[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123895-en":3,"doc-seo-123895-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123895,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Identification of Socioeconomic Factors Influencing Global Food Price Security Using Machine Learning","Global concern about food prices and security intensifies under armed conflicts, pandemics, and climate change, which complicates relationships between prices and socioeconomic drivers. This study moves beyond static linear regression by using machine learning to capture multidimensional, often non-linear associations. Data from 2000–2022 are modeled with key indicators sourced from World Development Indicators and FAO datasets, including feature extraction to focus on the most influential determinants. Support vector regression is applied to identify critical dimensions of food price security and enable correlation-based interpretation for data-driven policy.","Identification of socioeconomic factors influencing global food price security using machine learning  \nShan Shana  \na Sociology Department, Zhejiang University, Hangzhou 310058, China  \nCorrespondence: [shshan@zju.edu.cn](shshan@zju.edu.cn)  \n1Abbreviations  \nAbstract: Global concern over food prices and security has been exacerbated by the impacts of armed conflicts such as the Russia–Ukraine War, pandemic diseases, and climate change. Traditionally, analyzing global food prices and their associations with socioeconomic factors has relied on static linear regression models. However, the complexity of socioeconomic factors and their implications extend beyond simple linear relationships. By incorporating determinants, critical characteristics identification, and comparative model analysis, this study aimed to identify the critical socioeconomic characteristics and multidimensional relationships associated with the underlying factors of food prices and security.  \nMachine learning tools were used to uncover the socioeconomic factors influencing global food prices from 2000 to 2022. A total of 105 key variables from the World Development Indicators and the Food and Agriculture Organization of the United Nations were selected.  \n1 FAO Food and Agriculture Organization  \nGNI Gross national income  \nODA Official development assistance  \nSVR Support vector regression  \nWDI World Development Indicators  \nMachine learning identified four key dimensions of food price security: economic and population metrics, military spending, health spending, and environmental factors. The top 30 determinants were selected for feature extraction using data mining. The efficiency of the support vector regression model allowed for precise prediction-making and correlation analysis.  \nKeywords: environment and growth, global economics, price fluctuation, support vector regression  \n1. Introduction  \nAmidst the rapidly changing global landscape, rising food prices and security issues have emerged as prominent challenges. These concerns arise from a complex interplay offactors such as armed conflicts, large-scale health crises, and relentless climate change and require in-depth analysis and inventive solutions. The urgency has been made even clearer by recent global events, such as the Russia–Ukraine conflict and COVID-19 pandemic. The Food and Agriculture Organization (FAO) of the United Nations Food Price Index (FFPI, [https://www.fao.org/prices/en/](https://www.fao.org/prices/en/)) provides insights into the changing prices of food on the global market. It is a useful tool for monitoring and analyzing trends and fluctuations in food prices that can have significant impacts on food security, trade, and agricultural policy.  \nTraditional research methods often use linear regression models to evaluate these issues. While these models have contributed significantly to our understanding of food prices and security, they tend to simplify the multiple interactions between phenomena and their many socioeconomic determinants. The impact of recent events has increased the vulnerability of global food systems. Disrupted supply chains, pressure on agricultural productivity, and rising inflation have led to dramatic fluctuations in food prices, threatening global food security. These challenges highlight the urgent need for advanced data-driven  \nmethods to examine the complex determinants of food prices and security. The complexity of different socioeconomic systems often contradicts simple linear assumptions and requires more advanced analytical approaches (Bar-Yam, 2004). Machine learning methods are particularly useful for elucidating the complex interplay of systems, especially for social phenomena (Grimmer et al., 2021) . When coupled with feature extraction techniques such as principal component analysis, machine learning can improve the efficiency of data analysis by identifying the most influential variables (Awan et al., 2019) . This not only reduces the","cbCaiq934epuhhnY","https://ap.wps.com/l/cbCaiq934epuhhnY","pdf",7719191,1,37,"English","en",105,"# Abstract\n# Introduction\n## Motivation and background\n## Limits of linear regression\n# Methods\n## Data\n## Modeling approach\n## Feature selection and determinants","[{\"question\":\"What problem does the study address?\",\"answer\":\"It addresses how socioeconomic determinants influence global food price security amid conflicts, health crises, and climate change.\"},{\"question\":\"Which machine learning method is used to model food prices and security?\",\"answer\":\"The study uses support vector regression to uncover key relationships and support precise prediction and correlation analysis.\"},{\"question\":\"What data and indicators are used for the analysis?\",\"answer\":\"It uses the FAO Food Price Index-derived food price security indicator as the target variable and selects variables from World Development Indicators and FAO-related sources.\"}]","Identification of Socioeconomic Factors Influencing Global Food Price Security Using Machine Learning | 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