Enhancing Village Ranking: Leveraging Cluster Analysis and Machine Learning

Abstract

The research presented in this paper addresses the critical challenge of ranking Indian villages based on their potential for socio-economic growth, inspired by the objectives of the Shyam Prasad Mukerji Rurban Mission (SPMRM). Leveraging a novel algorithm known as ClusterRank, an extension of the PageRank algorithm, we endeavor to provide a timely, efficient, and accurate solution for village ranking. The ClusterRank algorithm, an extension of the well-known PageRank algorithm, serves as the cornerstone of our approach. It excels in its ability to efficiently and accurately rank villages, outperforming alternative ranking methods. Notably, our findings reveal the superior convergence speed of the ClusterRank algorithm, affirming its capacity to produce rapid results. To assess the efficacy of ClusterRank, we employ a range of statistical ranking coefficients, including the Spearman, Kendall, and Point Biserial correlation coefficients. Our analysis demonstrates a strong positive correlation, with a Spearman correlation coefficient of 0.89, between the rankings generated by ClusterRank and the ground truth rankings provided by SPMRM. This correlation underscores the effectiveness of ClusterRank in identifying villages with high growth potential and highlights its alignment with the objectives of the SPMRM. This research presents a compelling case for the adoption of the ClusterRank algorithm by SPMRM as a valuable tool in the identification of rural areas poised for rurban development. Such adoption not only promises substantial time and resource savings but also directs the focus of SPMRM towards the implementation of targeted interventions and initiatives to uplift rural communities. Moreover, our supplementary analyses using Kendall and Point Biserial correlation coefficients further validate the robustness of the ClusterRank algorithm.