PDF(Pubmed)
Abstract：
Big data and data analysis methods and models are important tools in food security (FS) studies for gap analysis and preparation of appropriate analytical frameworks. These innovations necessitate the development of novel methods for collecting, storing, processing, and extracting data.
The primary goal of this study was to conduct a critical review of agricultural big data and methods and models used for FS studies published in peer-reviewed journals since 2010. Approximately 130 articles were selected for full content review after the pre-screening process.
There are different sources of data collection, including but not limited to online databases, the internet, omics, Internet of Things, social media, survey rounds, remote sensing, and the Food and Agriculture Organization Corporate Statistical Database. The collected data require analysis (i.e., mining, neural networks, Bayesian networks, and other ML algorithms) before data visualization using Python, R, Circos, Gephi, Tableau, or Cytoscape. Approximately 122 models, all of which were used in FS studies worldwide, were selected from 130 articles. However, most of these models addressed only one or two dimensions of FS (i.e., availability and access) and ignored the other dimensions (i.e., stability and utilization), creating a gap in the global context.
There are certain FS gaps both worldwide and in the United Arab Emirates that need to be addressed by scientists and policymakers. Following the identification of the drivers, policies, and indicators, the findings of this review could be used to develop an appropriate analytical framework for FS and nutrition.
The primary goal of this study was to conduct a critical review of agricultural big data and methods and models used for FS studies published in peer-reviewed journals since 2010. Approximately 130 articles were selected for full content review after the pre-screening process.
There are different sources of data collection, including but not limited to online databases, the internet, omics, Internet of Things, social media, survey rounds, remote sensing, and the Food and Agriculture Organization Corporate Statistical Database. The collected data require analysis (i.e., mining, neural networks, Bayesian networks, and other ML algorithms) before data visualization using Python, R, Circos, Gephi, Tableau, or Cytoscape. Approximately 122 models, all of which were used in FS studies worldwide, were selected from 130 articles. However, most of these models addressed only one or two dimensions of FS (i.e., availability and access) and ignored the other dimensions (i.e., stability and utilization), creating a gap in the global context.
There are certain FS gaps both worldwide and in the United Arab Emirates that need to be addressed by scientists and policymakers. Following the identification of the drivers, policies, and indicators, the findings of this review could be used to develop an appropriate analytical framework for FS and nutrition.
摘要：
大数据和数据分析方法和模型是粮食安全（FS）研究中的重要工具，用于差距分析和准备适当的分析框架。这些创新需要开发新的收集方法，存储,processing,并提取数据。
这项研究的主要目标是对自2010年以来在同行评审期刊上发表的用于FS研究的农业大数据以及方法和模型进行批判性审查。在预筛选过程后，大约130篇文章被选择用于完整的内容审查。
有不同的数据收集来源，包括但不限于在线数据库，互联网,组学，物联网,社交媒体,调查回合，遥感,和粮食及农业组织公司统计数据库。收集的数据需要分析(即，采矿,神经网络，贝叶斯网络，和其他ML算法)在使用Python进行数据可视化之前，R,Circos,Gephi,Tableau,或者Cytoscape.大约122个模型，所有这些都被用于全球的FS研究，从130篇文章中选出。然而,这些模型中的大多数只针对FS的一个或两个维度(即，可用性和访问)，并忽略其他维度(即，稳定性和利用率)，在全球范围内造成差距。
科学家和政策制定者需要解决全球和阿拉伯联合酋长国的某些FS差距。在确认司机身份后，政策,和指标，本综述的结果可用于为FS和营养建立适当的分析框架.
这项研究的主要目标是对自2010年以来在同行评审期刊上发表的用于FS研究的农业大数据以及方法和模型进行批判性审查。在预筛选过程后，大约130篇文章被选择用于完整的内容审查。
有不同的数据收集来源，包括但不限于在线数据库，互联网,组学，物联网,社交媒体,调查回合，遥感,和粮食及农业组织公司统计数据库。收集的数据需要分析(即，采矿,神经网络，贝叶斯网络，和其他ML算法)在使用Python进行数据可视化之前，R,Circos,Gephi,Tableau,或者Cytoscape.大约122个模型，所有这些都被用于全球的FS研究，从130篇文章中选出。然而,这些模型中的大多数只针对FS的一个或两个维度(即，可用性和访问)，并忽略其他维度(即，稳定性和利用率)，在全球范围内造成差距。
科学家和政策制定者需要解决全球和阿拉伯联合酋长国的某些FS差距。在确认司机身份后，政策,和指标，本综述的结果可用于为FS和营养建立适当的分析框架.
