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Quiz yourself on Fieldwork Presentation and Evaluation SkillsGet your flashcardsBuild my revision planTrack my progress🗂️ 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)
| Data Situation | Best Technique | Why This Technique Works |
|---|---|---|
| One set of values at different named locations or categories | Bar chart | Clear comparison between discrete categories; height shows relative size instantly |
| Values changing over time or continuously along a transect | Line graph | Shows trend direction and rate of change; the line implies continuity between measurements |
| Showing proportions of a whole at one location | Pie chart or proportional circle | Each segment's area is proportional to its share of the total; comparisons of parts to the whole are immediate |
| Testing whether two continuous variables are related | Scatter graph | Each point represents one measurement pair; correlation direction and strength are visible; line of best fit can be added |
| Showing how values are spread across a range at different sites | Dispersion diagram | Shows every individual value; median and quartiles can be added; reveals spread that a mean alone would hide |
| Frequency distribution of continuous measurements (e.g. pebble sizes) | Histogram | Bars are joined (continuous scale on x-axis); bar area = frequency; shows distribution shape clearly |
| Abundance or density of species/features along a transect | Kite diagram | Width at each point shows frequency; the "kite" shape makes patterns along transect immediately visible; multiple species can be overlaid |
Qualitative and Locational Data
| Data Situation | Best Technique | Why This Technique Works |
|---|---|---|
| Showing where features are located | Annotated map or sketch map | Preserves locational context; annotations add geographical meaning beyond just marking positions |
| Showing volume and direction of movement (people, traffic, goods) | Flow map / desire lines | Line width = quantity; direction = route; makes spatial patterns in movement immediately visible |
| Showing relative values across different areas | Choropleth map | Shading intensity communicates relative values per area; spatial patterns across zones are clear |
| Comparing multiple variables measured at different sites (e.g. EQS results) | Radar / spider diagram | Each axis = one variable; overlapping polygons for different sites allow multi-variable comparison at a glance |
| Recording primary evidence of the fieldwork site | Field sketch / annotated photograph | Direct visual evidence; annotations must add geographical interpretation, not just label what is visible |
Quick Check: A student is investigating whether land value decreases with distance from the city centre. They have 15 paired measurements (distance in km, estimated land value in £/m²). Which presentation technique is most appropriate and why?
A scatter graph is most appropriate. Both variables are continuous (distance is measured on a continuous scale; land value is a continuous numerical measurement). The student wants to test whether a relationship (correlation) exists between the two variables — this is exactly what a scatter graph is designed to show. Each of the 15 data pairs is plotted as a single point; the overall pattern of points shows whether there is a positive, negative or no correlation. A line of best fit can be added and Spearman's rank correlation coefficient calculated to test the strength of the relationship statistically. A bar chart would be wrong here because it cannot show the relationship between two continuous variables.
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