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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Mapping with splot and PySAL
Imports
Step1:  Data Preparation
Load example data into a geopandas.GeoDataFrame and inspect column names. In this example we will use the columbus.shp file containing neighborhood crime data of 1980.
Step2:  We extract two arrays x (housing value (in $1,000)) and y (residential burglaries and vehicle thefts per 1000 households).
Step3:  Create Value-by-Alpha Choropleths using the splot.mapping functionality
What is a Value by Alpha choropleth?
In a nutshell, a Value-by-Alpha Choropleth is a bivariate choropleth that uses the values of the second input variable y as a transparency mask, determining how much of the choropleth displaying the values of a first variable x is shown. In comparison to a cartogram, Value-By-Alpha choropleths will not distort shapes and sizes but modify the alpha channel (transparency) of polygons according to the second input variable y.
Let's look at a couple of examples generated with splot.
Step4:  We can create a value by alpha map using splot's vba_choropleth functionality.
We will plot a Value-by-Alpha Choropleth with x defining the rgb values and y defining the alpha value. For comparison we plot a choropleth of x with gdf.plot()
Step5:  You can see the original choropleth is fading into transparency wherever there is a high y value.
You can use the option to bin or classify your x and y values. splot uses mapclassify to bin your data and displays the new color and alpha ranges
Step6:  Instead of using a colormap you can also pass a list of colors
Step7:  Sometimes it is important in geospatial analysis to actually see the high values and let the small values fade out. With the revert_alpha = True argument, you can revert the transparency of the y values.
Step8:  You can use the divergent argument to display divergent alpha values. This means values at the extremes of your data range will be displayed with an alpha value of 1. Values towards the middle of your data range will be mapped more and more invisible towards an alpha value of 0.
Step9:  Create your own cmap for plotting
Sometimes you need to display divergent values with a natural midpoint not overlapping with he median of your data. For example if you measure the temperature over a country ranging from -2 to 10 degrees Celsius. Or if you need to assess whether a certain threshold is reached.
For cases like this splot provides a utility function to shift your colormap.
Step10:  Add a legend
If your values are classified, you have the option to add a legend to your map.
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Python Code:
import pysal.lib as lp
from pysal.lib import examples
import geopandas as gpd
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
%matplotlib inline
Explanation: Mapping with splot and PySAL
Imports
End of explanation
link_to_data = examples.get_path('columbus.shp')
gdf = gpd.read_file(link_to_data)
gdf.columns
Explanation: Data Preparation
Load example data into a geopandas.GeoDataFrame and inspect column names. In this example we will use the columbus.shp file containing neighborhood crime data of 1980.
End of explanation
x = gdf['HOVAL'].values
y = gdf['CRIME'].values
Explanation: We extract two arrays x (housing value (in $1,000)) and y (residential burglaries and vehicle thefts per 1000 households).
End of explanation
from pysal.viz.splot.mapping import vba_choropleth
Explanation: Create Value-by-Alpha Choropleths using the splot.mapping functionality
What is a Value by Alpha choropleth?
In a nutshell, a Value-by-Alpha Choropleth is a bivariate choropleth that uses the values of the second input variable y as a transparency mask, determining how much of the choropleth displaying the values of a first variable x is shown. In comparison to a cartogram, Value-By-Alpha choropleths will not distort shapes and sizes but modify the alpha channel (transparency) of polygons according to the second input variable y.
Let's look at a couple of examples generated with splot.
End of explanation
# Create new figure
fig, axs = plt.subplots(1,2, figsize=(20,10))
# use gdf.plot() to create regular choropleth
gdf.plot(column='HOVAL', scheme='quantiles', cmap='RdBu', ax=axs[0])
# use vba_choropleth to create Value-by-Alpha Choropleth
vba_choropleth(x, y, gdf, rgb_mapclassify=dict(classifier='quantiles'),
               alpha_mapclassify=dict(classifier='quantiles'),
               cmap='RdBu', ax=axs[1])
# set figure style
axs[0].set_title('normal Choropleth')
axs[0].set_axis_off()
axs[1].set_title('Value-by-Alpha Choropleth')
# plot
plt.show()
# Create new figure
fig, axs = plt.subplots(1,2, figsize=(20,10))
# create a vba_choropleth
vba_choropleth(x, y, gdf, rgb_mapclassify=dict(classifier='quantiles'),
               alpha_mapclassify=dict(classifier='quantiles'),
               cmap='RdBu', ax=axs[0],
               revert_alpha=False)
# set revert_alpha argument to True
vba_choropleth(x, y, gdf, rgb_mapclassify=dict(classifier='quantiles'),
               alpha_mapclassify=dict(classifier='quantiles'),
               cmap='RdBu', ax=axs[1],
               revert_alpha = True)
# set figure style
axs[0].set_title('divergent = False')
axs[1].set_title('divergent = True')
# plot
plt.show()
Explanation: We can create a value by alpha map using splot's vba_choropleth functionality.
We will plot a Value-by-Alpha Choropleth with x defining the rgb values and y defining the alpha value. For comparison we plot a choropleth of x with gdf.plot():
End of explanation
# Create new figure
fig, axs = plt.subplots(2,2, figsize=(20,10))
# classifier quantiles
vba_choropleth(x, y, gdf, cmap='viridis', ax = axs[0,0],
               rgb_mapclassify=dict(classifier='quantiles', k=3), 
               alpha_mapclassify=dict(classifier='quantiles', k=3))
# classifier natural_breaks
vba_choropleth(x, y, gdf, cmap='viridis', ax = axs[0,1],
               rgb_mapclassify=dict(classifier='natural_breaks'), 
               alpha_mapclassify=dict(classifier='natural_breaks'))
# classifier std_mean
vba_choropleth(x, y, gdf, cmap='viridis', ax = axs[1,0],
               rgb_mapclassify=dict(classifier='std_mean'), 
               alpha_mapclassify=dict(classifier='std_mean'))
# classifier fisher_jenks
vba_choropleth(x, y, 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()
Explanation: You can see the original choropleth is fading into transparency wherever there is a high y value.
You can use the option to bin or classify your x and y values. splot uses mapclassify to bin your data and displays the new color and alpha ranges:
End of explanation
color_list = ['#a1dab4','#41b6c4','#225ea8']
vba_choropleth(x, y, gdf, cmap=color_list,
               rgb_mapclassify=dict(classifier='quantiles', k=3), 
               alpha_mapclassify=dict(classifier='quantiles'))
plt.show()
Explanation: Instead of using a colormap you can also pass a list of colors:
End of explanation
# Create new figure
fig, axs = plt.subplots(1,2, figsize=(20,10))
# create a vba_choropleth
vba_choropleth(x, y, gdf, rgb_mapclassify=dict(classifier='quantiles'),
               alpha_mapclassify=dict(classifier='quantiles'),
               cmap='RdBu', ax=axs[0],
               revert_alpha=False)
# set revert_alpha argument to True
vba_choropleth(x, y, gdf, rgb_mapclassify=dict(classifier='quantiles'),
               alpha_mapclassify=dict(classifier='quantiles'),
               cmap='RdBu', ax=axs[1],
               revert_alpha = True)
# set figure style
axs[0].set_title('divergent = False')
axs[1].set_title('divergent = True')
# plot
plt.show()
Explanation: Sometimes it is important in geospatial analysis to actually see the high values and let the small values fade out. With the revert_alpha = True argument, you can revert the transparency of the y values.
End of explanation
# create new figure
fig, axs = plt.subplots(1,2, figsize=(20,10))
# create a vba_choropleth
vba_choropleth(x, y, gdf, cmap='RdBu',
               divergent=False, ax=axs[0])
# set divergent to True
vba_choropleth(x, y, gdf, cmap='RdBu',
               divergent=True, ax=axs[1])
# set figure style
axs[0].set_title('revert_alpha = False')
axs[0].set_axis_off()
axs[1].set_title('revert_alpha = True')
# plot
plt.show()
Explanation: You can use the divergent argument to display divergent alpha values. This means values at the extremes of your data range will be displayed with an alpha value of 1. Values towards the middle of your data range will be mapped more and more invisible towards an alpha value of 0.
End of explanation
from pysal.viz.splot._viz_utils import shift_colormap
# shift the midpoint to the 80st percentile of your datarange
mid08 = shift_colormap('RdBu', midpoint=0.8)
# shift the midpoint to the 20st percentile of your datarange
mid02 = shift_colormap('RdBu', midpoint=0.2)
# create new figure
fig, axs = plt.subplots(1,2, figsize=(20,10))
# vba_choropleth with cmap mid08
vba_choropleth(x, y, gdf, cmap=mid08, ax=axs[0], divergent=True)
# vba_choropleth with cmap mid02
vba_choropleth(x, y, gdf, cmap=mid02, ax=axs[1], divergent=True)
# plot
plt.show()
Explanation: Create your own cmap for plotting
Sometimes you need to display divergent values with a natural midpoint not overlapping with he median of your data. For example if you measure the temperature over a country ranging from -2 to 10 degrees Celsius. Or if you need to assess whether a certain threshold is reached.
For cases like this splot provides a utility function to shift your colormap.
End of explanation
fig = plt.figure(figsize=(15,10))
ax = fig.add_subplot(111)
vba_choropleth(x, y, gdf,
               alpha_mapclassify=dict(classifier='quantiles', k=5),
               rgb_mapclassify=dict(classifier='quantiles', k=5),
               legend=True, ax=ax)
plt.show()
Explanation: Add a legend
If your values are classified, you have the option to add a legend to your map.
End of explanation
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