IDEAS home Printed from https://ideas.repec.org/a/jss/jstsof/v056i03.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.jstatsoft.org/index.php/jss/article/view/v056i03/v56i03.pdf
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v056i03/SSN_1.1.2.tar.gz
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v056i03/v56i03.R
    Download Restriction: no
    File URL: https://libkey.io/http://hdl.handle.net/10.18637/jss.v056.i03?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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. Finley, Andrew O. & Banerjee, Sudipto & Carlin, Bradley P., 2007. "spBayes: An R Package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 19(i04).
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pebesma, Edzer & Bivand, Roger & Ribeiro, Paulo Justiniano, 2015. "Software for Spatial Statistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i01).
    2. 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).
    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. 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.
    5. Finley, Andrew O. & Banerjee, Sudipto & Gelfand, Alan E., 2015. "spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i13).
    6. Yirigui Yirigui & Sang-Woo Lee & A. Pouyan Nejadhashemi & Matthew R. Herman & Jong-Won Lee, 2019. "Relationships between Riparian Forest Fragmentation and Biological Indicators of Streams," Sustainability, MDPI, vol. 11(10), pages 1-24, May.
    7. 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.
    8. Bakar, Khandoker Shuvo & Sahu, Sujit K., 2015. "spTimer: Spatio-Temporal Bayesian Modeling Using R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i15).
    9. Gianfranco Piras & Mauricio Sarrias, 2023. "Heterogeneous spatial models in R: spatial regimes models," Journal of Spatial Econometrics, Springer, vol. 4(1), pages 1-32, December.
    10. Tilman M. Davies & Sudipto Banerjee & Adam P. Martin & Rose E. Turnbull, 2022. "A nearest‐neighbour Gaussian process spatial factor model for censored, multi‐depth geochemical data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(4), pages 1014-1043, August.
    11. David O'Donnell & Alastair Rushworth & Adrian W. Bowman & E. Marian Scott & Mark Hallard, 2014. "Flexible regression models over river networks," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 47-63, January.
    12. Sameh Abdulah & Yuxiao Li & Jian Cao & Hatem Ltaief & David E. Keyes & Marc G. Genton & Ying Sun, 2023. "Large‐scale environmental data science with ExaGeoStatR," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    13. Jhorland Ayala-García & Sandy Dall’Erba, 2021. "The impact of preemptive investment on natural disasters," Documentos de trabajo sobre Economía Regional y Urbana 301, Banco de la Republica de Colombia.
    14. Rodeschini, Jacopo & Fassò, Alessandro & Finazzi, Francesco & Fusta Moro, Alessandro, 2024. "Scenario analysis of livestock-related PM2.5 pollution based on a new heteroskedastic spatiotemporal model," Socio-Economic Planning Sciences, Elsevier, vol. 96(C).
    15. Fangpo Wang & Anirban Bhattacharya & Alan E. Gelfand, 2018. "Process modeling for slope and aspect with application to elevation data maps," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(4), pages 749-772, December.
    16. Matthew Heiner & Matthew J. Heaton & Benjamin Abbott & Philip White & Camille Minaudo & Rémi Dupas, 2023. "Model-Based Clustering of Trends and Cycles of Nitrate Concentrations in Rivers Across France," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(1), pages 74-98, March.
    17. Gerber, Florian & Furrer, Reinhard, 2015. "Pitfalls in the Implementation of Bayesian Hierarchical Modeling of Areal Count Data: An Illustration Using BYM and Leroux Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(c01).
    18. Maria Terres & Alan Gelfand, 2015. "Using spatial gradient analysis to clarify species distributions with application to South African protea," Journal of Geographical Systems, Springer, vol. 17(3), pages 227-247, July.
    19. Seoncheol Park & Hee‐Seok Oh, 2022. "Lifting scheme for streamflow data in river networks," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 467-490, March.
    20. Giorgi, Emanuele & Diggle, Peter J., 2017. "PrevMap: An R Package for Prevalence Mapping," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i08).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jss:jstsof:v:056:i03. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum (email available below). General contact details of provider: http://www.jstatsoft.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.