mlsanityData to 14 Oct 2026

RelMap: Reliable Spatiotemporal Sensor Data Visualization via Imputative Spatial Interpolation

arXiv 2508.01240 ↗First posted 2 Aug 2025cs.LG cs.HCcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 75 references that are in the map. First posted on arXiv 2 Aug 2025.

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24 of 75 references.

References · 24

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12 more
  1. 2015Structural-RNN: Deep Learning on Spatio-Temporal Graphscited by 234
  2. 2021Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networkscited by 95
  3. 2020Inductive Graph Neural Networks for Spatiotemporal Krigingcited by 91
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  5. 2018Processing of missing data by neural networkscited by 37
  6. 2023Kriging Convolutional Networkscited by 31
  7. 2021Spatial Aggregation and Temporal Convolution Networks for Real-time Krigingcited by 27
  8. 2021Positional Encoder Graph Neural Networks for Geographic Datacited by 20
  9. 2020Topology Density Map for Urban Data Visualization and Analysiscited by 8
  10. 2020Uncertainty-Oriented Ensemble Data Visualization and Exploration using Variable Spatial Spreadingcited by 5
  11. 2024SalienTime: User-driven Selection of Salient Time Steps for Large-Scale Geospatial Data Visualizationcited by 2
  12. 2020Communicating Uncertainty and Risk in Air Quality Mapscited by 1

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