Graphs

Bipolar Graphs

Purpose

Bipolar analysis is a technique used for comparing areas, people or gathered data. It can be used as a visual portrayal of the results of a questionnaire or survey - for example, a comparison between two shopping centres or comparing the environment and amenities of three areas.

Analysing a bipolar chart

Once completed, the results can be analysed to highlight their similarities and/or differences. Be aware that the results are based on people's perceptions and bellefs. As such, although useful, they may not be true. Bear this in mind when considering any conclusions from the analysis of the data.

Considerations/limitations of bipolar analysis

Advantages:

  • a large amount of data can be plotted in a relatively small space

  • patterns can be identified quickly and easily.

Disadvantages:

  • the results are based on people's perceptions, which may be biased

  • the technique can only be used for two or three areas, otherwise the graph will become cluttered and confusing to analyse

Dispersion diagrams

Purpose

Dispersion diagrams are used to display the main patterns in the distribution of data. The graph shows each value plotted as an individual point against a vertical scale. it shows the range of data and the distribution of each piece of data within that range. It therefore enables a comparison of the degree of clustering of two sets of data - for example, comparing the quality of life of people living in urban areas with that of people living in rural areas.

Analysing a dispersion diagram

Analysis of a completed dispersion diagram should show the similarities and differences between the data sets. The use of appropriate statistical techniques will allow testing to see whether the differences are statistically significant.

Reference to relevant geographical knowledge and understanding is often required in the interpretation of the data.

Considerations/limitations of dispersion diagrams

Advantages

  • show the spread from the mean

  • are easily understood

  • give an indication of the reliability of the data

  • allow the calculation of the mean, range, mode, median, lower quartile, upper quartile and interquartile range

  • can compare graphs easily

  • anomalies can be shown can calculate the standard deviation.

Disadvantages

  • data must be in a form that can be placed along a number line

  • work better with lots of data

  • the standard deviation can easily be manipulated and can be biased.

Kite diagrams

Purpose

A kite graph displays the density and distribution of plant or animal species in a particular habitat. A kite diagram can be used to show the abundance of key marine species as you move from the shoreline to below the low-tide mark on a rocky shore or can show changes in vegetation coverage near ecosystem boundaries, such as between a meadow and a forest - see pp. 10 and 11 (beach profile), pp. 16 and 17 (slope analysis), pp. 22 and 23 (vegetation analysis) and pp. 80 and 81 (transects).

A quadrat is usually a square made of wire. It may contain further wires to mark off smaller areas inside, such as 5 × 5 or 10 × 10 squares. The organisms within each square, usually plants, can be identified and counted.

Analysing a kite diagram

The analysis of a kite diagram should indicate where the various species occur and the frequency of each species. It should also include an explanation of the spatial patterns in the diagram, highlighting changes over the distance of the transect and the possible relationships between individual species and/or other related variables.

Considerations/limitations of kite diagrams

Advantages

  • useful for displaying changes over distance

  • visually clear and easy to distinguish one category from another

  • comparisons can easily be made

Disadvantages

  • visually subjective, as the scale influences the visual effect

  • only works with a specific range of data

  • time-consuming to construct by hand

Kite diagrams may need to be broken down into sections for larger studies over 100m. An alternative graphing technique is a histogram.

Logarithmic graphs

Purpose

Logarithmic graphs are used when the values on the scales are so large that they are difficult to show on linear graph paper or when the graph is to be used to compare the rates of growth (exponential growth) of the variables over time. A logarithmic scale increases by multiplications in value rather than additions (eg. 1, 10, 100, 1000 rather than 1, 2, 3, 4).

The value by which the scale is multiplied is usually ten (ie. log-base 10). Both scales may be logarithmic, or just one scale logarithmic and the other linear (semi-logarithmic graphs).

Semi-logarithmic graphs are commonly used in geography to compare the rates of increase or decrease in economic data, changes in population size, agricultural yields and energy production.

Analysing a logarithmic graph

When analysing logarithmic graphs, be mindful of the following points:

  • whether you are interpreting a two-scale logarithmic or semi- (one-scale)
    logarithmic graph

  • the scale values on the x-axis and y-axis

  • the direction of the line - it is easy to interpret these graphs in the same way as linear graphs and to obtain a completely wrong meaning from the displayed data

  • evaluate whether the changes in the line direction actually show an increase, decrease, or a constant value

  • a constant proportional rate of change (an exponential change) is represented by a straight line on a logarithmic graph (rather than a curved line on a linear graph) - this means that logarithmic graphs are good for comparing rates of change.

Considerations/limitations of logarithmic graphs

Advantages

  • useful for studying data that change exponentially

  • can display a much larger range of data than a linear scale

  • allow you to see increased detail at smaller values, whereas larger values are compressed

Disadvantages

  • zero cannot be plotted

  • positive and negative values cannot be plotted on the same graph

  • small values occupy a larger proportion of the scale than larger values

  • on a linear scale, unless the graph paper is very large, smaller values would be too small to see properly

  • allow comparison between trends in small and large values.

  • can be difficult to plot values accurately difficult to interpret as scale is distorted.

Polar graphs