Advertisement

GeoJournal

, Volume 81, Issue 6, pp 965–968 | Cite as

CyberGIS and spatial data science

  • Shaowen Wang
Article

In today’s geospatially connected world, regional and urban analysis has become increasingly essential to understand coupled environmental and human systems. The complexities of such systems and their connectivity at various spatial and temporal scales have posed daunting challenges to effective solutions to a variety of regional problems and urban sustainable development. Conventional scientific approaches to such challenges, however, tend to be fragmented in space and time and constrained by the inability to take advantage of spatial big data, which make extrapolation over the connectedness across large and multiple spatial and temporal scales difficult or infeasible. Major scientific breakthroughs and technological innovations are urgently needed to discover and understand complex and dynamic spatial connections between people and places. Interdisciplinary approaches combining rich and complex spatial data, analysis and models are highly demanded to ignite transformative geospatial...

Keywords

Cloud Computing Spatial Data Green Infrastructure Urban Green Infrastructure Open Science Grid 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Anselin, L., & Rey, S. (2012). Spatial econometrics in an age of CyberGIScience. International Journal of Geographical Information Science, 26(12), 2211–2226.CrossRefGoogle Scholar
  2. de Andrade, F. G., Baptista, C. D. S., & Leite, F. L, Jr. (2011). Using federated catalogs to improve semantic integration among spatial data infrastructures. Transactions in GIS, 15(5), 707–722.CrossRefGoogle Scholar
  3. Lin, T., Wang, S., Rodríguez, L. F., Hu, H., & Liu, Y. Y. (2015). CyberGIS-enabled decision support platform for biomass supply chain optimization. Environmental Modelling and Software, 70, 1364–8152. doi: 10.1016/j.envsoft.2015.03.018.CrossRefGoogle Scholar
  4. Wang, S. (2010). A CyberGIS framework for the synthesis of cyberinfrastructure, GIS, and spatial analysis. Annals of the Association of American Geographers, 100(3), 535–557.CrossRefGoogle Scholar
  5. Wang, S. (forthcoming). CyberGIS. In D. Richardson, N. Castree, M. F. Goodchild, A. L. Kobayashi, W. Liu, & R. Marston (Eds.), The international encyclopedia of geography: People, the earth, environment, and technology. Wiley-Blackwell and The Association of American Geographers. doi: 10.1002/9781118786352.wbieg0931.
  6. Wang, S., Hu, H., Lin, T., Liu, Y., Padmanabhan, A., & Soltani, K. (2014). CyberGIS for data-intensive knowledge discovery. ACM SIGSPATIAL Newsletter, 6(2), 26–33.Google Scholar
  7. Wang, S., Liu, Y., & Padmanabhan, A. (2015). Open cyberGIS software for geospatial research and education in the big data era. SoftwareX,. doi: 10.1016/j.softx.2015.10.003.Google Scholar
  8. Wright, D. J., & Wang, S. (2011). The emergence of spatial cyberinfrastructure. Proceedings of the National Academy of Sciences, 108(14), 5488–5491.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.CyberGIS Center for Advanced Digital and Spatial StudiesUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  2. 2.CyberInfrastructure and Geospatial Information LaboratoryUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  3. 3.Department of Geography and Geographic Information ScienceUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  4. 4.National Center for Supercomputing ApplicationsUniversity of Illinois at Urbana-ChampaignUrbanaUSA

Personalised recommendations