Walkability Index within Areas


This tool is a “sandwich with the lot”, combining the three composite elements of the built urban form – connectivity, land use mix and population density – to provide you with a comprehensive walkability index for your neighbourhoods in a single workflow.


This tutorial relies on you having first completed the workflow outlined in the Neighbourhood Generator tool, as the outputs of that workflow are required to complete this use case.

When you have completed that tool, you will need one more additional dataset to drive the Complete With Gross Density (Regions) workflow:

  • Select MB Mesh Block 2011 Census for Australia ensuring that you have the blue Geometry attribute, and the Mesh Block Category and Total Usual Residential Population 2011 attributes selected. This is because we need both Land Use data and Population Data to generate the scores for our neighbourhoods.

We are now ready to generate the output datasets. Open the  tool (Tools → Walkability → Walkability Index with Gross Density) and enter your parameters as shown below. These are also explained under the image.

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  • Network dataset: Line data set representing a road or pedestrian network. We select PSMA Street Network in this instance
  • Regions: This is where specify the neighbourhoods that will have their walkability ranked. Select the neighbourhoods that you just created with the Neighbourhood Generator tool (named Output: walkability001-points-to-regions XXX)
  • Land use polygon dataset: This is the dataset that we use to specify the different land uses to be included in the Land Use Mix component of the walkability index. Select MB Mesh Block 2011 Census for Australia
  • Land use classification attribute: This is where specify the attribute that has the different land uses within it. In this instance we select Mesh Block Category
  • Land use classifications dataset again select the dataset that has the different land uses in it, i.e. select MB Mesh Block 2011 Census for Australia
  • Land use classifications attribute:  again select the attribute which has the different land uses within it i.e. select Mesh Block Category
  • Classification categories values select the land uses you would like to include within your land use mix calculations. We have selected all values here, but you might avoid ‘water’ or other land use you are not interested in walking from/to
  • Population dataset select the dataset that has the population counts for your regions. In this instance, the MB Mesh Block 2011 Census for Australia dataset has population counts for each mesh block, so we select that
  • Population attribute select the attribute within your population dataset that contains the population counts – in this instance, it is called Total Usual Residential Population 2011

Once you have entered your parameters, click Add and Run to  execute the tool


Once your tool has run, click on the Display button to bring up the output of the tool. This is a table, with a large amount of information for about each of the catchments around the 41 train stations in the analysis (shown below). These are explained in some detail under the image

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  • Connectivity: The total number of connections per square kilometer
  • Area: The total area in square metres of each walking catchment
  • Connections The total number of connections in each of the walking catchment
  • LUM_X: The total square metres of each land use falling within each walking catchment
  • LandUseMixMeasure: This is an ‘entropy measure’, measuring the extent to which there is an equal distribution of each land use within the catchments. It is calcluated by:


\({\sum_{l = 1}^{l} \left(P_{li}\right)\cdot ln\left(1/{P_{li}}\right)}\over{ln\left(n\right)} \)

Where Pli is the proportion that each landuse l contributes to each catchment i and where n represents the total number of landuse categories available. Values of the land use mix range form 0 (the lowest mix) to 1 (the highest possible mix)

  • AverageDensity: The average population per hectare for each of the catchments. This is the Gross Density rather than Net Density
  • XXX_ZScore: These are the scores for the three different components (connectivity, land use and average density) converted into Z scores, 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.

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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.

  • SumZScore: This is the final Walkability Index for your catchments – and represents the sums of each of the different component Z score

We will now take a look at the distribution of the Walkability Index across our study areas. To do this, create a Choropleth of the Sum Z Score, choosing a Diverging palette type so that the middle colour represents the mean values. It should look something like the image below. We can see that the better walkability scores (blue) tend to be found more south and east. If you hover over each of the catchments, you can see its individual attributes, and determine which of the different components let down or improved its overall walkability index

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