Understanding the geospatial jobs market through machine learning

Understanding the geospatial jobs market through machine learning

Real-time jobs data

The Geospatial Commission wanted to know about the pattern of geospatial jobs in the UK.

We explored this using Burning Glass data, which is compiled by exhaustively scraping all online job adverts and flagging characteristics such as occupation and the skills specified in the advert, capturing nearly 10 million adverts per year, and over 8000 different skills. We identified a core set of geospatial skills and used these to identify relevant jobs.

Where are the jobs?

We used a clustering algorithm to identify different groups of geospatial jobs, on the basis of the required skill mix, finding distinct clusters of jobs such as GIS technician, surveyor, or engineering design. Outside of core areas such as research, and specialised technical services, we found a very diverse sectoral spread, with geospatial jobs often found in larger organisations, such as in retail or public administration. We also identified geospatial skills that were rising in usage over time, as well as co-occurring skills that are used alongside geospatial skills.

Informing the UK’s geospatial strategy

Our study informs the UK’s geospatial strategy, in particular the mission to enhance skills and capabilities to meet rising demand and technological changes. A skills forum bringing together industry stakeholders used this evidence to agree coordinated approaches to specific challenges.