Data acquisition, analysis and visualization are critical components of intelligence solutions. Of significant interest, is improving on these solutions to accommodate multi-dimensional, spatiotemporal data that permits actionable intelligence in real-time. Current systems, however, suffer from reduced scalability and inability to handle spatiotemporal data. Researcher at the University of Louisiana at Lafayette have overcome these limitations by development of a novel real-time solution for forecasting emerging trends within a multi-dimensional, spatiotemporal network of sensors. A key aspect of this technology is incorporation of historical observations between spatially coupled sensors. This technology provides an end-to-end scalable visual analytic framework that supports sensor data processing, analysis and visualization to enable real-time business intelligence. Further, user visual exploration is achieved though virtual reality methods and/or consumer-level devices.