Mimar Sinan Güzel Sanatlar Üniversitesi Açık Bilim, Sanat Arşivi

Açık Bilim, Sanat Arşivi, Mimar Sinan Güzel Sanatlar Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve yayınların etkisini artırmak için telif haklarına uygun olarak Açık Erişime sunar.

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dc.contributor.authorErsen, Mert
dc.contributor.authorBuyuklu, Ali Hakan
dc.contributor.authorErpolat, Semra Tasabat
dc.date.accessioned2025-01-09T20:08:04Z
dc.date.available2025-01-09T20:08:04Z
dc.date.issued2021
dc.identifier.issn2071-1050
dc.identifier.urihttps://doi.org/10.3390/su131911039
dc.identifier.urihttps://hdl.handle.net/20.500.14124/7970
dc.description.abstractTraffic accidents, which continue to increase from year to year in Turkey and in the world, have become a huge problem that can result in serious traumas, injuries, and deaths, as well as their material and moral consequences. Many studies have been carried out in the world and in Turkey to reduce the number of traffic accidents, but these studies have not been very effective in reducing accidents. In this study, 3105 fatal or injured traffic accidents between 2010-2017 in Sariyer district of Istanbul, Turkey's largest city in terms of population, were discussed. We analyzed the statistical information on the subject in detail within the framework of geographic information systems. It has been tried to determine the sections where traffic accidents are concentrated in this region with studies based on spatial methods. Thematic accident map was created according to the accident types. In this context, the advantages and disadvantages of these methods were compared using Point Density, Kernel Density, Getis Ord Gi*, and Anselin Local Moran's I (LISA) Spatial Autocorrelation. In addition, in order to observe the change in accidents, thematic accident and Kernel Density maps were created separately according to accident occurrence types in the beginning and last year. From this point of view, the changes that occurred in the accidents were interpreted. The current study determined that the most accidents were on some streets and these streets divided into regions in a plan. The cases were examined with statistical analyses according to accident types and using the Kernel Density method. In addition, it has been observed that Kernel Density method gives better visual results than other spatial methods. In this study, spatial analysis and statistical analysis methods were used to evaluate traffic accidents more realistically. The day of the week effect and month of the year effect on traffic accidents was investigated for the first time. In addition, it is proposed to bring a new approach to the prevention of traffic accidents by using hotspot, accident type, and day of the week effect.en_US
dc.language.isoengen_US
dc.publisherMdpien_US
dc.relation.ispartofSustainabilityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjecttraffic accidentsen_US
dc.subjectspatial statisticsen_US
dc.subjectspatial analysisen_US
dc.subjectpoint densityen_US
dc.subjectkernel densityen_US
dc.subjectGetis Ord Gi*en_US
dc.subjectAnselin Local Moran's I (LISA)en_US
dc.subjectgeographic information systemsen_US
dc.subjectaccident analyticsen_US
dc.titleAnalysis of Fatal and Injury Traffic Accidents in Istanbul Sariyer District with Spatial Statistics Methodsen_US
dc.typearticleen_US
dc.authoridBUYUKLU, ALI HAKAN/0000-0002-4174-4538
dc.authoridErsen, Mert/0000-0001-5643-4690
dc.departmentMimar Sinan Güzel Sanatlar Üniversitesien_US
dc.identifier.doi10.3390/su131911039
dc.identifier.volume13en_US
dc.identifier.issue19en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.wosqualityQ2
dc.identifier.wosWOS:000707982500001
dc.identifier.scopus2-s2.0-85116780927
dc.identifier.scopusqualityQ1
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.snmzKA_20250105


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