Skip to main content

Enhanced Geographical Information System Architecture for Geospatial Data

  • Chapter
New Trends in Computational Vision and Bio-inspired Computing

Abstract

Geospatial data is processed using a geographical information system, geospatial data is mainly of two kind’s vector and raster data. Vector data largely includes point data and arcs whereas raster data is the used to represent surfaces. This data is processed and the insights are used for demographical analysis, geological studies, map route optimizations, creating hydrological models among other uses making GIS important for both academia and industry. The suggested system is aimed to further the cause of data standardization as well as web-based GIS tools. The system explores the uses of open source application in order to provide the base for a system that enable the system to provide the GIS functionalities over a portal in form of WPS. The proposed system intends to provide services that enable the user’s access standardized methods of publishing geospatial data and use various services.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Lupp, M.: Open geospatial consortium. In: International Conference in IT Convergence and Security, In Encyclopedia of GIS, pp. 815–815. Boston, MA. (2008)

    Google Scholar 

  2. Foerster, T., & Stoter, J.: Establishing an OGC web processing service for generalization processes. In ICA workshop on Generalization and Multiple Representation. (2006)

    Google Scholar 

  3. Hassler, P., Lutz, M., & Pins, M.: Geo-server interface. U.S. Patent Application No. 10/285,282. (2004)

    Google Scholar 

  4. Youngblood, B..: GeoServer Beginner’s Guide. Packt Publishing Ltd. (2013)

    Google Scholar 

  5. Peterson, A. T., & Cohoon, K. P..: Sensitivity of distributional prediction algorithms to geographic data completeness. Ecological modelling. 117(1), 159–164 (1999)

    Article  Google Scholar 

  6. Virrantaus, K., Markkula, J., Garmash, A., Terziyan, V., Veijalainen, J., Katanosov, A., & Tirri, H..: Developing GIS-supported location-based services. In Proceedings of the Second International Conference on Web information systems engineering, IEEE (2001)

    Google Scholar 

  7. Patil, S., Bhattacharjee, S., & Ghosh, S. K..: A spatial web crawler for discovering geo-servers and semantic referencing with spatial features. In International Conference on Distributed Computing and Internet Technology, 68–78 (2014)

    Google Scholar 

  8. Breunig, M..: An approach to the integration of spatial data and systems for a 3D geo-information system. Computers & Geosciences, 25(1) 39–48. (1999)

    Article  Google Scholar 

  9. Jayapandian, N., Zubair Rahman, A.M.J.Md.: Secure and efficient online data storage and sharing over cloud environment using probabilistic with homomorphic encryption. Cluster Computing, Springer. 20, 1561–1573 (2017)

    Google Scholar 

  10. Graser, A.: Learning QGIS 2.0. Packt Publishing Ltd (2013)

    Google Scholar 

  11. Graser, A., & Olaya, V..: Processing: A python framework for the seamless integration of geoprocessing tools in QGIS. ISPRS International Journal of Geo-Information, 4(4) 2219–2245. (2015)

    Article  Google Scholar 

  12. Cagnacci, F., & Urbano, F.: Managing wildlife: a spatial information system for GPS collars data. Environmental Modelling & Software, 23(7) 957–959. (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to N. Jayapandian .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Cite this chapter

Singh, M., Agarwal, S., Ajay Prasanna, Y., Jayapandian, N., Kanmani, P. (2020). Enhanced Geographical Information System Architecture for Geospatial Data. In: Smys, S., Iliyasu, A.M., Bestak, R., Shi, F. (eds) New Trends in Computational Vision and Bio-inspired Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-41862-5_4

Download citation

Publish with us

Policies and ethics