Plotting a choropleth map (with geopandas) using a user_defined classification scheme

Question

I'm kind of new to python, so I'm hoping that the answer to my question is relatively straight forward.

I'm trying to make a choropleth map using geopandas. However, since I'm making multiple maps that need to be compared to each other, it is indispensable that I use a custom data classification scheme (rather than quantiles or jenks). Hence, I've been trying to work with the User_Defined scheme, and I'm able to create the bins but I don't know how to apply them to the map itself.

This is what I did to create my classification scheme:

    import pysal.esda.mapclassify as ps
    from pysal.esda.mapclassify import User_Defined

    bins = [5, 20, 100, 600, 1000, 3000, 5000, 10000, 20000, 400000]
    ud = User_Defined(projected_world_exports['Value'], bins)

(where 'Value' is the column I plot in the map)

And then when I try to plot the choropleth map I don't know what the scheme is meant to be called

    projected_world_exports.plot(column='Value', cmap='Greens', scheme = ?????)

If anyone could help I would be hugely appreciative!

Thanks x

Answer 1

I took a look at the code of geopandas plotting function (https://github.com/geopandas/geopandas/blob/master/geopandas/plotting.py) but I guess the plot method only accepts one of the three name ("quantiles", "equal_interval", "fisher_jenks") but not directly a list of bins or a pysal.esda.mapclassify classifier such as User_Defined.
(I guess it could be linked to that issue where the last comment is about defining an API for "user defined" binning).

However for now I guess you can achieve this by slightly modifying and reusing the functions from the file I linked. For example you could rewrite you're own version of plot_dataframe like this :

import numpy as np

def plot_dataframe(s, column, binning, cmap,
                   linewidth=1.0, figsize=None, **color_kwds):
    import matplotlib.pyplot as plt

    values = s[column]
    values = np.array(binning.yb)

    fig, ax = plt.subplots(figsize=figsize)
    ax.set_aspect('equal')

    mn = values.min()
    mx = values.max()

    poly_idx = np.array(
        (s.geometry.type == 'Polygon') | (s.geometry.type == 'MultiPolygon'))
    polys = s.geometry[poly_idx]
    if not polys.empty:
        plot_polygon_collection(ax, polys, values[poly_idx], True,
                                vmin=mn, vmax=mx, cmap=cmap,
                                linewidth=linewidth, **color_kwds)

    plt.draw()
    return ax

Then you would need to define the functions _flatten_multi_geoms and plot_polygon_collection by copying them and you are ready to use it like this :

bins = [5, 20, 100, 600, 1000, 3000, 5000, 10000, 20000, 400000]
ud = User_Defined(projected_world_exports['Value'], bins)

plot_dataframe(projected_world_exports, 'Value', ud, 'Greens')