Disaster Risk Gateway - User contributions [en]

ECA (Event Coincidence Analysis)

2024-07-01T15:19:07Z

Hquintal:

<div style="text-align:justify">
'''Year of publication''': 2016

'''Access''': Open

'''Link''': https://link.springer.com/article/10.1140/epjst/e2015-50233-y

'''Organisation(s) / Author(s)''': Potsdam Institute for Climate Impact Research, Telegrafenberg A31, 14473 Potsdam,
Germany / J.F. Donges, J.F. Siegmund, C.-F. Schleussner, R.V. Donner; Stockholm Resilience Centre, Stockholm University, Kr¨aftriket 2B, 114 19 Stockholm, Sweden / J.F. Donges, ; Climate Analytics, Friedrichstr. 231, Haus B, 10969 Berlin, Germany / , C.-F. Schleussner; Institute of Earth and Environmental Science, University of Potsdam,Karl-Liebknecht-Str. 24-25, 14476 Potsdam-Golm, Germany / J.F. Siegmund

'''Description''': Studying event time series is a powerful approach for analyzing the dynamics of complex dynamical systems in many fields
of science. The method of event coincidence analysis provides 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. 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.

'''Technical considerations''': R package paper: https://www.sciencedirect.com/science/article/pii/S0098300416305489 R package: https://github.com/JonatanSiegmund/CoinCalc

'''Keywords''': Event Coincidence Analysis, Time Series Analysis, Temporal Dependence

[[Category:CategoryPageName]]
</div>

ECA (Event Coincidence Analysis)

2024-07-01T15:18:32Z

Hquintal: Created page with "<div style="text-align:justify"> '''Year of publication''': 2017 '''Access''': Open '''Link''': https://link.springer.com/article/10.1140/epjst/e2015-50233-y '''Organisation(s) / Author(s)''': Potsdam Institute for Climate Impact Research, Telegrafenberg A31, 14473 Potsdam, Germany / J.F. Donges, J.F. Siegmund, C.-F. Schleussner, R.V. Donner; Stockholm Resilience Centre, Stockholm University, Kr¨aftriket 2B, 114 19 Stockholm, Sweden / J.F. Donges, ; Climate Analytic..."

<div style="text-align:justify">
'''Year of publication''': 2017

'''Access''': Open

'''Link''': https://link.springer.com/article/10.1140/epjst/e2015-50233-y

'''Organisation(s) / Author(s)''': Potsdam Institute for Climate Impact Research, Telegrafenberg A31, 14473 Potsdam,
Germany / J.F. Donges, J.F. Siegmund, C.-F. Schleussner, R.V. Donner; Stockholm Resilience Centre, Stockholm University, Kr¨aftriket 2B, 114 19 Stockholm, Sweden / J.F. Donges, ; Climate Analytics, Friedrichstr. 231, Haus B, 10969 Berlin, Germany / , C.-F. Schleussner; Institute of Earth and Environmental Science, University of Potsdam,Karl-Liebknecht-Str. 24-25, 14476 Potsdam-Golm, Germany / J.F. Siegmund

'''Description''': Studying event time series is a powerful approach for analyzing the dynamics of complex dynamical systems in many fields
of science. The method of event coincidence analysis provides 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. 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.

'''Technical considerations''': R package paper: https://www.sciencedirect.com/science/article/pii/S0098300416305489 R package: https://github.com/JonatanSiegmund/CoinCalc

'''Keywords''': Event Coincidence Analysis, Time Series Analysis, Temporal Dependence

[[Category:CategoryPageName]]
</div>

DBSCAN (Density Based Clustering of Applications with Noise)

2024-07-01T15:04:49Z

Hquintal: Created page with "<div style="text-align:justify"> '''Year of publication''': 2022 '''Access''': Open '''Link''': https://cdn.aaai.org/KDD/1996/KDD96-037.pdf?source=post_page--------------------------- '''Organisation(s) / Author(s)''': Institute for Computer Science, University of Munich / Martin Ester, Hans-Peter Kriegel, Jorg Sander, Xiaowei Xu '''Description''': Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application..."

<div style="text-align:justify">
'''Year of publication''': 2022

'''Access''': Open

'''Link''': https://cdn.aaai.org/KDD/1996/KDD96-037.pdf?source=post_page---------------------------

'''Organisation(s) / Author(s)''': Institute for Computer Science, University of Munich / Martin Ester, Hans-Peter Kriegel, Jorg Sander, Xiaowei Xu

'''Description''': Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. The clustering algorithm DBSCAN relies on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. Tilloy et al., 2022 has conducted a more recent application of DBSCAN for the evaluation of multi-hazards, which can be found at the following link: https://esd.copernicus.org/articles/13/993/2022/esd-13-993-2022.html

'''Technical considerations''':

R package(s): https://cran.r-project.org/web/packages/dbscan/index.html
python package: https://pypi.org/project/dbscan/

'''Keywords''':

spatiotemporal clustering, multi-hazard modeling, hazard footprints

[[Category:CategoryPageName]]
</div>