Point Map


Using the Point Map visualisation tool is an easy way to investigate the attributes of your point dataset and visualise it based on its different variables.



For this worked example, we will map South Australian Government Schools point data and explore the different subtypes.

To do this:

  • Select Greater Adelaide (gccsa_2016/4GADE) as your area.
  • Select SA DE – Schools – Enrolments (point) Term 3 2017 as your dataset, making sure to include the following variables:
    • id
    • Geometry
    • Subtype name

Once you have added this dataset, you are ready to create your point map. Follow the inputs instructions below to see how to do this.



We are now ready to create a point map of the various government school subtypes.

To do this open the Point Map tool (Interactive Maps & Charts → Mapping Points → Point Map)Enter your parameters as shown in the image below.

  • Select a dataset: Here you can choose which of the datasets you would like to display as a map. Select SA DE – Schools – Enrolments (points) Term 3 2017.
  • Select an attribute: This is the field that you want to map. If you want your map to make sense, and actually display the variable you are interested in, it is important to make sure you have selected the right attribute to map together with right classifier. Select Subtype name.
  • Select a classifier: Here we define how we break up our range of values in the attribute. For an attribute that is numerical in format (either an integer or a decimal),  the default setting for this field is Jenks (Natural Breaks), which breaks your data up into intuitive groups based on the shape of the distribution of values. You can select Quantiles or Equal Intervals. If your attribute is categorical – that is, if it is a description or a word (such as a land-use zone, or a name, or any kind of “string”) then the parameter will automatically set to Pre-classified. Select Pre-classified. 
  • Number of Classes: This slider allows you to define the number of breaks in your data (minimum of 3, maximum of 12). The number that you choose should depend on the distribution of your values, the number of data points (areas) and the information that you are trying to portray with your data. Select 7.
  • Select a palette type: Here you can choose the type of colour scheme for your data – Sequential, which shifts from a shade of one colour to another; Qualitative, where the colours are unique along the palette (used for Pre-classified); and Diverging, where colours shift to two colours from a central point along a natural spectrum. Select Qualitative.
  • Palette: This allows you to choose the actual colours of your palette (you can switch the ends of the palette around by clicking the Reverse Palette box at the bottom of the box. AURIN uses colours generated by Colour Brewer. Select Set1.
  • Default Opacity: This slider allows you to define how opaque your map is over the base map. 0.00 indicates completely transparent, 1.00 indicates completely opaque. Select 0.50.
  • Stroke/Line Opacity: This slider allows you to define how opaque your polygon borders will be, with the same values as the Default and Hover Opacity. Select 0.85.
  • Reverse palette: This reverses the order of the colour in its palette which may be useful if you want its colours on opposing ends. Untick this box.
  • Hide Null Values: This will not give a class to any null values if they exist in your dataset. Untick this box.
  • Save Visualisation as: It’s a good idea to change the name of this from the default value to something that reflects the data, particularly if you plan on having multiple choropleth maps from different datasets. The name that you choose here will also be displayed in the legend automatically generated for your map. Type Government School Subtypes.

Once you have selected your parameters click Add.



Once you click Add on the input box, a point map will appear automatically in your viewer (shown below). You will also see an entry appear in your Visualise panel, with a small graph icon next to it.

If you hover over any of the dots, it will be highlighted with a red circle, with the values of the variables coming up. Hovering over any of the points on the graph will show its corresponding area on the map, and vice versa, as shown in the image below.

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