RelMap: Reliable Spatiotemporal Sensor Data Visualization via Imputative Spatial Interpolation

2025-08-02

AI Summary

To address the low reliability of conventional spatial interpolation and insufficient visualization credibility caused by sparse and irregularly distributed sensor networks, this paper proposes an end-to-end graph neural network (GNN)-based interpolation framework. The method integrates Principal Neighborhood Aggregation (PNA) with Geographic Position Encoding (GPE) to enhance interpolation accuracy via GNN-driven reference data completion. Additionally, it introduces an explicit uncertainty visualization technique based on static heatmaps, encoding model prediction uncertainty into perceptible visual channels. Experiments on real-world environmental and meteorological datasets demonstrate that the proposed approach significantly outperforms baseline methods in both interpolation accuracy and data imputation performance. A user study further confirms its effectiveness in communicating uncertainty and improving decision-making trustworthiness.

Technology Category

• Reasoning under Uncertainty: Graphical Models
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Web data visualization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Improving reliability of spatiotemporal sensor data visualization
• Enhancing interpolation accuracy with GNN imputation data
• Communicating uncertainty in heatmaps for better decision-making

Innovation

• GNNs enhance visualization reliability and resolution
• PNA and GPE learn spatiotemporal dependencies effectively
• Static visualization encodes interpolation uncertainties clearly