Smart Events: how to use mobile data for crowd counting
Karel De Grote is a Belgian University College with many different research departments. The expertise centre of Public Impact is one of them. Their aim is to construct lively and liveable social spaces where impactful meetings stimulate economic, societal and ecological progress. This is done through multidisciplinary research where the centre draws on the expertise of many different sectors.
Objectives of the cooperation
The aim of the expertise centre was to provide concrete handles to municipalities and cities when organising events. More concretely, they wanted to provide them with knowledge and tools to calculate the ROI of events so that they could justify them. Knowing how many people are present, is one of the most important parameters to calculate the ROI. However, it is also one of the most difficult ones to measure. That is why the goal was to test different counting methods on events where there was another benchmark (e.g. tickets sold) and compare them afterwards. One of the counting methods tested was mobile technology.
Orange and CROPLAND were contacted to count the visitors of three events. The counting was done based on anonymous mobile phone data. CROPLAND and Orange measured how many unique visitors were present at the three different events. Afterwards, the data were processed by the expertise centre and turned into meaningful insights.
With the collected data from Orange and CROPLAND, the expertise centrum developed a tool for cities and municipalities that describes the counting method that is the most suited for particular events. The centre also developed a document that recommended which supplier of counting methods would suit what event best. The cooperation was concluded with a study day on event safety and crowd counting at the Karel De Grote University College. The findings of the research were presented and CROPLAND was invited to elaborate on its counting method.
Technology BigData Analytics I Mobile data I Python programming I Data pipelines