# Gross Density (Regions)

## Introduction

This tool allows you to calculate the average population density within walkability catchments across your study area. Alternatively, the tool can be used to calculate the population density within a polygon dataset of your choice. We will run both of the analyses here to show their differences.

## Inputs

We will run our analyses in Inner Melbourne

• Select Melbourne – Inner SA4 as your area
• Select the following datasets:
•  PSMA Street Network, making sure that you have the blue Geometry attribute selected
•  PSMA Railway Stations, making sure that you have the blue Geometry attribute selected
•  MB Mesh Block 2011 Census for Australia ensuring that you have the blue Geometry attribute, and the Total Usual Residential Population 2011 attributes selected. This is because we need Population Data to generate the scores for our neighbourhoods.

#### Walkability Catchment Density

We will first look at the average density of walking catchments around railway stations. You first need to generate some neighbourhoods around the railway stations (select 800m as your walking distance and 50m as your trim distance.

Once you have done this, open the Gross Density (Regions) tool (Tools → Walkability → Gross Density (Regions)) and enter your parameters as shown below (also explained below the image)

[Click to Enlarge]

• Regions: the areas that you would like to include for your analysis. In this instance we select the walking neighbourhoods we generated, named Output: walkability001-points-to-regions XXX
• Population dataset: This is the dataset that contains your population count. In this instance, we choose MB Mesh Block 2011 Census for Australia
• Population attribute: This is the column within your population dataset that contains your population counts. Here we select Total Usual Resident Population 2011

#### Mesh Block Density

For this analysis, we run it essentially the same way as above, except for Regions we choose MB Mesh Block 2011 Census for Australia, as shown below:

[Click to Enlarge]

Again, once you have entered your parameters, click Add and Run. Be patient with this, as it can take several minutes. The AURIN routines are tenacious and persistent, so it will run if you have some patience with it.

## Outputs

#### Walkability Catchment Density

Once your tool has run, click Display to open up the table. It should look something like the table below.

[Click to Enlarge]

The final two columns represent the Average Density in people per hectare for each of the walkability catchments, and the Z score for that value, where the mean for the different catchments is zero, and the numbers indicate how many standard deviations each score is above or below the mean. Essentially, the more positive the number, the better relative score for that attribute, and the more negative number, the worse relative score for that attribute. This is represented in the image below, and is calculated by the formula

## $$Z_{i} = {X_{i} – \overline{X}}\over{s}$$

where $$X_{i}$$ is the individual score for observation $$i$$, $$\overline{X}$$ is the mean of all the scores, and $$s$$ represents the sample standard deviation.

[Click to Enlarge]

We recommend that you make sure you have a relatively large number of observations (a minimum of 30) before using Z scores in any discussion, as they rely on robust mean and standard deviation calculations, which are less reliable at smaller samples sizes.

If you create a choropleth of the Z scores (using a Diverging palette) it should look something like the image below

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We can see that average density tends to be higher torwards the centre of the study area and lower further away

#### Mesh Block Density

The outputs of this routine are the same as for the walkability catchments (i.e. a table with Average Density and Z Scores that you can view if you would like to). However, the density has been calculated for every single meshblock, where the Portal has calculated the area of each meshblock and generated a density of people per hectare.

However, we are interested in looking at the spatial distribution of the density, which we have mapped in the two images below. The first is the distribution of Average Density for each mesh block in Melbourne (in people per hectare)

[Click to Enlarge]

The second is the distribution of the Z Scores for Average Density for each mesh block in Melbourne

[Click to Enlarge]