Cartography and Neural Networks: A Scientometric Analysis Based on CiteSpace

Author:

Cheng Shiyuan1234ORCID,Zhang Jianchen1234ORCID,Wang Guangxia1234,Zhou Zheng1234,Du Jin56,Wang Lijun1234,Li Ning1234,Wang Jiayao1234

Affiliation:

1. College of Geography and Environmental Science, Henan University, Kaifeng 475004, China

2. Henan Industrial Technology Academy of Spatial-Temporal Big Data, Henan University, Zhengzhou 450046, China

3. Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng 475004, China

4. Henan Technology Innovation Center of Spatio-Temporal Big Data, Henan University, Zhengzhou 450046, China

5. Key Research Institute of Yellow River Civilization and Sustainable Development, Henan University, Kaifeng 475001, China

6. Henan Urban Planning Institute & Corporation, Zhengzhou 450053, China

Abstract

Propelled by emerging technologies such as artificial intelligence and deep learning, the essence and scope of cartography have significantly expanded. The rapid progress in neuroscience has raised high expectations for related disciplines, furnishing theoretical support for revealing and deepening the essence of maps. In this study, CiteSpace was used to examine the confluence of cartography and neural networks over the past decade (2013–2023), thus revealing the prevailing research trends and cutting-edge investigations in the field of machine learning and its application in mapping. In addition, this analysis included the systematic categorization of knowledge clusters arising from the fusion of cartography and neural networks, which was followed by the discernment of pivotal clusters in the field of knowledge mapping. Crucially, this study diligently identified the critical studies (milestones) that have made significant contributions to the development of these elucidated clusters. Timeline analysis was used to track these studies’ origins, evolution, and current status. Finally, we constructed collaborative networks among the contributing authors, journals, institutions, and countries. This mapping aids in identifying and visualizing the primary contributing factors of the evolution of knowledge mapping encompassing cartography and neural networks, thus facilitating interdisciplinary and multidisciplinary research and investigations.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Henan Province

Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources

Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions (Henan University) and the Ministry of Education open project

Henan Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains

Science and Technology Development Project of Henan Province

Publisher

MDPI AG

Reference150 articles.

1. Some thoughts on deep learning enabling cartography;Tinghua;Acta Geod. Et Cartogr. Sin.,2021

2. Cartography in the age of spatio-temporal big data;Jiayao;Acta Geod. Et Cartogr. Sin.,2017

3. Konečný, M., and Cartwright, W. (2010). Joint Board of Geospatial Information Societies (JB GIS), United Nations Office for Outer Space Affairs (UNOOSA).

4. Deep Mapping—A Critical Engagement of Cartography with Neuroscience;Zhong;Geomat. Inf. Sci. Wuhan Univ.,2022

5. Semiology of graphics: Diagrams, Networks, Maps;Bertin;Ann. Assoc. Am. Geogr.,1983

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