All UN Member States acceded to the 2030 Program for Sustainable Development in 2015, and it prepares an extensive framework for peace and well-being for people and the planet both now and in the coming time. The 17 Sustainable Development Goals (SDGs), a high-priority call to action for all developed and developing nations, are central to this initiative. An array of indicators is used to surveil progress in the direction of each SDG as a part of the National Indicator Framework for the SDGs in India. These indicators align with international hands created by the United Nations and include various social, economic, and environmental characteristics. National surveys, administrative records, censuses, and other data sources are used to assemble information for the SDGs in India. The National Sample Survey (NSS), the Annual Survey of Industries (ASI), the Census of India, the National Data and Analytics Platform (NDAP), NITI Aayog, and several sector-specific surveys performed by various ministries and departments are some of the primary data sources. International organizations, including the World Bank, the United Nations Development Programme (UNDP), and other research institutions, also produce data and scrutinize the SDGs in India in addition to the government. As per their own data gathering and analysis, these organizations offer penetrative assessments of India's progress in reaching the SDGs. A spatial analysis and regression model has been developed on the collected dataset to assess spatiotemporal progress toward achieving the SDGs in the Indian context. This study exhibits the use of geographically referenced information analysis in mapping the SDGs as presented in the SDG India Index: Composite score, reported by the National Data and Analytics Platform. In Spatial Analysis, we have checked the spatial autocorrelation of the SDG India Index Composite score and its supporting indices across 36 states. In Spatial Regression, an OLS model has been developed by taking the SDG India Index: Reduced Inequality as the dependent variable and other SDG Goals as independent variables. However, only 8 SDG Goals have been finalized as independent variables to make the model significant. Spatial dependence in the developed linear model has been checked based on Lagrange multiplier diagnostics. The statistics in Lagrange multiplier diagnostics are the simple LM test for error dependence, the simple LM test for the sake of a missing spatially lagged dependent variable, variants robust to the presence of the other, and a portmanteau test. Lagrange Multiplier Test is the procedure suggested in (Anselin, 2005) to choose the appropriate model form. After estimating the proper model, the hypotheses are tested. After the analysis, we have opted for the OLS model, as suggested by Lagrange multiplier diagnostics. A Spatial Durbin linear model has been constructed on the developed OLS model to probe the impact of neighboring regions' independent variable values on the dependent variable, SDG India Index: Reduced Inequality.
