A Systematic Review of COVID-19 Geographical Research: Machine Learning and Bibliometric Approach

Abstract

The rampant COVID-19 pandemic swept the globe rapidly in 2020, causing a tremendous impact on human health and the global economy. This pandemic has stimulated an explosive increase of related studies in various disciplines, including geography, which has contributed to pandemic mitigation with a unique spatiotemporal perspective. Reviewing relevant research has implications for understanding the contribution of geography to COVID-19 research. The sheer volume of publications, however, makes the review work more challenging. Here we use the support vector machine and term frequency-inverse document frequency algorithm to identify geographical studies and bibliometrics to discover primary research themes, accelerating the systematic review of COVID-19 geographical research. We confirmed 1,171 geographical papers about COVID-19 published from 1 January 2020 to 31 December 2021, of which a large proportion are in the areas of geographic information systems (GIS) and human geography. We identified four main research themes—the spread of the pandemic, social management, public behavior, and impacts of the pandemic—embodying the contribution of geography. Our findings show the feasibility of machine learning methods in reviewing large-scale literature and highlight the value of geography in the fight against COVID-19. This review could provide references for decision makers to formulate policies combined with spatial thinking and for scholars to find future research directions in which they can strengthen collaboration with geographers.

2020年, 新冠肺炎流行病迅速席卷全球, 对人类健康和全球经济造成了巨大影响。这次流行病激发了各个学科研究的爆炸性增长。其中, 地理学研究以独特的时空角度, 为流行病治理做出了贡献。对有关研究进行综述, 有助于理解地理学对新冠肺炎研究的贡献。然而, 海量的文献使得这个综述更具挑战性。为了加快对新冠肺炎地理研究的系统性综述, 我们利用支持向量机和词频-反文档频率算法寻找文献中的地理学研究, 利用文献计量学发掘主要研究题目。本文确认了2020年1月1日至2021年12月31日发表的1,171篇新冠肺炎地理学论文, 其中多数文章属于地理信息系统和人文地理学领域。确定了体现地理学贡献的四个主要研究题目：流行病传播、社会管理、公众行为和流行病影响。研究结果表明了利用机器学习方法去开展海量文献综述的可行性, 强调了地理学在抗击新冠肺炎的价值。该文献综述有助于决策者制定具备空间思维的政策, 也有助于学者们寻求加强与地理学者合作的未来研究方向。

La desenfrenada pandemia del COVID-19 se extendió con gran rapidez por todo el globo en 2020, ocasionando tremendo impacto en la salud humana y en la economía mundial. Esta pandemia ha estimulado un incremento explosivo de estudios relacionados en diferentes disciplinas, la geografía incluida, disciplina que ha contribuido a la mitigación de la pandemia, con una perspectiva espaciotemporal única. La revisión de la investigación relevante tiene implicaciones para entender la contribución de la geografía en la investigación del COVID-19. Sin embargo, el gran volumen de publicaciones hace más desafiante el trabajo de revisión. Dentro de ese contexto, usamos la máquina de vectores de apoyo y el algoritmo de frecuencia del término por frecuencia inversa de documento para identificar los estudios geográficos y la bibliometría, con el fin de descubrir los temas primarios de investigación, acelerando así la revisión sistemática de la investigación geográfica del COVID-19. Confirmamos la existencia de1.171 escritos geográficos sobre COVID-19, publicados entre el 1 de enero de 2020 y el 31 de diciembre de 2021, de los cuales una alta proporción se encuentra en las áreas de los sistemas de información geográfica (SIG) y de la geografía humana. Identificamos cuatro temas principales de investigación –la propagación de la pandemia, el manejo social de la crisis, el comportamiento público y los impactos de la pandemia– en los que se involucró la geografía. Nuestros hallazgos muestran la idoneidad de los métodos de aprendizaje automático para la revisión de literatura a gran escala y destacan el valor de la geografía en la lucha contra el COVID-19. Esta revisión podría aportar referencias para quienes son responsables de tomar decisiones en la formulación de políticas combinadas con el pensamiento espacial; y para los estudiosos puede ofrecer indicios para identificar direcciones futuras hacia la investigación en las que ellos puedan fortalecer la colaboración con los geógrafos.

Key Words:

• COVID-19
• geography
• machine learning
• review

关键词:

• 新冠肺炎
• 地理学
• 机器学习
• 综述

Palabras clave:

• aprendizaje automático
• COVID-19
• geografía
• revisión

Acknowledgments

We are grateful to Ling Bian and two anonymous reviewers for their insightful comments.

Supplemental Material

Supplemental data for this article can be accessed on the publisher’s site at: https://doi.org/10.1080/24694452.2022.2130143

Notes

1 The Web of Science Web site is available at https://clarivate.com/webofsciencegroup/solutions/web-of-science/ (accessed July 9, 2021).

2 Note that in WOS, a paper can be assigned to multiple categories. Here we only retain papers that have been assigned to only one WC and WC= (“Multidisciplinary Sciences”).

3 See http://www.gsc.org.cn/content.aspx?id=1108.

4 There are thirty-five papers from the journal HELIYON, for which WOS did not assign the attribute category quartile.

Additional information

Funding

Jianghao Wang acknowledges research support from the National Natural Science Foundation of China (No. 42222110 and 41971409), and the Youth Innovation Promotion Association of the Chinese Academy of Sciences (No. 2020052). The funders had no role in study design, data collection and analysis, decision to publish, or manuscript preparation.

Notes on contributors

Jinglun Xi

JINGLUN XI is a PhD Student in the State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Chaoyang 100101, Beijing, China. E-mail: [email protected]. His research interests include quantitative geoscience and knowledge graphing.

Xiaolu Liu

XIAOLU LIU is a PhD Student in the State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Chaoyang 100101, Beijing, China. E-mail: [email protected]. Her research interests include geoscience knowledge graphing.

Jianghao Wang

JIANGHAO WANG is an Associate Professor in the State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Chaoyang 100101, Beijing, China. E-mail: [email protected]. His research interests include geospatial analysis and modeling.

Ling Yao

LING YAO is an Associate Professor in the State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Chaoyang 100101, Beijing, China. E-mail: [email protected]. His research interests include remote sensing and geographical analysis.

Chenghu Zhou

CHENGHU ZHOU is a Professor in the State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Chaoyang 100101, Beijing, China. E-mail: [email protected]. His research interests include GIScience and geographical analysis.