splot.mapping.vba_choropleth

splot.mapping.vba_choropleth(x_var, y_var, gdf, cmap='GnBu', divergent=False, revert_alpha=False, alpha_mapclassify=None, rgb_mapclassify=None, ax=None, legend=False)

Value by Alpha Choropleth

Parameters

x_varstring or array

The name of variable in gdf determined by color or an array of values determined by color.

y_varstring or array

The name of variable in gdf determining alpha value or an array of values determined by color.

gdfgeopandas dataframe instance

The Dataframe containing information to plot.

cmapstr or list of str

Matplotlib Colormap or list of colors used to create vba_layer

divergentbool, optional

Creates a divergent alpha array with high values at the extremes and low, transparent values in the middle of the input values.

revert_alphabool, optional

If True, high y values will have a low alpha and low values will be transparent. Default = False.

alpha_mapclassifydict

Keywords used for binning input values and classifying alpha values with mapclassify. Note: valid keywords are eg. dict(classifier=’quantiles’, k=5, hinge=1.5). For other options check splot.mapping.mapclassify_bin.

rgb_mapclassifydict

Keywords used for binning input values and classifying rgb values with mapclassify. Note: valid keywords are eg.g dict(classifier=’quantiles’, k=5, hinge=1.5).For other options check splot.mapping.mapclassify_bin.

axmatplotlib Axes instance, optional

Axes in which to plot the figure in multiple Axes layout. Default = None

legendbool, optional

Adds a legend. Note: currently only available if data is classified, hence if alpha_mapclassify and rgb_mapclassify are used.

Returns

figmatplotlip Figure instance

Figure of Value by Alpha choropleth

axmatplotlib Axes instance

Axes in which the figure is plotted

Examples

Imports

>>> from libpysal import examples
>>> import geopandas as gpd
>>> import matplotlib.pyplot as plt
>>> import matplotlib
>>> import numpy as np
>>> from splot.mapping import vba_choropleth

Load Example Data

>>> link_to_data = examples.get_path('columbus.shp')
>>> gdf = gpd.read_file(link_to_data)

Plot a Value-by-Alpha map

>>> fig, _ = vba_choropleth('HOVAL', 'CRIME', gdf)
>>> plt.show()

(Source code, png, hires.png, pdf)

Plot a Value-by-Alpha map with reverted alpha values

>>> fig, _ = vba_choropleth('HOVAL', 'CRIME', gdf, cmap='RdBu',
...                         revert_alpha=True)
>>> plt.show()

(png, hires.png, pdf)

Plot a Value-by-Alpha map with classified alpha and rgb values

>>> fig, axs = plt.subplots(2,2, figsize=(20,10))
>>> vba_choropleth('HOVAL', 'CRIME', gdf, cmap='viridis', ax = axs[0,0],
...                rgb_mapclassify=dict(classifier='quantiles', k=3), 
...                alpha_mapclassify=dict(classifier='quantiles', k=3))
>>> vba_choropleth('HOVAL', 'CRIME', gdf, cmap='viridis', ax = axs[0,1],
...                rgb_mapclassify=dict(classifier='natural_breaks'), 
...                alpha_mapclassify=dict(classifier='natural_breaks'))
>>> vba_choropleth('HOVAL', 'CRIME', gdf, cmap='viridis', ax = axs[1,0],
...                rgb_mapclassify=dict(classifier='std_mean'), 
...                alpha_mapclassify=dict(classifier='std_mean'))
>>> vba_choropleth('HOVAL', 'CRIME', gdf, cmap='viridis', ax = axs[1,1],
...                rgb_mapclassify=dict(classifier='fisher_jenks', k=3), 
...                alpha_mapclassify=dict(classifier='fisher_jenks', k=3))
>>> plt.show()

(png, hires.png, pdf)

Pass in a list of colors instead of a cmap

>>> color_list = ['#a1dab4','#41b6c4','#225ea8']
>>> vba_choropleth('HOVAL', 'CRIME', gdf, cmap=color_list,
...                rgb_mapclassify=dict(classifier='quantiles', k=3), 
...                alpha_mapclassify=dict(classifier='quantiles'))
>>> plt.show()

(png, hires.png, pdf)

Add a legend and use divergent alpha values

>>> fig = plt.figure(figsize=(15,10))
>>> ax = fig.add_subplot(111)
>>> vba_choropleth('HOVAL', 'CRIME', gdf, divergent=True,
...                alpha_mapclassify=dict(classifier='quantiles', k=5),
...                rgb_mapclassify=dict(classifier='quantiles', k=5),
...                legend=True, ax=ax)
>>> plt.show()

(png, hires.png, pdf)