Progress of Spatial Geographic Phenomenon Visualization Methods Based on Multi-Source Data

The generation of massive and complex data in the context of the big data era, the rapid development of 5G technology and mathematical and statistical methods have made the spatial visualization methods of multi-source data more and more abundant. Different methods are applicable to different fields and data forms; therefore, the analysis for research methods has become an important topic. 698 papers from CNKI and Web of Science Core Collection from 2015–2021 are selected as samples. Citespace is used to analyze the papers to find out the research hotspots and the development history of the past seven years at home and abroad about the field of spatial visualization based on multi-source data, then concluding that domestic research areas are mostly focused on first-tier cities. The foreign research areas mostly focus on climate change, precipitation and green space, and both domestic and foreign research methods are dominated by machine learning. Further, three research methods commonly used in spatial visualization are summarized and analyzed, especially the machine learning method, to provide more scientific and effective new ideas for spatial visualization research.