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This article outlines the patterns of internal migration from Murshidabad district of West Bengal during the Covid-19 Pandemic. We found that seasonal/temporary male migration from the study area has been evolving continuously over time. More than half of the total migrants move within the state boundaries. The inter-state migrants are often destined to economically better off states than the study area. We have shown that landlessness, lack of jobs, low wages, decaying of traditional craftsmanship and household pressure are among the factors leading young males to migrate. These moves are motivated by jobs and income, and movers often shift from agricultural work to building and construction and petty businesses. Migrants and their families are almost exclusively dependent on domestic remittances to meet the household expenditures, repay loans and meet children’s educational expenses. Social networks, especially friends and relatives, are crucial in facilitating the migration process. Covid-19 Pandemic raised essential questions about the future of these vulnerable sections of the population as migration became difficult and remittances dropped significantly.

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The world has seen road accident and its related societal and economical impact as one of the live, persisting problem in the last 2-3 decades and its prominence has been observed in the developing countries of the Asian subcontinent. With no exception, all major cities in India are facing the various challenges related to road accidents, mostly due to the large population density. Among the major cities in India, Bhubaneswar is a very fast growing city with aim to be the most livable city in Asia in the coming few years. With the city of Bhubaneswar as our study area, we address the issues related to road safety by determining the degree of severity of road accidents. We study the accident related data collected for the last decade using the spatial tools of Geographical Information System (GIS). Then using a GIS-map based analysis and a fuzzybased model, we have found the spatio-temporal distribution of accident vulnerable locations with their degree of severity. Our experimental results show the accident hot-spots with values of selected contributing parameters such as timing, traffic density, vehicle speed, road intersections.