Abstract
Geographic information system is built on the basis of spatial data model, no GIS system can get rid of the data model and exist alone. Essentially, some of the key functions and data management of GIS are determined by the spatial data model. The most important point of the GIS driving system is that it can achieve the visualization of geographic information with the help of the GIS topsoil rendering, which greatly improves the data application rate of spatial map** and makes the design of object model of spatial map** gradually precise and scientific. Especially in recent years, due to the continuous improvement of spatial data collection methods and technologies, the efficiency of data collection has been greatly improved. How to use a large amount of data in spatial map** and how to build models and preserve information has gradually become the focus of research. This paper, based on GIS drive, USES the basic mathematical algorithm to design and apply the object model of spatial cartography, and puts forward the advantages of the object model of spatial cartography in cartography. It provides some reference for the later related research.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Song, ** for action recognition. IEEE Transactions on Circuits and Systems for Video Technology 24(13): 243–257.
Jothibasu, A., and S. Anbazhagan. 2017. Spatial map** of groundwater potential in Ponnaiyar River basin using probabilistic-based frequency ratio model. Modeling Earth Systems & Environment 3 (1): 33.
Mccrink, K., J. Perez, and E. Baruch. 2017. Number prompts left-to-right spatial map** in toddlerhood. Developmental Psychology 53 (7): 356–358.
Dueas, Maria Emilia, Jeffrey J. Essner, and Young ** Lee. 2017. 3D MALDI mass spectrometry imaging of a single cell: Spatial map** of lipids in the embryonic development of zebrafish. Scientific Reports 7 (1): 14946.
Clemments, A.M., P. Botella, and C.C. Landry. 2017. Spatial map** of protein adsorption on mesoporous silica nanoparticles by stochastic optical reconstruction microscopy. Journal of the American Chemical Society 139 (11): 3978–3981.
Jo, A., J. Ryu, and H. Chung. 2018. Applicability of various interpolation approaches for high resolution spatial map** of climate data in Korea. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences 28 (32): 178–192.
Anyz, Jiri, Lenka Vyslouzilova, and Tomas Vaculovic. 2017. Spatial map** of metals in tissue-sections using combination of mass-spectrometry and histology through image registration. Scientific Reports 39 (12): 401–412.
Navarro, José Fernández, Joel Sjöstrand, and Fredrik Salmén. 2017. ST Pipeline: An automated pipeline for spatial map** of unique transcripts. Bioinformatics 33 (16): 174–189.
Wu, Zuxuan, ** Wang, and Yu-Gang Jiang. 2017. Modeling spatial-temporal clues in a hybrid deep learning framework for video classification. Proceedings of the 23rd ACM International Conference on Multimedia, vol. 24, no. 19, pp. 239–257.
Nittel, S., J. Yang, and R.R. Muntz. 2017. Map** a common geoscientific object model to heterogeneous spatial data repositories. In Proceedings of the 4th ACM International Workshop on Advances in Geographic Information Systems, vol. 46, no. 23, pp. 435–439.
Oskard, Morton S., Tsai-Hong Hong, and Clifford A. Shaffer. 2017. Spatial map** system for autonomous underwater vehicles. Proceedings of SPIE The International Society for Optical Engineering 33 (13): 249–257.
Carpenter, Gail A., Stephen Grossberg, and Gregory W. Lesher. 2017. The what-and-where filter: A spatial map** neural network for object recognition and image understanding. Computer Vision and Image Understanding 69 (1): 1–22.
Majumder, Raja, Gouri Sankar Bhunia, Poly Patra. 2019. Assessment of flood hotspot at a village level using GIS-based spatial statistical techniques. Arabian Journal of Geosciences 12 (13): 45–53.
Mandal, Sujit, and Kanu Mandal. 2018. Modeling and map** landslide susceptibility zones using GIS based multivariate binary logistic regression (LR) model in the Rorachu river basin of eastern Sikkim Himalaya, India. Modeling Earth Systems & Environment 4 (1): 69–88.
Gimpel, A., V. Stelzenmüller, S. Töpsch, et al. A GIS-based tool for an integrated assessment of spatial planning trade-offs with aquaculture. Science of the Total Environment 627 (428): 1644–1655.
Acknowledgements
This work was supported by Hnky2019-91.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, F., Fu, Y. (2020). Design and Application of Spatial Map** Object Model Driven by GIS. In: Yang, CT., Pei, Y., Chang, JW. (eds) Innovative Computing. Lecture Notes in Electrical Engineering, vol 675. Springer, Singapore. https://doi.org/10.1007/978-981-15-5959-4_136
Download citation
DOI: https://doi.org/10.1007/978-981-15-5959-4_136
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5958-7
Online ISBN: 978-981-15-5959-4
eBook Packages: Computer ScienceComputer Science (R0)