Clustering of Countries According to Tourism Potentials Using K-Means Method

Cem Gürler

Makale detayları

Clustering of Countries According to Tourism Potentials Using K-Means Method

Yazarlar:

Cem Gürler

Yayıncı:

Kyrgyz-Turkish Manas University

Dergi:

MANAS Sosyal Araştırmalar Dergisi Vol.12 Issue 1, pp.353-363

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001282912

pISSN:

1694-7215

Anahtar kelimeler:

Özet:

 The tourism sector has a serious economic importance for countries. After the coronavirus pandemic, the size of the travel and tourism sector decreased by approximately 50 to  4,671 Trillion. With the lifting of restrictions, the size of this sector is expected to catch up with pre-pandemic again in the near future. In this context, countries want to get more shares from the travel and tourism sector in order to reduce the damage caused by the pandemic and to continue their economic growth. There are different strategies in the tourism sector and countries follow different policies to attract more visitors. The aim of the current study is to identify countries that are similar to each other in the travel and tourism sector by cluster analysis. In this context, firstly, the number of clusters was determined as 4 using the GAP statistics. After determining the number of clusters, cluster analysis was performed using the k-means method. After the cluster analysis, the average index score was calculated for each cluster and the clusters were ranked according to this calculated score.

 The tourism sector has a serious economic importance for countries. After the coronavirus pandemic, the size of the travel and tourism sector decreased by approximately 50 to  4,671 Trillion. With the lifting of restrictions, the size of this sector is expected to catch up with pre-pandemic again in the near future. In this context, countries want to get more shares from the travel and tourism sector in order to reduce the damage caused by the pandemic and to continue their economic growth. There are different strategies in the tourism sector and countries follow different policies to attract more visitors. The aim of the current study is to identify countries that are similar to each other in the travel and tourism sector by cluster analysis. In this context, firstly, the number of clusters was determined as 4 using the GAP statistics. After determining the number of clusters, cluster analysis was performed using the k-means method. After the cluster analysis, the average index score was calculated for each cluster and the clusters were ranked according to this calculated score.

 The tourism sector has a serious economic importance for countries. After the coronavirus pandemic, the size of the travel and tourism sector decreased by approximately 50 to  4,671 Trillion. With the lifting of restrictions, the size of this sector is expected to catch up with pre-pandemic again in the near future. In this context, countries want to get more shares from the travel and tourism sector in order to reduce the damage caused by the pandemic and to continue their economic growth. There are different strategies in the tourism sector and countries follow different policies to attract more visitors. The aim of the current study is to identify countries that are similar to each other in the travel and tourism sector by cluster analysis. In this context, firstly, the number of clusters was determined as 4 using the GAP statistics. After determining the number of clusters, cluster analysis was performed using the k-means method. After the cluster analysis, the average index score was calculated for each cluster and the clusters were ranked according to this calculated score.

 The tourism sector has a serious economic importance for countries. After the coronavirus pandemic, the size of the travel and tourism sector decreased by approximately 50 to  4,671 Trillion. With the lifting of restrictions, the size of this sector is expected to catch up with pre-pandemic again in the near future. In this context, countries want to get more shares from the travel and tourism sector in order to reduce the damage caused by the pandemic and to continue their economic growth. There are different strategies in the tourism sector and countries follow different policies to attract more visitors. The aim of the current study is to identify countries that are similar to each other in the travel and tourism sector by cluster analysis. In this context, firstly, the number of clusters was determined as 4 using the GAP statistics. After determining the number of clusters, cluster analysis was performed using the k-means method. After the cluster analysis, the average index score was calculated for each cluster and the clusters were ranked according to this calculated score.

Makale detayları

Clustering of Countries According to Tourism Potentials Using K-Means Method

Yazarlar:

Cem Gürler

Yayıncı:

Kyrgyz-Turkish Manas University

Dergi:

MANAS Sosyal Araştırmalar Dergisi Vol.12 Issue 1, pp.353-363

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001282912

pISSN:

1694-7215

Tourism

Competitiveness

Cluster Analysis

Turizm

Rekabetçilik

Kümeleme Analizi

Sayı

Makale detayları

Clustering of Countries According to Tourism Potentials Using K-Means Method

Yazarlar: Cem Gürler

Yayıncı: Kyrgyz-Turkish Manas University

Dergi: MANAS Sosyal Araştırmalar Dergisi Vol.12 Issue 1, pp.353-363

Belge Tipi: Makale

Yayın Tarihi: 1/2023

Belge Dili: Türkçe

Document ID (Org No.): 2001282912

pISSN: 1694-7215

Tourism

Competitiveness

Cluster Analysis

Turizm

Rekabetçilik

Kümeleme Analizi

Sayı

Yazarlar:

Cem Gürler

Yayıncı:

Kyrgyz-Turkish Manas University

Dergi:

MANAS Sosyal Araştırmalar Dergisi Vol.12 Issue 1, pp.353-363

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001282912

pISSN:

1694-7215