Identifying Higher-Risk Buildings in QGIS
This describes a step-by-step guide using QGIS how you can identify if a building in occupation is classified as Higher-Risk by the Building Safety Act 2022 and the Higher-Risk Buildings (Descriptions and Supplementary Provisions) Regulations 2023 using OS NGD data.
This article follows on from Identifying higher-risk buildings recipe guide on the OS Docs platform that will guide you through accessing the data needed for the article below.
Create a new QGIS Project and add your preferred backdrop mapping. In this example, OS Maps API Light is being used.
Add the 2 feature types you have downloaded from OS Data Hub to a map and order them in table of contents so that main.add_gb_builtaddress sits above main.bld_fts_building.
Change the symbology as desired.
Go to the Processing toolbox (usually docked on the right side of window) and type join into the search box.
Select and double click the Joint attributes by location (summary) tool. The tool dialog box will open.
Set Joint to features in as the main.bld_fts_building layer and By comparing to as the main.add_gb_builtaddress layer.
Keep the default ‘intersect’ for Where the features.
Open the Fields to summarise option and select ‘uprn’. Return to main parameters dialog
Open the Summaries to calculate option and select ‘count’. Return to main parameters dialog
Ensure Discard records which could not be joined is selected.
Open the Joined layer option and select an appropriate output location, file type and name.
Ensure Open output file after running algorithm is selected.
Run the tool.
Viewing the new layer you will see that a reduced number of building features have been returned however, there isan additional step required to ensure all the criteria have been met. Open the layers attribute table and sort thenewly created ‘uprn_count’ attribute in ascending order. You will see that there are a number of buildings with fewerthan 2
One of the criteria for a Higher-Risk Building is that there are 2 or more residential addresses. From reviewing the Join_Count attribute, we can see that there are several buildings with only a single join. This is complicated by the fact that serviced apartments / aparthotels often don’t have individual addresses but do fall within the criteria for Higher-Risk Buildings. We will use an attribute query in the Select By Expression dialogue box to identify non-commercial buildings where is only a single address.
Now select the Edit option from the top ribbon and Delete the selected rows. When prompted, accept the deletions and then save your edits. You now have a feature class that contains all buildings that are considered Higher-Risk.
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