机器学习促进可持续发展:为农村发展倡议的村庄排名

IF 2 4区 社会学 Q3 ENVIRONMENTAL STUDIES
Akhbar Sha, S Madhan, Moturi Karthikeya, R Megha, Krishna R Dhanush, Dhruvjyoti Swain, G. Gopakumar, M Geetha
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引用次数: 0

摘要

像印度 Shyama Prasad Mukherji Rurban Mission(SPMRM)这样的农村发展计划需要高效的方法来确定具有高社会经济增长潜力的村庄。传统的规划方法依赖于调查和专家意见,但由于网上有大量翔实的数据,这种方法已经过时。本文提出了一个新颖的框架--eRurban,它利用机器学习来自动进行村庄排名和分析,以促进印度的农村发展。eRurban 利用来自 25 万个克村民委员会(村庄集群)的数据,通过聚类技术将具有相似发展轨迹的村庄分组。一项关键的创新是引入了 ClusterRank 算法,这是一种新颖的排序方法,利用梯度下降来训练排序系数,从而提高了准确性和效率。与 SPMRM 报告生成的村庄排名相比,ClusterRank 的斯皮尔曼相关系数高达 0.89,这证明了 ClusterRank 的有效性。这一具有成本效益的框架为印度的农村发展规划提供了宝贵的见解和指导。通过自动进行村庄排名和分析,eRurban 解决了传统方法的局限性,为优化资源分配和促进农村地区的可持续增长提供了数据驱动的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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 Traditional planning methods, reliant on surveys and expert opinions, are becoming outdated due to the abundance of informative data available online.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.

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来源期刊
CiteScore
3.80
自引率
5.30%
发文量
57
期刊介绍: Description The journal has an applied focus: it actively promotes the importance of geographical research in real world settings It is policy-relevant: it seeks both a readership and contributions from practitioners as well as academics The substantive foundation is spatial analysis: the use of quantitative techniques to identify patterns and processes within geographic environments The combination of these points, which are fully reflected in the naming of the journal, establishes a unique position in the marketplace. RationaleA geographical perspective has always been crucial to the understanding of the social and physical organisation of the world around us. The techniques of spatial analysis provide a powerful means for the assembly and interpretation of evidence, and thus to address critical questions about issues such as crime and deprivation, immigration and demographic restructuring, retailing activity and employment change, resource management and environmental improvement. Many of these issues are equally important to academic research as they are to policy makers and Applied Spatial Analysis and Policy aims to close the gap between these two perspectives by providing a forum for discussion of applied research in a range of different contexts  Topical and interdisciplinaryIncreasingly government organisations, administrative agencies and private businesses are requiring research to support their ‘evidence-based’ strategies or policies. Geographical location is critical in much of this work which extends across a wide range of disciplines including demography, actuarial sciences, statistics, public sector planning, business planning, economics, epidemiology, sociology, social policy, health research, environmental management.   FocusApplied Spatial Analysis and Policy will draw on applied research from diverse problem domains, such as transport, policing, education, health, environment and leisure, in different international contexts. The journal will therefore provide insights into the variations in phenomena that exist across space, it will provide evidence for comparative policy analysis between domains and between locations, and stimulate ideas about the translation of spatial analysis methods and techniques across varied policy contexts. It is essential to know how to measure, monitor and understand spatial distributions, many of which have implications for those with responsibility to plan and enhance the society and the environment in which we all exist.   Readership and Editorial BoardAs a journal focused on applications of methods of spatial analysis, Applied Spatial Analysis and Policy will be of interest to scholars and students in a wide range of academic fields, to practitioners in government and administrative agencies and to consultants in private sector organisations. The Editorial Board reflects the international and multidisciplinary nature of the journal.
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