Massive spatial data collected from numerous sources are increasingly used to instrument our natural, human and social systems at unprecedented scales while providing us with tremendous opportunities to gain dynamic insight into complex phenomena. Though such big data streams play crucial roles in many scientific domains and promise to enable a wide range of decision-making practices with significant societal impacts, exploiting them successfully poses significant challenges. On one hand, spatial and location attributes serve as a common key to many types of data such as census and population, land use and cover, floodplain, and vegetation distribution. Oftentimes perceived as significant benefits, spatial data synthesis can be used to link disparate pieces of data that pertain to common spatial references and units. On the other hand, however, there are diverse spatial references and units for data collection and management and they are based on different representation models and assumptions.
To break through these challenges, this project aims to establish a suite of scalable capabilities for spatial data synthesis enabled by innovative cloud computing and cyberGIS and driven by multiple scientific communities. Such capabilities will also be designed to support integration with cyberGIS analytics and workflow for solving scientific problems. The project establishes core capabilities through a spiral approach by initially developing the capabilities for solving specific scientific problems and later moving on to engage broader communities for validating and improving the core capabilities. The scientific problems will revolve around two interrelated themes: 1) measuring urban sustainability based on a number of social, environmental, and physical factors and processes; and 2) examining population dynamics by synthesizing multiple states of the art population data sources with social media data.
Sponsored by: National Science Foundation (NSF)
People: Anand Padmanabhan, Kate Keahey, Shaowen Wang
Publications:
- Keahey K., Riteau P. and Timkovich N. 2017. “LambdaLink: an Operation Management Platform for Multi-Cloud Environment”. Proceedings of the 10th International Conference on Utility and Cloud Computing. Dec 5-8. Austin, Texas.
- Social sensing of urban land use based on analysis of Twitter users mobility patterns Soliman, A., Soltani, K., Yin, J., Padmanabhan, A. and Wang, S., 2017, PLos One.
- Soltani, K., Soliman, A., Padmanabhan, A., and Wang, S. 2016. “UrbanFlow: Large-scale Framework to Integrate Social Media and Authoritative Landuse Maps”. Proceedings of the 2016 Annual Conference on Extreme Science and Engineering Discovery Environment (XSEDE’16). July 17-21. Miami, Florida.
- Soliman, A., Yin, J., Soltani, K., Padmanabhan, A., and Wang, S. 2015. “Where Chicagoans tweet the most: Semantic analysis of preferential return locations of Twitter users”. Proceedings of the First ACM SIGSPATIAL International Workshop on Smart Cities and Urban Analytics (UrbanGIS’15).
- Lin, T., Wang, S., Rodríguez, L. F., Hu, H., and Liu, Y.Y.. “CyberGIS-Enabled Decision Support Platform for Biomass Supply Chain Optimization,” Environmental Modelling & Software, 2015.
- Wang, S., Hu, H., Lin, T., Liu, Y., Padmanabhan, A., and Soltani, K.. “CyberGIS for Data-Intensive Knowledge Discovery,” ACM SIGSPATIAL Newsletter, 2014.
- Wang, S., Liu, Y., and Padmanabhan, A.. “Open CyberGIS Software for Geospatial Research and Education in the Big Data Era,” SoftwareX, 2015. doi:DOI:10.1016/j.softx.2015.10.003
- Hu, Hao and Lin, Tao and Wang, Shaowen and Rodriguez, Luis F. “A cyberGIS approach to uncertainty and sensitivity analysis in biomass supply chain optimization,” Applied Energy, v.203, 2017, p. 26–40. doi:https://doi.org/10.1016/j.apenergy.2017.03.107
- Wang, Shaowen. “CyberGIS and spatial data science,” GeoJournal, v.81, 2016, p. 965–968.
- Yin, Junjun and Soliman, Aiman and Yin, Dandong and Wang, Shaowen. “Depicting urban boundaries from a mobility network of spatial interactions: a case study of Great Britain with geo-located Twitter data,” International Journal of Geographical Information Science, v.31, 2017, p. 1293–131. doi:10.1080/13658816.2017.1282615
