<p>The pandemic of infectious diseases worldwide poses a severe threat to human life. Studying the built environment (BE) is paramount in reducing threats to human life. This study investigates and spatially analyzes the morphology and function of streets as cities' most critical public assets and their impact on the spread of COVID-19 in Gorgan. Street morphology data were obtained through the Space Syntax toolkit in QGIS while street function data were extracted from Open Street Map. In addition, which was the only hospital designated for COVID-19 patients in the city during the pandemic. Data were analyzed using Global Moran's I, Ordinary Least Squares (OLS), and forest based classification and regression (FCR). Moran's analysis indicated that the distribution of COVID-19 patients in urban neighborhoods follows a clustered pattern. The results indicate that the morphological characteristics of streets cannot serve as strong predictors for COVID-19 transmission at the neighborhood level. Furthermore, it was determined that the role and function of urban pathways in facilitating interactions, human contacts, and subsequent virus transmission are significantly more important than their morphological features. The research reveals that areas containing numerous streets with dominant accessibility and high selectivity characteristics face higher risks of COVID-19 outbreaks than other areas. The study delivers essential information that helps urban decision-makers reinforce BE resilience during pandemic situations like COVID-19.</p>

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Exploring the influence of urban street morphology and functionality on COVID-19 spread: insights from Gorgan, Iran

  • Masoud Zamanipoor,
  • Mohammad Rahim Rahnama,
  • Farzaneh Zamanipour,
  • Iman Heidari

摘要

The pandemic of infectious diseases worldwide poses a severe threat to human life. Studying the built environment (BE) is paramount in reducing threats to human life. This study investigates and spatially analyzes the morphology and function of streets as cities' most critical public assets and their impact on the spread of COVID-19 in Gorgan. Street morphology data were obtained through the Space Syntax toolkit in QGIS while street function data were extracted from Open Street Map. In addition, which was the only hospital designated for COVID-19 patients in the city during the pandemic. Data were analyzed using Global Moran's I, Ordinary Least Squares (OLS), and forest based classification and regression (FCR). Moran's analysis indicated that the distribution of COVID-19 patients in urban neighborhoods follows a clustered pattern. The results indicate that the morphological characteristics of streets cannot serve as strong predictors for COVID-19 transmission at the neighborhood level. Furthermore, it was determined that the role and function of urban pathways in facilitating interactions, human contacts, and subsequent virus transmission are significantly more important than their morphological features. The research reveals that areas containing numerous streets with dominant accessibility and high selectivity characteristics face higher risks of COVID-19 outbreaks than other areas. The study delivers essential information that helps urban decision-makers reinforce BE resilience during pandemic situations like COVID-19.