Spatial Aggregation of IoT Data in Sustainable Mobility scenarios

The increasing deployment of Internet of Things (IoT) devices in active mobility scenarios enables the collection of large-scale data for urban infrastructure and environmental monitoring. Many times the goal is to obtain a spatial sampling of the quantities of interest starting from a geo-referenced timeseries. This paper presents a spatial aggregation methodology for mobility data collected from IoT-enabled electric bicycles. The proposed approach maps sensor readings to predefined geographic reference nodes, producing compact and interpretable spatial representations. The method addresses typical challenges associated with mobile sensing data, including asynchronous sampling, variable spatial coverage, and heterogeneous user behavior. The framework is validated using multi-sensor data acquired during the 2024 Giro-E cycling event, where participants were equipped with Garmin Fenix 7S smartwatches. Three representative stages are analyzed to assess the effectiveness of the aggregation in terms of spatial alignment and data retention. The results indicate that the method achieves a favorable trade-off between positional accuracy and data compactness, supporting its applicability in infrastructure monitoring, behavioral analysis, and urban planning.