Geographical Skills: Maps, Graphs and Data Techniques

Ordnance Survey (OS) maps are examined directly in Papers 1, 2 and 3. Understanding every element of the map is essential.

Grid references — locating features:

The OS National Grid uses numbered grid lines to allow precise location of features.

Type	How to read	Example
Four-figure	Easting (across), then northing (up); two digits each; identifies a 1 km × 1 km square	5273 = easting 52, northing 73
Six-figure	Easting (estimate tenths across the square), northing (estimate tenths up); three digits each; pinpoints a point within ~100 m	524738 = easting 52.4, northing 73.8

Memory technique: "Along the corridor, then up the stairs" — eastings before northings.

Scale and distance:

• 1:25,000 (Explorer): 4 cm = 1 km; 1:50,000 (Landranger): 2 cm = 1 km
• Straight-line distance: measure with a ruler, then convert using the scale bar
• Curved route: use a piece of string; lay it along the route, then measure the string against the scale bar

Direction:

• 8 compass points: N, NE, E, SE, S, SW, W, NW
• Bearings: measured clockwise from north (0°); N = 000°, E = 090°, S = 180°, W = 270°

Spot heights and triangulation points:

• A spot height is a black dot on the map with a number indicating the exact height (in metres) of that point above sea level; found on summits, road junctions, and any precise surveyed location
• A triangulation pillar (trig point) is a concrete pillar on a hilltop used in historical triangulation surveys; marked on maps by a blue triangle with a dot and the height value (e.g. △ 294)
• Spot heights give more precise elevation than reading between contours

Contours:

• Contour lines connect points of equal height above sea level
• On 1:25,000 maps: contour interval = 10 m; index contours (labelled) every 50 m
• Close contours = steep slope; widely spaced = gentle slope; concentric circles = hilltop; V pointing uphill = valley; V pointing downhill = spur

Cross-sections:

1. Place a strip of paper along the transect line
2. Mark where each contour crosses; record its height
3. Transfer marks to graph paper; plot height against distance; join smoothly
4. The result is a continuous profile — not a staircase

Describing physical features on large-scale maps:

• Coastal: identify cliffs (closely spaced contours reaching the coast), beaches (very low/flat ground at coast), headlands/bays from map shape
• Fluvial (river): V-shaped valleys appear on the map as V-shaped contour patterns with the V pointing upstream; wide, flat flood plains appear as a broad band of low-lying land; meanders visible in plan view
• Glacial: U-shaped troughs appear as broad, flat-bottomed valleys with steep sides; corries identifiable as armchair-shaped hollows with tightly curved contours; ribbon lakes (elongated lake on valley floor)

Inferring human activity from map evidence:

• Settlement: clusters of buildings, named roads, pub symbols (PH), places of worship, post offices → evidence of a village or town
• Tourism: tourist information symbol (ℹ), visitor centres, car parks near amenities, footpath networks, National Park boundary → tourist area
• Farming: field patterns, farm names, orchards (symbol), plantation woodland
• Industry: factory buildings, railway sidings, electricity pylons, large rectangular buildings near road/rail links

Atlas maps:

• Latitude and longitude: latitude lines (parallels) run east–west and measure distance north/south of the Equator (0°–90° N/S); longitude lines (meridians) run north–south and measure distance east/west of the Prime Meridian (0° through Greenwich, 0°–180° E/W)
• Using atlas maps: identify distribution patterns of a phenomenon (e.g. where tropical storms occur, where LICs are concentrated); describe relationships between thematic maps (e.g. compare a map of rainfall with a map of food insecurity — patterns may coincide)

Maps in association with photographs:

• Sketch maps: simplified maps drawn to show key features from a photograph or site; annotate with labels pointing to specific features
• Aerial and satellite photographs: bird's-eye view (aerial) or satellite imagery; identify features from shape, size, colour, and texture; cross-reference with an OS map extract to confirm location and add detail (e.g. road numbers, settlement names, contour heights)
• Drawing sketches from photographs: observe a landscape photograph and reproduce a simplified line drawing capturing the key features; divide the view into foreground, middleground, and background; draw outlines rather than detail; annotate with labels (e.g. "V-shaped valley — evidence of river erosion", "flood plain — flat, low-lying land adjacent to channel", "settlement on valley side — avoids flood risk"); a sketch communicates spatial relationships more clearly than a written description

Choropleth maps:

• Areas shaded in different colours/densities to show how a variable varies between regions
• Darker = higher value; lighter = lower value; strength: easy to read regional patterns; weakness: hides variation within each shaded area

Isoline maps (isopleth):

• Lines connect points of equal value (temperature isotherms, pressure isobars, rainfall isohyets, noise contours)
• Reading gradient: isolines close together = steep gradient (rapid change over short distance); isolines far apart = gentle gradient
• Reading value: if a point lies between isolines 300 and 400, its value is between 300 and 400; interpolate proportionally (a point halfway between the lines ≈ 350)

Flow-line maps:

• Lines of varying width show movement between places; wider line = larger flow
• Direction shown by arrow; use: migration, trade, traffic volume; limitation: becomes cluttered with many flows

Proportional symbol maps:

• Symbols (circles, squares, dots) drawn at each location with size proportional to the value represented
• Use: population size, GDP, earthquake magnitude; strength: easy to compare quantities at a glance; weakness: difficult to estimate precise values from symbol size

Desire line maps:

• Straight lines drawn between an origin and destination to show movement, regardless of actual route taken
• Use: shopping catchment areas, commuting patterns, migration journeys; unlike flow-line maps, do not show the actual path taken

Line graphs:

• Continuous data over time; describe: overall trend, rate of change, peaks and troughs; quote data values

Bar graphs:

• Discrete categories; vertical or horizontal; compound/stacked bar: components within each bar; divided bar (100% bar): proportional

Histograms:

• Show frequency distribution of data grouped into equal class intervals; bars touch each other (no gaps) — this distinguishes histograms from bar charts
• The x-axis shows the continuous variable (e.g. rainfall amount, age group); the y-axis shows frequency; useful for showing whether data is normally distributed, skewed, or bimodal

Pictograms:

• Use symbols or pictures to represent data; each symbol represents a fixed number (e.g. each figure = 1 million people)
• Advantage: visually engaging; easy to read; limitation: imprecise (half-symbols are hard to read accurately)

Pie charts:

• Proportional composition; 360° = 100%; calculate degrees: (percentage ÷ 100) × 360

Triangular graphs:

• Three-axis graph for data summing to 100% across three variables (e.g. % primary, secondary, tertiary employment); read each axis parallel to its own grid lines

Plotting data on a provided graph: When given a pre-drawn graph with labelled axes and a scale, plot the given data accurately. For a bar chart: draw a bar to the correct value. For a line graph: place a cross (×) at each coordinate, then join with a straight line. For a scatter graph: plot each (x, y) pair as a point. Read the scale carefully — a common error is misreading the interval (e.g. treating 2-unit intervals as 1-unit intervals).

Dispersion diagrams:

• All individual data values plotted on a single vertical scale; show spread, clustering, and outliers; less useful than a line graph for showing temporal patterns

Population pyramids:

• Bar chart of age-sex structure; females right, males left; youngest at base

Shape	Characteristics	Development stage
Wide base, rapid taper	High birth rate; high death rate; short life expectancy	LIC / DTM Stage 2
Narrowing base, expanding middle	Falling birth rate; falling death rate	NEE / DTM Stage 3
Narrow base, bulge in middle	Low birth rate; low death rate; ageing population	HIC / DTM Stage 4–5

Scatter graphs:

• Show the relationship (correlation) between two variables; each point = one observation
• Positive correlation: as x increases, y increases (GNI per capita vs. life expectancy)
• Negative correlation: as x increases, y decreases (birth rate vs. GNI per capita)
• No correlation: random scatter; no relationship

Line of best fit:

• A straight line through the middle of the point cluster; roughly equal numbers of points on each side
• Used for interpolation: estimating the y-value for an x-value within the data range by reading off the best-fit line
• Used for extrapolation: estimating a value beyond the data range by extending the best-fit line; less reliable because the trend may not continue

Prediction from scatter plots: "If a country has a GNI per capita of $10,000,whatwouldyoupredictitslifeexpectancytobe?"\to find$10,000 on x-axis, go up to the line of best fit, read across to the y-axis.

Cumulative frequency graphs:

• Plot cumulative (running total) frequency against the upper boundary of each class interval; produces a curve (S-shaped)
• Reading quartiles from cumulative frequency curve:
  • Median (Q2): draw horizontal line from 50% of total frequency; read down to x-axis
  • Lower quartile (Q1): from 25% frequency
  • Upper quartile (Q3): from 75% frequency
  • Interquartile range (IQR): Q3 − Q1; the middle 50% of the data
• Reading percentiles: draw horizontal line from desired percentage (e.g. 90th percentile from 90%); read down to x-axis

Measures of central tendency:

Measure	Calculation	Best for
Mean	Sum all values ÷ number of values	Normally distributed data with no extreme outliers
Median	Middle value when ranked in order	Data with outliers (house prices, income)
Mode	Most frequently occurring value; modal class — the class interval containing the highest frequency in grouped data	Categorical or discrete data; histogram bar charts

Measures of spread:

• Range: highest − lowest; simple but sensitive to outliers
• Quartiles: Q1 (25th percentile), Q2 (median/50th), Q3 (75th percentile); divide the ranked dataset into four equal quarters
• Interquartile range (IQR): Q3 − Q1; the range of the middle 50% of data; resistant to outliers
• Percentiles: the value below which a given percentage of data falls; the 90th percentile is the value below which 90% of observations fall

Percentages and percentage change:

• Percentage = (part ÷ whole) × 100
• Percentage change = ((new − original) ÷ original) × 100; can exceed 100% for an increase; cannot fall below −100%

Ratios:

• Express the relationship between two quantities (1 doctor per 500 people = 1:500); useful for comparing resource availability between countries

Weaknesses in selective statistical presentation: Statistics can be used selectively to mislead. Critical awareness of these techniques is required:

• Cherry-picking the time period: a graph starting just before a peak and ending at a trough can make a rising trend look like a fall
• Truncated y-axis: starting a bar graph y-axis at 90 (not zero) makes small differences look dramatic; a full y-axis from zero would show the differences are modest
• Mean vs. median: using the mean for highly skewed data (e.g. house prices) overstates the typical value; the median would be more appropriate
• Percentage vs. absolute numbers: a 200% increase sounds large, but if the original number was 1, it means only 3 now; context of the base matters
• Misleading map shading: choropleth map class intervals can be chosen to make distributions look more or less extreme than they are

Fieldwork skills are assessed in Paper 3, Section B. You must discuss both your actual fieldwork experience and the methods used.

Quantitative data collection: Data collected as numbers; can be counted, measured, and statistically analysed.

Method	Example	Strength	Weakness
Structured questionnaire	Shoppers asked to tick boxes about shopping frequency	Comparable; analysable statistically	Sample bias; socially desirable responses
Environmental quality survey	Bipolar rating scale for noise/litter/traffic on a 1–5 scale	Quick; quantitative; comparable	Subjective; needs standardised criteria
Pebble/sediment size measurement	Measuring b-axis of 20 pebbles every 10 m along a beach	Direct measurement; high precision	Time-consuming; small sample possible
Traffic count	Counting vehicles passing a point for 5-minute periods	Direct; replicable	Varies by time of day/week

Qualitative data collection: Data collected as descriptions, observations, images, or opinions; not numerical.

• Field sketches and annotations: drawing and labelling physical or human features observed in the field; captures spatial arrangement and key features that photographs miss
• Photographs: record visual evidence; annotate with date, location, compass direction, and what you are trying to show
• Interviews and open-ended questionnaires: allow respondents to give detailed, nuanced answers; produce rich descriptive data; harder to analyse statistically
• Land use mapping: systematically recording land use type across an area using a base map and colour codes; produces spatial data that can be compared at different times or locations

Sampling strategies:

Strategy	Description	Use
Systematic	Sample at regular intervals (every 10 m, every 5th person)	Ensures coverage; easy to replicate
Random	Use random number tables to select sites/respondents	Removes observer bias
Stratified	Ensure sample reflects known proportions in the population (e.g. same % of age groups as the actual population)	More representative for surveys

Data presentation and analysis:

• Match technique to data type: time-series → line graph; categories → bar chart; spatial variation → choropleth or dot map; correlation → scatter graph
• Label all axes; include a key; give a title; quote data in descriptions
• Analysis: refer to your original hypothesis; use statistics (mean, median, IQR) to describe your data; identify and explain anomalies; acknowledge limitations (sample size, time of day, weather, single visit, observer error)

1. Confusing four-figure and six-figure grid references

Four-figure = 1 km × 1 km square (two-digit easting, two-digit northing). Six-figure = 100 m precision (three digits each). Read eastings before northings in both cases. Practice until this is automatic.

2. Describing a graph without quoting data

"Population increased over time" earns minimal marks. "Global population grew from 2.5 billion in 1950 to approximately 8.1 billion in 2024 — an increase of over 200%" earns full credit. Quote values, units, and dates from the source in every graph description.

3. Drawing a cross-section as a staircase

A cross-section is a smooth landscape profile, not a step-graph. Plot each contour as a point and join them with a smooth curve.

4. Confusing correlation direction on scatter graphs

Positive correlation: both variables increase together (up-right pattern). Negative: one increases as the other decreases (down-right). State the direction explicitly and quote an example ("As GNI per capita increases, infant mortality decreases").

5. Treating fieldwork analysis as a factual description

Fieldwork answers expect more than a description of what you did. The higher mark bands require: reasons why you chose your method; acknowledgement of limitations; how the limitations could be reduced; a conclusion linked back to your hypothesis. Evidence + explanation = marks.