splot.giddy.dynamic_lisa_composite

splot.giddy.dynamic_lisa_composite(rose, gdf, p=0.05, figsize=(13, 10))

Composite visualisation for dynamic LISA values over two points in time. Includes dynamic lisa heatmap, dynamic lisa rose plot, and LISA cluster plots for both, compared points in time.

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.

gdfgeopandas dataframe instance

The GeoDataFrame containing information and polygons to plot.

pfloat, optional

The p-value threshold for significance. Default =0.05.

figsize: tuple, optional

W, h of figure. Default =(13,10)

Returns

figMatplotlib Figure instance

Dynamic lisa composite figure.

axsmatplotlib 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_composite

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_composite(rose, gdf)
>>> plt.show()

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

customize plot

>>> fig, axs = dynamic_lisa_composite(rose, gdf)
>>> axs[0].set_ylabel('1996')
>>> axs[0].set_xlabel('2009')
>>> axs[1].set_title('LISA cluster for 1996')
>>> axs[3].set_title('LISA clsuter for 2009')
>>> plt.show()

(png, hires.png, pdf)