Spatiotemporal Analysis of Covid-19 in Turkey.

Aral N., Bakır H.

Sustainable cities and society, vol.76, pp.103421, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 76
  • Publication Date: 2022
  • Doi Number: 10.1016/j.scs.2021.103421
  • Journal Name: Sustainable cities and society
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
  • Page Numbers: pp.103421
  • Keywords: Coronavirus, pandemic, Spatial analysis, Spatial statistics, Spatial autocorrelation, Turkey, ACUTE RESPIRATORY SYNDROME, SARS, EPIDEMIC, DYNAMICS, PATTERN
  • Bursa Uludag University Affiliated: Yes


The Covid-19 pandemic continues to threaten public health around the world. Understanding the spatial dimension of this impact is very important in terms of controlling and reducing the spread of the pandemic. This study measures the spatial association of the Covid-19 outbreak in Turkey between February 8 and May 28, 2021 and reveals its spatiotemporal pattern. In this context, global and local spatial autocorrelation was used to determine whether there is a spatial association of Covid-19 infections, while the spatial regression model was employed to reveal the geographical relationship of the potential factors affecting the number of Covid-19 cases. As a result of the analyzes made in this context, it has been observed that there are spatial associations and distinct spatial clusters in Covid-19 cases at the provincial level in Turkey. The results of the spatial regression model showed that population density and elderly dependency ratio are very important in explaining the model of Covid-19 case numbers. Additionally, it has been revealed that Covid-19 is affected by the Covid-19 numbers of neighboring provinces, apart from the said explanatory variables. The findings of the study revealed that spatial analysis is helpful in understanding the spread of the pandemic in Turkey. It has been determined that geographical location is an important factor to be considered in the investigation of the factors affecting Covid-19.