tutorial: urban economy

In this tutorial we will highlight some of the useful datasets available for you to integrate into your Urban Economy assessments.

We will also take you through the GenerateDissimilarity Index and Location Quotient tools.

Potential Datasets

Below are some of the datasets that you might wish to include in your analyses. These are at Statistical Area Level 2.

Each dataset links out to its data catalogue entry at our catalogue tool, www.data.aurin.org.au

Exercise One: Using the Generate Tool

Introduction

The Generate tool allows you to create new columns and variables that are the result of combining two other columns by a mathematical function – allowing you to calculate proportions, sums, products or differences.

Set Up

For this worked example we will calculate the proportion total dwellings that are separate houses for SA2s across Melbourne

To do this:

  • Select Greater Melbourne (gccsa_2016/2GMEL) as your area
  • Select the following datasets and variables:
DatasetVariables
SA2-G32 Dwelling Structure-Census 2016SA2 Code 2016
SA2 Name 2016
Occupied private dwellings: Separate house, Dwellings
Occupied private Dwellings:Total Occupied private dwellings, Dwellings

Once you have added these datasets, you are ready to use the Generate tool – click the Inputs tab above to see how to do this

Inputs

We are now ready to generate a new column: in this instance, we will be working out the  proportion of dwellings that are separate houses in Greater Melbourne. This will be equivalent to:


Total Occupied Separate (i.e. detached) Houses / Total Occupied Dwellings


To do this click the Tools button in the Analyse panel, click Data Manipulation and then Generate. Enter your parameters as shown in the image below and click the Add and Run button. These parameters are also explained below


Generate tool Parameters


  • Dataset Input: This is the dataset that contains the columns you would like to include in the calculation. In this example we use SA2-G32 Dwelling Structure-Census 2016
  • Operand 1: This represents the ‘left hand side’ of the equation. In this instance we will use the Occupied private dwellings Separate house Dwellings column
  • OperatorThis represents the mathematical function that you would like to use to create the new column. In this instance, we are creating a proportion, so we will use the divide function ( ‘/’ )
  • Other operators include:
    • + add
    • – subtract
    • * multiply
    • / divide
    • == is equal to
    • != is not equal to
    • < is less than > is greater than
    • <= is less than or equal to
    • => is greater than or equal to
  • Operand 2This represents the ‘right hand side’ of the equation. In this instance we will use the Total private dwellings Dwellings  column
  • New Column NameThis will be the new column in the output table. It is important that you only include letters, numbers and underscores (no spaces or other characters!) in this column. Also, it can only start with a letter – no number at the start! We use Prop_House

Outputs

Once the tool has run, a pop up box will appear asking you to display your results (shown below). Click Display to open the output table. You will see that there has been an entirely new table created (also shown below), which now has an additional column at the end (Prop_House) which represents the mathematical outcome of dividing one of your original columns by another. You should now rename this datasets to something meaningful and easy to recognise



You can now map this output dataset and column as you normally would using the choropleth visualisation function



Exercise Two: Using the Dissimilarity Index

Introduction

The Dissimilarity Index is a measure of the evenness with which two groups are distributed across the geographic units that make up a larger area of study, measuring how similar or dissimilar two groups are with respect to their geographic spread within a larger region. The index of dissimilarity can also be used as a measure of inequality.

The Dissimilarity Index is applicable to any categorical variable (whether demographic or not) and because of its simple properties is useful for input into multidimensional scaling and clustering programs. It has been used extensively in the study of social mobility to compare distributions of origin (or destination) occupational categories.

DI values close to 1 indicate a high dissimilarity, and DI values close to zero indicate low dissimilarity (high similarity) in the geographic spread of the two variables.

Set Up

For this worked example we will calculate the Dissimilarity Index for the spread of wage earners and those receiving the Newstart Allowance across Greater Melbourne

To do this:

  • Select Greater Melbourne (gccsa_2016/2GMEL) as your area
  • Select the following datasets and variables:
DatasetVariables
ABS - Data by Region - Income (Including Government Allowances) (SA2) 2011-2017
SA2 Code 2016
SA2 Name 2016
Year: 2016
Estimates Of Personal Income Employee Income Earners No.
Selected Government Pensions & Allowances Newstart Allowance No.

Once you have added these datasets, you are ready to use the Dissimilarity tool – click the Inputs tab above to see how to do this

Inputs

To do this click the Tools button in the Analyse panel, click Indices and then Dissimilarity Index. Enter your parameters as shown in the image below and click the Add and Run button.


Dissimilarity Index tool Parameters


 

Outputs

Once the tool has run, a pop up box will appear asking you to display your results (shown below). Click Display to open the text box. You will see the output Dissimilarity Index of 0.2; recall that these range from 0 to 1, with those closer to 1 indicating more dissimilarity between the distribution of the two variables. Our Dissimilarity Index reveals that, although there are some differences between the spatial distribution of wage recipients and those receiving the Newstart Allowance, the dissimilarity is not stark



 

Exercise Three: Using the Location Quotient Tool

Introduction

Location Quotients are proportional measures which show how much the incidence of something in an area or spatial unit differs from a larger area or region in which it sits. It may be used, for example, to show the reliance on an industry within a city compared to the country as a whole, or the percentage of people residing within a local government area, compared to the state as a whole.

The standard output of the LQ is a ratio, indicating how much more or less the spatial unit’s incidence is, compared to the larger area.

For example, an LQ of 1 indicates no difference between area of interest and the overall incidence; an LQ of 0.65 would indicate an incidence in the area of interest is 35% lower than the overall incidence; an LQ of 1.35 would indicate an incidence 35% higher than the overall incidence.

Set Up

For this worked example we will calculate the Location Quotient for the number of households in each of Melbourne’s SA2s that are in the most disadvantaged quartile of households across Australia.

To do this:

  • Select Greater Melbourne (gccsa_2016/2GMEL) as your area
  • Select the following datasets and variables:
DatasetVariables
ABS - Index of Household Advantage and Disadvantage (IHAD) (SA2) 2016
SA2 9-Digit Code 2016
Number Of Households In The IHAD (a): Quartile 1
Occupied Private Dwellings (b)

Once you have added these datasets, you are ready to use the Generate tool – click the Inputs tab above to see how to do this

Inputs

We now want to open the Location Quotient tool (Tools → Indicies → Location Quotient). Enter your parameters as shown below (the meanings of all these inputs are explained below), and click Add and Run.


Location Quotient tool Parameters


  • Dataset Input: The dataset that you would like to run the location quotient tool on. Here we select ABS – Index of Household Advantage and Disadvantage (IHAD) (SA2) 2016
  • Location Quotient Key Column: The region or spatial unit key for the areas of interest. Here we select SA2 9-Digit Code 2016
  • Location Quotient Region Variable: The attributes you would like to analyse, to see if their incidence is higher or lower in each area than overall. Here we select Number Of Households In The IHAD (a): Quartile 1
  • Location Quotient Region Total: The total count for each region (usually people). Here we select Occupied Private Dwellings (b)
  • Location Quotient Number of Intervals: This is used to specify how many “groups” of LQ values you want to create – the tool will produce an LQ value for each area; this input allows you to group those into a set number of classes or groups.
  • Location Quotient Upper Limit of Intervals: This is used to specify the boundaries of the groups that you want to create. For example if you wanted the group of the lowest LQ values to be between 0 and 0.5, you would specify “0.5” and so on. Each value needs to be separated by a comma, and you need to enter the same number of values as the number of intervals that you specified in the parameter above i.e. if you set the number of intervals to 4, you need to have 4 upper limits, each separated by a comma.

Outputs

Once your analysis has run a dialogue box will open. Click Display to open the output table. For the example used here, it should look something like the image below. The output of the Location Quotient tool is a table. The table has the Location Quotient for each variable ([xxx]_LQ). In addition, each of the variables is placed into a group, based on it’s falling into one of the interval ranges ([xxx]_range).



You can now map this output dataset and the two column as you normally would using the choropleth visualisation function