Using cluster analysis methods for multivariate mapping of traffic accidents
Küçük Resim Yok
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Sciendo
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Many 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.
Açıklama
Anahtar Kelimeler
Traffic Accidents, Multivariate Mapping, Data Mining, Cluster Analysis, Visualization
Kaynak
Open Geosciences
WoS Q Değeri
Q4
Scopus Q Değeri
Q2
Cilt
10
Sayı
1
