[WIP] Ch7 dab lastpass

[darribas]

Not to merge yet.

[darribas]

OK, on dcfdc5a I have a couple of updates that I'd love if @sjsrey and/or @ljwolf could have a look at as I'm a bit unsure about what I'm getting:

a) I've added the code for the local joint counts but, since it's the first time I'm actually applying the statistic and the results are not necessarily what I'd expect from the general narrative around Brexit (eg. NE of England Leave cluster), I could use with a bit of help sane checking what I'm getting is correct/OK

b) I now have a working example for the raster LISA that returns something a bit more sensible to me. @ljwolf the issue seems to be again on the handling of different types (xref libpysal#446), but I could use another pair of eyes to confirm this looks OK and the issue is then on libpysal's handling of dtypes.

Once this is more or less stable/understood, I'll move on with building the narrative around these two sections, but that should be a pretty straightforward affair.

[ljwolf]

I'm going to say no on the LJC. Something is off to me, and I need time to dig into it. I was concerned that you're detecting a leave cluster for many places that voted remain. I'm not sure if the implementation of the statistic is off (I tried estimating the statistic after resetting the db index and rebuilding the weights... got a "slice mismatch" error from numba) or if the issue is in our interpretation, but I'd say that I'm not cool w/ including it until I understand why areas that vote remain are getting called leave clusters.

[ljwolf]

OK, I believe I have resolved the issue with the rasters. It seems to me that the p-value threshold was not set correctly, and this was affecting the generation of the cluster map. Lowering the threshold back down to .01 gives a cluster map that conforms to my expectations:

[sjsrey]

This looks a lot more aligned than what I saw last week.

If I play around with the significance level, things continue to be aligned on the left and right figures. So I think this is sorted.

I'm not sure what the original data is so I can't comment, but if it is interpolated population data we might want to add a note about induced spatial dependence?

[ljwolf]

do not forget to add image captions!

[ljwolf]

After working on the LJC this afternoon, I still cannot get it to work with the symmetrized knn weights. it does work with the queen weights. I am not sure why.

With the symmetric KNN, I'm still getting LJC > 0 for cases where y = 0. This should never happen, as y==1 is a prerequisite for there to be any 1-1 joins.