Walktime analysis using OS Multi-modal Routing Network and QGIS
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Drive and walk time analysis are concepts used to determine which locations are accessible from a point, within a certain time or distance, either on foot or using a vehicle. This is based on a network, as opposed to a straight line ‘as the crow flies’.
However, this doesn’t only apply to driving on roads. Routing analysis can be undertaken along path, rail/tram or ferry networks.
In this tutorial, we will be focusing on walktime analysis, using the OS Multi-modal Routing Network dataset.
OS MRN is a fully connected routable network. It is made up of road, rail, path and ferry networks and therefore dedicated for multi-modal routing .
Find OS MRN on the OS Data Hub and click ‘Add Data Package’.
Select your area of interest and choose GeoPackage format.
Extract the GPKG file and load into QGIS.
MRN is available in the geographic coordinate system, WGS84 (EPSG:4326). The routing plugin wants the network data to be in a projected coordinate system. Therefore, you may need to re-project the dataset to British National Grid (EPSG:27700).
MRN contains three feature types: transport_link, transport_node and turn_restriction. In this example, we will be using the transport_link.
The data contains various attributes which indicate what type of transport can use a particular link (e.g., on foot, bicycle, coach). As a result, you can filter the data by these attributes, to keep only the links that are suitable for your mode(s) of travel.
In this exercise, we will do this for walking, but the filtering steps would be the same for other modes of travel.
For this analysis, we are only interested in routes that are navigable on foot.
· In QGIS, right click on the transport_link layer and select ‘Filter’.
· In the ‘Provider Specific Filter Expression’ section, copy and paste the following code
· "foot" = 'designated' OR "foot" = 'yes'
We only want the transport links which have values of ‘designated’ or ‘yes’ under the ‘foot’ attribute, denoting them as being accessible by pedestrians, to be included in the analysis.
You will also need a point dataset, which the routing tool will use as starting points for the analysis. The one used here contains two starting points.
QGIS has built in tools for network analytics such as service area and shortest path. However, for a visually clearer output, we will be using a plugin called QNEAT3.
Select ‘Plugins’ > ‘Manage and Install Plugins’ and search for ‘QNEAT3’. Select ‘Install Plugin’.
Once the plugin is installed, it will appear in the processing toolbox. The tool offers various analysis options, but for now select QNEAT3 – Qgis Network Analysis Toolbox > Iso-Areas > Iso-Area as Polygons (from Layer).
Populate the fields as shown below (However, feel free to experiment with your own values!).
The ‘size of Iso-Area (distance or time value)’ field allows you to specify the max distance or time (in seconds) for the walktime analysis. In this exercise, we have specified a max walktime of 3,600 seconds (60 minutes), with intervals of 900 seconds (15 minutes).
As this analysis is for walking, we can accept the default speed value of 5 km/h, further down in the menu. If you were to conduct analysis using different modes of travel, MRN does include average road speeds.
If you wish to save the output polygon, you can do so at the bottom of the menu.
Once you are happy, select ‘run’.
Right click the output polygon and select ‘Properties’.
· Select ‘Symbology’
· At the top, select ‘Categorised’
· In ‘Value’, select ‘1.2 cost_level’
· Select a suitable colour ramp
· Select ‘Classify’
· Untick the symbol for ‘all other values’
Add a base map of your choice. We have used the OS Maps API Light Style and made it slightly transparent so that the walktime analysis output polygons show underneath.
We have also converted the cost level from seconds to minutes in the legend.
Analysis using multiple modes of travel possible by manipulating one dataset
Compatible with open-source software and plugins
Simple routing analysis using this data is straightforward
Versatile dataset- research of various contexts for different use cases possible
