Register now for the Spatial-Stream-Network (SSN) Model Training Workshop (Boise, ID)

Register now for the Spatial-Stream-Network (SSN) Model Training Workshop (Boise, ID)

Spatial-Stream-Network (SSN) Model Training Workshop

AGENDA
Day 1: Overview of spatial statistical network models: theory, software, and applications
Days 2 & 3: Work 1-on-1 with instructors to apply the spatial models to your datasets

COST $700 (students)
$1,000 (professionals)

DATES Oct 14–16, 2026

TIME 8:30 – 5:00

LOCATION Idaho Water Center
322 E Front Street
Boise, Idaho

Course Instructors
Dr. Erin Peterson (QUT)
Dr. Mike Dumelle (EPA)
Dr. Jay Ver Hoef (NOAA)
Dr. Dan Isaak (USFS)

FREE SOFTWARE PACKAGES
SSNbler GIS toolset: An Introduction to ‘SSNbler’: Assembling Spatial Stream Network (`SSN`) Objects in R • SSNbler

SSN2 package for the R statistical software: CRAN: Package SSN2

Spatial statistical stream-network (SSN) models are a new type of model based on covariance structures that account for flow direction, flow volume, and the branching structure of stream networks to provide a valid means of interpolating predictions between sample locations. SSNs are 0useful for describing spatial patterns in common stream attributes (e.g., water quality, habitat conditions, biological surveys) and often outperform traditional analytical techniques applied to stream data. SSNs also provide parameter estimates for covariate fixed effects and can be used to describe the spatial autocorrelation structure (i.e., non-independence) among measurements, which has utility for designing efficient monitoring strategies and spatial surveys.

  • Share free user-friendly software tools for SSN analysis:
    • SSNbler package for processing spatial data sets using GIS
      and the R statistical software
    • SSN2 package for analysis in the R statistical software
  • Introduction to the new R packages, SSNbler and SSN2, including
    new data structures and functionality
  • Demonstrate the data processing steps used to calculate the spatial
    information for fitting SSN models in R
  • Demonstrate how SSN2 is used for:
    • Exploratory data analysis
    • Modeling continuous, presence/absence, and count data
      using spatial linear, mixed-effects, and generalized models
    • Model diagnostics and selection
    • Prediction (geostatistical kriging)
    • Uncertainty estimation
    • Simulation and spatial visualization techniques for stream
      data using ggplot2

REQUIREMENTS A good working knowledge of statistics and the R statistical program are useful to obtain the greatest benefit from the workshop. Participants must bring a laptop with access to the R statistical software and a GIS of your choice (Recommended: ArcGIS Pro with standard or advanced license or QGIS software). Please contact the organizers if you cannot meet these requirements.

If you have questions about course enrollment, email: disaak67@gmail.com and support@spatialstreamnetworks.com