The right amount of math to make it applicable, but not so much that it remove the focus on what is happening. Thanks a lot for putting this quality content up on youtube!
Thank you Mathias for the feedback. I love math, but I love accessible education even more! I'm always trying to balance! I'm glad to hear that the content is useful!
Excellent lecture! I have used Kriging for more than a decade but did not have the insights that you presented in your 36 min lecture. Also, the excel spreadsheet very useful in getting an intuitive understanding of the Kriging method. Much appreciated!
Yes it is awesome! I watched all your lecture series on Data Analytics and Geostatistics from 1a Data Analytics Reboot: Statistics Concepts to the last. As a graduate student in environmental science with biological sciences as undergrad who has limited statistics and mathematical background, your videos and teaching materials are very helpful as they are easier to understand than papers/books which are also difficult to obtain. Thank you very much for your generosity in sharing your expertise to wannabe geostatisticians: your lecture materials (PDF format), videos, program codes in R and Python. I'm experienced in R but not in Python, but your Python workflows motivated me and now I can program in Python as well!
@@renatojrfolledo5728, thank you for sharing this. Now I'm totally stoked. This is why I do it! We are all one scientific community and more data analytics will improve science everywhere! Thank you, Michael
Great lecture!!!. (correct me if I am wrong) the main assumption here is to have a signal noise representation of the signal f=m+e, then for predicting a new signal f*=m*+e*, kriging assumes that e*=sum_i a_i e_i = sum_i a_i (f_i-m_i) which gives the equation f*=sum_i a_i f_i + m*- sum_i a_im_i. Then weights a_i are founded by minimizing the variance with constraint sum_i a_i=1. (1) All this start by modeling e*=sum_i a_i e_i right?, 2) If we assume that f's are realization of Gaussian process, then f* can be estimated using equation 2.19 or 2.25 from this book www.gaussianprocess.org/gpml/chapters/RW.pdf i.e., the approach you are using wil be equivalent (equal) than the showed in that book, right?). Thank for sharing this video
Thank you for the great lecture i have a question about the percentage of measurement error in ARC map while using any kriging technique it assumes that the measurement error equals to 100% of the error. this negatively ompact the generated geostatistical layer contour lines and the values at the easured rain gauges are significantly impacted. when i use a zero percent error the generated geostatistical layers for both t universal and ordinary kriging are identical
That is a great idea, Nazmul. I mentioned universal kriging in class for completeness to expand on the idea that common kriging variants are driven by various stationarity assumptions. I'll put together some content on this. Aside, given my geoscience-oriented engineering background I generally prefer mapped trend models. Thank you, Michael
@@CK-vy2qv, you are correct. That is Darby, my rescue dog! She likes to join in my recorded lectures. I'm glad that you are finding the content useful!
Thanks a lot for the great lecture. Learning from this lecture that Kriging accounts for distance (i.e., in the lecture, increase/decrease weights if a sampling point is closer/further away from the unknown location) when assigning weights, would you still recommend to perform data declustering and calculate the weights for data? I just wonder if account for data closeness twice would be redundant? Thank you very much in advance!
Great lecture !! I've noticed a small mistake at 14:17 : The indexes are C(u_i,u_j) instead of C(u_i,u_i) on the two equations ! Thanks again I hope I'm not wrong
Howdy Solima, you're welcome and check out my ExcelNumericalDemos repository, I have simple kriging, indicator kriging and collocated cokriging by-hand. Hope this helps.
Good question! Dr. Journel told us not to put kriging estimates in maps! They are the best estimates at each location, but jointly they are incorrect, because they do not honor the histogram nor the variogram. The kriging variance is the missing variance in kriging and a measure of uncertainty in the estimate.
Howdy Olatunde, you could cite the book, Pyrcz and Deutsch, 2014, Geostatistical Reservoir Modeling, 2nd edition. I'm glad the content is useful to you, Michael
Hello sir. Thanks again for your great and straightforward content on geostatistics. I owe you a debt of gratitude. I had a question about ordinary kriging. In the 2nd half of this video on kriging theory by Luc Anselin, he said that in ordinary kriging the mean is constant and does not vary locally, there exists a stationary condition, and it is the case in a universal kriging model that the mean varies locally, while you said that in ordinary kriging we relax the stationarity condition: ua-cam.com/video/AoIUcE0vvq8/v-deo.html Am I right or this is some kind of misunderstanding?
1:20 Spatial estimation
8:00 Weighting scheme
10:00 derivation of Simple Kriging
15:27 Kriging definition
16:00 Linear system for Simple Kriging
17:50 Properties of Simple Kriging
25:45 Excel Demo
31:35 Ordinary Kriging
32:45 Kriging: Summary
Sir I appreciate you posting such valuable lectures for public learning. Kudos to you.
My pleasure! I love being a professor and getting to help so great many people on their scientific journey! Thank you, Michael
The right amount of math to make it applicable, but not so much that it remove the focus on what is happening. Thanks a lot for putting this quality content up on youtube!
Thank you Mathias for the feedback. I love math, but I love accessible education even more! I'm always trying to balance! I'm glad to hear that the content is useful!
Thanks professor I was lost until I saw your videos , greetings of a Ecuadorian from the Netherlands
Excellent lecture! I have used Kriging for more than a decade but did not have the insights that you presented in your 36 min lecture. Also, the excel spreadsheet very useful in getting an intuitive understanding of the Kriging method. Much appreciated!
You are a life saver. I understood more from this video than all articles I read combined.
Now that is accessible! I'm glad to hear that that content is helpful, Kalebe.
Thank you for your spreadsheet demonstration, it is much easier to understand an equation or an algorithm by playing around with the variable.
Thank you very much for making this public, now I fully understand what Kriging is
That's what I'm talking about! I love to hear this. Isn't spatial data analytics awesome?
Yes it is awesome! I watched all your lecture series on Data Analytics and Geostatistics from 1a Data Analytics Reboot: Statistics Concepts to the last. As a graduate student in environmental science with biological sciences as undergrad who has limited statistics and mathematical background, your videos and teaching materials are very helpful as they are easier to understand than papers/books which are also difficult to obtain. Thank you very much for your generosity in sharing your expertise to wannabe geostatisticians: your lecture materials (PDF format), videos, program codes in R and Python. I'm experienced in R but not in Python, but your Python workflows motivated me and now I can program in Python as well!
@@renatojrfolledo5728, thank you for sharing this. Now I'm totally stoked. This is why I do it! We are all one scientific community and more data analytics will improve science everywhere! Thank you, Michael
@@GeostatsGuyLectures Only when magicians like you Explain it... Thank you, Sir.
Great lecture!!!. (correct me if I am wrong) the main assumption here is to have a signal noise representation of the signal f=m+e, then for predicting a new signal f*=m*+e*, kriging assumes that e*=sum_i a_i e_i = sum_i a_i (f_i-m_i) which gives the equation f*=sum_i a_i f_i + m*- sum_i a_im_i. Then weights a_i are founded by minimizing the variance with constraint sum_i a_i=1. (1) All this start by modeling e*=sum_i a_i e_i right?, 2) If we assume that f's are realization of Gaussian process, then f* can be estimated using equation 2.19 or 2.25 from this book www.gaussianprocess.org/gpml/chapters/RW.pdf i.e., the approach you are using wil be equivalent (equal) than the showed in that book, right?). Thank for sharing this video
Thank you for the great lecture i have a question about the percentage of measurement error in ARC map while using any kriging technique it assumes that the measurement error equals to 100% of the error. this negatively ompact the generated geostatistical layer contour lines and the values at the easured rain gauges are significantly impacted. when i use a zero percent error the generated geostatistical layers for both t universal and ordinary kriging are identical
Thanks a lot for this lecture! I think at 16:34, some of the indices in the third equation line should be u3 instead of u1
Great eye! You are correct. I'll get that corrected. I appreciate the great help! Michael
Very simple but very powerful demonstration. Excellent lecture. Could you please also discuss universal kriging?
That is a great idea, Nazmul. I mentioned universal kriging in class for completeness to expand on the idea that common kriging variants are driven by various stationarity assumptions. I'll put together some content on this. Aside, given my geoscience-oriented engineering background I generally prefer mapped trend models. Thank you, Michael
I found this lecture very interesting thank you for sharing.
I try to keep it lively! The topic is fascinating. That's how I got pulled into it and never looked back. Thank you, Michael
Anyone else noticed the ghost at 13:17? :)
Howdy Chronis, great catch! I should get someone in to look at that!
@@GeostatsGuyLectures Haha - my best guess is that it was the dog :) BTW thanks for your videos, they are great!
@@CK-vy2qv, you are correct. That is Darby, my rescue dog! She likes to join in my recorded lectures. I'm glad that you are finding the content useful!
I was wondering if you have posted something related to kriging neighborhood analysis?
Thank you for your lecture! I think at 14:35 there should be a 2 in the second equation on the right side, hope I'm not mistaken.
Hi Professor are you planning to introduce RBF in your class?
Thanks a lot for the great lecture. Learning from this lecture that Kriging accounts for distance (i.e., in the lecture, increase/decrease weights if a sampling point is closer/further away from the unknown location) when assigning weights, would you still recommend to perform data declustering and calculate the weights for data? I just wonder if account for data closeness twice would be redundant? Thank you very much in advance!
Hi Professor, I wanted to ask. Do you know any method I can use to run regression kriging on arcmap or which tool can I use?
Thanks, this is an amazing explanation + lecture content!
Thank you! I'm glad that you are finding it useful!
thnk you sir for making such valuable contents available to us
Great lecture !! I've noticed a small mistake at 14:17 : The indexes are C(u_i,u_j) instead of C(u_i,u_i) on the two equations !
Thanks again I hope I'm not wrong
I've noticed it too, I think you're right.
Where can i get the spreadsheet sir??
Many thanks but do you have excell sheet for establishing krigiing
Howdy Solima, you're welcome and check out my ExcelNumericalDemos repository, I have simple kriging, indicator kriging and collocated cokriging by-hand. Hope this helps.
@@GeostatsGuyLectures many thanks for your help could you pls send me your email for some quiz
Best lecture! Thanks a lot
Great content sir, at 16:50 i noticed the third equation should be C(u3,u2) and C(u3,u3). I hope i'm not mistaken.
That' s gone be so viable.Thank a lot
thank you sooooooo much professor!!!!!!!!!!!!!!!!!!!!!!thank you!!!!
How i interpret the kriging and kriging variance map?
Good question! Dr. Journel told us not to put kriging estimates in maps! They are the best estimates at each location, but jointly they are incorrect, because they do not honor the histogram nor the variogram. The kriging variance is the missing variance in kriging and a measure of uncertainty in the estimate.
Thank you, indeed!
Thank you.
You're welcome, William. I hope the content is useful!
@@GeostatsGuyLectures it may actually end up in my dissertation.
@@WGLTubaman, cool! Cite it and the channel will be famous! Glad to see more folks finding the content! Good luck on writing, Michael
hello SIR
THANKS SO MUCH FOR THIS LECTURE
CAN YOU EXPLAIN FACTORIAL KRIGING PLZ
Cool mic. Great sound quality.
Thank you xdsf! I'm improving the quality over time. I'm thinking about getting a better camera.
Thank you so much Geostatguy ..
What is data redundancy?
Never mind, it is data clustering. Thank you for your videos, they make geostatistics really enjoyable!
wooow thank you for you share
Thanks so much
Thank you so much for this lecture sir. I have really learned a lot from it. Please which of your works on Kriging can I cite in my publication?
Howdy Olatunde, you could cite the book, Pyrcz and Deutsch, 2014, Geostatistical Reservoir Modeling, 2nd edition. I'm glad the content is useful to you, Michael
awesome!
Thank you. I hope the content is helpful!
thanks sir
Hello sir. Thanks again for your great and straightforward content on geostatistics. I owe you a debt of gratitude. I had a question about ordinary kriging. In the 2nd half of this video on kriging theory by Luc Anselin, he said that in ordinary kriging the mean is constant and does not vary locally, there exists a stationary condition, and it is the case in a universal kriging model that the mean varies locally, while you said that in ordinary kriging we relax the stationarity condition:
ua-cam.com/video/AoIUcE0vvq8/v-deo.html
Am I right or this is some kind of misunderstanding?