People can interpret citizen science data alone or as a group. The benefit of doing it with peers is a more thorough understanding of results. Conclusions, trends, patterns and anomalies can be comprehended more quickly and easily when people pore over datasets together. (As the old wisdom goes, two heads are better than one.) One particular format tested in COMPAIR as a form of communal data analysis is data cafe.

The concept revolves around community generated data. However, it is not the goal of a data cafe to present participants with raw data and let them play with it, like in a hackathon. People who come to a data cafe can include individuals with little or even no knowledge of coding and data analysis. They are valued not so much for their technical skills but for the insights they have on the local setting as a resident, for example. This knowledge is then used to provide the context for data interpretation. So, at a data cafe, participants usually work with data that has already been preprocessed and made available for interactive engagement through intuitive visuals and front-ends.

Herzele data cafe

In June 2023, citizens of Herzele were invited to a data cafe dedicated to the schoolstreet. The event didn’t follow the traditional approach where a series of presentations normally conclude with a Q&A. Instead, a less structured, more informal format was adopted. This involved setting up a makeshift bar in a public location, with participants able to move in and out freely while the cafe lasted. People could join any group at the table to discuss the results of the citizen science campaign displayed on the Policy Monitoring Dashboard.

Data cafe in Herzele

The outcome was a list of recommendations for the local authority on how to improve the effectiveness of future schoolstreet measures. Top three suggestions included 1) building more cycling lanes to support modal shift, 2) introducing speed limits in surrounding areas and 3) coordinating school opening/closing hours so that they do not coincide. Feedback received from the participants showed that many felt a stronger sense of involvement in policy evaluation after attending the data cafe. Furthermore, they found discussing results with peers as being more insightful than doing it alone.

Lessons learned from citizen led traffic monitoring

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