splot.giddy.dynamic_lisa_vectors

splot.giddy.dynamic_lisa_vectors(rose, ax=None, arrows=True, **kwargs)

Plot vectors of positional transition of LISA values in Moran scatterplot

Parameters

rosegiddy.directional.Rose instance

A Rose object, which contains (among other attributes) LISA values at two points in time, and a method to perform inference on those.

axMatplotlib Axes instance, optional

If given, the figure will be created inside this axis. Default =None.

arrowsboolean, optional

If True show arrowheads of vectors. Default =True

**kwargskeyword arguments, optional

Keywords used for creating and designing the matplotlib.pyplot.plot()

Returns

figMatplotlib Figure instance

Figure of dynamic LISA vectors

axmatplotlib Axes instance

Axes in which the figure is plotted

Examples

>>> import geopandas as gpd
>>> import pandas as pd
>>> from libpysal.weights.contiguity import Queen
>>> from libpysal import examples
>>> import numpy as np
>>> import matplotlib.pyplot as plt

>>> from giddy.directional import Rose
>>> from splot.giddy import dynamic_lisa_vectors

get csv and shp files

>>> shp_link = examples.get_path('us48.shp')
>>> df = gpd.read_file(shp_link)
>>> income_table = pd.read_csv(examples.get_path("usjoin.csv"))

calculate relative values

>>> for year in range(1969, 2010):
...     income_table[str(year) + '_rel'] = (
...         income_table[str(year)] / income_table[str(year)].mean())

merge to one gdf

>>> gdf = df.merge(income_table,left_on='STATE_NAME',right_on='Name')

retrieve spatial weights and data for two points in time

>>> w = Queen.from_dataframe(gdf)
>>> w.transform = 'r'
>>> y1 = gdf['1969_rel'].values
>>> y2 = gdf['2000_rel'].values

calculate rose Object

>>> Y = np.array([y1, y2]).T
>>> rose = Rose(Y, w, k=5)

plot

>>> dynamic_lisa_vectors(rose)
>>> plt.show()

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

customize plot

>>> dynamic_lisa_vectors(rose, arrows=False, color='r')
>>> plt.show()

(png, hires.png, pdf)