ÇOK DEĞİŞKENLİ HARİTALAMA İÇİN KÜMELEME YÖNTEMLERİNİN KULLANILMASI

Çok değişkenli haritalama mekânsal objelere ait birden çok özelliğin harita kullanılarak görsel sunumudur. Çeşitli veri hazırlama ve istatistiksel sınıflandırma teknikleri kullanılarak mekânsal objelere ait birden çok özellik görsel olarak incelenebilir ve kartografik işaretlerle gösterilebilir. Bu kapsamda kümeleme analizi yöntemleri de çok değişkenli haritalama için kullanılabilir. Bu çalışmada kümeleme analiz yöntemlerinden k-ortalama yöntemi, k-temsilci yöntemi ve Birleştirici Hiyerarşik Kümeleme yöntemi ele alınmıştır. Bu yöntemlerle Türkiye’deki üç ayrı yıla ait trafik kaza verileri kullanılarak oluşturulan sınıflar ve üretilen çok değişkenli haritalar kullanılarak bu yöntemlerin karşılaştırılması yapılmış, bu yöntemlerle üretilen haritaların risk yönetimi ve planlamada kullanılabilirliği üzerinde durulmuştur.

Anahtar Kelimeler:

Çok değişkenli haritalama, veri madenciliği, kümeleme analizi

USING CLUSTER ANALYSIS METHODS FOR MULTIVARIATE MAPPING

Multivariate mapping is the visual exploration of spatial objects with multiple attributes using a map. 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. In this study, among clustering analysis methods, k-means method, k-medoids method and Agglomerative Hierarchical Clustering method were selected. For this purpose, multivariate maps created from traffic accident data of two different years in Turkey were used. The methods were compared using the maps produced with these methods and effectiveness of these maps in risk management and planning were discussed.

Keywords:

Multivariate mapping, data mining, cluster analysis,

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