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SSN: An R Package for Spatial Statistical Modeling on Stream Networks

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  • Ver Hoef, Jay
  • Peterson, Erin
  • Clifford, David
  • Shah, Rohan

Abstract

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, for a generalized linear mixed model framework. Plotting and diagnostic functions are provided. Prediction (kriging) can be performed for missing data or for a separate set of unobserved locations, or block prediction (block kriging) can be used over sets of stream segments. This article summarizes the SSN package for importing, simulating, and modeling of stream network data, including diagnostics and prediction.

Suggested Citation

  • Ver Hoef, Jay & Peterson, Erin & Clifford, David & Shah, Rohan, 2014. "SSN: An R Package for Spatial Statistical Modeling on Stream Networks," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 56(i03).
    • Handle: RePEc:jss:jstsof:v:056:i03
    DOI: http://hdl.handle.net/10.18637/jss.v056.i03
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    References listed on IDEAS

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    1. Peterson, Erin & Ver Hoef, Jay, 2014. "STARS: An ArcGIS Toolset Used to Calculate the Spatial Information Needed to Fit Spatial Statistical Models to Stream Network Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 56(i02).
    2. Smith, Brian J. & Yan, Jun & Cowles, Mary Kathryn, 2008. "Unified Geostatistical Modeling for Data Fusion and Spatial Heteroskedasticity with R Package ramps," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i10).
    3. Ver Hoef, Jay M. & Peterson, Erin E., 2010. "A Moving Average Approach for Spatial Statistical Models of Stream Networks," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 6-18.
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    Cited by:

    1. Ying Man & Fangwen Zhou & Baoshan Cui, 2023. "Process–Based Identification of Key Tidal Creeks Influenced by Reclamation Activities," Sustainability, MDPI, vol. 15(10), pages 1-11, May.
    2. Peterson, Erin & Ver Hoef, Jay, 2014. "STARS: An ArcGIS Toolset Used to Calculate the Spatial Information Needed to Fit Spatial Statistical Models to Stream Network Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 56(i02).
    3. Eric W Fox & Jay M Ver Hoef & Anthony R Olsen, 2020. "Comparing spatial regression to random forests for large environmental data sets," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-22, March.
    4. Eric Craig Watson & Heejun Chang, 2018. "Relation Between Stream Temperature and Landscape Characteristics Using Distance Weighted Metrics," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 1167-1192, February.
    5. Santos-Fernandez, Edgar & Ver Hoef, Jay M. & Peterson, Erin E. & McGree, James & Isaak, Daniel J. & Mengersen, Kerrie, 2022. "Bayesian spatio-temporal models for stream networks," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).
    6. Tsung-Ta David Hsu & Danlin Yu & Meiyin Wu, 2023. "Predicting Fecal Indicator Bacteria Using Spatial Stream Network Models in A Mixed-Land-Use Suburban Watershed in New Jersey, USA," IJERPH, MDPI, vol. 20(6), pages 1-17, March.
    7. Pebesma, Edzer & Bivand, Roger & Ribeiro, Paulo Justiniano, 2015. "Software for Spatial Statistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i01).

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