from scipy2018 import PySAL==2.0rc2

Returning from scipy 2018

My wrap-up from an exciting week full of stories about code and community at scipy 2018. First: I am even more convinced to keep on working on open source software in a fantastic community where scipy conference t-shirts are worn somewhat similar to festival t-shirts. Second: I was very impressed by the amount of new ideas, creative solutions and jokes (reference to the ‘catterplot’ and all lightning-talks) to encounter at the conference. Lastly, Dani asked me during the end of the sprints, “What changed your life this week?” – “Of course meeting the pysal team, my mentors and experiencing the spirit of coding together to finish the release of splot!”

I also had the change to introduce splot to the broader python community in a lightning talk, which you can see if you follow the link.

First splot release

This big announcement this week is that we successfully preliminary released splot as part of PySAL 2.0rc2.

You can install and access splot via PySAL 2.0:

pip install PySAL==2.0rc2
You can download PySAL-2.0 file from pypi.org. Release notes and some statistics about PySAL - 2.0 can be accessed here. More information about migrating to PySAL 2.0 can be found here. And our brand new team website which can be accessed here (which is partly still in the making).

Extended functionality

Next to these exciting news, I have continued extending and fine-tuning splot‘s functionality to make it more user friendly. For example, you can now use splot.esda.moran_scatterplot() to plot all esda.moranobjects, instead of calling functions specific to Moran, Moran_Local, Moran_BV, ….
from splot.esda import moran_scatterplot

fig, axs = plt.subplots(2, 2, figsize=(10,10),
                        subplot_kw={'aspect': 'equal'})

moran_scatterplot(moran, p=0.05, ax=axs[0,0])
moran_scatterplot(moran_loc, p=0.05, ax=axs[1,0])
moran_scatterplot(moran_bv, p=0.05, ax=axs[0,1])
moran_scatterplot(moran_loc_bv, p=0.05, ax=axs[1,1])
plt.show()
 
Furthermore, I implemented moran_facet() which allows to plot moran statistics calculated for a variety of attributes:
from splot.esda import moran_facet

fig, axarr = moran_facet(moran_matrix)
plt.show()

Milestone 2: Sprinting towards an`splot` release

I am happy to announce that a first experimental release of splot is near! The whole mentoring and PySAL development team including the GSoC student, me, will be meeting at SciPy 2018 to prepare a common release of all PySAL sub-packages. You will find us coding together at lunch, in coffee breaks and during the sprints at the end of the conference to get the release ready by next weekend.

From first steps to mid sprint

After the decision was made how the API should look like and the focus was set on the implementation of views in matplotlib, I was busy creating and implementing new visualisations for esda and libpysal.

Levi John Wolf, recently created libpysal functionality that allows to “snap” neighbouring polygons back together, to correct incorrectly separated nodes and edges, stemming from data digitisation errors. This error of “non-touching” polygons is common and needs to be corrected for spatial analysis. A typical workflow to assess this error using esda and splot could look like this:

First we import all necessary packages.

import libpysal.api as lp
import libpysal
from libpysal import examples
import matplotlib.pyplot as plt
import geopandas as gpd
from splot.libpysal import plot_spatial_weights

Second, we load the data we want to assess into a geopandas dataframe and calculate spatial weights. (We will use existing `libpysal.example` data.)

gdf = gpd.read_file(libpysal.examples.get_path('43MUE250GC_SIR.shp'))

weights = lp.Queen.from_dataframe(gdf)

libpysal  automatically warns us if our dataset contains islands. Islands are polygons that do not share edges and nodes with adjacent polygones. This can for example be the case if polygons are truly not neighbouring, eg. when two land parcels are seperated by a river. However, these islands often stems from human error when digitizing features into polygons.

/Users/steffie/code/libpysal/libpysal/weights/weights.py:189: UserWarning: There are 30 disconnected observations
  warnings.warn("There are %d disconnected observations" % ni)
/Users/steffie/code/libpysal/libpysal/weights/weights.py:190: UserWarning: Island ids: 0, 1, 5, 24, 28, 81, 95, 102, 108, 110, 120, 123, 140, 170, 176, 224, 240, 248, 254, 255, 256, 257, 262, 277, 292, 295, 304, 322, 358, 375
  warnings.warn("Island ids: %s" % ', '.join(str(island) for island in self.islands))

This unwanted error can now be assessed using splot.libpysal.plot_spatial_weights functionality:

plot_spatial_weights(weights, gdf)
plt.show()
 

This visualisation depicts the spatial weights network, a network of connections of the centroid of each polygon to the centroid of its neighbour. As we can see, there are many polygons in the south and west of this map, that are not connected to its neighbors. We can use libpysal.weights.util.nonplanar_neighbors to correct this error and visualise the result with splot.libpysal.plot_spatial_weights.

wnp = libpysal.weights.util.nonplanar_neighbors(weights, gdf)

plot_spatial_weights(wnp, gdf)
plt.show()


As we can see, all erroneous islands are now stored as neighbors in our new weights object, depicted by the new joins displayed in orange. This example and more ca be tested by users via splot‘s extended documentation in jupyter notebooks.

From mid sprint to full sprint

Additionally, the splot dev team has started to reach out to the geopandas and Yellowbrick dev teams in order to share knowledge and collaborate. splot functionality will depend on a data input as geopandas dataframe in future. Therefore we would like to start a collaboration on eventually coordinated release dates and potential joint visualisation projects, which will be discussed this week. Results and ideas will be collected in this issue.

Yellowbrick extends the Scikit-Learn API with diagnostic visualisations to steer machine learning processes. Its mission to extend an existing API by offering visualisations that can steer a data analysis process is very close to splot. Since Yellowbrick is already an established and popular package and seems to have had similar decisions to make, we decided to contact its dev team and are looking forward to a conversation in two weeks.

See you all at SciPy 2018!