Moran's I : Data Science Concepts

[@sigurorton6287]

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

[@meganrussell9728]

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

[@taniotanev1918]

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.

[@karimvaliev8031]

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

[@yannikwohnsdorf8944]

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

[@tapfumapashapa9798]

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

[@谢润志]

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

[@rifatamanna7895]

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

[@ritvikmath]

Thanks :)

[@jaeyeollee9243]

Huge respect for sharing your precious knowledge making it so approachable

[@ritvikmath]

Thanks !

[@mozhganchamani4248]

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

[@ritvikmath]

Thanks!

[@NickKravitz]

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.

[@anjanaaji1863]

Thank you so much ur way of teaching ...

[@igorzambiassi6841]

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.

[@KXYZ32]

Thanks for uploading. Very crystal.

[@ritvikmath]

Thanks for your words!

[@TheTunamouse]

This was so incredibly clear, thank you so much!

[@pankajsingh6921]

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

[@ramiismael7502]

can you use graph to build a deeper understanding?

[@runrunrun6517]

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

[@coltonlapp4193]

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?

[@golibsanaev]

Very clear explanation. Thank you!

[@brianacervantes7850]

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?

[@caothanhlong7577]

It's super intuitive.

[@julianunland6618]

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?

[@TheSnowsore]

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

[@haolunshan5092]

super clear, very makesense

[@shirok2460]

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

[@teegnas]

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

[@ritvikmath]

Cool! I'll have to look into those topics

[@aa-yi9zu]

Thank you so much!

[@paulglombick4634]

Thanks!

[@s.c.1903]

Thank you

[@gezahagnnegash9740]

Thanks for sharing

[@raduandreinegrila215]

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

[@ritvikmath]

Good point! Thanks

[@raduandreinegrila215]

@@ritvikmath much obliged. Thank you for the clear explanations!

[@anttirask7877]

What about the chess board? Would that be a good example of this or am I totally off?

[@yulinliu850]

👍👍👍👍

[@FersotJ]

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.

[@ritvikmath]

Nice! Thanks for the info. Curious what it will be for this year.

[@yellowrasta1460]

Thanks man...

[@runrunrun6517]

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

[@lovelyjain9485]

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

[@deepaksurya776]

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

[@ritvikmath]

Interesting idea! I will look into it. Thanks!

[@JK-sy4ym]

Genius

[@sgifford1000]

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

[@ritvikmath]

Good example!

[@kamyaryazdani8800]

that is boss

[@sgifford1000]

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

[@sgifford1000]

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.