Data Presentation and Analysis

Presenting Fieldwork Data: Cartographic Techniques

Opening a weather app instantly shows you where it is raining using colour codes.

In geography, a Choropleth Map works similarly by shading geographical areas to show spatial patterns, like population density or deprivation. While they clearly show broad regional trends, their negative features are that they mask internal variations and falsely suggest abrupt changes at administrative boundaries.

To construct a choropleth map, first gather data for specific areas and determine the range (maximum minus minimum). Next, divide the data into 4-6 class intervals using methods like Equal Interval or Quantile Classification. Finally, create a shading scale from light (lowest values) to dark (highest values) and shade the base map accordingly, remembering to add a clear key.

For point data like visitor numbers, we use Proportional Symbols, such as circles. These allow easy comparison of precise locations without land-area bias, though symbols can overlap in dense areas and exact numerical values are hard to extract.

Crucially, the area of the symbol, not the radius, must be proportional to the data value to avoid a visual lie. The formula uses a scale factor ($k$):

$$r = \sqrt{\text{Data Value}} \times k$$

Worked Example

If 144 pedestrians are represented by a circle with a 12 mm radius, what is the radius required for 324 pedestrians?

Step 1: Find the square root of the new data value.

• $\sqrt{324} = 18$

Step 2: Apply the scale factor ($k$).

• Since $\sqrt{144} = 12$, the scale factor $k = 1$.
• Radius = $18 \times 1 = 18\text{ mm}$

Step 3: State the final answer with units.

• The radius is $18\text{ mm}$.

Digital frameworks called GIS (Geographical Information Systems) can layer these techniques, such as placing proportional circles over a choropleth base map.

Mapping Movement and Relationships

Commuters rarely travel in a perfectly straight line from home to work.

However, a Desire-line map draws exactly that: straight lines connecting an origin to a destination, completely ignoring the actual route taken. If we want to show the exact route and volume of movement, like river discharge or traffic, we use a Flow-line map where the line thickness is proportional to the volume.

To construct flow-lines, first establish a scale for thickness (e.g., $1\text{ mm} = 50\text{ vehicles}$). Divide your raw data by this scale to calculate the exact line width in millimetres. Finally, draw the lines connecting origins and destinations, adding arrowheads to indicate directionality.

When investigating the relationship between two continuous variables, known as Bivariate Data, a Scatter Graph is used. The independent variable (the controlled basis) goes on the x-axis, and the dependent variable (the measured outcome) goes on the y-axis.

Points are plotted as small crosses and never connected point-to-point. Instead, a line of best fit is drawn as a single straight line with an equal number of points above and below it. Scatter graphs highlight the Correlation (positive, negative, or none) and make it easy to spot an Anomaly.

They cannot easily label individual data points and show relationships rather than definite causes. We can use the line of best fit for Interpolation (estimating a value within the measured range) or Extrapolation (predicting values beyond the measured range).

Processing Data with Statistics

Raw data numbers are just a messy list until you find the hidden story inside them.

Statistical Analysis involves identifying patterns, trends, and relationships, which goes beyond simply describing what the data shows. Using the Mean (average) and Range (spread) helps identify trends across different fieldwork zones.

To prove the strength of a relationship scientifically, geographers use Spearman's Rank Correlation. If your calculated value exceeds a critical value (e.g., at 95% confidence), it proves a statistically significant correlation.

Another vital processing tool is calculating Percentage Change, which compares how much a value has grown or shrunk relative to its starting point.

Worked Example

The average pedestrian count in the central business district increased from 450 to 585. Calculate the percentage change.

Step 1: Write the formula.

• $\frac{\text{Difference between values}}{\text{Original value}} \times 100$

Step 2: Calculate the difference between the values.

• $585 - 450 = 135\text{ pedestrians}$

Step 3: Divide by the original value and multiply by 100.

• $\frac{135}{450} = 0.3$
• $0.3 \times 100 = 30\%$

Explaining Physical Geography Theories

A pebble picked up at a river's source looks entirely different from one found near its mouth.

The Bradshaw Model predicts how a river changes downstream. Discharge (measured in cumecs), channel depth, average velocity, and the actual water width—known as the Occupied Channel Width—all increase downstream. Meanwhile, gradient, channel bed roughness, and load particle size decrease.

Geographers must explain the Causal Mechanism behind these patterns. Discharge increases downstream due to added water from tributaries and groundwater. This increased discharge provides more energy for lateral and vertical erosion, increasing width and depth.

Velocity increases because the larger channel becomes more efficient; a higher Hydraulic Radius means less water is in contact with the bed and banks, losing less energy to friction. Sediment size decreases downstream because rocks collide and break apart, a process called Attrition.

In coastal fieldwork, similar mechanisms apply. Longshore Drift transports sediment in a zig-zag pattern due to prevailing winds causing swash at an angle and gravity causing backwash at 90 degrees. This leads to sediment sorting, with smaller pebbles found further downdrift.

To measure these coastal changes, geographers use a Transect from the water's edge to the back of the beach, stopping at every Break of Slope to measure the angle with a clinometer. Pebble shape is categorized using the qualitative Powers' Scale of Roundness, and the data is often presented as a Histogram showing the frequency of size classes.

Explaining Human Geography Theories

Why are the tallest skyscrapers always clustered tightly in the very centre of a city?

The answer lies in Bid-Rent Theory, which states that competition for accessible land in the centre drives up prices. This creates a causal mechanism: only high-profit commercial businesses can afford the centre, while residential housing is pushed to the cheaper periphery.

This results in Distance Decay, where land value and density decline as you move further from the centre. Students often compare their primary fieldwork data to classical urban models, such as the Burgess (Concentric Zone) Model or the Hoyt (Sector) Model.

Fieldwork might involve classifying land using the RICEPOTS system or measuring the Environmental Quality Index (EQS) on a bi-polar scale. For example, a hypothesis might state that EQS scores will increase as distance from the centre increases.

However, a key negative feature of these traditional models is that they are based on an Isotropic Surface (a flat, featureless plain) and often fail to account for modern realities like out-of-town retail parks or inner-city gentrification.

Evaluating Methodologies and Anomalies

Even the most careful scientist will occasionally record a result that makes no sense.

An anomaly is a data point that breaks the geographical rule. For example, coastal theory dictates that sediment gets smaller downdrift due to attrition, but you might find large pebbles unexpectedly. High-level evaluation requires linking these anomalies to site-specific factors, such as human-made groynes trapping larger sediment.

River anomalies are often caused by hard local geology preventing erosion or human interference like weirs. Anomalies can also stem from methodological errors, such as holding a ranging pole at an angle, which impacts the Reliability of the data.

To evaluate fully, apply the "So What?" rule by explaining how an anomaly partially or fully undermines your initial hypothesis. Finally, always contextualise primary findings with at least two forms of secondary data, such as historical OS maps or Census data.