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.

翻译：贫困是一个多层面现象，与家庭获得可持续生计的能力缺失相关，现越来越多地采用多维指标进行评估。其空间格局取决于社会、经济、政治和区域变量。人工智能在分析贫困的复杂性和细微差别方面展现出巨大潜力。本项目旨在基于生活质量和生计指标，研究1990-2022年间印度农村的贫困状况。将各地区划分为“先进”、“追赶”、“落后”和“滞后”四类。项目计划整合多种数据源，包括传统的全国性大样本家庭调查、人口普查数据，以及卫星影像的白天/夜间灯光数据、通信网络等代理变量，以提供地区层面的全面贫困视图。项目还意图通过因果分析和纵向分析探究贫困成因。由于人口增长与集聚型政策，发展中国家的贫困与不平等可能正在加剧。因此，瞄准滞后地区和脆弱人口对于消除贫困、改善生活质量、实现“零贫困”目标至关重要。本研究还重点关注人口中边缘群体占比高于全国平均水平的地区，以追踪其发展指标表现及其与贫困的关联性。