After a long search, I finally found the explanation I was looking for. You break down complex concepts really well, keep it up, I am sure your channel will grow exponentially.
Thank you for this, man. I somehow ended up in an advanced geostatistics class as a complete newbie and so far I've just been following the instructions without even understanding what's going on. You present these matters in a way that is super easy to follow, irregardless of the persons background. :)
Very well done! When you say, this formula, what it means in English ..., this is exactly the missing part from so many people trying to pass the same message, this is why most of them fail I think. Bringing math to everyone's real life. This is how we recognise Masters as well. Big thank you.
man, that was the most indispensable video lacking on UA-cam in the area of data science. wish the younger me were able to watch this video instead of sifting through the papers about kriging. thanks aplenty!
Awesome video. This guy has all the potential to become a great teacher. This would be nice if you can make a video on different types of kriging models, and how to implement them on a GIS framework :)
WOW EVERYTHING IS MAKING SO MUCH MORE SENSE NOW!!! Thank you so much, your videos are saving me rn. I would love to see more spatial statistics content if you ever were thinking about it!! Wow though, thank you a thousand times.
I started my research reading recently and was feeling struggled with understanding the definition and formula. This video gives me a brief and clear intro to Spatial Stat. It helps me a lot! LOL
i have two questions : the first is do you try moving least squares method , the second is how can we overcome the inconvenient you have mentioned at the end of the video ? in otherwords , is there another interpolation method which permit to predict a lot of point ( not just one point ) with a cheap price ?
Thank you for such a clear and concise explanation of kriging! I have been struggling a bit to understand the 'why' of kriging and how to interpret my data- following labs like a cookbook and just 'getting through them' will not help me with a GIS career, so I appreciate you so much!! Subscribed! I will look for more videos, thank you again!!!
Great video, easy to digest, and as an on-going bachelor in earth science, i really recommend this for you whom have absolutely zero idea about kriging model (imma recommend this too for my colleagues lol). However, one question. At timemark 9:56 - 10:10, the matrice equation includes matrice b which has the semi-variogram calculation between Xnew and Xi. The calculation itself supposed to knew Ynew, right (as showed in the video)? But Ynew is our main objective, which is unknown...bit entangled on this one.
I have exactly the same question. This is why I was going through the comments. Thank you. Perhaps the answer is given in another video. We can still solve the equation, but with 5 + 1 = 6 unknown variables (w1, w2, w3, w4, w5, Ynew) instead of w1, w2, w3, w4, w5.
Wow this video is a lifesaver.. Thank you so much.. This is what you call a crystal clear explanation. Please go ahead with the mathematical concepts behind this as well. Thank you very much again.
Thank you very much for this video! :) It's the best explanation I've seen so far. I'd like to hear more about the math behind the Kriging model. It would be also interesting to hear more about other applications, e.g. engineering application of the surrogate (Kriging) model.
Excellent explanation, thank you! This is how all mathematical topics should be explained in my opinion. Bad communication in math is like bad communication in software development, sure you can write all the thousands of lines of code in one line, the compiler will understand it! But this is not optimal if you're trying to convey an idea to others.
This is a really good video. I was able to understand and I'm not even a statistics or mathematics student. I'm studying Geographic Information Systems and Kriging is one of the most common Spatial Interpolation methods.
Thank you for this video. It’s very clear. Gamma of h is more precisely the expected value of the squared differences between all pairs of point a distance h apart. Although to discuss the intuition behind it you can consider a single pair of points as you have done.
Man your explanation is so clear :D you did a great job, hopefully you can make another video about krigging model like ordinary, simple, etc.. you really inspiring me :)
It's just an AMAZING explanation, I saw the video more than 10 times and that's is one of the best educational video I've ever seen. Question please : How can we know the matrix B while Ynew is unknown ? because B is a matrix of variogram : Gamma(Xnew,Xi) = 1/2(Ynew - Yi)² , and Ynew is unknown.
Can u please teach the mathematics involved in kriging, if possible can u please teach kriging using python. I am very much interested in learning kriging model. Thank you
Really clear video about the topic, thank you very much. I would love to see video explanation of the math being the kriging model, if it is explained as well as this video. Thank you!
Great video, thanks. What if you wanted to predict something at the exact location of a measured site - can you borrow strength from near by sites and also get an estimate of uncertainty at that site (rather than just relying on the measurement)?
Nice video. I am a bit confused about your notation - gamma(x_i, x_j) = 0.5(y_i-y_j)^2? These are different variables... Did you mean gamma(y_i, y_j) or 0.5(x_i-x_j)^2? Also, if it is equal to 0.5(y_i-y_j)^2, then how can you calculate b? You do not know X_new, so you could not calculate b...
Man, this video explanation is clear as water for me. The quality is good, well done dude :)
Thanks!
Can confirm. I learned more from this video that from a whole (poorly made) lecture series on geostatistics.
damn so good explanation. This helped me a lot. Better than my professor...
After a long search, I finally found the explanation I was looking for. You break down complex concepts really well, keep it up, I am sure your channel will grow exponentially.
Thank you for this, man. I somehow ended up in an advanced geostatistics class as a complete newbie and so far I've just been following the instructions without even understanding what's going on. You present these matters in a way that is super easy to follow, irregardless of the persons background. :)
One of the best educational videos ive ever seen, clear, concise and well structured! Congrats
Glad you liked it!
Very well done!
When you say, this formula, what it means in English ...,
this is exactly the missing part from so many people trying to pass the same message, this is why most of them fail I think.
Bringing math to everyone's real life.
This is how we recognise Masters as well.
Big thank you.
man, that was the most indispensable video lacking on UA-cam in the area of data science. wish the younger me were able to watch this video instead of sifting through the papers about kriging. thanks aplenty!
Glad it was helpful!
This is an amazing explanation of the Kriging Model. Its super impressive how you do this in one cut!
Thank you very much!
Awesome video. This guy has all the potential to become a great teacher.
This would be nice if you can make a video on different types of kriging models, and how to implement them on a GIS framework :)
I would like to know the mathematics involved in Kriging. Please make a dedicated video if you can.
Me too! XD
After a long search, I finally found the explanation I was looking for. You break down complex concepts really well thanks
WOW EVERYTHING IS MAKING SO MUCH MORE SENSE NOW!!! Thank you so much, your videos are saving me rn. I would love to see more spatial statistics content if you ever were thinking about it!! Wow though, thank you a thousand times.
I'm so glad!
Awesome video! Clearly explained. Big shout out from Brazil.
I started my research reading recently and was feeling struggled with understanding the definition and formula. This video gives me a brief and clear intro to Spatial Stat. It helps me a lot! LOL
Man , such a pro! Love the way you explain crucial concepts on spatial statistics so easy, greetings from colombia
Currently studying geostatistics in an advanced GIS class just a massive shout out that this is an incredibly good explanation!
Thank you for not making this a "1-hour" ordeal.. I enjoyed this session to the latter..thank you again
Thank you very much!!! I am a Chinese student, your videos help me a lot!
oh man, this is a great explanation of kriging
the best explanation of Kriging model i found so far!! thanks man!
Glad it helped!
Bless your soul, man. Explained better than my prof
Dude. This video is frigging awesome. Very well explained, crystal clear!
i have two questions : the first is do you try moving least squares method , the second is how can we overcome the inconvenient you have mentioned at the end of the video ? in otherwords , is there another interpolation method which permit to predict a lot of point ( not just one point ) with a cheap price ?
Great explaination! I have done this before, but needed a refresher. This was perfect. Thanks!
Thank you for such a clear and concise explanation of kriging! I have been struggling a bit to understand the 'why' of kriging and how to interpret my data- following labs like a cookbook and just 'getting through them' will not help me with a GIS career, so I appreciate you so much!! Subscribed! I will look for more videos, thank you again!!!
best explaination ever. better than some claiming solid.
I can't explain how amazing this video is...!!!
This is great explanation. Please teach the mathematics involved in kriging.
Super helpful! Thank you, it made sense all the way and witthout watering down any important notes on the Kriging Model 👍
Great video, easy to digest, and as an on-going bachelor in earth science, i really recommend this for you whom have absolutely zero idea about kriging model (imma recommend this too for my colleagues lol).
However, one question. At timemark 9:56 - 10:10, the matrice equation includes matrice b which has the semi-variogram calculation between Xnew and Xi. The calculation itself supposed to knew Ynew, right (as showed in the video)? But Ynew is our main objective, which is unknown...bit entangled on this one.
I have exactly the same question. This is why I was going through the comments. Thank you.
Perhaps the answer is given in another video.
We can still solve the equation, but with 5 + 1 = 6 unknown variables (w1, w2, w3, w4, w5, Ynew) instead of w1, w2, w3, w4, w5.
Exactly! I have the same remark :) I hope that @ritvikmath can give us an explanation
Wow this video is a lifesaver.. Thank you so much.. This is what you call a crystal clear explanation. Please go ahead with the mathematical concepts behind this as well. Thank you very much again.
Thank you very much for this video! :) It's the best explanation I've seen so far. I'd like to hear more about the math behind the Kriging model. It would be also interesting to hear more about other applications, e.g. engineering application of the surrogate (Kriging) model.
You explain the concepts better than my uni lecturer... thank you!
Excellent explanation, thank you! This is how all mathematical topics should be explained in my opinion. Bad communication in math is like bad communication in software development, sure you can write all the thousands of lines of code in one line, the compiler will understand it! But this is not optimal if you're trying to convey an idea to others.
This is a really good video. I was able to understand and I'm not even a statistics or mathematics student. I'm studying Geographic Information Systems and Kriging is one of the most common Spatial Interpolation methods.
Wonderful explanation. Great use of visuals and introduction of terms with symbols.Thank you!
I wish I could like this video twice! you explained it so well I felt like I just got spoon fed the information
what a wonderful way of teaching 👏👏👏.loved it
This comment is really from the heart
keep doing what you are doing
Good and clear illustration of widely used but less understood model
Many thanks for the introduction about the Kriging model. Best! I would love to know more about the math behind the kriging model and variogram.
Thank you for this video. It’s very clear. Gamma of h is more precisely the expected value of the squared differences between all pairs of point a distance h apart. Although to discuss the intuition behind it you can consider a single pair of points as you have done.
clear and straightforward. Thank you!
Man your explanation is so clear :D you did a great job, hopefully you can make another video about krigging model like ordinary, simple, etc.. you really inspiring me :)
Noted!
Thanks, really helpful, watched from Sudan.
It's just an AMAZING explanation, I saw the video more than 10 times and that's is one of the best educational video I've ever seen.
Question please : How can we know the matrix B while Ynew is unknown ?
because B is a matrix of variogram : Gamma(Xnew,Xi) = 1/2(Ynew - Yi)² , and Ynew is unknown.
Echo
Tricky topic, but beatifully explained. Thanks.
thanks for the good and clear explanation , you make it very easy to follow and understand
Never thought about this being used for anything other than mining, but it makes sense that it will work for any spatial estimation
Totally!
Awesome, many thanks!
Of course!
Do more spatial and geostatistics please! I'd like to see the math behind dependence tests.
thank u .. u make it easy to understand how the kriging work 🌹
Thank you very much for your excellent video !
Great Explanation. Thankyou. finally found a good explanation.
Did you ever go through the full mathematical model? would love to see that vid.
This was explained really well. Good job!
Great succinct explanation. Would love more on this subject
Good explanation to dive into the topic. Thank you very much!
Awesome work!
Excellent video! Thank you so much!
Impressive. Keep doing things even close to this well and you will become well known among students and researchers.
This video is perfect for me!!!, thank uuuuuuuu !! you saved me from the final!!
Thank you for super good video! Now, I start to understand
This is such a good explanation, thank you!
Thank you very much sir for clean and neat explanation.
Cmon, This is too simple now! Thanks dude
so elegant, I love this intuition of math！
Great explanation
Can u please teach the mathematics involved in kriging, if possible can u please teach kriging using python. I am very much interested in learning kriging model.
Thank you
Great idea! I will look into it
This was amazing. Thank you so much
Very well explained man!! Thanks for doing it and hope to see more videos about geostats from you!!
I think that we need more geostatistics lessons from you.
Thanks - great explanation of the variogram!
Thanks!
Great video, excellent explanation!! Thanks a lot!
Nice work ... would like to see your suggested more detailed delve into the math and krihing variance ...
Very nice video, you really cleared my problem of understanding this concept of kriging/
very great explanation of kriging, Thanks man
Glad it was helpful!
Thank you soo much for this great and awesome explanation..
I would be keen to see the video with more mathematics that you mention here.
Please upload the video describing the maths behind it! You explanation was very clear, as I am trying to get grip of this topic.
Thanks that was amazing
Really great video.
Great Explanation thanks 🙏🏼
Awesome video, thanks! :)
So the kriging model was more of a 'guess-how-my-cousin-look' game by looking at the "relatives" got it, thanks!
Haha good analogy!
crystal clear explanation on the topic. Thanks.
Glad it was helpful!
Really nice video, thank you!
Glad you liked it!
Clear and easy explanation, thanks a lot!
Well explained ❤❤❤ thanks so much
Can you explain the built-in feature about errors?
Really clear video about the topic, thank you very much. I would love to see video explanation of the math being the kriging model, if it is explained as well as this video. Thank you!
Great suggestion!
Awesome explanation. So helpful, thank you 🙏🏻
Glad it was helpful!
great video cleared a lot of doubts. Interested in knowing the mathematics behind it.
Great video thanks! I'll be learning from all your videos 😁
Great video, thanks. What if you wanted to predict something at the exact location of a measured site - can you borrow strength from near by sites and also get an estimate of uncertainty at that site (rather than just relying on the measurement)?
Insanely good thank you so much!
Very clear video. Thank u
Excellent
Great simple explanation!
Great explanation! Thanks :)
Nice video. I am a bit confused about your notation - gamma(x_i, x_j) = 0.5(y_i-y_j)^2? These are different variables... Did you mean gamma(y_i, y_j) or 0.5(x_i-x_j)^2? Also, if it is equal to 0.5(y_i-y_j)^2, then how can you calculate b? You do not know X_new, so you could not calculate b...
exactly my question too..
me too
Very clearly explained, however, how does the number of neighbors got decided? Is that all points have a distance below range? Thanks!