data+presentation

=**Data Presentation**=

Scattergraphs
The following table shows data from Tokyo. There are two **variables**; distance from the CBD and noise level.


 * Noise pollution data for a transect in Tokyo ||
 * distance from CBD (km) || noise level (decibels) ||
 * 0.5 || 92 ||
 * 1.2 || 88 ||
 * 1.7 || 82 ||
 * 1.8 || 79 ||
 * 2 || 68 ||
 * 2.19 || 82 ||
 * 2.4 || 65 ||
 * 3.1 || 90 ||
 * 3.8 || 63 ||
 * 5.7 || 54 ||
 * 5.7 || 54 ||

The most appropriate way to present this data would be as a **scattergraph**. A scattergraph is a good method of data presentation to show how one variable affects another. In this case, we might expect that as distance from the CBD increases, noise levels decrease. To check whether there is any **relationship** then we would plot our data onto a graph. In this example, noise level depends on distance from the CBD so noise level is the **dependent variable** and should be plotted on the y axis. Distance from the CBD is the **independent variable** and should be plotted on the x axis.

What do you think your graph will look like? Click on the scattergraph file below to find out.



The **line of best fit** on the scattergraph slopes downwards, showing a **negative relationship**. As distance from the CBD increases, noise level decreases. To analyse this data further, you would then need to try to explain why you think we have this relationship. You would need to suggest reasons why it might be very noisy in the CBD and less noisy further out in the suburbs. There may be some **anomalies**, that do not fit this general pattern. You should try to explain these anomolies. Look at where the data was taken. It could be that you have an unusually quiet reading close to the CBD because the data was collected in a park. Alternatively you may have a particularly noisy reading a long way from the CBD becase the data was gathered at a busy traffic intersection.

It is important to **quantitatively analyse** your data (using numbers) but do not forget a **qualitative** interpretation too.

Bar Graphs
A bar graph is a good way of plotting traffic count data.

A simple bar graph will allow you plot data to show how many vehicles you counted at different times or at different locations throughout the day.

A composite bar graph allows you to plot different types of vehicle on on single bar by using different colours, so that you can compare cars, vans and buses at different locations.

Pictograms
A pictogram is a type of bar chart, which uses pictures to show the frequency of data. It gives a good visual impression and is a useful way of displaying traffic count data. Go to this link to have a go at creating your own pictogram. http://gwydir.demon.co.uk/jo/numbers/pictogram/pictogram.htm

Mapping Techniques
A good way of showing the spatial distribution of your data is to plot it onto a base map. It is possible to create a located bar graph or a located proportional pie chart by plotting lots of small bar graphs onto a map, showing the locations where the data were collected. This allows you to compare different locations easily.

The link below shows an example which combines proportional pie charts and located bar graphs.

http://www.geocities.com/stevejford/USA_Population.jpg

You may like to use these digital copies of pollution base maps in your project

Flow Charts
Flow charts use proportional lines. They often show traffic movement. Here are two examples. http://www.transman.hu/Projektek/belso.jpg

http://www.infovis.info/visuals/qdig-files/converted-images/Atlas_of_Cyberspaces/Census/sm_telegeography_large.gif