ICA-Proc Proceedings of the ICA ICA-Proc Proc. Int. Cartogr. Assoc. 2570-2092 Copernicus Publications Göttingen, Germany 10.5194/ica-proc-2-79-2019 Research and implementation of big data visualization based on WebGIS Lu Wenjuan 1 Liu Aiguo 2 Zhang Chengcheng 1 Chinese Academy of Surveying and Mapping, China Beijing Aerospace Hongtu Information Technology Co., Ltd. China 10 07 2019 2 79 Copyright: © 2019 Wenjuan Lu et al. 2019 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://ica-proc.copernicus.org/articles/ica-proc-2-79-2019.html The full text article is available as a PDF file from https://ica-proc.copernicus.org/articles/ica-proc-2-79-2019.pdf

With the development of geographic information technology, the way to get geographical information is constantly, and the data of space-time is exploding, and more and more scholars have started to develop a field of data processing and space and time analysis. In this, the traditional data visualization technology is high in popularity and simple and easy to understand, through simple pie chart and histogram, which can reveal and analyze the characteristics of the data itself, but still cannot combine with the map better to display the hidden time and space information to exert its application value. How to fully explore the spatiotemporal information contained in massive data and accurately explore the spatial distribution and variation rules of geographical things and phenomena is a key research problem at present. Based on this, this paper designed and constructed a universal thematic data visual analysis system that supports the full functions of data warehousing, data management, data analysis and data visualization. In this paper, Weifang city is taken as the research area, starting from the aspects of rainfall interpolation analysis and population comprehensive analysis of Weifang, etc., the author realizes the fast and efficient display under the big data set, and fully displays the characteristics of spatial and temporal data through the visualization effect of thematic data. At the same time, Cassandra distributed database is adopted in this research, which can also store, manage and analyze big data. To a certain extent, it reduces the pressure of front-end map drawing, and has good query analysis efficiency and fast processing ability.