ABSTRACT
Most cities in developing countries suffer environmental degradation caused by the growth of unplanned areas that sprawl in the cities. In the current paper, we attempted to integrate a set of selected UN-based urban indicators based on the New Urban Agenda (NUA) within a GIS framework to observe and assess some aspects of urban vulnerability among city districts based on deprivation. The vulnerability map for the districts in Assiut City was created through a spatial multicriteria evaluation model. Thirteen sub-indicators related to shelter, social environmental and economic situations have been assessed in the model using standardization, weighting and aggregation methods. Results revealed that: districts, namely, El Thaltha, El Owla, El Thania, and El Rabaa are most vulnerable in most scenarios, while districts, namely, El Sheyakha El Sabaa, and El Sadsa, El-Walidya El Qiblia and El-Hamra El Thania are among the least vulnerable zones. Results also revealed that vulnerable districts encompass the highest percentage of slums, highest density of population, highest rates for urban growth and poor connection to services. Eventually, we assume that the most vulnerable zones in the city are under the highest risk of airborne diseases including COVID-19 epidemic. Eventually, a subset of selected urban vulnerability indicators that could be triggering the spread of the pandemic was chosen for another spatial multcriteria model to delineate city zones under risk. The result revealed that expected high-risk areas exist in the south-west of the city and include El Thaltha, El Owla, El Thania and El Rabaa districts, while the least risk district is El-Walydia El-Qeblia. The applied methodology and its outputs could support decision makers in reviewing priorities, setting contingency plans, allocation of funds and raising resilience among the city districts.
ABSTRACT
COVID-19 has affected over 170 countries around the world. Alarming rate has increased with the increase of infected cases and death rates. Whereas, the World Health Organization (WHO) had declared the COVID-19 virus as a pandemic on 11th March 2020. Preparations were made to face the spread of COVID-19, as predicting the most probable risk areas by using spatial models. Prediction spatial models of COVID-19 risk areas can help the governmental authorities to generate sustainable strategies and set up suitable protocols to control the pandemic. This research presents an attempt of a potential spatial prediction modeling of COVID-19 risk areas in Cairo governorate-Egypt. Four indicator models (demographic, residential, environmental and topographic) were developed using geomatics technology based on the guidelines of the UN-habitat sustainable development goals (SDGs) target (11 & 3). Five predicted scenarios were generated for the most pandemic probability areas by the integration of the four indicator models. The results showed that there are common areas in all scenarios for highly COVID-19 pandemic risk areas. These common risk areas were found in (El Marag, El Salam, Ain Shams, El Mataria, El Gammaleya, Manshiat Nasser, El Mosky , Bolak , Hadaak El Koba, and El Sharbeya) districts. The hotspots zones are characterized by overcrowding, high population density and economic activities, large family size, poor infrastructure service and low rate of education. Moreover, it was noticed that crowding points resulted in traffic density and air pollution, which may affect the pandemic spread. The accuracy assessment results displayed that, the environmental predicted scenario was more consistent with the official data of the Egyptian Ministry of Health and Population) MOHP), while the residential one was less convenient. The result of this study supports the health sector by predicting the hot spots areas. The present study is aimed to develop a proactive plan to confront the pandemic before spreading in the Cairo governorate-Egypt. Also, the proposed prediction model can be an effective aid for decision-makers across the world working on containment strategies to minimize the spread of Coronavirus.