Smart Survey Implementation

New Approach in Statistics Uses Smartphones to collect Diary information

Hbits together with 11 other governmental or university organisations from 7 different countries have received a grant for a new project of the European Commission and EUROSTAT, the statistical office of the European Union. The goal of the project is to develop and test intelligent, smartphone-based survey methods for official statistics in Europe.

Every ten years, EUROSTAT conducts a survey in Europe to find out how much time people spend on various activities, for example on paid work, housework, care work, social activities, travel, and leisure. Every five years, there is another survey for statistical purposes to find out how people use the money they earn and what they spend their money on. Up to now, those participating in EUROSTAT surveys had to answer the questions on paper or use simple computer logs. Now, intelligent applications (smart surveys) are to be developed and tested.

hbits wants to bring existing survey methods into the 21st century and make it possible to use a smartphone app for daily tracking of expenses or time making use of newly developed microservices using AI/ML models.

The subproject at hbits will be responsible in developing microservices for receipt scanning, geolocation and energy use. These smart environments are then later connect within the business process of collecting Time Use Data and Household Budget Data.

The MOTUS data collection platform is one of the platforms to which these new services will be integrated to.

The project will start on 1 May 2023. In addition to Belgium, Germany, the Netherlands, Norway, France, Italy, and Slovenia are also part of the project.

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Project goals

Seven European countries participate in the collaborative “Smart Survey Implementation” project. EUROSTAT expects results that can be applied to all countries and that make smart survey methods the standard everywhere in a few years.

Simultaneously, these advancements are expected to reduce respondent burden while enhancing the quality of the gathered data.

Cases