Analysing Spatial Patterns of the COVID-19 Outbreak in Turkey

Year 2020, , 27 - 40, 28.12.2020

Fatma Zeren , Veli Yılancı

https://doi.org/10.33399/biibfad.789117

Cited By: 5

Abstract

Türkiye'de COVID-19 ilk olarak 12 Mart 2020'de tespit edildi ve o günden bu yana 100 binden fazla kişiye bulaştı. Bu çalışmada hem COVID-19 salgını açısından riskli illeri belirlemeyi hem de il bazındaki verileri kullanarak Türkiye'deki salgının mekansal dinamiklerini keşfetmeyi hedefliyoruz. COVID-19' un mekansal yapısını ortaya çıkarmak amacıyla mekansal bağımlılık istatistiklerinden Moran-I istatistiği kullanılmıştır. Ayrıca, “hot point” ve “cold point” alanları belirlemek amacıyla mekansal etkileşim istatistiklerinden Lokal Moran I-LISA istatistiği kullanılmıştır. İstatistiksel olarak anlamlı bulunan Moran I katsayısı tüm ülke bağlamında mekansal etkileşimin güçlü olmadığını göstermektedir. LISA istatistiğine göre ise Düzce, Kocaeli, Ordu, Tekirdağ ve Trabzon illeri veri dönemi için “hot point”alanındaki şehirlerdir. Yani bu şehirlerin COVID-19 salgını açısından en riskli alanlardır ve komşu şehirlerle daha fazla mekansal etkileşimleri bulunmaktadır. COVID-19 değişkeni açısından, “hot point” şehirler ve onların komşularında tedbirler yoğunlaştırılmalı ve kontrol artırılmalıdır.

Keywords

COVID-19, mekansal otokorelasyon, hot-spot, cold spot, Moran I, LISA, Türkiye

Analysing Spatial Patterns of the COVID-19 Outbreak in Turkey

Abstract

COVID-19 is first detected on 12 March 2020 in Turkey, and since that day more than 100 thousand people are infected. In this study, we aim to determine risky provinces in terms of COVID-19 outbreak and also explore the spatial dynamics of the outbreak in Turkey using province-level data. To analyze spatial patterns of COVID-19, we employ spatial dependence statistics Moran-I. Also, we employ Local Indicator Spatial Association-LISA to detect the hot-spots and cold-spots. Moran-I coefficient found as low and statistically significant that shows spatial interaction is not strong in the context of the whole country. Also using LISA, we found Düzce, Kocaeli, Ordu, Tekirdağ, and Trabzon as hot-spots for data period, which indicates these cities can be classified as risky in terms of COVID-19 outbreak. There are more spatial interaction with their neighbours cities. In terms of the COVID-19 variable, in hot-spot provinces and neighboring provinces of these provinces, measures should be intensified, and control should be increased. 

Keywords

COVID-19, spatial autocorrelation, hot-spot, cold spots, Moran I, LISA, Turkey

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