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Space-Time Analytics for Spatial Dynamics
Abstract
The so-called Big Data Challenge poses not only issues with massive volumes of data, but issues with the continuing data streams from multiple sources that monitor environmental processes or record social activities. Many statistics tools and data mining methods have been developed to reveal embedded patterns in large data sets. While patterns are critical to data analysis, deep insights will remain buried unless we develop means to associate spatiotemporal patterns to the dynamics of spatial processes that essentially drive the formation of patterns in the data. This chapter reviews the literature with the conceptual foundation for space-time analytics dealing with spatial processes, discusses the types of dynamics that have and have not been addressed in the literature, and identifies needs for new thinking that can systematically advance space-time analytics to reveal dynamics of spatial processes. The discussion is facilitated by an example to highlight potential means of space-time analytics in response to the Big Data Challenge. The example shows the development of new space-time concepts and tools to analyze data from two common General Circulation Models for climate change predictions. Common approaches compare temperature changes at locations from the NCAR CCSM3 and from the CNRM CM3 or animate time series of temperature layers to visualize the climate prediction. Instead, new space-time analytics methods are shown here the ability to decipher the differences in spatial dynamics of the predicted temperature change in the model outputs and apply the concepts of change and movement to reveal warming, cooling, convergence, and divergence in temperature change across the globe.
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