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dc.contributor.authorDorffer,Nina
dc.contributor.authorBruns,Julian
dc.contributor.authorAbecker,Andreas
dc.contributor.authorLossow,Stefan
dc.contributor.editorDemmler, Daniel
dc.contributor.editorKrupka, Daniel
dc.contributor.editorFederrath, Hannes
dc.date.accessioned2022-09-28T17:10:11Z
dc.date.available2022-09-28T17:10:11Z
dc.date.issued2022
dc.identifier.isbn978-3-88579-720-3
dc.identifier.issn1617-5468
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/39503
dc.description.abstractDue to climate change and the effect on human health, there is an urgency to observe and understand the environment. To achieve this goal, knowledge about the development of environmental parameters over time and space is necessary. The analysis of the underlying data can therefore be done with spatial, temporal or even spatio-temporal methods. These methods can also be combined: First spatial,then temporal analyses and vice versa. In this work, we compare the effect of the decision whether to first analyze the data spatially, temporally or both simultaneously. We chose temperature data in Baden-Württemberg, yearly and monthly aggregated, for our comparison.en
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2022
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-326
dc.subjectEnvironmental Computer Science
dc.subjectSpatio-Temporal Statistics
dc.subjectEvaluation
dc.titleComparison Spatio-Temporal Prediction Approaches of Point-Referenced Environmental Dataen
mci.reference.pages1505-1516
mci.conference.sessiontitleKünstliche Intelligenz in der Umweltinformatik (KIU-2022)
mci.conference.locationHamburg
mci.conference.date26.-30. September 2022
dc.identifier.doi10.18420/inf2022_129


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