We develop a new stochastic spatio-temporal cointegration (SSTC) framework to model spatial and temporal dependence dynamics in high-dimensional non-stationary spatio-temporaldata of geological hazards. Such data, of...
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We develop a new stochastic spatio-temporal cointegration (SSTC) framework to model spatial and temporal dependence dynamics in high-dimensional non-stationary spatio-temporaldata of geological hazards. Such data, often collected from realtime remote sensing, are common in complex geophysical systems such as landslides, earthquakes and volcano eruptions. Our framework employs cointegrated vector autoregression to characterize the spatial-temporal dependence dynamics with error-correction. The framework is justified both statistically and by the domain mechanism underlying the data. By applying the SSTC method to only a small number of empirical dynamic quantile series that summarize the original large-scale data, we have achieved computational scalability with insignificant loss of spatio-temporal dynamic information. In this paper, we focus on deriving the SSTC framework and estimating the best SSTC model(s) by the maximum quasi-likelihood principle. We demonstrate the forecasting efficacy by applying our SSTC framework to radar data comprising displacement measurements recorded at 1803 locations and 5090 time states over 21.5 days. Broadly, our results provide new insights on modeling, dimension-reduction, estimation and prediction in large-scale and non-stationary, spatio-temporaldata analytics. Regarding landslide forecasting, this study delivers much-needed results for timely predictions of dynamics of an impending landslide from big and dense spatio-temporaldata. (C) 2020 Elsevier B.V. All rights reserved.
Considering basic characteristics of moving objects, like temporal,spatial,multi-dimensional,massive,the paper proposes approach to spatio-temporal data modeling for moving objects data management,which represents in ...
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Considering basic characteristics of moving objects, like temporal,spatial,multi-dimensional,massive,the paper proposes approach to spatio-temporal data modeling for moving objects data management,which represents in the model abstract data types and processes,dynamic attributes, spatio-temporal topological relationship,and spatio-temporal *** also presents how to use the model for applications of various moving objects query processing.
The paper researched on rendering art of large scale 3D city sight,and accomplished spatio-temporal data model of city objects taking geometry and action data into account integrated;Referred to the idea of LOD algori...
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The paper researched on rendering art of large scale 3D city sight,and accomplished spatio-temporal data model of city objects taking geometry and action data into account integrated;Referred to the idea of LOD algorithm of terrain quadtree,constituted scene quadtree,and improve search and cull efficiency of discrete city objects;proposed and accomplished a fast collision detection algorithm base on ZUFFER value,this method take full advantage of transform matrix and depth information,accomplished fast collision detection and response when user roaming in the fictitious scene as first *** these technologies,accomplished real time dynamic rendering of large scale 3D city scene.
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