The northeastern region of India presents intra-regional disparity, which is reflected in every aspect of development. The transport sector, especially railway transportation, is one of the important aspects, and the development of railway infrastructure seems to be very different in every region. The research question addressed in this study was “Which factors, geo-physical or socio-economic, influenced the variation in the level of railway development in Northeast India?” The aim of the study was to delineate regional differentiation on railway development in Northeast India and to analyse the reasons for different development patterns of railway lines among the northeastern states. The research was based on secondary data collected from multiple sources, and the existing synthetic indicator was applied for the classification of eight states based on their railway infrastructural status. An alternative approach called the alternative synthetic indicator has been proposed and found to be more efficient than the existing synthetic indicator. The degree of inequality among the northeastern states by considering railway infrastructural variables was measured by plotting a Lorenz curve; the corresponding Gini coefficient specifies the unequal distribution of railway infrastructure among all the northeastern states. The causality of such unequal development has been analysed through a correlation test by defining the composite dimension index. The analysis revealed that all the externalities of regional inequality significantly influence the development of railway lines in northeastern states. Environmental determinism plays a crucial role in railway development in Northeast India, but political willingness is also crucial for creating an actual state of differentiation and will play a special role in the future.

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Purulia is a malaria-prone district in West Bengal, India, with approximately half of the blocks defined as malaria endemic. We analyzed the malaria case in each block of the Purulia district from January 1, 2016, to December 31, 2020. As per the API, 20 blocks of Purulia were assigned to four different categories (0–3) and mapped using ArcGIS software. An exponential decay model was fitted to forecast the trend of malaria cases for each block of Purulia (2021–2025). There was a sharp decrease in total malaria cases and API from 2016 to 2020 due to the mass distribution of LLINs. The majority of cases (72.63%) were found in ≥ 15-year age group. Males were more prone to malaria (60.09%). Malaria was highly prevalent among Scheduled Tribes (48.44%). Six blocks were reported in Category 3 (high risk) and none in Category 0 (no risk) in 2016, while no blocks were determined to be in Category 3, and three blocks were in Category 0 in 2020. The exponential decay model prediction is oriented towards gaining malaria-free status in thirteen blocks of Purulia by 2025. This study will incite the government to uphold and strengthen the current efforts to meet the malaria elimination goals.

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