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RelMap: Reliable Spatiotemporal Sensor Data Visualization via Imputative Spatial Interpolation.

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    This study introduces a new method for visualizing spatiotemporal sensor data, improving reliability and uncertainty communication. The approach uses Graph Neural Networks (GNNs) for better data imputation and visualization.

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    Area of Science:

    • Geographic Information Science
    • Environmental Monitoring
    • Data Visualization

    Background:

    • Spatiotemporal sensor data visualization is vital for decision-making.
    • Traditional interpolation methods struggle with limited and irregular sensor data.
    • Existing methods lack reliable uncertainty visualization.

    Purpose of the Study:

    • To develop a novel spatial interpolation pipeline for reliable spatiotemporal data visualization.
    • To create a heatmap representation encoding uncertainty information.
    • To improve data imputation and temporal resolution using Graph Neural Networks (GNNs).

    Main Methods:

    • Leveraging imputation reference data from Graph Neural Networks (GNNs).
    • Integrating Principal Neighborhood Aggregation (PNA) and Geographical Positional Encoding (GPE) to learn spatiotemporal dependencies.
    • Developing an extrinsic, static visualization technique for uncertainty communication.

    Main Results:

    • Demonstrated superior performance in data imputation compared to traditional methods.
    • Showcased improved interpolant quality with reference data.
    • Validated the effectiveness of the visualization design in communicating uncertainties through use cases, evaluations, and user studies.

    Conclusions:

    • The novel pipeline provides reliable spatiotemporal data interpolation and visualization.
    • The GNN-based approach enhances data imputation and visualization reliability.
    • The proposed visualization effectively communicates map uncertainties to users.