Plotting a choropleth map (with geopandas) using a

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')

Answer 2

Here is an alternative approach that does not require modifying the geopandas code. It involves first labeling the bins so that you can create a custom colormap that maps each bin label to a specific color. A column must then be created in your geodataframe that specifies which bin label is applied to each row in the geodataframe, and this column is then used to plot the choropleth using the custom colormap.

from matplotlib.colors import LinearSegmentedColormap

bins = [5, 20, 100, 600, 1000, 3000, 5000, 10000, 20000, 400000]

# Maps values to a bin.
# The mapped values must start at 0 and end at 1.
def bin_mapping(x):
    for idx, bound in enumerate(bins):
        if x < bound:
            return idx / (len(bins) - 1.0)

# Create the list of bin labels and the list of colors 
# corresponding to each bin
bin_labels = [idx / (len(bins) - 1.0) for idx in range(len(bins))]
color_list = ['#edf8fb', '#b2e2e2', '#66c2a4', '#2ca25f', '#006d2c', \
              '#fef0d9', '#fdcc8a', '#fc8d59', '#e34a33', '#b30000']

# Create the custom color map
cmap = LinearSegmentedColormap.from_list('mycmap', 
                                         [(lbl, color) for lbl, color in zip(bin_labels, color_list)])
projected_world_exports['Bin_Lbl'] = projected_world_exports['Value'].apply(bin_mapping)
projected_world_exports.plot(column='Bin_Lbl', cmap=cmap, alpha=1, vmin=0, vmax=1)