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    Visual Analytics-enabled Bayesian Network Approach to Reasoning about Public Camera Data

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    Date
    2018
    Author
    Chuprikova, Ekaterina
    MacEachren, Alan M.
    Cron, Juliane
    Meng, Liqiu
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    Abstract
    The Visual Analytics (VA) approach has become an important tool for gaining insights on various data sets. Thus, significant research has been conducted to integrate statistical methods in the interactive environment of VA where data visualization provides support to analysts in understanding and exploring the data. However, much of the data explored with VA is inherently uncertain due to limits of our knowledge about a phenomenon, randomness and indeterminism, and vagueness. The Bayesian Network (BN) is a graphical model that provides techniques for reasoning under conditions of uncertainty in a consistent and mathematically rigorous manner. While several software tools for visualizing and editing BNs exist, they have an evident shortcoming when spatial data. In this study, we propose a Visual Analytics-enabled BN approach for reasoning under uncertainty. We describe the implementation procedure using an example of heterogeneous data that includes locations of security surveillance cameras installed in public places.
    BibTeX
    @inproceedings {10.2312:eurorv3.20181141,
    booktitle = {EuroVis Workshop on Reproducibility, Verification, and Validation in Visualization (EuroRV3)},
    editor = {Kai Lawonn and Noeska Smit and Lars Linsen and Robert Kosara},
    title = {{Visual Analytics-enabled Bayesian Network Approach to Reasoning about Public Camera Data}},
    author = {Chuprikova, Ekaterina and MacEachren, Alan M. and Cron, Juliane and Meng, Liqiu},
    year = {2018},
    publisher = {The Eurographics Association},
    ISBN = {978-3-03868-066-6},
    DOI = {10.2312/eurorv3.20181141}
    }
    URI
    http://dx.doi.org/10.2312/eurorv3.20181141
    https://diglib.eg.org:443/handle/10.2312/eurorv320181141
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA