Emerging patterns in multi-sourced data modeling uncertainty

Kolovos, Alexander;Smith, Lynette M.;Schwab-McCoy , Aimee;Gengler, Sarah;Yu, Hwa-Lung
(2016) Spatial Statistics — Vol. 18, n° Part A, p. 300-317 (2016)

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Authors
  • Kolovos, AlexanderSpaceTimesWorks, San Diego, CA, USA
    Author
  • Smith, Lynette M.Medical Center, University of Nebraska, Omaha, NE, USA
    Author
  • Schwab-McCoy , AimeeDepartment of Mathematics, Xavier University, Cincinnati, OH, USA
    Author
  • Gengler, SarahUCLouvain
    Author
  • Yu, Hwa-LungDepartment of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan
    Author
Abstract
The abundance of spatial and space–time data in many research fields has led to an increasing interest in the analytics of spatial data information. This development has renewed the attention to predictive spatial methodologies and advancing geostatistical tools. In this context, the present work reviews a series of cross-discipline studies that utilize multiple monitoring sources,and promote applied approaches in spatial and spatiotemporal modeling to improve our understanding of uncertainty. As multisourced information gives birth to new aspects of uncertainty, we explore emerging patterns in dealing with uncertainty in sources across structured, unstructured, and incomplete spatial data. We also illustrate how additional forms of information, such as secondary data and physical models, can further support and benefit research in the characterization and modeling of natural attributes.
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Kolovos, A., Smith, L. M., Schwab-McCoy, A., Gengler, S., & Yu, H.-L. (2016). Emerging patterns in multi-sourced data modeling uncertainty. Spatial Statistics, 18(Part A), 300-317. https://doi.org/10.1016/j.spasta.2016.05.005 (Original work published 2016)