Jobmap visualizes the job landscape within Flanders. It illustrates how professions relate to each other within the job market.
Within VDAB, the Competent ontology is employed. This is a standardized description of occupations and their corresponding competencies, serving as a common language in the labor market. The drawback of such a standard is that it lags a bit behind the latest changes in the job market. Through Jobmap, we provide a data-driven application that helps keep the Competent standard up to date with the latest developments in the labor market.
For example, the tool offers insights into clusters of jobs vacancies where no standardized profession exists. If a person searches for terms like data, development, and intelligence, the result is often a mix of professions, while that cluster actually represents the skills of a specific profession such as a data scientist. From these insights, it becomes easier to identify and define new, emerging professions.
The solution also assists job seekers by offering them a visual search for vacancies. For instance, when a teacher enters the term “Pedagogy”, he can visually see which potential job vacancies align with this search term. You can think of it as an eye- opener in your search for suitable employment.
Jobmap is based on a t-SNE reduction technique. This is a machine learning algorithm suitable for converting the complex accuracy of data – or embedding it – into coordinates of lower dimensions (2D or 3D), which are easier to interpret for a user. The application is written in Angular with regl-scatterplot/webgl, a visualization method to illustrate correlations between points on a graph.