Event coincidence analysis for quantifying statistical interrelationships between event time series

Donges, Jonathan F.; Schleußner, Carl-Friedrich; Siegmund, Jonatan; Donner, Reik V.

doi:10.1140/epjst/e2015-50233-y

Cited by 138 publications

(179 citation statements)

References 59 publications

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“…ECA is a method tailored for quantifying and testing statistical interrelationships between event series while allowing to specify explicitly the coincidence interval, lag, and directionality (in terms of precursor and trigger coincidences) of these interrelationships (24). In this study, we perform two coincidence tests: (i) the risk enhancement test, which is based on armedconflict outbreak and tests for coincidences of natural disasters co-occurring with or preceding conflict events, and (ii) the trigger test based on climaterelated natural disaster occurrence, which tests for coincidences with armedconflict outbreaks following or co-occurring with a disaster event (24). Both tests differ with regard to the considered set of countries and the definition of the coincidence interval, but otherwise the same methodology is applied.…”

Section: Methodsmentioning

confidence: 99%

“…Among others, event synchronization (25) has been widely used for studying climatological extreme events in various contexts (60,61). Donges et al (24) provide a more detailed discussion of ECA in comparison with related approaches.…”

Section: Methodsmentioning

confidence: 99%

“…Although we find no indications that environmental disasters directly trigger armed conflicts, our results imply that disasters might act as a threat multiplier in several of the world's most conflict-prone regions. event time series such as regime shifts in African paleoclimate and the appearance and disappearance of hominin species during the Plio-Pleistocene (23), plant growth response to climatic extremes (27,28), or the role of flood events as triggers of epidemic outbreaks (24).…”

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confidence: 99%

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Armed-conflict risks enhanced by climate-related disasters in ethnically fractionalized countries

Schleußner

Donges

Donner

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

297

175

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Social and political tensions keep on fueling armed conflicts around the world. Although each conflict is the result of an individual context-specific mixture of interconnected factors, ethnicity appears to play a prominent and almost ubiquitous role in many of them. This overall state of affairs is likely to be exacerbated by anthropogenic climate change and in particular climate-related natural disasters. Ethnic divides might serve as predetermined conflict lines in case of rapidly emerging societal tensions arising from disruptive events like natural disasters. Here, we hypothesize that climate-related disaster occurrence enhances armed-conflict outbreak risk in ethnically fractionalized countries. Using event coincidence analysis, we test this hypothesis based on data on armed-conflict outbreaks and climate-related natural disasters for the period 1980-2010. Globally, we find a coincidence rate of 9% regarding armed-conflict outbreak and disaster occurrence such as heat waves or droughts. Our analysis also reveals that, during the period in question, about 23% of conflict outbreaks in ethnically highly fractionalized countries robustly coincide with climatic calamities. Although we do not report evidence that climate-related disasters act as direct triggers of armed conflicts, the disruptive nature of these events seems to play out in ethnically fractionalized societies in a particularly tragic way. This observation has important implications for future security policies as several of the world's most conflict-prone regions, including North and Central Africa as well as Central Asia, are both exceptionally vulnerable to anthropogenic climate change and characterized by deep ethnic divides.climate-related natural disasters | ethnic fractionalization | armed conflicts | event coincidence analysis

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Armed-conflict risks enhanced by climate-related disasters in ethnically fractionalized countries

Schleußner

Donges

Donner

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

297

175

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“…Multivariate approaches in geoscience make use of anomalies occurring simultaneously in multiple data streams, often referred to as coincidences or co-exceedances (e.g., Donges et al, 2011b;Rammig et al, 2015;Zscheischler et al, 2015;Donges et al, 2016;Guanche et al, 2016;Siegmund et al, 2016). An alternative is the copula approach introduced to the field by Schoelzel and Friedrichs (2008) and Durante and Salvadori (2010).…”

Section: Introductionmentioning

confidence: 99%

Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques

et al. 2017

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Abstract. Today, many processes at the Earth's surface are constantly monitored by multiple data streams. These observations have become central to advancing our understanding of vegetation dynamics in response to climate or land use change. Another set of important applications is monitoring effects of extreme climatic events, other disturbances such as fires, or abrupt land transitions. One important methodological question is how to reliably detect anomalies in an automated and generic way within multivariate data streams, which typically vary seasonally and are interconnected across variables. Although many algorithms have been proposed for detecting anomalies in multivariate data, only a few have been investigated in the context of Earth system science applications. In this study, we systematically combine and compare feature extraction and anomaly detection algorithms for detecting anomalous events. Our aim is to identify suitable workflows for automatically detecting anomalous patterns in multivariate Earth system data streams. We rely on artificial data that mimic typical properties and anomalies in multivariate spatiotemporal Earth observations like sudden changes in basic characteristics of time series such as the sample mean, the variance, changes in the cycle amplitude, and trends. This artificial experiment is needed as there is no "gold standard" for the identification of anomalies in real Earth observations. Our results show that a well-chosen feature extraction step (e.g., subtracting seasonal cycles, or dimensionality reduction) is more important than the choice of a particular anomaly detection algorithm. Nevertheless, we identify three detection algorithms (k-nearest neighbors mean distance, kernel density estimation, a recurrence approach) and their combinations (ensembles) that outperform other multivariate approaches as well as univariate extreme-event detection methods. Our results therefore provide an effective workflow to automatically detect anomalies in Earth system science data.

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“…A common approach for multivariate analysis in geoscience is to look for those events where multiple variables present abnormal behavior simultaneously, often called co-exceedances (Donges et al 2011;Donges et al 2016). This approach is based on fixing a threshold at each variable and analyzing the probability of occurrence of events above those thresholds either simultaneously or with a certain lag between variables.…”

Section: Introductionmentioning

confidence: 99%

Extreme anomaly event detection in biosphere using linear regression and a spatiotemporal MRF model

et al. 2018

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Detecting abnormal events within time series is crucial for analyzing and understanding the dynamics of the system in many research areas. In this paper, we propose a methodology to detect these anomalies in multivariate environmental data. Five biosphere variables from a preliminary version of the Earth System Data Cube have been used in this study: Gross Primary Productivity, Latent Energy, Net Ecosystem Exchange, Sensible Heat and Terrestrial Ecosystem Respiration. To tackle the spatiotemporal dependencies of the biosphere variables, the proposed methodology after preprocessing the data is divided into two steps: a feature extraction step applied to each time series in the grid independently, followed by a spatiotemporal event detection step applied to the obtained novelty scores over the entire study area. The first step is based on the assumption that the time series of each variable can be represented by an autoregressive moving average (ARMA) process, and the anomalies are those time instances that are not well represented by the estimated ARMA model. The Mahalanobis distance of the ARMA models' multivariate residuals is used as a novelty score. In the second step, the obtained novelty scores of the entire study are treated as time series of images. Markov random fields (MRFs) provide an effective and theoretically well-established methodology for integrating spatiotemporal dependency into the classification of image time series. In this study, the classification of the novelty score images into three classes, intense anomaly, possible anomaly, and normal, is performed using unsupervised K-means clustering followed by multi-temporal MRF segmentation applied recursively on the images of each consecutive L ! 1 time steps. The proposed methodology was applied to an area covering Europe and Africa. Experimental results and validation based on known historic events show that the method is able to detect historic events and also provides a useful tool to define sensitive regions.

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Event coincidence analysis for quantifying statistical interrelationships between event time series

Cited by 138 publications

References 59 publications

Armed-conflict risks enhanced by climate-related disasters in ethnically fractionalized countries

Armed-conflict risks enhanced by climate-related disasters in ethnically fractionalized countries

Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques

Extreme anomaly event detection in biosphere using linear regression and a spatiotemporal MRF model

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