  Poverty is a multifaceted phenomenon linked to the lack of capabilities of
households to earn a sustainable livelihood, increasingly being assessed using
multidimensional indicators. Its spatial pattern depends on social, economic,
political, and regional variables. Artificial intelligence has shown immense
scope in analyzing the complexities and nuances of poverty. The proposed
project aims to examine the poverty situation of rural India for the period of
1990-2022 based on the quality of life and livelihood indicators. The districts
will be classified into `advanced', `catching up', `falling behind', and
`lagged' regions. The project proposes to integrate multiple data sources,
including conventional national-level large sample household surveys, census
surveys, and proxy variables like daytime, and nighttime data from satellite
images, and communication networks, to name a few, to provide a comprehensive
view of poverty at the district level. The project also intends to examine
causation and longitudinal analysis to examine the reasons for poverty. Poverty
and inequality could be widening in developing countries due to demographic and
growth-agglomerating policies. Therefore, targeting the lagging regions and the
vulnerable population is essential to eradicate poverty and improve the quality
of life to achieve the goal of `zero poverty'. Thus, the study also focuses on
the districts with a higher share of the marginal section of the population
compared to the national average to trace the performance of development
indicators and their association with poverty in these regions.
