RelMap: Reliable Spatiotemporal Sensor Data Visualization via Imputative Spatial Interpolation

TL;DR

This paper presents RelMap, a novel visualization pipeline that improves the reliability of spatiotemporal sensor data interpolation by integrating GNN-based imputation, uncertainty encoding, and an effective static visualization technique.

Contribution

It introduces a new spatial interpolation method using GNNs with PNA and GPE, and a static visualization approach that effectively communicates uncertainty in sensor data maps.

Findings

Enhanced data imputation accuracy with GNNs.

Improved visualization of uncertainty in heatmaps.

Superior performance demonstrated on real-world datasets.

Abstract

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.