# Calculate Connectivity

The street network used to calculate the number of intersections act as a proxy to show the potential mobility of pedestrians within the neighbourhood. It is acknowledged that this is an abstraction of how pedestrians might move through a neighbourhood, but it is readily achievable with available data and past research has shown that it is significantly associated with walking for transport. Southworth (2005), for example, uses Connectivity as one of six criteria in pedestrian network planning.

Connectivity is measured as the count of three (or more) way street intersections over the area of the participant’s neighbourhood. These neighbourhoods can be generated from a line or point dataset by using the Catchment Generator tool.

The produced outputs of the tool provide us with four indicators:

•  Connectivity – the number of connections per square kilometre.
•  Area – the square metres of each neighbourhood polygon.
•  Connections  – the number of intersections between three or more paths.
•  Connectivity Z-Score  – the connectivity score normalised to a Z score by the following formula:

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

where $X_{i}$ is the non-normalised score of observation $i$, $\overline{X}$ is the sample mean and $s$ is the sample standard deviation. The Z-score will tell you how much higher or lower than the rest of the neighbourhoods that a single neighbourhood is (where the mean is 0).

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

For this example, we will map road connectivity in Baulkham Hills, NSW in catchments generated from the National Register of Education Services in Australia dataset.

First, we set up your area of study:

• Select Baulkham Hills SA3 as your area (Australia → New South Wales → Greater Sydney  → Baulkham Hills and Hawkesbury → Baulkham Hills).
• Select OpenStreetMap – Lines (Australia) 2017 as your dataset, selecting the following variables:
• Unique Identifier
• Geometry Field
• Select National Register Education Services in Australia (Point) 09/06/2017 as your second dataset, selecting the following variables:
• Unique Identifier
• Geometry Field
• Calculate the catchment areas using the Catchment Generator tool with (this is also illustrated in the image below):
• Road Network: OpenStreetMap – Lines (Australia) 2017
• Points: National Register Education Services in Australia (Point) 09/06/2017
• Maximum walk distance: 600
• Trim Distance: 30

### Inputs

Once you have selected these, open the Calculate Connectivity input window (Tools → Walkability → Calculate Connectivity) and enter the parameters below.

• Road Network: The road or path network that you want to measure connectivity along. Select OpenStreetMap – Lines (Australia) 2017.
• Regions: The digital neighbourhoods that you want to measure connectivity in, this is the road network’s generated neighbourhoods. In this tutorial, we built neighbourhoods around education services. Select the output dataset from the catchment generator tool.

Once you have entered your parameters, click Run Tool.

### Outputs

Once your tool has run, click the Display Output button that appears on the pop-up dialogue box. Your outputs should look like the table below.

The output columns provide information about each neighbourhood’s connectivity as explained in the introduction. You can also view these neighbourhoods by using the Choropleth tool, using the Connectivity_ZScore as the attribute, 4 classes, a Diverging palette type and a RdYlBu palette. All other options can stay as default.

Your output should look something like the image below. The regions in blue and red illustrate higher and lower road connectivity respectively. In this example, we can see that the small neighbourhoods built around education facilities may provide us with insight into the pedestrian accessibility of already-built infrastructure and may provide insights into future urban planning.

Southworth, M. (2005). Designing the Walkable City. Journal of Urban Planning and Development, 131(4), 246–257.

### Looking for Spatial Data?

You can browse the AURIN Data Discovery: