Innovation in data science

Pilot projects within FSO’s data innovation strategy.

This document is the FSO’s first response to the wider subject of digitalisation. More specifically, it focuses on the application of complementary analysis methods (e.g. predictive analysis using advanced statistical techniques, data science and machine learning) that enable the current production of official statistics to be increased or completed. Five pilot projects have been chosen to implement this strategy and are in progress. Each project is described below.



Symbol image - Area Statistics Deep Learning (ADELE)

Area Statistics Deep Learning (ADELE)

The FSO’s land use statistics are an invaluable tool for long-term land observation. Thisproject involves learning and mastering the use of artificial intelligence (AI) technologiesto eventually automate (even partially) the visual interpretation of aerial images in order todetect and classify changes.

Symbol image - Automation of NOGA coding - NOGAuto

Automation of NOGA coding (NOGAuto)

Automation of the coding of the economic activity of enterprises using Machine Learningmethods applied to data already available within the FSO (data from surveys, descriptionsin the commercial register, keywords, explanatory notes for classifications etc.) to supportcoding.

Symbol image - Machine Learning SoSi (ML_SoSi)

Machine Learning SoSi (ML_SoSi)

The clustering of typical prospective trajectories patterns concerning the receipt ofbenefits in the social security system as well as employment and the estimation of clusteraffiliation through the use of individual characteristics and retrospective process dataapplying a machine learning approach.

Symbol image - Data validation with machine learning

Data validation with machine learning

The aim of this project is to extend and speed up data validation in the FSO by means of machine learning algorithms and at the same time to improve data quality.

Symbol image - Evaluate the potential of small area estimation methods for the Job Statistics (JOBSTAT)

Evaluate the potential of small area estimation methods for the Job Statistics (JOBSTAT)

A project team of 15 people evaluates the potential of small area estimation methods for the Job Statistics. The aim is to produce reliable estimates of the total number of jobs and FTEs for cantons, major towns and NOGA levels that were not anticipated in the sampleplan.