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Conventional monitoring of physical spaces uses manual sample collection to build coarse-grained spatiotemporal models of monitored phenomena. With the increased availability and affordability of low power sensors, using data from these sensors to validate and evolve existing models presents both a challenge and an opportunity. Conventional methods have undergone long-term testing yet they only provide coarse-grained information, while low power sensor networks are relatively new yet provide high resolution spatial and temporal data. The DSS Group is working on new methods for fusing the different data sources from sensor networks and conventional methods for more reliable and representative spatiotemporal models of the underlying environment. We have already applied this capability to the problem of mine rehabilitation, where mining companies must maintain a lease on the mine site until they can show they have restored the ecosystem to its original state.




K. Zhao, R. Jurdak, "Understanding the spatiotemporal pattern of grazing cattle movement,", Nature Scientific Reports, August 2016. (in press

 K. Zhao, R. Jurdak, J. Liu, D. Westcott, B. Kusy, H. Parry, P. Sommer, A. McKeown, ''Optimal Lévy-flight foraging in a finite landscape", Journal of the Royal Society Interface 12, 20141158 January 2015.

R. Jurdak, K. Zhao, J. Liu, M. AbouJaoude, M. Cameron, D. Newth, “Understanding Human Mobility from Twitter,” Accepted at PLOS ONE, JunePLOS ONE, 10(7): e0131469. doi:10.1371/journal.pone.0131469. July, 2015.

B. Kusy, C. Richter, S. Bhandari, R. Jurdak, M. Ngugi, G. Nelder, “Evidence-based Landscape Rehabilitation through Microclimate Sensing,” In proceedings of the Twelfth Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Seattle, USA, June 2015.