2015/08/31 by Jonathan F. Donges, Carl‐Friedrich Schleussner, Carl-Friedrich Schleussner +2 · 3 citations
Environmental Science · Mathematics · Physics and Astronomy · #Biology #Climate variability and models #Coincidence #Computer science #Data mining #Econometrics #Ecosystem dynamics and resilience #Event (particle physics) #Hydrology and Drought Analysis #Mathematics #Null hypothesis #Poisson distribution #Series (stratigraphy) #Statistical hypothesis testing #Statistics #Time series #physics.data-an #physics.soc-ph #stat.ME
paper · pdf · doi:10.1140/epjst/e2015-50233-y
published as European Physical Journal Special Topics, 225(3), 471-487 (2016) · 18 pages, 4 figures
arxiv created 2016/04/06 · openalex publication_date 2016/05/01 · openalex created_date 2016/06/24 · arxiv updated 2016/07/06 · openalex updated_date 2026/08/05
Studying event time series is a powerful approach for analyzing the dynamics of complex dynamical systems in many fields of science. In this paper, we describe the method of event coincidence analysis to provide a framework for quantifying the strength, directionality and time lag of statistical interrelationships between event series. Event coincidence analysis allows to formulate and test null hypotheses on the origin of the observed interrelationships including tests based on Poisson processes or, more generally, stochastic point processes with a prescribed inter-event time distribution and other higher-order properties. Applying the framework to country-level observational data yields evidence that flood events have acted as triggers of epidemic outbreaks globally since the 1950s. Facing projected future changes in the statistics of climatic extreme events, statistical techniques such as event coincidence analysis will be relevant for investigating the impacts of anthropogenic climate change on human societies and ecosystems worldwide.