Uncertainty of a detected spatial cluster in 1D: quantification and visualization

Junho Lee, Ronald E. Gangnon, Jun Zhu, Jingjing Liang

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Spatial cluster detection is an important problem in a variety of scientific disciplines such as environmental sciences, epidemiology and sociology. However, there appears to be very limited statistical methodology for quantifying the uncertainty of a detected cluster. In this paper, we develop a new method for the quantification and visualization of uncertainty associated with a detected cluster. Our approach is defining a confidence set for the true cluster and visualizing the confidence set, based on the maximum likelihood, in time or in one-dimensional space. We evaluate the pivotal property of the statistic used to construct the confidence set and the coverage rate for the true cluster via empirical distributions. For illustration, our methodology is applied to both simulated data and an Alaska boreal forest dataset. Copyright © 2017 John Wiley & Sons, Ltd.
Original languageEnglish (US)
Pages (from-to)345-359
Number of pages15
Issue number1
StatePublished - Oct 19 2017

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: Funding has been provided by a USDA Cooperative State Research, Education and Extension Service (CSREES) McIntire-Stennis project. We thank the School of Natural Resources and Agricultural Sciences, University of Alaska Fairbanks, for the development and maintenance of the Cooperative Alaska Forest Inventory, and thank the Global Forest Biodiversity Initiative (GFBI) for establishing data-sharing protocols and standards for international collaborative research projects. We also thank Thomas Malone for his insights on Alaska boreal forest.


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