Geospatial Analysis of COVID-19 Spread in Cairo Governorate Using Geographic Information System | ||||
| Bulletin de la Société de Géographie d'Egypte | ||||
| Article 2, Volume 94, Issue 1, 2021, Page 24-43 PDF (932 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/bsge.2021.208304 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Shimaa Elsayed Abd Elnaby; Asmaa Bdeer Ali Al qersh | ||||
| Department of Geography, Faculty of Arts, Alexandria University | ||||
| Abstract | ||||
| COVID-19 has caused a global public health crisis affecting most countries including Egypt Due to the widespread pandemic; the spread of COVID-19 has drawn increasing global attention. This study aims to analyze the reported Covid-19 cases by spatial statistical methods, exploring the geographical distribution patterns of the COVID-19 pandemic in Cairo from 14 February 2020 to 18 of May 2021. In order to better understand of the spread of this new virus, The spatial patterns of Covid-19 cumulative cases have been characterized by using ArcGIS ver. 10.3.1 based on spatial autocorrelation and cluster analysis using Global Moran’s I (Moran, 1950), Local Moran’s I and Getis-Ord Gi to explore the spatial gathering hotspots and cold spots of the COVID-19 pandemic .Up to 18 may 2021, Cairo Province had reported 79,130 Covid-19 cases. The top five districts with respect to incidence were Al Mouski (0.097/100000), Badr (0.088/100000), Qasr Al niel (0.088/100000), Al Gammalia (0.066/100000) and Al Zamalek (0.064/100000). Global Moran’s I analysis results showed that the Morbidity rate, recovery and fatality of COVID19 was in the clustering pattern, and there was less than 1% likelihood that this clustering pattern could be the result of random chance. While local spatial correlation analysis showed that the value of 71 sheikhs belonging to the HH aggregation class.The Getis -OrdGi statistic was applied and the results showed that the clustering of high values (hot spots) for Morbidity with a confidence level of 99% distributed in eastern, southern and the northeast of Cairo. | ||||
| Keywords | ||||
| COVID-19; GIS; Spatial autocorrelation; Hot/cold spots; Cairo | ||||
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