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India was the second highest COVID-19 affected country in the world with 2.1 million cases by 11th August. This study focused on the spatial transmission of the pandemic among the 640 districts in India over time, and aimed to understand the urban-centric nature of the infection. The connectivity context was emphasized that possibly had inflicted the outbreak. Using the modes of transmission data for the available cases, the diffusion of this disease was explained. Metropolitans contributed three-fourths of total cases from the beginning. The transport networks attributed significantly in transmitting the virus from the urban containment zones. Later, there was a gradual shift of infections from urban to rural areas; however, the numbers kept increasing in the former. The massive reverse migration after lockdown spiked the infected cases further. Districts with airports reported more with influx of international passengers. A profound east–west division in April with higher infections in the southern and western districts existed. By mid-May eastern India saw a steep rise in active cases. Moran's I analysis showed a low autocorrelation initially which increased over time. Hotspot clustering was observed in western Maharashtra, eastern Tamil Nadu, Gujarat and around Kolkata by the second week of August. The diffusion was due to travel, exposure to infected individuals and among the frontline workers. Spatial regression models confirmed that urbanization was positively correlated with higher incidences of infections. Transit mediums, especially rail and aviation were positively associated. These models validated the crucial role of spatial proximity in diffusion of the pandemic. [ABSTRACT FROM AUTHOR]

Settlements, cities and regions function as integrated systems. This resulted in the creation of functional connections and when linked, networks within which trade occurred. This played an important role in the creation of scope economies. This is based on the notion that various networks formed a unique exchange environment from which economic development benefitted. Furthermore, networks evolve and grow when independent cities or regions strive to co-operate and in the process created higher and lower-order settlements which support the outcomes of economic space development and cities as sites of renewed economic dynamism. Networks consist of two important elements: (1) nodes, denoting location and size and (2) links, denoting distance and capacity that display the forces of interaction. These forces of interaction are the underlying principles of the complex relationships that exist between different urban centres (gravitational properties) due to the agglomeration of economic activities. Applying a regional network model, considering gravitational properties provides a solution, whereby a city's functionality within a network is established. The model provides a framework denoting a city's rise in performance as either mega, primary, secondary or intermediate which ultimately measures a city's role and 'place' within economic space development. The framework allows cities to realise their 'place' of potential allowing them to respond proactively and innovatively to develop and promote economic development. [ABSTRACT FROM AUTHOR]