Hi There,
I have been building data visualization models for a while now and have used the kriging and IDW to good effect. One thing that has come up is that the data forms stripes along survey lines. Is there any instruction on how to de-stripe data so that it can be better visualized and not have the long linear features that are an artifact of the search radius? I know SAGA has a de-stripe tool but am unclear on how this works.
Any instructions that can be given on how to use the de-stripe function (or similar to remove artifacts) would be very welcomed.
Thanks for the help.
Pat.
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this is difficult to answer without any information about which kind of data you are trying to visualize. An image of the results would also help.
But I think that these kinds of interpolation effects (or actually errors) should be prevented in the first place - the striping indicates that the interpolation is wrong and that you get erroneous results between your survey lines. This should be fixed by tuning the parameters of the interpolation tools or by preprocessing the data before interpolating it. In the worst case you might need to lower your grid resolution in order to match the properties of your input data.
Best regards,
Volker
👍
1
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Dear Pat,
maybe you could try Multilevel B-Spline Interpolation instead of IDW.
It also seems to us that it is often IMHO better than Kriging which is time consuming to calculate and complex to set up.
there are no dedicated tools for pre-processing in this case and in general this is difficult. There would be two possibilities: dropping input data (points) or performing special interpolations to densify the data to close gaps.
Best is to workaround the problem but adjusting the interpolation parameters.
❤️
1
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The striping artifacts you're seeing are a common issue in spatial data interpolation, especially with kriging and IDW, where survey line density and directional bias can influence the results. Understanding and addressing these issues often requires access to specialized knowledge and tools, which is why using online educational resources becomes so crucial. By leveraging online tutorials, courses, and forums, learners and professionals alike can gain the expertise needed to navigate complex challenges in data visualization, ensuring they can apply the right techniques to improve their results and avoid common pitfalls like striping artifacts.
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I agree with Frank. Striping artifacts can indeed be a major challenge in spatial data interpolation, especially when using kriging or IDW. The issue often arises due to survey line density and directional bias, making it essential to understand how different interpolation methods handle spatial relationships. One way to deepen your knowledge is by engaging with online communities and educational resources. For instance, discussions like this one https://customwriting.com/forum/threads/pay-someone-to-write-my-essay-need-real-advice-from-experienced-students.9/ can be incredibly useful, as they connect learners with experienced individuals who can provide real-world insights. Whether it’s refining interpolation techniques or troubleshooting visualization issues, leveraging online tutorials, forums, and expert advice can make a significant difference in mastering spatial data analysis and avoiding common pitfalls like stripping artifacts.
Last edit: Derrick Bozem 2025-02-05
If you would like to refer to this comment somewhere else in this project, copy and paste the following link:
Hi There,
I have been building data visualization models for a while now and have used the kriging and IDW to good effect. One thing that has come up is that the data forms stripes along survey lines. Is there any instruction on how to de-stripe data so that it can be better visualized and not have the long linear features that are an artifact of the search radius? I know SAGA has a de-stripe tool but am unclear on how this works.
Any instructions that can be given on how to use the de-stripe function (or similar to remove artifacts) would be very welcomed.
Thanks for the help.
Pat.
Hi Pat,
this is difficult to answer without any information about which kind of data you are trying to visualize. An image of the results would also help.
But I think that these kinds of interpolation effects (or actually errors) should be prevented in the first place - the striping indicates that the interpolation is wrong and that you get erroneous results between your survey lines. This should be fixed by tuning the parameters of the interpolation tools or by preprocessing the data before interpolating it. In the worst case you might need to lower your grid resolution in order to match the properties of your input data.
Best regards,
Volker
Dear Pat,
maybe you could try Multilevel B-Spline Interpolation instead of IDW.
It also seems to us that it is often IMHO better than Kriging which is time consuming to calculate and complex to set up.
regards
Jan
Thanks for the input Volker and Jan.
I will try all the suggestions because I am also getting circles in the processing and not just the straight lines on the orientation of my data.
What tools would I use for the pre-processing of the data Volker?
Thanks for your help,
Pat.
Hi Pat,
there are no dedicated tools for pre-processing in this case and in general this is difficult. There would be two possibilities: dropping input data (points) or performing special interpolations to densify the data to close gaps.
Best is to workaround the problem but adjusting the interpolation parameters.
Thanks Volker
The striping artifacts you're seeing are a common issue in spatial data interpolation, especially with kriging and IDW, where survey line density and directional bias can influence the results. Understanding and addressing these issues often requires access to specialized knowledge and tools, which is why using online educational resources becomes so crucial. By leveraging online tutorials, courses, and forums, learners and professionals alike can gain the expertise needed to navigate complex challenges in data visualization, ensuring they can apply the right techniques to improve their results and avoid common pitfalls like striping artifacts.
I agree with Frank. Striping artifacts can indeed be a major challenge in spatial data interpolation, especially when using kriging or IDW. The issue often arises due to survey line density and directional bias, making it essential to understand how different interpolation methods handle spatial relationships. One way to deepen your knowledge is by engaging with online communities and educational resources. For instance, discussions like this one https://customwriting.com/forum/threads/pay-someone-to-write-my-essay-need-real-advice-from-experienced-students.9/ can be incredibly useful, as they connect learners with experienced individuals who can provide real-world insights. Whether it’s refining interpolation techniques or troubleshooting visualization issues, leveraging online tutorials, forums, and expert advice can make a significant difference in mastering spatial data analysis and avoiding common pitfalls like stripping artifacts.
Last edit: Derrick Bozem 2025-02-05