RelMap: Reliable Spatiotemporal Sensor Data Visualization via Imputative Spatial Interpolation
This addresses the challenge of producing reliable visualizations for decision-makers in fields like environmental monitoring, though it appears to be an incremental improvement combining existing techniques.
The paper tackles the problem of unreliable spatial interpolation for spatiotemporal sensor data visualization by introducing a novel pipeline that integrates GNN-based imputation with uncertainty-aware heatmaps, demonstrating superior performance in data imputation and effective uncertainty communication through real-world evaluations.
Accurate and reliable visualization of spatiotemporal sensor data such as environmental parameters and meteorological conditions is crucial for informed decision-making. Traditional spatial interpolation methods, however, often fall short of producing reliable interpolation results due to the limited and irregular sensor coverage. This paper introduces a novel spatial interpolation pipeline that achieves reliable interpolation results and produces a novel heatmap representation with uncertainty information encoded. We leverage imputation reference data from Graph Neural Networks (GNNs) to enhance visualization reliability and temporal resolution. By integrating Principal Neighborhood Aggregation (PNA) and Geographical Positional Encoding (GPE), our model effectively learns the spatiotemporal dependencies. Furthermore, we propose an extrinsic, static visualization technique for interpolation-based heatmaps that effectively communicates the uncertainties arising from various sources in the interpolated map. Through a set of use cases, extensive evaluations on real-world datasets, and user studies, we demonstrate our model's superior performance for data imputation, the improvements to the interpolant with reference data, and the effectiveness of our visualization design in communicating uncertainties.