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#kriging — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #kriging, aggregated by home.social.

  1. Back to topic.
    Modelling has changed and improved a lot. Mostly due to more powerful computers allowing more brute force analysis. The math tools really haven't changed that much. Early commercial modelling included programs like #Surfer decades ago and used #Kriging, (#Gaussian peocess regression) of geostatistical data. Most current geospatial modelling uses this technique, often enhanced with #BayesianDataAnalysis (read the book by that name). So now the table is mostly set. 5/

  2. Back to topic.
    Modelling has changed and improved a lot. Mostly due to more powerful computers allowing more brute force analysis. The math tools really haven't changed that much. Early commercial modelling included programs like #Surfer decades ago and used #Kriging, (#Gaussian peocess regression) of geostatistical data. Most current geospatial modelling uses this technique, often enhanced with #BayesianDataAnalysis (read the book by that name). So now the table is mostly set. 5/

  3. Back to topic.
    Modelling has changed and improved a lot. Mostly due to more powerful computers allowing more brute force analysis. The math tools really haven't changed that much. Early commercial modelling included programs like #Surfer decades ago and used #Kriging, (#Gaussian peocess regression) of geostatistical data. Most current geospatial modelling uses this technique, often enhanced with #BayesianDataAnalysis (read the book by that name). So now the table is mostly set. 5/

  4. Back to topic.
    Modelling has changed and improved a lot. Mostly due to more powerful computers allowing more brute force analysis. The math tools really haven't changed that much. Early commercial modelling included programs like #Surfer decades ago and used #Kriging, (#Gaussian peocess regression) of geostatistical data. Most current geospatial modelling uses this technique, often enhanced with #BayesianDataAnalysis (read the book by that name). So now the table is mostly set. 5/

  5. Back to topic.
    Modelling has changed and improved a lot. Mostly due to more powerful computers allowing more brute force analysis. The math tools really haven't changed that much. Early commercial modelling included programs like #Surfer decades ago and used #Kriging, (#Gaussian peocess regression) of geostatistical data. Most current geospatial modelling uses this technique, often enhanced with #BayesianDataAnalysis (read the book by that name). So now the table is mostly set. 5/

  6. QGIS Processing Toolbox tool for Variogram Modeling and Ordinary Kriging using GSTools
    This tool automates variogram modeling and kriging within QGIS, providing a user-friendly interface for spatial interpolation.

    github.com/geosaber/geostat

  7. QGIS Processing Toolbox tool for Variogram Modeling and Ordinary Kriging using GSTools
    This tool automates variogram modeling and kriging within QGIS, providing a user-friendly interface for spatial interpolation.
    #qgis #geostatistics #kriging #prediction #variogram #python #gstools #gstat
    github.com/geosaber/geostat

  8. QGIS Processing Toolbox tool for Variogram Modeling and Ordinary Kriging using GSTools
    This tool automates variogram modeling and kriging within QGIS, providing a user-friendly interface for spatial interpolation.
    #qgis #geostatistics #kriging #prediction #variogram #python #gstools #gstat
    github.com/geosaber/geostat

  9. QGIS Processing Toolbox tool for Variogram Modeling and Ordinary Kriging using GSTools
    This tool automates variogram modeling and kriging within QGIS, providing a user-friendly interface for spatial interpolation.
    #qgis #geostatistics #kriging #prediction #variogram #python #gstools #gstat
    github.com/geosaber/geostat

  10. QGIS Processing Toolbox tool for Variogram Modeling and Ordinary Kriging using GSTools
    This tool automates variogram modeling and kriging within QGIS, providing a user-friendly interface for spatial interpolation.
    #qgis #geostatistics #kriging #prediction #variogram #python #gstools #gstat
    github.com/geosaber/geostat

  11. @marie_ahoi I did my dissertation using spatial data but it involved mortality and in the US that's outside of IRB (for this analysis anyway). For our large study of #COPD ( #COPDGene ) though the participants are present and protected by #HIPPA so - higher standard. I think that going forward we'll pull all of the shape files, do all of our #kriging and other work before we even think about merging the spatial identifiers. Now what does THAT grant look like??!

  12. @marie_ahoi I did my dissertation using spatial data but it involved mortality and in the US that's outside of IRB (for this analysis anyway). For our large study of #COPD ( #COPDGene ) though the participants are present and protected by #HIPPA so - higher standard. I think that going forward we'll pull all of the shape files, do all of our #kriging and other work before we even think about merging the spatial identifiers. Now what does THAT grant look like??!

  13. @marie_ahoi I did my dissertation using spatial data but it involved mortality and in the US that's outside of IRB (for this analysis anyway). For our large study of #COPD ( #COPDGene ) though the participants are present and protected by #HIPPA so - higher standard. I think that going forward we'll pull all of the shape files, do all of our #kriging and other work before we even think about merging the spatial identifiers. Now what does THAT grant look like??!

  14. @marie_ahoi I did my dissertation using spatial data but it involved mortality and in the US that's outside of IRB (for this analysis anyway). For our large study of #COPD ( #COPDGene ) though the participants are present and protected by #HIPPA so - higher standard. I think that going forward we'll pull all of the shape files, do all of our #kriging and other work before we even think about merging the spatial identifiers. Now what does THAT grant look like??!

  15. New release of ! Version 0.3.5, and:

    - the package is computationally stable (no more `LinAlgErrors` in system, or even if they are raised, the users will know what went wrong),
    - a lot of optimization,
    - emphasis put on directional variograms.

    The following steps: directional Poisson Kriging and more tutorials in the documentation.