Abstract
With the development of computer science, the era of big data has arrived. Facing the new era and new challenges, the traditional analytical methods of problems in various fields have been unable to meet the needs. Data visualization is a rapidly developing discipline, it has significant advantages in analyzing problems, so data visualization shines in the era of big data. As people are very concerned about the fields of energy and environment, we choose to conduct data visualization studies in two areas, energy and the environment. According to the different characteristics of data in different fields, we propose targeted data visualization processes and design data visualization solutions. For energy data, we follow the process of data processing, visualization design, and data visualization. Based on the principle of high efficiency and intuitiveness, we add timeline and a combination of various charts to our design, and finally show a dynamic effect. We also propose a multi-dimensional visual mapping visualization scheme. The scheme can refine and enrich the visual results. For environmental data, we follow the process of goal analysis, data processing, visualization and analysis, the work shows the importance of visualization in information analysis and decision-making.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ebert, D.: Changing the world with visual analytics. In: 2017 IEEE Pacific Visualization Symposium, Seoul, South Korea, p. 8. IEEE Computer Society (2017)
Chang, Y.: The data visualization method in the era of big data. Electron. Technol. Softw. Eng. 2018(5), 156 (2018)
Qiao, S., Nurbol, Su, R.: Map analysis for research status and development trend of data visual analysis. Mod. Electron. Tech. 41(14), 161–169 (2018)
Chang, Y.: Visual analysis of web data under big data background. China CIO News 2018(5), 148–150 (2018)
Lee, B., Riche, N.H., Isenberg, P., Carpendale, S.: More than telling a story: transforming data into visually shared stories. IEEE Comput. Graphics Appl. 35(5), 84–90 (2015)
Dai, S., Dong, J., Xue, J.: Big data visualization analysis and application in scientific computing. J. Eng. Stud. 6(3), 275–281 (2014)
Ma, C., Li, L., Xue, W.: Research on visualization of air pollution characteristics and distribution. J. Northwest. Polytechnical Univ. 35(6), 1073–1078 (2017)
Chen, S.: Analysis of the evolution of energy consumption structure in American four states. Chem. Enterp. Manag. 2018(11), 30–32 (2018)
Li, B., Mao, B.: Energy consumption analysis and visualization based on CityGML-a case study of Swedish Smart City. Geomatics World 24(4), 48–52 (2017)
Wang, Y., Yu, J., Wu, F.: Data analysis and visualization based on geographic information in the background of Global Energy Internet. Electr. Power Inf. Commun. Technol. 14(3), 49–54 (2016)
Xu, N., Luo, J.: Statistical analysis of data map visualization. Technol. Innov. Appl. 2018(22), 67–68 (2018)
Brehmer, M., Lee, B., Bach, B., Riche, N.H., Munzner, T.: Timelines revisited: a design space and considerations for expressive storytelling. IEEE Trans. Visual Comput. Graphics 23(9), 2151–2164 (2017)
Wang, R., Zhou, M., Wang, Y., Liu, Y.: Mapping spatial and temporal patterns of air condition in Northeast China. Bull. Surveying Mapp. 2017(8), 88–91 (2017)
An, Q., An, H., Wang, L.: Analysis of embodied energy flow network between Chinese industries. J. Syst. Eng. 29(6), 754–762 (2014)
Acknowledgments
This research is funded by The National Science Foundation of China (61601053) and National Natural Science Foundation of China (Grant No. 61602051) and the Fundamental Research Funds for the Central Universities under Grant 2017RC11.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Guo, X., He, M., Chen, M., Zhao, X., Tian, Y. (2019). Research on Data Visualization in Different Scenarios. In: Tang, Y., Zu, Q., RodrÃguez GarcÃa, J. (eds) Human Centered Computing. HCC 2018. Lecture Notes in Computer Science(), vol 11354. Springer, Cham. https://doi.org/10.1007/978-3-030-15127-0_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-15127-0_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-15126-3
Online ISBN: 978-3-030-15127-0
eBook Packages: Computer ScienceComputer Science (R0)