I couldn't understand trend phenomena during the class but now I do. Also, I really like how you interpret the variogram plot at 22:50. Interesting!! Thank you, Dr. Michael
In a standardised dataset (mean=0, std=1) even if the min might be say -4 and max +4, what reasons could the variogram have to rise above sill=1? I looked at several books and papers and I don't seem to find a reasonable answer...
Hi Dr. Pyrcs, you said that zonal anisotropy is typical for data, that has a periodicity or a trend. But aren't the existence of a trend or a cycle going against the assumption of spatial stationary? Why is it OK do disregard this violation of stationary? Edit: I guess I should have watched the video to the end first xD Great lecture series you are providing here!
I'm not sure his answer but I'm using universal kriging because I'm sure stationarity isn't an assumption. Though it is kind of considered an old method
Hi, I'm Ph.D. student studying at skku in Korea. It is very enjoyable to watch your lecture. I've translated some of your course videos in Korean myself and find out it is better for me to translate directly on youtube than using other tools. As I know, posting my work is up to your decision. I would like to translate through the youtube translation platform, whether the work will be posted or not. So What I would like to request is activating "the community contribution function" of your video clip. support.google.com/youtube/answer/6052538?hl=en-GB Thank you.
I couldn't understand trend phenomena during the class but now I do. Also, I really like how you interpret the variogram plot at 22:50. Interesting!! Thank you, Dr. Michael
Trust all is well Alwarda! I'm glad the content was helpful.
An amazing lecture. Thank you.
Great Video
In a standardised dataset (mean=0, std=1) even if the min might be say -4 and max +4, what reasons could the variogram have to rise above sill=1? I looked at several books and papers and I don't seem to find a reasonable answer...
Hi Dr. Pyrcs, you said that zonal anisotropy is typical for data, that has a periodicity or a trend. But aren't the existence of a trend or a cycle going against the assumption of spatial stationary? Why is it OK do disregard this violation of stationary?
Edit: I guess I should have watched the video to the end first xD
Great lecture series you are providing here!
I'm not sure his answer but I'm using universal kriging because I'm sure stationarity isn't an assumption. Though it is kind of considered an old method
Hi, I'm Ph.D. student studying at skku in Korea. It is very enjoyable to watch your lecture. I've translated some of your course videos in Korean myself and find out it is better for me to translate directly on youtube than using other tools. As I know, posting my work is up to your decision. I would like to translate through the youtube translation platform, whether the work will be posted or not. So What I would like to request is activating "the community contribution function" of your video clip.
support.google.com/youtube/answer/6052538?hl=en-GB
Thank you.
what is you handle on twitter ?
GeostatsGuy