Other types of diagram

We have only described a few general-purpose graphical methods for describing data. These graphs can be applied to data from a wide range of applications and are fairly easy to understand without training. In general, publications that are intended for a wide readership should avoid more complex graphics.

However we briefly note the existence of other ways to present data graphically that are particularly useful in some situations.

The following two examples are only included to show that we have not covered all types of graphical display.

Always use the simplest graphical method that will convey the information that you want.

Monthly rainfall in Samaru

In most of Africa, the most important climatic variable is rainfall. Rainfall is usually highly seasonal and failure of crops is normally associated with late arrival of rain or low rainfall. A better understanding of the distribution of rainfall can affect the crops that are grown and when they are planted.

The diagram below is based on monthly rainfall in Samaru, Northern Nigeria between 1928 and 1983. For each month, the diagram and table show:

The bands in the diagram join up these values for different months.

Click on any month in the table to link the graph to the tabulated values.

This diagram is a useful way to describe how rainfall varies throughout the year and to help assess the likelihood of 'ten-year droughts and floods'. It does however require more explanation than would be acceptable in most publications for public consumption.

Road accidents in Israel

The diagram below was published recently (by D. G. Feitelson) to introduce a new type of diagram called a spie chart. It is based on a standard pie chart of the age and sex distribution in Israel — the angle (and area) of each segment of the basic circle is in proportion to the number of that age and sex.

The darker segments describe the ages of all road accident casualties in Israel in 2002. These segments use the same slices as for the overall population distribution with their radius adjusted to make the areas of the slices in proportion to the number of casualties in that age group and gender.

If casualties followed the same distribution as the rest of the population, the darker segments would form a complete circle; where they bulge out, there are a disproportionate number of casualties. The diagram therefore effectively illustrates the disproportionate number of male casualties and the particular over-representation of males aged 15-44.

Take care with interpretation of the graph. The bulge in male casualties aged 15-54 could be caused by more reckless drivers of this age, but they probably also spend more time driving.

Although the diagram would be informative to policy makers in transport and health, it would require too much explanation for inclusion in publications intended to be read by the general public.