# Walkability Index Around Points

The Walkability Index Around Points tool is a “sandwich with the lot – from scratch” for creating a Walkability Index. It combines the Catchment Generator tool with the three elements of the built urban form – Calculate Connectivity, Calculate Land Use Mix, and Calculate Gross Density.

Using this tool you can calculate a comprehensive walkability index for your neighbourhoods in a single workflow. It produces the same outputs as the Walkability Index Within Areas tool and does not rely on you using the Catchment Generator tool first.

Note: The walkability tools work optimally on regions around the size of local government areas. Study areas larger than this may be faced with long processing times.

### SET UP

We will run this tool for our study area Launceston, analysing the walkability of child care centres.

• Select Launceston SA3 (Australia →Tasmania → Rest of Tasmania → Launceston and North East → Launceston) as your area.
• Select OpenStreetMap – Lines (Australia) 2018 selecting the following attributes:
• Unique Identifier
• Geometry Field
• Select ACECQA – National Register Education Services in Australia (Point) selecting the following attributes:
• Unique Identifier
• Geometry Field
• Select ABS – Usual Residential Population and Dwelling Count (MB) 2016 selecting the following attributes:
• Mesh Block Category
• Mesh Block Code
• Total Usual Resident Population 2016
• Geometry

Note: The walkability tools work optimally on regions around the size of local government areas. Study areas larger than this may be faced with long processing times.

### Inputs

We are now ready to generate the output datasets. Open the tool (Tools → Walkability → Walkability Index Around Points) and enter your parameters as shown below, they can also be seen in the image below:

• Road Network: The line data set representing a road or pedestrian network. Select OpenStreetMap – Lines (Australia) 2017.
• Points: The dataset with the points which will form the centre of our walking catchments. Select National Register Education Services in Australia (Point) 09/06/2017 for the point location of child care facilities.
• Maximum walk distance: The maximum distance (in metres) along the line network data to be traversed from each input point. Enter 800.
• Trim distance: The width (in metres) of buffer to be applied to the traversable line, network segments. Enter 30.
• Land use polygon dataset: 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 ABS – Usual Residential Population and Dwelling Count (MB) 2016.
• Land use classification attribute: The attribute that contains the different land uses within it. Select Mesh Block Category.
• Land use classifications: The land uses you would like to include within your land use mix calculations. Select Commercial, Parkland, Education, Residential, and Hospital/Medical, as these are the kinds of places you would want to walk to/from and through.
• Population dataset: The dataset that contains the population counts for your regions. Select ABS – Usual Residential Population and Dwelling Count (MB) 2016.
• Population attribute: The attribute within your population dataset that contains the population counts. Select Total Usual Residential Population 2016.

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

### Outputs

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

• Connectivity: The total number of connections per square kilometre.
• 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 $X$ 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 calculated by:

$LUM = \frac{- {\sum_{i = 1}^{n} \left(p_{i}\cdot ln \left(p_{i}\right)\right)}}{ln\left(n\right)}$

Where $P_{li}$ is the proportion that each land use $l$ contributes to each catchment $i$ and where $n$ represents the total number of land-use 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 the relative score for that attribute, and the more negative number, the worse the 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.

Note: 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 Centroid Map 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. If you hover over each of the child care facilities, you can see its individual attributes, and determine which of the different components let down or improved its overall walkability index:

### Looking for Spatial Data?

You can browse the AURIN Data Discovery: