The First Geographic Big Data Competition of China (2024)

Jiang, Z. C.  Liao, X. H.  Liu, C.^*

Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Abstract: Approved by the organizing committee of the ??Data Factor ???? competition of the Nati­o­n­al Data Administration, the Geographical Society of China is hosting the ??Geographic Big Data Co­m-

petition (2024)?? as a sub-competition event of the ??Data Factor ???? competition of China. The compe­ti­tion focuses on the field of technological innovation (Track 6), with the theme of ??Geographic Data-Driven Technology, Educational Innovation, and Social Sustainable Development??. It is divi­ded into five questions: the collection, publication, and sharing of geographic data, the dev­elop­ment of geographic data supported technology models, geographic data assisting scientific res­ea­rch and technological innovation, geographic data accelerating the exploration of new paradigms in scientific research, and geographic data education and science popularization. The competition was officially launched in July 2024 and a series of procedures were successfully completed from July to September, including application, qualification review, preliminary round, final defense, evaluation, public announcement, and award presentation. Of 33 teams from 39 organizations and 136 participants, 12 won awards. This competition has played a positive role in promoting technological innovation and sustainable development of society.

Keywords: Data Factor ??; competition; Geographical Society of China; geographic big data; technology innovation; 2024

DOI: https://doi.org/10.3974/geodp.2024.04.10

CSTR: https://cstr.escience.org.cn/CSTR:20146.23.2024.04.10

1 Introduction

In the current wave of technological revolution and industrial transformation, the importance of data is increasingly stronger, becoming one of the core production factors. To achieve the amplification effect, superposition effect, and multiplication effect of data, promoting the construction of a digital economy system centered on data, is the key path to achieving high-quality economic development. In order to thoroughly leverage the role of data in promoting economic and social development, the Chinese government has provided relevant work guidance. Based on this, the National Data Administration and 17 departments jointly issued the ??Data Factor ???? Three-year Action Plan (2024-2026) on December 31, 2023, aiming to fully leverage the multiplier effect of data factors, and empower economic and social development^[1].

On this basis, to promote the in-depth application of data in key industries and fields, on May 6, 2024, the National Data Administration, together with the Central Cyberspace Office, and 13 other departments, jointly issued the Notice on Holding the 2024 ??Data Factor ???? Competition (GSP [2024] No. 53)[1]. The competition is themed ??Empowering Data with Multipliers?? and revolves around 12 industry sectors deployed in the national ??Data Factor ???? Three-year Action Plan (2024-2026), including industrial manufacturing, modern agriculture, commercial circulation, and transportation. Corresponding to these sectors, 12 tracks are set up to explore good technologies and solutions for the developing and utilizing of data factors through competition and data aggregation. The aim is to make more data ??active, useful, and dynamic??. This competition is the first in China to focus on the developing and applying of data factors, and is divided into local competitions and national finals.

As an authoritative academic institution in the field of geography nationwide, the Geographical Society of China (GSC) deeply recognizes the important role of data in geography. Data not only provides fundamental support for geographical research but also promotes the development of geographical theory and innovation in practical applications. Given this, the society attaches great importance to data and established the Geographic Big Data Working Committee in 2018 to promote the construction of the big data geography discipline system, strengthen the construction of the big data geography talent team, and promote the widespread application of geographic big data in various fields of geography and social sustainable development. This measure marks the establishment of the national academic team for geographic big data in China, aiming to unite scientific and technological workers in the field of geographic big data nationwide, implement the national big data strategy, and add glory to the GSC, which has a hundred-year history^[2].

According to the National Data Administration??s 2024 ??Data Factor ???? Competition notice, GSC entrusted its Geographical Big Data Working Committee to participate in the local sub-competition. In May 2024, it applied to host the 2024 Geographic Big Data Competition, to promote the widespread application of geographical big data, explore the potential and application prospects of geographical data in technology, education, and social sustainable development, and accelerate the innovation and development of modern geographical research methods.

According to the ??Announcement on the Qualification Review Results of the National Finals of the 2024 ??Data Factor ???? Competition??, the Geographic Big Data Competition (2024) hosted by the GSC has been identified as one of the first third-party events to obtain the recommendation qualifications for the national finals of the competition. The competition is hosted by the GSC and organized by its Big Data Working Committee. It was successfully held from July to September 2024 and completed smoothly.

This competition focuses on technological innovation in the ??Data Factor ???? field (Track 6). The competition questions include: the collection, publication, and sharing of geographic data, the development of geographic data-supported technology models, geographic data supporting scientific research and technological innovation, geographic data accelerating the exploration of new paradigms in scientific research, and geographic data education and science popularization.

2 Competition Organization

2.1 Competition Launch

According to the notice of the 2024 ??Data Factor ???? organizing committee the GSC issued the first notice of the Geographic Big Data Competition (2024) on July 12, 2024, officially launching the competition. The competition leadership group, qualification review and evaluation group, supervision group, and secretary group were established to determine the competition??s specific schedule and evaluation rules[2].

(1) Track: ??Data Factor ???? Technology Innovation Field (Track 6)

(2) Competition theme: Geographic Data-Driven Technology, Educational Innovation, and Social Sustainable Development

(3) Competition questions setting: Five topic groups, i.e.,

1) The collection, publication, and sharing of geographic data

2) The development of geographic data-supported technology models

3) Geographic data supporting scientific research and technological innovation

4) Geographic data accelerating the exploration of new paradigms in scientific research

5) Geographic data education and science popularization

(4) Competition leadership team

Team leader: Chen, Fahu, Chairman of the GSC, academician of the CAS Member

Team members:

Li, Xiaojuan, Vice Chairman of the GSC and Professor at Capital Normal University

Lyv, Guonian, Executive Director of the GSC and Professor at Nanjing Normal University

Song, Changqing, Vice Chairman of the GSC and Professor at Beijing Normal University

Zhang, Guoyou, Vice Chairman and Secretary General of the GSC

(5) Qualification review and evaluation team

Team leader: Liao, Xiaohan, Director of Geographic Big Data Working Committee of the GSC, and Professor in Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (IGSNRR-CAS)

Team members:

Li, Manchun, Deputy Director of Geographic Big Data Working Committee of the GSC and Professor at the School of Geography and Ocean Science, Nanjing University

Chen, Lijun, Deputy Director of Geographic Big Data Working Committee of the GSC, Senior Engineer of the National Geomatics Center of China

Zhang, Songmei, Deputy Director of Big Data Development Center, Ministry of Agriculture and Rural Affairs of the People?? s Republic of China

Li, Guoqing, Director of National Earth Observation Data Center, and Professor of Aerospace Information Research Institute, Chinese Academy of Sciences

Wang, Liming, Professor of IGSNRR-CAS

Wang, Junwei, Engineer at Beijing International Data Exchange

(6) Competition supervision team

Team leader: Fu, Bojie, Chairman of the Board of Supervisors of the GSC, academician of the CAS Member

Team members:

Chu, Mingruo, PhD student of IGSNRR-CAS, chairman of the graduate student union

Han, Lei, PhD student at Sun Yat-Sen University

Zhang, Yichuan, PhD student at Liaoning Normal University

Li, Kai, postgraduate student at Jiangsu Normal University and the chairman of the graduate student union

Wu, Jiashuo, postgraduate student at Southwest Jiaotong University and the president of the student union

Guo, Haohao, postgraduate student at Shanxi University

Ma, Feng, undergraduate student at Qinghai Normal University and the president of the student union

(7) Events schedule and evaluation rules

1) Schedule

July-September 2024

July 12th: Announcement No.1 was issued and application to begin

July 30th: Deadline for team application

August 5th: Release of approved list of participating teams

August 6-25: Preliminary round judges

August 26-30: Final defense

August 31st September 7th: Online public voting

September 8-14: Comprehensive evaluation to determine the winning team for the finals

September 15-19: Final results announcement

September 27th: Awards ceremony

2) Rules

Uniqueness: Teams participating in the final of the Geographic Big Data Competition (2024) are not allowed to participate in other regions. Repeated participants will have their eligibility for this competition canceled.

Topic direction: Each participating project is limited to one topic direction, and once the topic direction is selected, it cannot be changed.

Team members: Each team is composed of 1-5 people who abide by the law and have not committed any disciplinary violations in the past 5 years (July 2019 to July 2024). The preliminary team members cannot be changed and all will participate in the final.

Intellectual property: All works submitted by participating organizations should be original creations of the team/individual, and must strictly comply with relevant laws and regulations such as the Data Security Law and the Personal Information Protection Law. They have not infringed any third-party intellectual property rights, including but not limited to copyrights, patents, trademarks, and other intellectual property rights. Do not disclose personal privacy or sensitive information. If the work involves the use of third party resources, legal authorization has been obtained and the source will be clearly marked in the submitted materials.

Highlight innovation: The participating projects are required to highlight innovation that have already been practically applied, achieving good economic or social benefits, including but not limited to technologies, products, solutions, etc., with independent intellectual property rights.

Integrity in competition: Participating teams and individuals shall not engage in any form of cheating, including but not limited to forging data, greeting and guiding ratings, maliciously interfering with other participants, etc., to ensure the authenticity and fairness of the competition process. If there are any violations of the requirements of the 2024 ??Data Factor ???? competition during the event, the participation qualification will be canceled.

Presentation content: The final content of the participating teams should include but not be limited to the competition report, project presentation, including project overview, solutions, application value and benefits, business model, sustainable development prospects, team composition, etc. The competition content is open to the public.

Presentation format: Encourage teams to adopt different report formats, such as PPT presentations, videos, text, audio, animations, etc., to fully and accurately present the core content of the project. The reporting time is 20 minutes.

Scoring criteria: The competition combines expert review and online public voting. The scoring criteria are shown in Table 1.

Table 1  Scoring table for the finals of the Geographic Big Data Competition (2024)

2.2 Preliminary Selection

As of July 30th, nearly 40 teams from over 50 units across the country have participated in the application process. After strict review and preliminary evaluation by the qualification review and evaluation team of the competition, 33 approved teams were selected (Table 2), involving 136 contestants and 39 units, including 5 scientific research institutes, 22 universities, 7 enterprises, 3 government agencies, 1 middle school, and 1 professional association.

The preliminary selection results will be announced on August 5th.

Preliminary round evaluation will be taken from August 6 to25.

Table 2  List of teams selected for the preliminary round

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2.3 Finals

Based on the team collection, qualification review and preliminary contest, the final of the Geographic Big Data Competition (2024) was opened on August 26, 2024. Chen, Fahu, academician of the CAS Member, president of the GSC, and leader of the leading group of the Geographic Big Data Competition (2024), announced the opening of the competition. The whole final lasted five days and ended on August 30.

Online voting will be held from August 31 to September 7. As of September 7, a total of 26,992 people from 33 provinces (autonomous regions, municipalities, special administrative regions including Hong Kong, Macao, Taiwan) of China and 17 other countries, in which there are 26,305 voters from China and 687 voters form the world else, participated in the voting.

From the voting results, all provinces (regions, municipalities, and special administrative regions) in China participated in the voting (Figure 1). Although the number of participants in Macau and Taiwan was not large, it is gratifying that they were able to pay attention to and participate in the voting in the first data competition. The open public online voting in this competition has also attracted worldwide attention. In addition to Chinese voters, 687 people from 17 countries participated in the vote, including foreign voters from the United States, Japan, India, Australia, Singapore, Malaysia, Thailand, Pakistan, New Zealand, Indonesia, the Philippines, Myanmar, Bangladesh, Canada, South Korea, France, and Russia. Foreign voters account for 2.55% of all voters. Although the proportion is not large, it is enough to indicate that this activity has undeniably impacted on the world. Among them, the United States had the highest number of voters, reaching 303 people, accounting for 44.10% of foreign voters, followed by Japan, with 134 people participating in the vote, accounting for 19.51% of foreign voters (Figure 2).

After evaluation by the panels and public voting from 8 to 14 September, a comprehensive evaluation was conducted from two aspects, with public voting accounting for 20 points and expert review accounting for 80 points. Finally, 5 major awards were selected, including 12 outstanding award-winning teams. The list of award-winning teams is shown in Table 3.

Final results were announced from September 15 to 19.

During the public announcement process, no one or unit raised any objections, and the selection results became effective.

According to the requirements of the National Data Administration, the GSC recommends two teams to participate in the national finals of the 2024 ??Data Factor ???? Competition, namely the ??Big Data-Driven Geographical Indications Environment & Sustainability??

Table 3  List of winning teams from the Geographic Big Data Competition (2024)

Note: The specific competition topics and team members are listed in Table 2.

and the ??Exploration and Practice of GIS University-Industry Collaborative Education to Meet the Needs of the Times??Geospatial Big Data Experimental Teaching Platform??. Although these two teams were not selected for the national finals, their impact on technological innovation and social progress is profound.

On September 27th, at the 2024 China Geography Conference and 115th Anniversary Commemoration of the Establishment of the GSC, the award ceremony for the competition was held.

The award ceremony was presided over by Professor He, Shujin, Deputy Secretary General of the GSC. Professor Zhang, Guoyou, Vice Chairman and Secretary General of the GSC, announced the list of winners and presented awards to the winning teams (Figure 3).

At the award ceremony, the 12 winning teams from the five major awards of the competition, including the ??Geographic Data Collection and Publishing Sharing Award??, appeared one by one. In the presence of more than 3,000 senior and colleagues in the Chinese geography field, they received their award certificates (Figure 4, 5).

On September 28th, the winning teams summarized and shared their findings at the ??2024 China Geography Conference and 115th Anniversary Commemoration of the Establishment of the Geographical Society of China?? held in Nanjing. Liu, Qinhuo, Deputy Director of the Geographic Big Data Working Committee of the GSC, presided over the event. Representatives from each award-winning team summarized and shared the competition questions, once again showcasing their achievements and results in the field of geographic big data technology innovation to the public (Figures 6).

Figure 5  Winning team certificates

3 Main Methods and Distinctive Features

Geographic big data is an essential development element of new quality productivity that involves various related fields such as geography, resources, ecology, environment, and sustainable development. From the situation of the 33 teams participating in the finals, the content of the competition is rich, including specific database construction, data platform data center construction, data in teaching, scientific research, sustainable development, social services and other fields, as well as practical cases with strong effectiveness; The data content covers local, regional, national, and global scales. The impact of this competition not only radiated throughout the country, but also received widespread attention from relevant personnel in 17 countries around the world, including the United States of America, Japan, India, Australia, etc.

To ensure the fair and just conduct of the competition, the organizing committee will strengthen the construction of the competition system based on information openness, ensure that responsibilities are in place, and establish a public selection mechanism for the competition network. The specific method is as follows:

(1) In addition to complying with the participation conditions and requirements of the National Data Administration, the GSC has set up a leadership group, a judging group, and a supervision group to ensure the fairness and impartiality of the competition. The three parties work together to maintain the rigor and transparency of the competition.

(2) All information on the data competition should be fully open. The GSC has established a dedicated website to publish competition information. In addition, the systems and personnel related to the competition, such as the organizational structure and personnel of the competition, the rules and regulations of the competition, the schedule of the competition, the promotion videos of the competition teams, and the results of the competition, are all publicly disclosed and accepted for public supervision and feedback.

(3) To promote the popularity of big data events, increase public participation and interactivity, the competition has set up an 8-day public online voting session. The online public voting adopts anonymous voting method, and each voter (computer IP address) can only vote once, ensuring the transparency and fairness of the event, allowing the audience to directly participate in the selection process of the event, and also helping to increase the audience??s attention and stickiness to the event. In addition, by collecting and analyzing the data generated during the voting process, we can better understand audience preferences and provide data support for future event planning and marketing.

The significant feature of this event is the establishment of a bridge between scientific research data and social development needs. The integration of technological data with social needs and the provision of social development solutions have become the prominent features of this event. The content of the competition includes the China oasis database included in the United Nations Environment Programme??s Global Oasis Project, as well as the infrastructure and database of the national long-term support for domestically produced satellite ground verification systems.

The most outstanding team in this competition is the Geographical Indications Environment & Sustainability (GIES) team. The team related project was launched in 2021, based on the Global Change Research Data Publishing & Repository System that won the United Nations World Information Summit Award as the infrastructure, and in 2024, it was recognized by the United Nations Food and Agriculture Organization as the leading unit of data storage and technology transfer services for the One Country One Priority Product project of characteristic agricultural products (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences). This project has benefited more than 600,000 rural residents in 19 typical remote rural cases in 12 provinces (regions) in the past three years (through digital transformation of primary, secondary, and tertiary industries and big data IoT technology), and promoted the localization of landmark habitat related software and hardware equipment. The methods and technologies for promoting sustainable development in this project will gradually be promoted in the One Country One Priority Product plan of the FAO, which involves 85 countries worldwide. 20 countries in Asia, Latin America, Oceania, and Africa have been included in the 2024-2025 GIES Technology Promotion Plan.

4 Discussion

The effective organization of the Geographical Big Data Working Committee of the GSC in accordance with the requirements of the National Data Administration during this event is a favorable guarantee for the smooth progress of this competition. Although the mining and application of geographic big data have attracted widespread attention from the industry and academia, and have made significant progress in many fields, the overall social attention and participation in this geographic big data competition are still not comprehensive enough. In view of this, the organizing committee of the competition will closely monitor and promote the construction of domestic and international data sharing systems and mechanisms. At the same time, it will be committed to transforming geographic big data into new quality productivity, closely integrating big data, the Internet of Things, artificial intelligence, and sustainable development, further promoting the dissemination of geographic big data among hundreds of schools (townships), strengthening the close integration of data publishing and high-quality development of geographic regions, and focusing on these efforts to further stimulate the industry??s enthusiasm and potential ability to explore the value of geographic big data, thereby promoting the influence of geographic big data to a new level nationwide and even globally.

Author Contributions

Jiang, Z. C. was responsible for organizing the event and writing the event report, while Liao, X. H. led the qualification review and expert evaluation of the event; Liu, C. made overall planning and design for the entire event organization work.

Acknowledgements

The author expresses deep gratitude to the leadership group, expert group, supervision group, and every participating team member for their contributions to the Geographic Big Data Competition (2024), as well as to the guidance provided by the National Data Administration 2024 Data Competition Organizing Committee.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]        National Data Administration, Office of the central Cyberspace Affairs Commission, Ministry of Science and Technology of the People??s Republic of China, et al. ??Data Factor ???? Three Year Action Plan (2024-2026) [EB/OL]. https://www.cac.gov.cn/2024-01/05/c_1706119078060945.htm.

[2]        Geographical Society of China. The Big Geographical Data Working Committee of Geographical Society of China (GSC_BigData) established [J]. Journal of Global Change Data & Discovery, 2018, 2(3): 354-356. https://doi.org/10.3974/geodp.2018.03.18.

[3]        Zhai, M. J., Zhou, X., Tao, Z., et al. Dataset of water optical parameters and surface temperature for typical lakes in China [J]. Scientific Data [2025-01-03]. DOI: 10.1038/s41597-024-03704-3.

[4]        Lin, J. W., Gui, D. W., Zhang, S. Y., et al. Dataset development of the Hotan Oasis, water system, watershed, and elevation [J]. Journal of Global Change Data & Discovery, 2023, 7(3): 314?C320. https://doi.org/ 10.3974/geodp.2023.03.10. https://cstr.escience.org.cn/CSTR:20146.14.2023.03.10.

[5]        Peng, Q. Y., Shen, R., Li, X., et al. A twenty-year dataset of high-resolution maize distribution in China [J]. Scientific Data, 10(1): 658[2025-01-03]. DOI: 10.1038/s41597-023-02573-6.

[6]        Wang, J. L. Preface of ??Resource and environmental data of the Mongolian Plateau?? [J/OL]. China Scientific Data, 2023, 8(1): 7. DOI: 10.11922/11-6035.csd.2023.0085.zh.

[7]        Zhang, M. X., Sun, Y. W., Li, B., et al. GIES case dataset on Yanchi Tan sheep arid grassland inHuamachi Township, Ningxia Hui Autonomous Region, China [J]. Journal of Global Change Data & Discovery, 2021, 5(3): 249-260. https://doi.org/10.3974/geodp.2021.03.04. https://cstr.escience.org.cn/CSTR:20146.14.2021.03.04.

[8]        Zhang, M. X., Sun, Y. W., Li, B., et al. Yanchi Tan Sheep Huamachi Town arid grassland case data-set on ecosystem protection and sustainable development [J/DB/OL]. Digital Journal of Global Change Data Repository, 2021. https://doi.org/10.3974/geodb.2021.05.09.V1. https://cstr.escience.org.cn/CSTR:20146.11.2021.05.09.V1.

[9]        Shi, R. X., Ma, J. H, Liu, C., et al. Statistics and analysis of the Global Change Research Data Publishing & Sharing (2018) [J]. Journal of Global Change Data & Discovery, 2019, 3(1): 1?C9. https://doi.org/ 10.3974/geodp.2019.01.01.

[10]     Wang, Y. S., Yang, Y. Y., Du, L. M., et al. GIES case dataset on Yangxian black rice crested ibis (Nipponia nippon) habitat in Caoba village, Shaanxi province of China [J]. Journal of Global Change Data & Discovery, 2021, 5(3): 290?C299. https://doi.org/10.3974/geodp.2021.03.07. https://cstr.escience.org.cn/ CSTR:20146.14.2021.03.07.

[11]     Wang, Y. S., Yang, Y. Y., Du, L. M., et al. Dataset of GI environment protection and sustainable development of black rice and crested ibis (Nipponia nippon) habitat in Caoba Village, Yang County, Shaanxi Province of China [J/DB/OL]. Digital Journal of Global Change Data Repository, 2021. https://doi.org/10.3974/ geodb.2021.08.06.V1. https://cstr.escience.org.cn/CSTR:20146.11.2021.08.06.V1.

[12]     Li, C. Y., Wu, X., Qi, F. Y., et al. Exploration and research on global geological data publishing model [J]. Geology in China, 2017, 44(S1): 1‒7. DOI: 10.12029/gc2017Z101.

[13]     Fu, J. Y., Du, X. L., Zheng, Q. S., et al. GIES case dataset on rice permanent farmland in Lanjia village, Jilin province of China [J]. Journal of Global Change Data & Discovery, 2021, 5(3): 275?C289. https://doi.org/10.3974/geodp.2021.03.06. https://cstr.escience.org.cn/CSTR:20146.14.2021.03.06.

[14]     Fu, J. Y., Du, X. L., Zheng, Q. S., et al. Panshi rice Lanjia village case dataset on permanent basic farmland protection and sustainable development [J/DB/OL]. Digital Journal of Global Change Data Repository, 2021. https://doi.org/10.3974/geodb.2021.06.01.V1. https://cstr.escience.org.cn/CSTR:20146.11.2021.06.01.V1.

[15]     Liu, Q. H., Wen, J. G., Zhou, X., et al. 2023. Technique system of remote sensing product generation and validation of GF common products [J]. National Remote Sensing Bulletin, 27(3): 544?C562 DOI: 10.11834/jrs.20232531.

[16]     Wu, C., Zhang, F., Xia, J., et al. Building damage detection using U-Net with attention mechanism fom pre- and post-disaster remote sensing datasets [J]. Remote Sensing. 2021, 13(5): 905.

[17]     Wang, Z. Y., Wu, C. Y., Zhang, F., et al. Constructing an extensible building damage (EBD) dataset covering 12 natural disaster events by a semi-supervised fine-tuning method [J]. International of Remote Sensing (under review). https://doi.org/10.6084/m9.figshare.25285009.v2.

[18]     Zhu, B. X., Chen, S. B. Cao, Y. J., et al. A regional maize yield hierarchical linear model combining Landsat 8 vegetative indices and meteorological data: case study in Jilin Province [J]. Remote Sensing 2021, 13(356): 2072?C4292.

[19]     Yang, J., Huang, X. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019 [J]. Earth System Science Data, 2021, 13(8): 3907‒3925.

[20]     Duan, R. T., Liu, Y. T., Fu, C. L., et al. GIES case dataset on Baoshan Arabica coffee dry-hot valley in Xinzhai Village, Yunnan Province of China [J]. Journal of Global Change Data & Discovery, 2021, 5(3): 261?C274. https://doi.org/10.3974/geodp.2021.03.05. https://cstr.escience.org.cn/CSTR:20146.14.2021.03.05.

[21]     Duan, R. T., Liu, Y. T., Fu, C. L., et al. Baoshan coffee Xinzhai Village dry-hot valley case dataset on ecosystem protection and sustainable development [J/DB/OL]. Digital Journal of Global ChangeData Repository, 2021. https://doi.org/10.3974/geodb.2021.05.10.V1. https://cstr.escience.org.cn/CSTR:20146.11.2021.05.10.V1.

[22]     Duan, Z. Q., He, S. J. Academic influence of special columns and enlightenment: a case study of representative platform journals of Chinese Geographical and Resource Journals Cluster [J]. Chinese Journal of Scientific and Technical Periodicals, 2021, 32(7): 889‒894.

[23]     Liu, C., Gong, K., Liu, Y. H., et al. An innovative solution on geographical indications for environment & susta­­in­a­bility (GIES) [J]. Journal of Global Change Data & Discovery, 2021, 5(3): 237?C248. https:// doi.org/10.3974/geodp.2021.03.03. https://cstr.escience.org.cn/CSTR:20146.14.2021.03.03.