Anomaly detection and spatio-temporal analysis of global climate system
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
Geographic analysis & visualization of climate extremes for the Quadrennial Defense Review
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Introduction to data mining for sustainability
Data Mining and Knowledge Discovery
Spatiotemporal data mining in the era of big spatial data: algorithms and applications
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
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Knowledge discovery from temporal, spatial and spatiotemporal data is critical for climate change science and climate impacts. Climate statistics is a mature area. However, recent growth in observations and model outputs, combined with the increased availability of geographical data, presents new opportunities for data miners. This paper maps climate requirements to solutions available in temporal, spatial and spatiotemporal data mining. The challenges result from long-range, long-memory and possibly nonlinear dependence, nonlinear dynamical behavior, presence of thresholds, importance of extreme events or extreme regional stresses caused by global climate change, uncertainty quantification, and the interaction of climate change with the natural and built environments. This paper makes a case for the development of novel algorithms to address these issues, discusses the recent literature, and proposes new directions. An illustrative case study presented here suggests that even relatively simple data mining a proaches can provide new scientific insights with high societal impacts.