Emerging patterns in multi-sourced data modeling uncertainty

Alexander Kolovos, Lynette M. Smith, Aimee Schwab-McCoy, Sarah Gengler, Hwa Lung Yu

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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 multi-sourced 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.

Original languageEnglish (US)
Pages (from-to)300-317
Number of pages18
JournalSpatial Statistics
StatePublished - Nov 1 2016


  • Bayesian maximum entropy
  • Binomial kriging
  • Minimum norm approximations
  • Poisson
  • Uncertainty

ASJC Scopus subject areas

  • Statistics and Probability
  • Computers in Earth Sciences
  • Management, Monitoring, Policy and Law


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