Centroid Map


Centroid choropleths created using the Centroid Map tool are another common type of visualisation tool. Although conceptually similar to classic choropleth maps, centroid choropleths differ in that the central point (“centroid”) of an area (“polygon”) on a map is represented, with varying colour and symbol size, rather than the entire area being shaded according to the variable.

The centroid is shown in proportion to the measurement of a variable being displayed on the map, such as population density or average income, or as shown below, percentage of the population involved in volunteering. A choropleth centroid map allows for two datasets to be overlaid on top of each other, one as a choropleth (such as the map of mortgage stress in the worked example above), and the other as a choropleth centroid.



Note: This worked example assumes you have knowledge of the Choropleth map.

For this worked example, we will look at the distribution of rental costs across Tasmania.

To do this:

  • Select Tasmania (ste/6) as your area
  • Select ABS – Data by Region – Family & Community (SA2) 2011 – 2016 as your dataset with the following variables:
    • SA2 Code 2016
    • SA2 Name 2016
    • Year: 2016 (add Filter Value)
    • Household Stress: Households with Mortgage Repayments Greater Than or Equal To 30% of Household Income %
    • Household Stress: Households With Rent Payments Greater Than or Equal To 30% of Household Income %
  • Create a Choropleth map of using the Household Stress: Households with Mortgage Repayments Greater Than or Equal To 30% of Household Income % variable using all default values, and a Sequential, Reds palette. It should look like the example below.

Now you are are ready to create a choropleth centroid map – follow the inputs instructions below to see how to do this.


Now that you have added your dataset and created a choropleth map, you are ready to map areas to points and create a choropleth centroid map.

To do this, open the Centroid Map toolbox (Interactive Maps & Charts → Mapping Points Centroid Map) in the Visualise pane. This will bring up a range of fields that need to be populated. Enter your parameters as you see them below.

  • Select a dataset: Here you can choose which of the datasets you would like to display as a map. Select ABS – Data by Region – Family & Community (SA2) 2011 – 2016.
  • 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 Household Stress: Households with Rent Repayments Greater Than or Equal To 30% of Household Income %.
  • 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 Jenks (Natural Breaks).
  • 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 4.
  • 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 Sequential.
  • 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 Yellow – Green – Blue (YlGnBu).
  • 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 1.0.
  • 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 1.0.
  • 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: The default for this field is “[Attribute] -1” It’s a good idea to change the name of this 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 Percentage of Households spending 30% or more of income on rent.

Once you have selected your parameters click Add.


Once you click Add on the input box, a map will appear automatically in your viewer which should look something like the map shown below. This map shows both the choropleth and the choropleth centroid, but you will notice there are clusters of circles across towns and cities, due to the larger number of SA2s in those places.

If you zoom into one of these areas (in this instance, Hobart) you can start to see the pattern resolving, to which you can begin to ask the question: is there an inverse relationship between the proportion of households spending more than 30% of income on mortgage (darker, redder polygons) and the proportion of households spending more than 30% of their income on rent (larger, bluer circles)?


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