Abstract:
According to the World Health Organization (WHO), a “pandemic” is the worldwide spread of a new disease. Even if the scale of the pandemic is small it can take millions of lives. Today the world is in unprecedented times with the outbreak of novel coronavirus which have not just impacted the global public health but also has indirect impacts on social functioning and the urban economy of the globe. The goal of this thesis is to understand the spatial variability of COVID-19 incidence rates in Lucknow, Uttar Pradesh, India.
Based on the literature reviewed many of the researchers have identified different factors that can cause such spatial variation in the incidence of COVID-19. For this study, the variability is assessed by examining socio-demographic and built environment factors that may relate to infection rates.
The study area for this research was chosen based on a COVID-19 vulnerability assessment, state district COVID-19 incidence rates, and a city level study (a comparative study of four COVID-19 highest and lowest incidence rate wards, taking land use into account) to identify the case study area in the selected case study city.
The dataset was understood under five heads i.e Urban density, intra connectivity, activity, socio-demographic characteristic, and infrastructure, which were used to explain the spatial variability of COVID-19 using Multiple Linear Regression. Pearson Correlation Coefficient and Principal Component Analysis were used to help reduce the multicollinearity in the dataset.
The result concluded that COVID-19 Incidence rate is negatively related with the density of an area, positively related with intra connectivity, and positively related with the type of activities, across the wards of Lucknow. According to (Hamidi, Sabouri and Ewing, 2020) they have suggested that in the “transmission of the COVID-19 pandemic, connectivity is more important than density” which is also concluded through this research.
