To help subpackages that depend on libpysal, the API will change shortly to be in line with our desired migrating.pysal.org API. If you test your package against the pypi version of libpysal (which you get using pip install libpysal), you already know that your changes don’t break with respect to what exists. However, if you’d like to give yourself some lead time to detect if there are breaking changes in the development version of libpysal on github, feel free to follow these directions on how to set up optional tests on travis.

I’ve always been interested in how PySAL stacks up with NetworkX for building the dual graph of polygonal lattices, so I did some perftesting. This is by far the most common spatial operation I do on a day to day basis, and it looks like PySAL’s constructors still are the fastest for cases where we can assume planarity. But, with how simple the geopandas-only solution is, I look foward to the day when the geopandas.

I just finished attending the 2018 GIS Research UK conference at Leicester University. I presented twice; once on some new methods in spatial clustering and once for the CDRC brexit data analysis competition. I had a really good time participating in the data analysis competition, and it struck a chord with me reflecting on quite a few conversations I’ve had with my friend & colleague Taylor Oshan and something Morton O’Kelly said at this year’s annual American Association of Geographers meeting.

My entry in the Consumer Data Research Center’s Brexit Data Competition is called “Tension Points: A Theory & Evidence” (static), which I talked about at the 2018 GISRUK conference There is an abstract describing some of the work that I submitted to get to the final round, but if you’re computationally inclined, you’ll find everything sufficient to replicate my modelling & analysis in this Jupyter Notebook (raw). You’ll need scikit-learn, pystan, statsmodels, and geopandas at minimum to run.

This paper culminates a bit of work I’ve started on since seeing a talk by Phil Chodrow on a paper that eventually became his quite interesting NAS paper paper on segregation and entropy surfaces. I was intrigued by the prospect of using spectral clustering for constrained clustering problems. Specifically, I’d known that affinity matrix clustering could be adapted to constrained contexts ever since reading about hierarchical ward clustering, but I hadn’t seen a really convincing method that showed me how I could work this out for a general affinity-matrix clustering method.