Using cluster analysis methods for multivariate mapping of traffic accidents

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dc.contributor.authorSelvi, Huseyin Zahit
dc.contributor.authorCaglar, Burak
dc.date.accessioned2024-02-23T14:29:49Z
dc.date.available2024-02-23T14:29:49Z
dc.date.issued2018
dc.departmentNEÜen_US
dc.description.abstractMany factors affect the occurrence of traffic accidents. The classification and mapping of the different attributes of the resulting accident are important for the prevention of accidents. Multivariate mapping is the visual exploration of multiple attributes using a map or data reduction technique. More than one attribute can be visually explored and symbolized using numerous statistical classification systems or data reduction techniques. In this sense, clustering analysis methods can be used for multivariate mapping. This study aims to compare the multivariate maps produced by the K-means method, K-medoids method, and Agglomerative and Divisive Hierarchical Clustering (AGNES) method, which among clustering analysis methods, with real data. The results from the study will suggest which clustering methods should be preferred in terms of multivariate mapping. The results show that the K-medoids method is more appropriate in terms of clustering success. Moreover, the aim is to reveal spatial similarities in traffic accidents according to the results of traffic accidents that occur in different years. For this aim, multivariate maps created from traffic accident data of two different years in Turkey are used. The methods are compared, and the use of the maps produced with these methods for risk management and planning is discussed. Analysis of the maps reveals significant similarities for both years.en_US
dc.identifier.doi10.1515/geo-2018-0060
dc.identifier.endpage781en_US
dc.identifier.issn2391-5447
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85060396497en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage772en_US
dc.identifier.urihttps://doi.org/10.1515/geo-2018-0060
dc.identifier.urihttps://hdl.handle.net/20.500.12452/14855
dc.identifier.volume10en_US
dc.identifier.wosWOS:000457907800001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSciendoen_US
dc.relation.ispartofOpen Geosciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectTraffic Accidentsen_US
dc.subjectMultivariate Mappingen_US
dc.subjectData Miningen_US
dc.subjectCluster Analysisen_US
dc.subjectVisualizationen_US
dc.titleUsing cluster analysis methods for multivariate mapping of traffic accidentsen_US
dc.typeArticleen_US

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