# workshop tutorial: Huso2165 urban planning research

In this tutorial, we will explore and investigate the potential relationship between a number of variables across Greater Melbourne.

This tutorial is designed to get you familiar with the AURIN portal and its various tools and visualisations, the thousands of datasets covering a diverse range of topics, and the statistical and methodological considerations when investigating relationship between variables.

All of the datasets described in this tutorial are at Statistical Area Level 2 (SA2), which is largely equivalent to a suburb in major metropolitan regions like Melbourne.

Click on each of the first drop downs below to see some of the datasets you might include in your analyses

Below the data set catalog are a number of drop downs explaining how some of the tools work, and an additional drop down with a worked example of the kind of analysis you will need to undertake in your assessment.

User Guide: 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:
• – 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

User Guide: Merge Aggregated Datasets and Scatterplot Chart

### Introduction

Similar to the Tabular Inner Join, the Merge Aggregated Datasets tool allows you to join together datasets of the same geographical aggregation (and in the same place!) to allow you to compare and investigate the relationship between the two (or more) datasets. In this tool, if one row in a table does not have a corresponding row in the other table, it will be empty on that side in the output table (compare with Tabular Inner Join, where rows which do not have corresponding entries are excluded entirely). Merging tables allows us to continue with other processes such as scatterplot, which rely on variables being in the same dataset.

### Set Up

For this worked example we will investigate the relationship between housing stress and socio-economic status across Melbourne

To do this:

• Select Greater Melbourne (2016) as your area
• Select the following datasets and variables:
 Dataset Variables SA2 SEIFA 2016 - The Index of Relative Socio-economic Advantage and Disadvantage (IRSAD) A2 9-digit code 2016 SA2 Name 2016 IRSAD Score NATSEM - Social and Economic Indicators - Synthetic Estimates SA2 2016 SA2 Code SA2 Name Housing Stress (30/40 rule)

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

### Merge Inputs

We are now ready to merge our datasets

To do this click the Tools button in the Analyse panel, click Data Manipulation and then Merge Aggregated Datasets. Enter your parameters as shown in the image below and click the Add and Run button

### Merge 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 has connected each row (SA2s in Greater Melbourne).  You should now rename this datasets to something meaningful and easy to recognise

### Scatterplot Inputs

We are now ready to create a scatterplot of our two variables

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

• Dataset Input: For this we want to select our merged dataset: SEIFA + Housing Stress
• VariablesFor this we need to select the following variables in the order of X and then Y. In this case we want to put IRSAD Score as the X (horizontal) variable and Housing Stress (30/40 rule) as the Y (vertical) variable
• Use Variable TitlesDetermines whether we want the human readable name (titles) or computer readable name (names). Always tick this box
• Chart Title: Here we enter the title for the plot. In this instance we have chosen SEIFA IRSAD Score vs Percentage of Households in Housing Stress
• Grid: Select this if you want to choose gridlines for your graph
• GreyscaleSelect this if you want your graph to be in grey scale, rather than in the default colour

### Scatterplot 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 image. You will now see a scatterplot which shows the relationship between the IRSAD score (x axis) and the percentage of households in housing stress (y axis). You can see that as the IRSAD score increases (that is, as advantage increases) the percentage of households in housing stress decreases). You can right click on this image and export it as a .png image for inclusion in a report or document.

User Guide: Diversity Index and Dataset Attribute Filter

### Introduction

A diversity index measures the degree of specialisation or alternatively the degree of diversity across attributes within a spatial unit. This allows spatial units to be compared as to the mix of the characteristics being measured.

The diversity index for a particular region can range from 0 to 1, where a score approaching 0 indicates an increasing degree of diversity and a score approaching 1 indicates an increasing degree of specialisation. Determination of whether a region has a high or low diversity index is done by comparing the diversity index scores across all regions.

### Set Up

For this worked example we will calculate the diversity of dwelling structures for for SA2s across Melbourne

To do this:

• Select Greater Melbourne (gccsa_2016/2GMEL) as your area
• Select the following datasets and variables:
 Dataset Variables SA2-G32 Dwelling Structure-Census 2016 SA2 Code 2016 SA2 Name 2016 Occupied private dwellings Separate house Dwellings NATSEM - Social and Economic Indicators - Synthetic Estimates SA2 2016 Occupied private dwellings Semi detached row or terrace house townhouse etc with Total Dwellings Occupied private dwellings Other dwelling Improvised home tent sleepers out Dwellings Occupied private dwellings Other dwelling House or flat attached to a shop office etc Dwellings Occupied private dwellings Other dwelling Caravan Dwellings Occupied private dwellings Other dwelling Cabin houseboat Dwellings Occupied private dwellings Flat or apartment In a three storey block Dwellings Occupied private dwellings Flat or apartment In a one or two storey block Dwellings Occupied private dwellings Flat or apartment In a four or more storey block Dwellings Occupied private dwellings Flat or apartment Attached to a house Dwelling

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

Inputs

Once you have added this dataset to your cart, open the Diversity Index tool (Tools → Indices → Diversity Index) and enter the parameters as shown in the image below (these are also shown under the image)

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

Diversity Index Tool Parameters

• Dataset Input: the dataset that contains the variables you would like to include. In this instance, select SA2-G32 Dwelling Structure-Census 2016
• Key Columnthe column containing the unique identifier for each area. In this instance, select SA2 Code 2016
• Variables: the variables you would like to be included in the calculation of the index. In this instance, select all of the dwelling counts that you have included.

### 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 (Diversity Index) which represents the Diversity Index Score. If you sort the table, you will note that there are rows that have a diversity index score of zero. These are rows with zero counts for all different kinds of dwellings and will need to be removed with the next step. Before that, you should now rename this datasets to something meaningful and easy to recognise

### Filter Dataset Inputs

This tool allows you to keep some parts of a dataset based on their values.

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

###### DATASET ATTRIBUTE FILTER TOOL PARAMETERS

• Dataset Input: The dataset that you would like to run the filter over. In this instance, we are using Dwelling Diversity Index
• Attribute: The attribute that you would like to use to filter based on its values. In this instance we are using Diversity Index
• Operator: the rule for describing the data that you want to keep. In this instance, we are selecting Greater Than
• Attribute Value: The ‘value’ for applying to the attribute. In this instance we are selecting 0.

### Outputs

Once you have run the tool, click on the Display button. This will open up the new dataset now shown in your Data panel named Output: geojson_filter XXX (you should rename this something meaningful). If you open the dataset and sort lowest to highest, you will note that there are no SA2s in this dataset with a zero Diversity Index. You are now ready to map this

Recall that the Diversity Index ranges from 0 to 1 and that scores close to 0 indicate higher diversity. This means that when you create your choropleth for this particular variable, you should click the reverse palette options (shown below)