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The BYM approach models the spatial effect of region j as the sum of a spatially dependent component δsj and a spatially independent, or heterogeneity, component δhj, δj = δsj+δhj.
In LES models, the spatial resolution plays the role of a filter of scales smaller than the cell size.
Due mostly to aggregation and topological properties when modelling these models, the spatial complexity can now be also introduced [26, 47, 51].
We test spatial autocorrelation with six different spatial weight matrices (SWM) in three different spatial models: the spatial lag model, spatial error model, and general spatial model.
For each property, we design a branch of residual convolutional units, each of which models the spatial properties of crowd traffic.
Three primary tasks have been identified in most hydrologic applications with sophisticated computerized numerical models: the spatial database construction, integration of spatial model layers and the GIS and model interface.
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Based on these models, the spatial-temporal framework was further built using data collection and processing.
In the above studies, a single modelling framework was used to model the spatial and time-varying effects simultaneously.
for a parameter ξ > 0 modeling the spatial correlation length.
The whole 10 s window was used to model the spatial spread of the seizure.
The authors in [26, 27] used SSI to model the spatial distribution of potential interference sources.
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