a line — they are separate places, not points on a continuum).

Drawing a line graph correctly:

Critical Error: Wrong Technique for the Data Type

The most common error is using a line graph for discrete categories, or using a bar chart for continuous time-series data. Ask: "Are my x-axis values genuinely continuous, or are they separate named categories?" If they are named categories (sites, land uses, areas), use a bar chart. If they are points on a continuous scale (time, distance), use a line graph.

📉 Scatter Graphs and Correlation — The Relationship Test

Scatter graphs are designed for one purpose: testing whether a relationship (correlation) exists between two continuous variables. They are the standard presentation technique for any hypothesis of the form "as X increases, does Y also change?"

Drawing a Scatter Graph Correctly

Interpreting Correlation

Spearman's Rank Correlation Coefficient (rs)

The scatter graph shows you the direction of a correlation visually. Spearman's rank correlation coefficient gives you a numerical measure of its strength, from −1 (perfect negative) through 0 (no correlation) to +1 (perfect positive). An rs value above 0.7 or below −0.7 is generally considered a strong correlation. Most importantly, you can test whether the correlation is statistically significant — meaning it is unlikely to be caused by chance alone — by comparing your rs value against a critical values table at the 0.05 significance level.

When writing up: "The scatter graph shows a strong negative correlation between pebble size and distance from the cliff (rs = −0.87). This is significant at the 0.05 level, meaning there is less than a 5% probability that this relationship occurred by chance. This supports the hypothesis that attrition during transport progressively reduces pebble size with increasing distance."

The Critical Limitation: Correlation Is Not Causation

A scatter graph and Spearman's rank can only tell you that two variables are associated. They cannot prove that one variable causes the other. There may be a third variable (a confounding variable) that actually causes both. This is a standard evaluation point: "A limitation of the scatter graph is that correlation does not prove causation. A confounding variable — such as wave energy at different points along the transect — might explain both the distance and the pebble size patterns independently."

🗺️ Maps — Choropleth, Flow Maps, and Annotated Sketches

Map-based presentations are used when location matters — when the spatial pattern of the data is what you are trying to communicate. If you can answer your research question without knowing where things happened, you probably do not need a map. But if the spatial distribution, direction of movement, or relative values across areas are central to the enquiry, a map is essential.

Choropleth Maps

A choropleth map uses different shading intensities or colours to show how a variable varies across different areas. Darker shading conventionally means higher values. They are most commonly used for showing deprivation, population density, crime rates, or land use proportions across named zones or administrative areas.

Drawing a choropleth correctly:

Key limitation: a choropleth assumes values are uniform across each zone. In reality, a deprived ward might have very wealthy streets in one corner. The map hides this within-zone variation — a significant weakness when analysing at a fine-grained level.

Flow Maps and Desire Lines

A flow map (or desire line map) shows the movement of people, goods, or traffic between places. The width of each line is proportional to the quantity of flow; the direction shows where the flow goes. They are used in pedestrian survey data (where are people coming from?), migration studies, and trade flow analysis.

The term desire line specifically means a straight line drawn between an origin and destination, regardless of the actual route taken. It shows the direction and relative volume of movement without implying that this is the route followed.

Annotated Field Sketches and Photographs

A field sketch or annotated photograph is a form of primary evidence — it records what the site actually looked like. But its value for marks depends entirely on the quality of the annotations.

The difference between a description and an annotation:

Minimum requirements for a GCSE annotated photograph: at least 3 annotations, each making a geographical point (linking a visible feature to a geographical process, theory, or pattern). Simply labelling features with their names earns description marks only.

Isoline Maps

An isoline connects points of equal value on a map — the same principle as contour lines on an OS map, but applied to any measured variable (noise levels, temperature, pollution concentrations). Lines are drawn by interpolating between known data points. Isolines never cross; they are labelled with their value; they close or run to the edge of the map.

📐 Less Common Techniques — Pie Charts, Dispersion Diagrams, Kite Diagrams, and EQS Radar

Pie Charts and Proportional Circles

A pie chart shows how a total is divided into parts. Each segment's angle is calculated as: (value / total) × 360°. The first segment starts at the 12 o'clock position and segments are added clockwise. Pie charts work well for showing proportions at a single location or point in time, but become very difficult to read when there are more than 5-6 slices or when slices are very similar in size.

When NOT to use a pie chart: do not use a pie chart to show change over time (line graph is better); do not use multiple pie charts of the same size to compare totals at different sites (the reader cannot tell which total is bigger). Proportional circles solve this problem: each circle's area (not radius) is made proportional to the total, so you can compare both the total size and the internal proportions across sites simultaneously.

Dispersion Diagrams

A dispersion diagram plots every individual data value as a dot on a vertical scale, grouped by location or category. Unlike a bar chart (which only shows a mean or total), a dispersion diagram reveals how spread out the data is — are all values clustered closely together, or widely scattered? You can add a median line and inter-quartile range to make the comparison of spread more explicit.

Dispersion diagrams are particularly useful in fieldwork when you want to compare the consistency of measurements at different sites — not just the average. Two sites might have the same mean velocity but very different spreads (one highly variable, one consistent). A bar chart would show them as equal; a dispersion diagram would reveal the difference.

Kite Diagrams

A kite diagram is used almost exclusively for transect data, most commonly in sand dune or vegetation succession studies. The x-axis shows distance along the transect; the y-axis is symmetrical above and below a central line. The width of the "kite" at each measurement point represents the abundance, frequency, or cover of a species at that location. Multiple species can be shown on the same diagram, stacked vertically, allowing you to see how different species replace each other along the transect — the process of succession.

Environmental Quality Survey (EQS) Radar / Spider Diagrams

When you have measured multiple separate variables at multiple sites (the typical output of an Environmental Quality Survey — noise, litter, building condition, traffic, greenery, etc.), a radar diagram (also called a spider diagram) allows you to compare all variables simultaneously. Each axis of the polygon represents one variable; the polygon for each site is drawn by plotting the score for each variable and connecting the points. Overlapping polygons for different sites immediately reveal which site scores better across which dimensions.

✏️ Annotating Graphs, Maps, and Photographs Correctly

Annotations — explanatory notes added directly to a presentation — are where many students lose marks unnecessarily. They either forget annotations entirely, or add annotations that only describe what the graph already shows rather than adding geographical meaning.

What Every Graph Must Have

Adding Annotations to Graphs

A good graph annotation does one of three things: identifies a specific feature of interest (an anomaly, a peak, a change in trend), explains what caused that feature geographically, or links it to the broader enquiry hypothesis. Use an arrow from the annotation to the specific point or region being annotated.

Photo Annotations — The Three-Part Formula

Each annotation on a photograph should follow a three-part structure: (1) identify the feature, (2) explain the geographical process, (3) link to theory or hypothesis.

In practice, a photo annotation does not need all three parts in exhaustive detail — but it must go beyond identifying what is visible. The examiner is looking for geographical thinking, not description.

📝 Written Analysis — Moving from Description to Explanation to Evaluation

In any fieldwork write-up or exam question asking you to interpret presented data, there are three levels of response. Most students operate at Level 1. The marks are at Level 2 and Level 3.

Level 1 — Description (Low marks)

You state what the graph, map, or diagram shows. You use the data but only to describe it. There is no geographical explanation of why the pattern exists.

Level 2 — Explanation (Core marks)

You identify the pattern AND explain the geographical reason for it. You connect the data to a process, cause, or geographical principle.

Level 3 — Evaluation and Theoretical Linkage (Top marks)

You analyse the data in detail (using specific figures), link to geographical theory or models, identify and explain anomalies, and evaluate what the data does and does not tell you.

Key Phrases That Lift Mark Scores

These connecting phrases signal to the examiner that you are analysing rather than describing:

The Golden Rule: Always Use Specific Figures

"Beach sediment was smaller towards the sea" earns one mark. "Beach sediment size decreased from 42 mm at Site 1 (cliff base) to 11 mm at Site 5 (swash zone), a reduction of 74%" earns four marks. The examiner rewards precision. Make your data speak in numbers, not just in general patterns.