Common and Specialized Geographical Graph Types

Recognising what a graph represents

Geographical graphs turn observations into visible patterns. Different graph types organise those observations differently: a bar graph compares categories, a line graph follows change, and a scatter graph investigates a relationship between two variables. Recognising this structure tells you how to plot and read the data.

An important distinction is between discrete data, which have separate possible values, and continuous data, which can take any value within a range. The number of vehicles passing a site is discrete; their measured speeds are continuous. Continuous measurements can be grouped into class intervals before they are graphed.

Bar graphs: comparing categories and their parts

A vertical bar graph represents each category with a separate bar: its height gives the value. A horizontal bar graph uses bar length instead. These are two orientations of the same technique. Bars have equal widths and equal gaps, so their lengths—not their widths—communicate the quantities.

For example, a comparison of travel choices could have separate bars for walking, cycling, bus travel and car travel. Read the category label first, then trace the end of its bar to the numerical scale.

A divided bar graph splits each bar into sections to show its composition. A percentage version represents the whole as 100%. Imagine a bar divided into 40% walking, 20% cycling and 40% bus travel: each section occupies its share of the bar's total length. Several divided bars can compare the composition of different groups. Use the key to identify each section, and read its length between its two boundaries rather than treating its endpoint as its value.

Histograms: grouping continuous measurements

A histogram shows the frequency of continuous measurements grouped into intervals. Its bars touch because neighbouring intervals follow directly from one another, rather than representing separate named categories.

For OCR, the required histograms have equal class intervals. For example, river depths could be grouped into 0–less than 10 cm, 10–less than 20 cm and 20–less than 30 cm. These intervals are all 10 cm wide. Place depth intervals on the horizontal axis and frequency on the vertical axis; each bar's height represents the number of measurements in that interval.

A histogram preserves the distribution of measurements, but not every original value. A bar containing six observations tells you how many fall within the interval, not their six exact depths.

Pictograms: counting symbols with a key

A pictogram represents quantities using repeated pictures or symbols. It is useful for comparing discrete counts, such as the number of pedestrians recorded at different sites. Each complete symbol represents the same quantity, stated in a key; a partial symbol represents the corresponding fraction of that quantity.

Consider this pedestrian count, with the key one complete circle = 4 pedestrians:

• Site A: ● ● ◐ — two complete circles and one half-circle.
• Site B: ● ● ● — three complete circles.

Site A represents $2.5 \times 4 = 10$ pedestrians, while Site B represents $3 \times 4 = 12$ pedestrians. The half-circle is worth two pedestrians, not half a pedestrian. Site B therefore has two more pedestrians than Site A.

To construct a pictogram, choose a simple symbol and a convenient key value. Divide each category's count by the key value to find how many symbols to draw: ten pedestrians with four per symbol require two and a half symbols. Arrange equal-sized symbols neatly in labelled rows and include the key. Repetition communicates the quantity; enlarging individual symbols would change the meaning. Pictograms are accessible at a glance, but awkward fractions or very large numbers of symbols can make precise comparisons harder.

Line graphs: following change in order

A line graph plots values against an ordered numerical scale, commonly time or distance. Plot each pair of values and join neighbouring points in order. The connecting line makes increases, decreases and turning points easier to follow.

For a river study, measurements at 0 m, 100 m and 200 m downstream could be plotted against channel width. The order matters: joining these measurements follows the journey downstream. A rising section means width increases over that stretch. The graph shows the pattern, but does not by itself explain its cause.

Scatter graphs: investigating a relationship

A scatter graph gives each observation a position using two measured variables. For example, each river site could contribute one point showing channel width on the horizontal axis and channel depth on the vertical axis.

The points remain separate because they represent different paired observations, not successive steps along a line. If points tend upwards from left to right, the variables have a positive correlation. A downward pattern indicates negative correlation. A scattered cloud with no clear direction suggests little or no correlation.

A best-fit line summarises the overall pattern. Draw it through the centre of the scatter, with points reasonably balanced on either side; it need not pass through every point. Points close to the line indicate a stronger relationship than widely scattered points. An isolated point may be an anomaly worth investigating. Even a strong correlation does not establish that one variable causes the other.

Dispersion graphs: seeing the spread

A dispersion graph displays individual values on a numerical scale, usually vertically, with different datasets arranged side by side. Each observation is plotted at its measured value. This allows you to compare clusters, gaps, extreme values and the overall spread.

Imagine beach A has pebble lengths of 3, 4, 4 and 5 cm, while beach B has 1, 4, 4 and 7 cm. Both sets are centred on 4 cm, but beach B's values are much more dispersed. Plotting every measurement reveals that difference more clearly than showing only an average. Repeated values must remain visible rather than disappearing beneath a single overlapping mark.

Pie charts and proportional circles

A pie chart divides a circle into sectors representing parts of a whole. The full circle is 100%, or 360°. A larger share therefore occupies a larger angle and area. All categories must belong to the same total.

The sector-angle relationship is:

$$\text{sector angle} = \frac{\text{percentage}}{100}\times 360^\circ$$

Identify a sector using its label or key, then read its share of the total. Pie charts work best with a manageable number of categories; numerous small sectors are difficult to compare.

Pie charts can also be placed on maps, allowing the composition at different sites to be compared. They can be drawn as proportional circles, combining information about the whole with its division into categories. Keep the two questions separate: how large is the whole, and what share belongs to each category?

Proportional symbols: showing quantities through size

Proportional symbols vary in size according to the quantities they represent. On a map, a symbol's position identifies the location and its size communicates a value, such as population or traffic count. Unlike a pictogram, which repeats equal-sized symbols, a proportional-symbol display changes the size of a symbol.

For circles and squares, area is proportional to the quantity. A circle representing four times the quantity needs four times the area, not four times the radius. Since circle area increases with the square of its radius, doubling the radius gives four times the area.

For example, suppose a circle of radius 1 cm represents 100 vehicles counted during one hour at fictional Site A. Fictional Site B records 400 vehicles during the same period. Its circle needs four times the area, so draw it with a radius of 2 cm. Centre each circle on its site's marked position. This demonstrates sizing, not a real traffic distribution.

Include a size key containing labelled example circles. To read the display, first locate the site, then compare its circle with the key. The larger circle in this example represents four times the traffic count, even though its radius is only twice as large. Intermediate sizes can be compared with the key, but exact values may be difficult to judge without numerical labels.

Proportional symbols make differences between places visible, although large symbols can overlap and obscure nearby locations. Proportional divided circles add sectors: the circle's area represents the total, while sector angles represent the shares within that total.

Climate graphs: combining two kinds of information

A climate graph combines precipitation bars with a temperature line. Months run along the horizontal axis. Precipitation is measured in millimetres, while temperature is measured in degrees Celsius, so the two datasets need separate labelled scales.

Climate graphs commonly use long-term monthly averages over 30 years. To read one, follow the temperature line to identify the warmest and coolest months, then compare the precipitation bars to identify wetter and drier periods. Read each dataset against its own axis, even where a bar and the line appear at the same height.

The combination helps reveal seasonal patterns: for example, whether the wettest period coincides with higher or lower temperatures. Monthly averages conceal variation within a month and individual extreme weather events.

Cross-sections: representing a shape along a line

A cross-section shows the shape encountered along a particular line through a feature. A river-channel cross-section, for example, uses distance across the channel horizontally and depth vertically.

Plot a measured depth at each distance across the channel, then join neighbouring points to represent the channel's profile. Read the depth axis carefully to establish which way increasing depth is shown. The profile reveals where the channel is shallow or deep and whether its two sides have similar shapes.

Joining the measurements represents the shape between sampling points; it does not mean that every intervening point was measured. This distinguishes a profile from a scatter graph, where joining unrelated observations would create a misleading shape.

Population pyramids: comparing age and sex

A population pyramid uses paired horizontal bars to show age groups and sexes. Age groups are arranged vertically, with younger groups below older groups. The two sexes occupy opposite sides of the central age axis, and bar lengths show population numbers or percentages.

Read the labels to establish which side represents each sex and what units the horizontal scales use. Compare the two bars for the same age group to investigate differences between sexes; compare bars at different ages to investigate the age structure.

A broad lower section indicates a relatively large young population, while a substantial upper section indicates a larger older population. These shapes help identify groups of young and old dependants, but an age-group total does not describe every person's employment or circumstances. A population pyramid need not have a triangular outline.

Radial graphs and rose charts: reading outwards from a centre

A radial graph has several axes, or spokes, extending from a common centre. On a radar or spider chart, each spoke represents a variable. Plot a value along each spoke and join the points belonging to the same dataset to form a shape.

For example, environmental-quality scores before and after redevelopment could be plotted on the same spokes. Compare the two scores along each named spoke to identify which conditions improved or worsened. Use the marked scales: the apparent area of the polygon can be misleading, and the angles between spokes do not represent measured relationships.

A rose chart is a directional radial graph. Its axes follow compass directions, and its sectors represent observations associated with those directions. A wind rose can show how frequently wind comes from each direction. Read the compass label, then use the outward scale to find the frequency.

A stacked wind rose can additionally divide each directional sector into wind-speed groups. The sector's total shows frequency, while its subdivisions show the speed categories. The most frequent wind direction is therefore not necessarily the direction associated with the strongest winds.