Machine Learning for Sustainable Development: Ranking Villages for Rural Development Initiatives

Rural development initiatives like India’s Shyama Prasad Mukherji Rurban Mission (SPMRM) require efficient methods to identify villages with high socio-economic growth potential. Traditional planning methods, reliant on surveys and expert opinions, are becoming outdated due to the abundance of infor...

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Published in:Applied spatial analysis and policy 2025-03, Vol.18 (1), Article 6
Main Authors: Sha, Akhbar, Madhan, S, Karthikeya, Moturi, Megha, R, Dhanush, Krishna R, Swain, Dhruvjyoti, Gopakumar, G., Geetha, M
Format: Article
Language:English
Subjects:
Automation
Correlation coefficient
Data
Descent
Economic growth
Human Geography
Landscape/Regional and Urban Planning
Machine learning
Panchayats
Peri-urban areas
Ratings & rankings
Regional/Spatial Science
Resource allocation
Rural areas
Rural development
Social Sciences
Sustainable development
Towns
Villages
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container_title Applied spatial analysis and policy
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creator Sha, Akhbar
Madhan, S
Karthikeya, Moturi
Megha, R
Dhanush, Krishna R
Swain, Dhruvjyoti
Gopakumar, G.
Geetha, M
description Rural development initiatives like India’s Shyama Prasad Mukherji Rurban Mission (SPMRM) require efficient methods to identify villages with high socio-economic growth potential. Traditional planning methods, reliant on surveys and expert opinions, are becoming outdated due to the abundance of informative data available online. This paper proposes a novel framework, eRurban, that utilizes machine learning to automate village ranking and analysis for rural development in India. eRurban leverages data from 250,000 gram panchayats (village clusters) to group villages with similar development trajectories through clustering techniques. A key innovation is the introduction of the ClusterRank algorithm, a novel ranking method that utilizes gradient descent to train ranking coefficients for improved accuracy and efficiency. The effectiveness of ClusterRank is demonstrated by its high Spearman correlation coefficient (0.89) when compared to village rankings generated by SPMRM reports. This cost-effective framework offers valuable insights and guidance for rural development planning in India. By automating village ranking and analysis, eRurban addresses limitations of traditional methods and offers a data-driven solution for optimizing resource allocation and promoting sustainable growth in rural areas.
doi_str_mv 10.1007/s12061-024-09606-6
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subjects Automation
Correlation coefficient
Data
Descent
Economic growth
Human Geography
Landscape/Regional and Urban Planning
Machine learning
Panchayats
Peri-urban areas
Ratings & rankings
Regional/Spatial Science
Resource allocation
Rural areas
Rural development
Social Sciences
Sustainable development
Towns
Villages
title Machine Learning for Sustainable Development: Ranking Villages for Rural Development Initiatives
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