通过机器学习和可解释人工智能调查空间效应,解决巴基斯坦儿童营养不良问题

IF 2.8 3区 地球科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Xiaoyi Zhang, Muhammad Usman, Ateeq ur Rehman Irshad, Mudassar Rashid, Amira Khattak
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引用次数: 0

摘要

虽然区域健康不平等中的社会经济梯度已得到牢固确立,但社会经济贫困与气候脆弱性之间的协同互动在便利的邻近地区和邻里位置与健康差异之间的关系仍未得到充分探讨,因此需要在区域背景下进行深入了解。此外,在处理区域健康不平等这一持久问题时,不可避免地会忽视空间溢出效应的重要性以及协变量对儿童发育迟缓的非线性影响。本研究旨在调查巴基斯坦地区一级儿童发育迟缓的空间不平等现象,并验证空间滞后在预测儿童发育迟缓方面的重要性。此外,本研究还探讨了所选独立特征与儿童发育迟缓之间是否存在非线性关系。研究利用了 2017-2018 年多指标类集调查中与社会经济特征相关的数据和综合背景分析中的气候数据。研究采用了多模型方法来解决研究问题,其中包括普通最小二乘法回归(OLS)、各种空间模型、机器学习算法和可解释人工智能方法。首先,使用 OLS 分析和检验选定变量之间的线性关系。其次,使用空间杜宾误差模型(SDEM)来检测和捕捉空间溢出对儿童发育迟缓的影响。第三,采用 XGBoost 和随机森林机器学习算法来检查和验证空间滞后成分的重要性。最后,利用 EXAI 方法(如 SHapley)来识别潜在的非线性关系。研究发现,在儿童发育迟缓方面存在明显的空间聚类和地域差异,多维贫困、高气候脆弱性和早婚会加剧儿童发育迟缓。相比之下,气候脆弱性低、接触大众媒体多和妇女识字率高则会降低儿童发育迟缓的程度。机器学习算法,特别是 XGBoost 和随机森林的使用,突出了邻近地区的平均值在预测附近地区儿童发育迟缓方面的重要作用,证实了空间溢出效应不受地理边界的限制。此外,部分依存图等 EXAI 方法揭示了多维贫困与儿童发育迟缓之间存在非线性关系。研究结果为了解巴基斯坦儿童发育迟缓的空间分布提供了有价值的见解,强调了在预测儿童发育迟缓时考虑空间效应的重要性。接触大众媒体和妇女识字率等个人和家庭层面的因素对儿童发育迟缓有积极影响。这进一步说明了使用 EXAI 方法的合理性,以获得更好的见解并提出相应的定制干预政策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigating Spatial Effects through Machine Learning and Leveraging Explainable AI for Child Malnutrition in Pakistan
While socioeconomic gradients in regional health inequalities are firmly established, the synergistic interactions between socioeconomic deprivation and climate vulnerability within convenient proximity and neighbourhood locations with health disparities remain poorly explored and thus require deep understanding within a regional context. Furthermore, disregarding the importance of spatial spillover effects and nonlinear effects of covariates on childhood stunting are inevitable in dealing with an enduring issue of regional health inequalities. The present study aims to investigate the spatial inequalities in childhood stunting at the district level in Pakistan and validate the importance of spatial lag in predicting childhood stunting. Furthermore, it examines the presence of any nonlinear relationships among the selected independent features with childhood stunting. The study utilized data related to socioeconomic features from MICS 2017–2018 and climatic data from Integrated Contextual Analysis. A multi-model approach was employed to address the research questions, which included Ordinary Least Squares Regression (OLS), various Spatial Models, Machine Learning Algorithms and Explainable Artificial Intelligence methods. Firstly, OLS was used to analyse and test the linear relationships among selected variables. Secondly, Spatial Durbin Error Model (SDEM) was used to detect and capture the impact of spatial spillover on childhood stunting. Third, XGBoost and Random Forest machine learning algorithms were employed to examine and validate the importance of the spatial lag component. Finally, EXAI methods such as SHapley were utilized to identify potential nonlinear relationships. The study found a clear pattern of spatial clustering and geographical disparities in childhood stunting, with multidimensional poverty, high climate vulnerability and early marriage worsening childhood stunting. In contrast, low climate vulnerability, high exposure to mass media and high women’s literacy were found to reduce childhood stunting. The use of machine learning algorithms, specifically XGBoost and Random Forest, highlighted the significant role played by the average value in the neighbourhood in predicting childhood stunting in nearby districts, confirming that the spatial spillover effect is not bounded by geographical boundaries. Furthermore, EXAI methods such as partial dependency plot reveal the existence of a nonlinear relationship between multidimensional poverty and childhood stunting. The study’s findings provide valuable insights into the spatial distribution of childhood stunting in Pakistan, emphasizing the importance of considering spatial effects in predicting childhood stunting. Individual and household-level factors such as exposure to mass media and women’s literacy have shown positive implications for childhood stunting. It further provides a justification for the usage of EXAI methods to draw better insights and propose customised intervention policies accordingly.
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来源期刊
ISPRS International Journal of Geo-Information
ISPRS International Journal of Geo-Information GEOGRAPHY, PHYSICALREMOTE SENSING&nb-REMOTE SENSING
CiteScore
6.90
自引率
11.80%
发文量
520
审稿时长
19.87 days
期刊介绍: ISPRS International Journal of Geo-Information (ISSN 2220-9964) provides an advanced forum for the science and technology of geographic information. ISPRS International Journal of Geo-Information publishes regular research papers, reviews and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. The 2018 IJGI Outstanding Reviewer Award has been launched! This award acknowledge those who have generously dedicated their time to review manuscripts submitted to IJGI. See full details at http://www.mdpi.com/journal/ijgi/awards.
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