Newly emerging urban IoT infrastructures are enabling novel ways of sensing how urban spaces are being used. However, the data produced by these systems are largely context-agnostic, making it difficult to discern what patterns and anomalies in the data mean. We propose a hybrid data approach that combines the quantitative data collected from an urban IoT sensing infrastructure with qualitative data contributed by people answering specific kinds of questions in situ. We developed a public installation, Roam-io, to entice and encourage the public to walk-up and answer questions to suggest what the data might represent and enrich it with subjective observations. The findings from an in the wild study on the island of Madeira showed that many passers-by stopped and interacted with Roam-io and attempted to make sense of the data and contribute in situ observations.

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