Visualization of Joint Spatio-temporal Models via Feature Recognition with an Application to Wildland Fires

Devan G. Becker; Douglas G. Woolford; Charmaine B. Dean

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Paper

Visualization of Joint Spatio-temporal Models via Feature Recognition with an Application to Wildland Fires

Topics: Dynamic or Temporal Data Visualisation; Geographic Information Visualisation; Multivariate Data Visualisation; Visual Data Analysis and Knowledge Discovery; Visualization Algorithms and Technologies; Visualization Applications

In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISIGRAPP, 233-239, 2021

Authors: Devan G. Becker ¹ ; Douglas G. Woolford ¹ and Charmaine B. Dean ²

Affiliations: ¹ Statistical and Actuarial Sciences, University of Western Ontario, London, Ontario, Canada ; ² Statistics and Actuarial Science, Waterloo University, Waterloo, Ontario, Canada

Keyword(s): Image Recognition, Non-negative Matrix Factorization, Log-Gaussian Cox Processes, Dimension Reduction.

Abstract: Many spatial statistics applications result in a collection of spatial estimates, especially if a different (but possibly correlated) estimate is produced for a sequence of time epochs. For a small collection of epochs, the connections or trends between estimates and the prominent or common features can be found via inspection of the spatial estimates. As the number of spatial estimates grows, this task becomes much more difficult. We present a method of summarizing a sequence of estimates using an image recognition technique called NonNegative Matrix Factorization which results in a meaningful decomposition of the source images into basis functions and coefficients. This visualization technique allows for investigation of trends over time as well as common spatial features of the estimates without needing to fit a temporal model or use pre-specified spatial regions. We apply this technique to a sequence of models that jointly model the spatial location of wildland fires with the tot al burn area of each of the fires. We discuss the extensions of the visualization technique to the joint modelling framework and are able to draw new insights about the connection between the location and size of the fires. (More)

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Paper citation in several formats:

Becker, D.; Woolford, D. and Dean, C. (2021). Visualization of Joint Spatio-temporal Models via Feature Recognition with an Application to Wildland Fires. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - IVAPP; ISBN 978-989-758-488-6; ISSN 2184-4321, SciTePress, pages 233-239. DOI: 10.5220/0010319602330239

@conference{ivapp21,
author={Devan G. Becker. and Douglas G. Woolford. and Charmaine B. Dean.},
title={Visualization of Joint Spatio-temporal Models via Feature Recognition with an Application to Wildland Fires},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - IVAPP},
year={2021},
pages={233-239},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010319602330239},
isbn={978-989-758-488-6},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - IVAPP
TI - Visualization of Joint Spatio-temporal Models via Feature Recognition with an Application to Wildland Fires
SN - 978-989-758-488-6
IS - 2184-4321
AU - Becker, D.
AU - Woolford, D.
AU - Dean, C.
PY - 2021
SP - 233
EP - 239
DO - 10.5220/0010319602330239
PB - SciTePress