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pysal / segregation

Segregation Measurement, Inferential Statistics, and Decomposition Analysis
https://pysal.org/segregation/
BSD 3-Clause "New" or "Revised" License
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consider surface based S #102

Closed AnttiHaerkoenen closed 5 years ago

AnttiHaerkoenen commented 5 years ago

Described in https://doi.org/10.1111/j.1538-4632.2007.00699.x

renanxcortes commented 5 years ago

Described in https://doi.org/10.1111/j.1538-4632.2007.00699.x

We definitely want to continue improving the surface based measures. But, it is worth to mention that the approach followed in https://github.com/pysal/segregation/blob/master/notebooks/network_measures.ipynb is a surface based segregation measure!

AnttiHaerkoenen commented 5 years ago

I implemented this in my own project. Maybe I could contribute something?

renanxcortes commented 5 years ago

I implemented this in my own project. Maybe I could contribute something?

Sure, feel free to contribute!

knaaptime commented 5 years ago

would definitely welcome a PR :)

knaaptime commented 5 years ago

which index did you want to use? right now, you can use SpatialDissim, SpatialInformationTheory, or SpatialDivergence and pass a libpysal Kernel weights object to get a surface-based measure, exactly as described in the O'Sullivan and Wong paper. We haven't finished extending that to other indices indices yet though

AnttiHaerkoenen commented 5 years ago

which index did you want to use? right now, you can use SpatialDissim, SpatialInformationTheory, or SpatialDivergence and pass a libpysal Kernel weights object to get a surface-based measure, exactly as described in the O'Sullivan and Wong paper.

I want to use S which is not the same as D, although they are related as shown in formula 16 of their paper

renanxcortes commented 5 years ago

Closed as this was added in https://github.com/pysal/segregation/pull/116.