Analysis of tornado reports through replicated spatiotemporal point patterns

Jonatan A. González; Ute Hahn; Jorge Mateu

doi:10.1111/rssc.12375

Analysis of tornado reports through replicated spatiotemporal point patterns

Jonatan A. González^*, Ute Hahn, Jorge Mateu

^*Corresponding author for this work

Computer, Electrical and Mathematical Sciences and Engineering

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

Understanding the spatiotemporal distribution of tornado events is increasingly imperative, not only because of the natural phenomenon itself and its tremendous complexity but also because we can potentially reduce the risks that they entail. In particular, the US regions are particularly susceptible to tornadoes and they are the focus and motivation of our statistical analysis. Tornado reports can be treated as spatiotemporal point patterns, and we develop some methods for the analysis of replicated spatiotemporal patterns to identify significant structural differences between cold and warm seasons along the years. We extend some existing spatial techniques to the spatiotemporal context to test the null hypothesis that two (or more) observed spatiotemporal point patterns with replications are realizations of point processes that have the same second-order descriptors. In particular, we develop a non-parametric test to approximate the null distribution of the test statistics. We present intensive simulation studies that demonstrate the validity and power of our test and apply our methods to the motivating problem of tornadoes.

Original language	English (US)
Pages (from-to)	3-23
Number of pages	21
Journal	Journal of the Royal Statistical Society. Series C: Applied Statistics
Volume	69
Issue number	1
DOIs	https://doi.org/10.1111/rssc.12375
State	Published - Jan 1 2020

Bibliographical note

Funding Information:
The authors thank the Joint Editor and the two referees for their helpful suggestions and constructive comments that have led to an improved version of the paper. This work was supported by the Centre for Stochastic Geometry and Advanced Bioimaging and partially funded by grant MTM2016-78917-R from the Spanish Ministry of Science and Education and by a grant from the Villum Foundation.

Funding Information:
The authors thank the Joint Editor and the two referees for their helpful suggestions and constructive comments that have led to an improved version of the paper. This work was supported by the Centre for Stochastic Geometry and Advanced Bioimaging and partially funded by grant MTM2016‐78917‐R from the Spanish Ministry of Science and Education and by a grant from the Villum Foundation.

Publisher Copyright:
© 2019 Royal Statistical Society

Keywords

K-function
Non-parametric test
Permutation test
Separability
Spatiotemporal point process
Tornadoes

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1111/rssc.12375

Cite this

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abstract = "Understanding the spatiotemporal distribution of tornado events is increasingly imperative, not only because of the natural phenomenon itself and its tremendous complexity but also because we can potentially reduce the risks that they entail. In particular, the US regions are particularly susceptible to tornadoes and they are the focus and motivation of our statistical analysis. Tornado reports can be treated as spatiotemporal point patterns, and we develop some methods for the analysis of replicated spatiotemporal patterns to identify significant structural differences between cold and warm seasons along the years. We extend some existing spatial techniques to the spatiotemporal context to test the null hypothesis that two (or more) observed spatiotemporal point patterns with replications are realizations of point processes that have the same second-order descriptors. In particular, we develop a non-parametric test to approximate the null distribution of the test statistics. We present intensive simulation studies that demonstrate the validity and power of our test and apply our methods to the motivating problem of tornadoes.",

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Analysis of tornado reports through replicated spatiotemporal point patterns

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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