Moran's I : Data Science Concepts 

Killed it. One of the best explanation of Moran's I I've come across.

A negative spatial correlation natural example is the way passengers take seats in public transport. They tend to try to sit away from each other with even spacing.

The one and only explanation I have come across that actually explains this concept from a spatial perspective.

Instead of just showing formula building the understanding through example......your way of teaching is identical👍👍☺

The greatest explanation I’ve ever heard. Respect man.

you have a teaching gift! thanks for explaining and uploading!

Huge respect for sharing your precious knowledge making it so approachable

Really great and intuitive explanation of it, thank you! 👍

Well Done. I commented before I read any comment. This is a good explanation.

Thanks for uploading. Very crystal.

The concepts beyond formula are very interesting which are elaborated perfectly by you, thank you 👍

I'm working on my final paper about Spatial Econometrics and your explanation helped me a lot! But I think it's worth explaining the contiguity criterion: I'm used to using the Queen criterion instead of the Rook, and it confused me a lot.

This was so incredibly clear, thank you so much!

before watch this video I just understand how to calculate moran i using Python. Now I understand how it works!!

thanks for the explanation. this is what i was looking for. thanks again.

can you use graph to build a deeper understanding?

Great video! but you didn't answer the initial question.. what is the Moran's I for the 2016 election? My home city of NYC has a negative Moran's I - the poorest county in New York State (Bronx) borders on the richest (Manhattan). Every town in Queens houses a different ethnic group.

Thank you so much ur way of teaching ...

Very clear explanation. Thank you!

Your video is very helpful. Woud you mind translating the video and uploading it to a Chinese video website? Because we can’t use RU-vid in China, I hope my friends can learn from it too.

A potential example of negative spatial correlation - the location of retail stores? If there is a retail store from a chain that isn't highly correlated, the surrounding area probably has a lower rate of retail stores?

I was starting with watching Luc Anselin. He might teach something to you, but you teach something to me!

reminds of something in the intersection of Voronoi diagrams + clustering + Sammon's projection

It's super intuitive.

A negative spatial correlation natural example might be a bunch of mountain peaks/ranges with valleys in between

thank you so much for this video! you're a fantastic teacher. I do have one question: is it possible to end up with a positive, but small value for Moran's I that is statistically significant? if so, is there a difference in how you would interpret a larger positive value versus a smaller positive value if both are statistically significant?

super clear, very makesense

Thanks!

👍👍👍👍

Thank you so much!

How would you recommend computing Morans I for Vectors with values from [0,1]? My first idea was to just subtract x_i with x_mean and take the length of that vector. The problem here is, that its not possible to get a negative value for the length, which leads into getting bad value to interpret. Second idea, is to just compute Morans I for every entry in our vector independent from another and taking the mean of these. Is there any better way?

Thanks for sharing

Genius

Unless I don't know what borders are, I believe the I for the 2016 election is about 0.317, so pretty strongly physically correlated. This is using the naive weighting average above which is like the L_0 norm or something.

Please talk about how A/B testing is used in Data science. Thanks

can you do a video about local moran's i?

Thanks man...

Thank you

Can we report p-value from Moran's I value

that is boss

An example of Morans's I = 0 would be people who like to eat liver or durian fruit.

On second thought that is not correct since there is clustering.

I live in FL and Moran's I would be about 0 (that is equal democrats and republicans). How ever, republicans cluster in the center of the state versus the coastline. Micro vs. macro have different results.

I have a question about W. I counted 16 adjacent pairs not 20. Would you mind showing which pairs are adjacent so I can arrive at 20?