The SSN package for R provides a set of functions for modeling stream network data. The package can import geographic information systems data or simulate new data as a ‘SpatialStreamNetwork’, a new object class that builds on the spatial sp classes. Functions are provided that fit spatial linear models (SLMs) for the ‘SpatialStreamNetwork’ object. The covariance matrix of the SLMs use distance metrics and geostatistical models that are unique to stream networks; these models account for the distances and topological configuration of stream networks, including the volume and direction of flowing water. In addition, traditional models that use Euclidean distance and simple random effects are included, along with Poisson and binomial families...
Streams and rivers host a significant portion of Earth’s biodiversity and pro-vide important ecosyst...
Many statistical models are available for spatial data but the vast majority of these assume that sp...
This paper describes the STARS ArcGIS geoprocessing toolset, which is used to calcu-late the spatial...
The SSN package for R provides a set of functions for modeling stream network data. The package can ...
We develop spatial statistical models for stream networks that can estimate relationships between a ...
Streams and rivers host a significant portion of Earth's biodiversity and provide important ecosyste...
Scientists need appropriate spatial-statistical models to account for the unique features of stream ...
In this article we use moving averages to develop new classes of models in a flexible modeling frame...
In this article we use moving averages to develop new classes of models in a flexible modeling frame...
Stream monitoring data provides insights into the biological, chemical and physical status of runnin...
Stream monitoring data provides insights into the biological, chemical and physical status of runnin...
The application of existing geostatistical theory to the context of stream networks provides a numbe...
Spatial autocorrelation is an intrinsic characteristic in freshwater stream environments where neste...
1. Geostatistical models based on Euclidean distance fail to represent the spatial configuration, co...
Streams and rivers host a significant portion of Earth’s biodiversity and pro-vide important ecosyst...
Many statistical models are available for spatial data but the vast majority of these assume that sp...
This paper describes the STARS ArcGIS geoprocessing toolset, which is used to calcu-late the spatial...
The SSN package for R provides a set of functions for modeling stream network data. The package can ...
We develop spatial statistical models for stream networks that can estimate relationships between a ...
Streams and rivers host a significant portion of Earth's biodiversity and provide important ecosyste...
Scientists need appropriate spatial-statistical models to account for the unique features of stream ...
In this article we use moving averages to develop new classes of models in a flexible modeling frame...
In this article we use moving averages to develop new classes of models in a flexible modeling frame...
Stream monitoring data provides insights into the biological, chemical and physical status of runnin...
Stream monitoring data provides insights into the biological, chemical and physical status of runnin...
The application of existing geostatistical theory to the context of stream networks provides a numbe...
Spatial autocorrelation is an intrinsic characteristic in freshwater stream environments where neste...
1. Geostatistical models based on Euclidean distance fail to represent the spatial configuration, co...
Streams and rivers host a significant portion of Earth’s biodiversity and pro-vide important ecosyst...
Many statistical models are available for spatial data but the vast majority of these assume that sp...
This paper describes the STARS ArcGIS geoprocessing toolset, which is used to calcu-late the spatial...