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To obtain insights into the history of this relict biota, we modelled the spatial distribution of habitats during the Last Glacial Maximum (LGM) in the Russian Altai.
Based on the series of satellite images we modelled the spatial urban pattern and extent of built-up change in recent years with the help of a grid structure and we computed a synthetic indicator for the vegetation study, especially for evaluating human intervention through cultivated fields, farms and deforestation.
This was investigated in the Santa Fe River watershed (SFRW), Florida, where we modelled the spatial distribution of total C (TC) at four depths to 180 cm (0 30, 30 60, 60 120, and 120 180 cm) and at an aggregated depth of 0 100 cm.
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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.
To model the spatial correlation between the data points, an anisotropic variogram was built.
This relies on investigating and modelling the spatial autocorrelation among sample data by variogram analysis.
It is, however, an approximate method that does not model the spatial distribution of scatter [10].
Advances continue to be made in modelling the spatial pattern of habitat.
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