Scatter Plot

Introduction

A scatter plot is one of the easiest and more effective ways of investigating your datasets. The AURIN portal has two ways of creating a scatterplot from your data – the interactive scatterplot allows you to interact with each of the datapoints and where they fit on your map, while the scatterplot described here is more “bare-bones”, but allows you easy download of the image for incorporation into documents or presentations.

Inputs

To illustrate the Scatterplot, we will run it on an income and poverty dataset from Greater Melbourne

  •  Select Greater Melbourne GCCSA as your area
  •  Select SA2 OECD Indicators: Income, Inequality and Financial Stress 2011 as your dataset, and select the following attributes:
    •  SA2 Code
    •  SA2 Name
    •  Median Disposable Income (Synthetic Data)
    •  Poverty Rate (Synthetic Data)

Open the Scatter Plot tool (ToolsCharts → Scatterplot) and enter the following parameters as shown in the image below (Each of these are also explained below)

[Click to Enlarge]

[Click to Enlarge]

  • Dataset Input: For this we want to select SA2 OECD Indicators: Income, Inequality and Financial Stress 2011
  • Variable NameFor this we need to select the following variables in the order of X and then Y. In this case we want to put Median Disposable Income as the X (horizontal) variable and Poverty Rate as the Y (vertical) variable
  • Chart Title: Here we enter the title for the plot. In this instance we have chosen Median Disposable Income versus Poverty Rate
  • Grid: Select this if you want to choose gridlines for your graph
  • Greyscale: Select this if you want your graph to be in grey scale, rather than in the default colour

Once you have entered the parameters, click Add and Run

Outputs

Once you have run your scatter plot it will appear in your Visualise your data panel as an image. It should look something like the image below, indicating a negative relationship between median disposable income and poverty rates across Melbourne

[Click to Enlarge]

[Click to Enlarge]