Source code for pysal.viz.splot._viz_esda_mpl

import matplotlib.pyplot as plt
import matplotlib as mpl
import geopandas as gpd
import numpy as np
from pysal.lib.weights.contiguity import Queen
from pysal.lib.weights.spatial_lag import lag_spatial
import seaborn as sbn
from pysal.explore.esda.moran import (Moran_Local, Moran_Local_BV,
                        Moran, Moran_BV)
import warnings
from pysal.model.spreg import OLS

from matplotlib import patches, colors

from ._viz_utils import (mask_local_auto, moran_hot_cold_spots,
                         splot_colors)

"""
Lightweight visualizations for esda using Matplotlib and Geopandas

TODO
* geopandas plotting, change round shapes in legends to boxes
* prototype moran_facet using `seaborn.FacetGrid`
"""

__author__ = ("Stefanie Lumnitz <stefanie.lumitz@gmail.com>")


def _create_moran_fig_ax(ax, figsize):
    """
    Creates matplotlib figure and axes instances
    for plotting moran visualizations. Adds common viz design.
    """
    if ax is None:
        fig = plt.figure(figsize=figsize)
        ax = fig.add_subplot(111)
    else:
        fig = ax.get_figure()
    
    ax.spines['left'].set_position(('axes', -0.05))
    ax.spines['right'].set_color('none')
    ax.spines['bottom'].set_position(('axes', -0.05))
    ax.spines['top'].set_color('none')
    ax.spines['left'].set_smart_bounds(True)
    ax.spines['bottom'].set_smart_bounds(True)
    return fig, ax


[docs]def moran_scatterplot(moran, zstandard=True, p=None, ax=None, scatter_kwds=None, fitline_kwds=None): """ Moran Scatterplot Parameters ---------- moran : esda.moran instance Values of Moran's I Global, Bivariate and Local Autocorrelation Statistics zstandard : bool, optional If True, Moran Scatterplot will show z-standardized attribute and spatial lag values. Default =True. p : float, optional If given, the p-value threshold for significance for Local Autocorrelation analysis. Points will be colored by significance. By default it will not be colored. Default =None. ax : Matplotlib Axes instance, optional If given, the Moran plot will be created inside this axis. Default =None. scatter_kwds : keyword arguments, optional Keywords used for creating and designing the scatter points. Default =None. fitline_kwds : keyword arguments, optional Keywords used for creating and designing the moran fitline. Default =None. Returns ------- fig : Matplotlib Figure instance Moran scatterplot figure ax : matplotlib Axes instance Axes in which the figure is plotted Examples -------- Imports >>> import matplotlib.pyplot as plt >>> from pysal.lib.weights.contiguity import Queen >>> from pysal.lib import examples >>> import geopandas as gpd >>> from pysal.explore.esda.moran import (Moran, Moran_BV, ... Moran_Local, Moran_Local_BV) >>> from pysal.viz.splot.esda import moran_scatterplot Load data and calculate weights >>> link_to_data = examples.get_path('Guerry.shp') >>> gdf = gpd.read_file(link_to_data) >>> x = gdf['Suicids'].values >>> y = gdf['Donatns'].values >>> w = Queen.from_dataframe(gdf) >>> w.transform = 'r' Calculate esda.moran Objects >>> moran = Moran(y, w) >>> moran_bv = Moran_BV(y, x, w) >>> moran_loc = Moran_Local(y, w) >>> moran_loc_bv = Moran_Local_BV(y, x, w) Plot >>> fig, axs = plt.subplots(2, 2, figsize=(10,10), ... subplot_kw={'aspect': 'equal'}) >>> moran_scatterplot(moran, p=0.05, ax=axs[0,0]) >>> moran_scatterplot(moran_loc, p=0.05, ax=axs[1,0]) >>> moran_scatterplot(moran_bv, p=0.05, ax=axs[0,1]) >>> moran_scatterplot(moran_loc_bv, p=0.05, ax=axs[1,1]) >>> plt.show() """ if isinstance(moran, Moran): if p is not None: warnings.warn('`p` is only used for plotting `esda.moran.Moran_Local`\n' 'or `Moran_Local_BV` objects') fig, ax = _moran_global_scatterplot(moran=moran, zstandard=zstandard, ax=ax, scatter_kwds=scatter_kwds, fitline_kwds=fitline_kwds) elif isinstance(moran, Moran_BV): if p is not None: warnings.warn('`p` is only used for plotting `esda.moran.Moran_Local`\n' 'or `Moran_Local_BV` objects') fig, ax = _moran_bv_scatterplot(moran_bv=moran, ax=ax, scatter_kwds=scatter_kwds, fitline_kwds=fitline_kwds) elif isinstance(moran, Moran_Local): fig, ax = _moran_loc_scatterplot(moran_loc=moran, zstandard=zstandard, ax=ax, p=p, scatter_kwds=scatter_kwds, fitline_kwds=fitline_kwds) elif isinstance(moran, Moran_Local_BV): fig, ax = _moran_loc_bv_scatterplot(moran_loc_bv=moran, ax=ax, p=p, scatter_kwds=scatter_kwds, fitline_kwds=fitline_kwds) ax.xaxis.set_ticks_position('bottom') ax.yaxis.set_ticks_position('left') return fig, ax
def _moran_global_scatterplot(moran, zstandard=True, ax=None, scatter_kwds=None, fitline_kwds=None): """ Global Moran's I Scatterplot. Parameters ---------- moran : esda.moran.Moran instance Values of Moran's I Global Autocorrelation Statistics zstandard : bool, optional If True, Moran Scatterplot will show z-standardized attribute and spatial lag values. Default =True. ax : Matplotlib Axes instance, optional If given, the Moran plot will be created inside this axis. Default =None. scatter_kwds : keyword arguments, optional Keywords used for creating and designing the scatter points. Default =None. fitline_kwds : keyword arguments, optional Keywords used for creating and designing the moran fitline. Default =None. Returns ------- fig : Matplotlib Figure instance Moran scatterplot figure ax : matplotlib Axes instance Axes in which the figure is plotted Examples -------- Imports >>> import matplotlib.pyplot as plt >>> from pysal.lib.weights.contiguity import Queen >>> from pysal.lib import examples >>> import geopandas as gpd >>> from pysal.explore.esda.moran import Moran >>> from pysal.viz.splot.esda import moran_scatterplot Load data and calculate weights >>> link_to_data = examples.get_path('Guerry.shp') >>> gdf = gpd.read_file(link_to_data) >>> y = gdf['Donatns'].values >>> w = Queen.from_dataframe(gdf) >>> w.transform = 'r' Calculate Global Moran >>> moran = Moran(y, w) plot >>> moran_scatterplot(moran) >>> plt.show() customize plot >>> fig, ax = moran_scatterplot(moran, zstandard=False, ... fitline_kwds=dict(color='#4393c3')) >>> ax.set_xlabel('Donations') >>> plt.show() """ # to set default as an empty dictionary that is later filled with defaults if scatter_kwds is None: scatter_kwds = dict() if fitline_kwds is None: fitline_kwds = dict() # define customization defaults scatter_kwds.setdefault('alpha', 0.6) scatter_kwds.setdefault('color', splot_colors['moran_base']) scatter_kwds.setdefault('s', 40) fitline_kwds.setdefault('alpha', 0.9) fitline_kwds.setdefault('color', splot_colors['moran_fit']) # get fig and ax fig, ax = _create_moran_fig_ax(ax, figsize=(7, 7)) # set labels ax.set_xlabel('Attribute') ax.set_ylabel('Spatial Lag') ax.set_title('Moran Scatterplot' + ' (' + str(round(moran.I, 2)) + ')') # plot and set standards if zstandard is True: lag = lag_spatial(moran.w, moran.z) fit = OLS(moran.z[:, None], lag[:, None]) # plot ax.scatter(moran.z, lag, **scatter_kwds) ax.plot(lag, fit.predy, **fitline_kwds) # v- and hlines ax.axvline(0, alpha=0.5, color='k', linestyle='--') ax.axhline(0, alpha=0.5, color='k', linestyle='--') else: lag = lag_spatial(moran.w, moran.y) b, a = np.polyfit(moran.y, lag, 1) # plot ax.scatter(moran.y, lag, **scatter_kwds) ax.plot(moran.y, a + b*moran.y, **fitline_kwds) # dashed vert at mean of the attribute ax.vlines(moran.y.mean(), lag.min(), lag.max(), alpha=0.5, linestyle='--') # dashed horizontal at mean of lagged attribute ax.hlines(lag.mean(), moran.y.min(), moran.y.max(), alpha=0.5, linestyle='--') return fig, ax
[docs]def plot_moran_simulation(moran, ax=None, fitline_kwds=None, **kwargs): """ Global Moran's I simulated reference distribution. Parameters ---------- moran : esda.moran.Moran instance Values of Moran's I Global Autocorrelation Statistics ax : Matplotlib Axes instance, optional If given, the Moran plot will be created inside this axis. Default =None. fitline_kwds : keyword arguments, optional Keywords used for creating and designing the vertical moran fitline. Default =None. **kwargs : keyword arguments, optional Keywords used for creating and designing the figure, passed to seaborn.kdeplot. Returns ------- fig : Matplotlib Figure instance Simulated reference distribution figure ax : matplotlib Axes instance Axes in which the figure is plotted Examples -------- Imports >>> import matplotlib.pyplot as plt >>> from pysal.lib.weights.contiguity import Queen >>> from pysal.lib import examples >>> import geopandas as gpd >>> from pysal.explore.esda.moran import Moran >>> from pysal.viz.splot.esda import plot_moran_simulation Load data and calculate weights >>> link_to_data = examples.get_path('Guerry.shp') >>> gdf = gpd.read_file(link_to_data) >>> y = gdf['Donatns'].values >>> w = Queen.from_dataframe(gdf) >>> w.transform = 'r' Calculate Global Moran >>> moran = Moran(y, w) plot >>> plot_moran_simulation(moran) >>> plt.show() customize plot >>> plot_moran_simulation(moran, fitline_kwds=dict(color='#4393c3')) >>> plt.show() """ # to set default as an empty dictionary that is later filled with defaults if fitline_kwds is None: fitline_kwds = dict() figsize = kwargs.pop('figsize', (7, 7)) # get fig and ax fig, ax = _create_moran_fig_ax(ax, figsize) # plot distribution shade = kwargs.pop('shade', True) color = kwargs.pop('color', splot_colors['moran_base']) sbn.kdeplot(moran.sim, shade=shade, color=color, ax=ax, **kwargs) # customize plot fitline_kwds.setdefault('color', splot_colors['moran_fit']) ax.vlines(moran.I, 0, 1, **fitline_kwds) ax.vlines(moran.EI, 0, 1) ax.set_title('Reference Distribution') ax.set_xlabel('Moran I: ' + str(round(moran.I, 2))) return fig, ax
[docs]def plot_moran(moran, zstandard=True, scatter_kwds=None, fitline_kwds=None, **kwargs): """ Global Moran's I simulated reference distribution and scatterplot. Parameters ---------- moran : esda.moran.Moran instance Values of Moran's I Global Autocorrelation Statistics zstandard : bool, optional If True, Moran Scatterplot will show z-standardized attribute and spatial lag values. Default =True. scatter_kwds : keyword arguments, optional Keywords used for creating and designing the scatter points. Default =None. fitline_kwds : keyword arguments, optional Keywords used for creating and designing the moran fitline and vertical fitline. Default =None. **kwargs : keyword arguments, optional Keywords used for creating and designing the figure, passed to seaborne.kdeplot. Returns ------- fig : Matplotlib Figure instance Moran scatterplot and reference distribution figure ax : matplotlib Axes instance Axes in which the figure is plotted Examples -------- Imports >>> import matplotlib.pyplot as plt >>> from pysal.lib.weights.contiguity import Queen >>> from pysal.lib import examples >>> import geopandas as gpd >>> from pysal.explore.esda.moran import Moran >>> from pysal.viz.splot.esda import plot_moran Load data and calculate weights >>> link_to_data = examples.get_path('Guerry.shp') >>> gdf = gpd.read_file(link_to_data) >>> y = gdf['Donatns'].values >>> w = Queen.from_dataframe(gdf) >>> w.transform = 'r' Calculate Global Moran >>> moran = Moran(y, w) plot >>> plot_moran(moran) >>> plt.show() customize plot >>> plot_moran(moran, zstandard=False, ... fitline_kwds=dict(color='#4393c3')) >>> plt.show() """ figsize = kwargs.pop('figsize', (10, 4)) fig, axs = plt.subplots(1, 2, figsize=figsize, subplot_kw={'aspect': 'equal'}) plot_moran_simulation(moran, ax=axs[0], fitline_kwds=fitline_kwds, **kwargs) moran_scatterplot(moran, zstandard=zstandard, ax=axs[1], scatter_kwds=scatter_kwds, fitline_kwds=fitline_kwds) axs[0].set(aspect="auto") axs[1].set(aspect="auto") return fig, axs
def _moran_bv_scatterplot(moran_bv, ax=None, scatter_kwds=None, fitline_kwds=None): """ Bivariate Moran Scatterplot. Parameters ---------- moran_bv : esda.moran.Moran_BV instance Values of Bivariate Moran's I Autocorrelation Statistics ax : Matplotlib Axes instance, optional If given, the Moran plot will be created inside this axis. Default =None. scatter_kwds : keyword arguments, optional Keywords used for creating and designing the scatter points. Default =None. fitline_kwds : keyword arguments, optional Keywords used for creating and designing the moran fitline. Default =None. Returns ------- fig : Matplotlib Figure instance Bivariate moran scatterplot figure ax : matplotlib Axes instance Axes in which the figure is plotted Examples -------- Imports >>> import matplotlib.pyplot as plt >>> from pysal.lib.weights.contiguity import Queen >>> from pysal.lib import examples >>> import geopandas as gpd >>> from pysal.explore.esda.moran import Moran_BV >>> from pysal.viz.splot.esda import moran_scatterplot Load data and calculate weights >>> link_to_data = examples.get_path('Guerry.shp') >>> gdf = gpd.read_file(link_to_data) >>> x = gdf['Suicids'].values >>> y = gdf['Donatns'].values >>> w = Queen.from_dataframe(gdf) >>> w.transform = 'r' Calculate Bivariate Moran >>> moran_bv = Moran_BV(x, y, w) plot >>> moran_scatterplot(moran_bv) >>> plt.show() customize plot >>> moran_scatterplot(moran_bv, ... fitline_kwds=dict(color='#4393c3')) >>> plt.show() """ # to set default as an empty dictionary that is later filled with defaults if scatter_kwds is None: scatter_kwds = dict() if fitline_kwds is None: fitline_kwds = dict() # define customization scatter_kwds.setdefault('alpha', 0.6) scatter_kwds.setdefault('color', splot_colors['moran_base']) scatter_kwds.setdefault('s', 40) fitline_kwds.setdefault('alpha', 0.9) fitline_kwds.setdefault('color', splot_colors['moran_fit']) # get fig and ax fig, ax = _create_moran_fig_ax(ax, figsize=(7,7)) # set labels ax.set_xlabel('Attribute X') ax.set_ylabel('Spatial Lag of Y') ax.set_title('Bivariate Moran Scatterplot' + ' (' + str(round(moran_bv.I, 2)) + ')') # plot and set standards lag = lag_spatial(moran_bv.w, moran_bv.zy) fit = OLS(moran_bv.zy[:, None], lag[:, None]) # plot ax.scatter(moran_bv.zx, lag, **scatter_kwds) ax.plot(lag, fit.predy, **fitline_kwds) # v- and hlines ax.axvline(0, alpha=0.5, color='k', linestyle='--') ax.axhline(0, alpha=0.5, color='k', linestyle='--') return fig, ax
[docs]def plot_moran_bv_simulation(moran_bv, ax=None, fitline_kwds=None, **kwargs): """ Bivariate Moran's I simulated reference distribution. Parameters ---------- moran_bv : esda.moran.Moran_BV instance Values of Bivariate Moran's I Autocorrelation Statistics ax : Matplotlib Axes instance, optional If given, the Moran plot will be created inside this axis. Default =None. fitline_kwds : keyword arguments, optional Keywords used for creating and designing the vertical moran fitline. Default =None. **kwargs : keyword arguments, optional Keywords used for creating and designing the figure, passed to seaborne.kdeplot. Returns ------- fig : Matplotlib Figure instance Bivariate moran reference distribution figure ax : matplotlib Axes instance Axes in which the figure is plotted Examples -------- Imports >>> import matplotlib.pyplot as plt >>> from pysal.lib.weights.contiguity import Queen >>> from pysal.lib import examples >>> import geopandas as gpd >>> from pysal.explore.esda.moran import Moran_BV >>> from pysal.viz.splot.esda import plot_moran_bv_simulation Load data and calculate weights >>> link_to_data = examples.get_path('Guerry.shp') >>> gdf = gpd.read_file(link_to_data) >>> x = gdf['Suicids'].values >>> y = gdf['Donatns'].values >>> w = Queen.from_dataframe(gdf) >>> w.transform = 'r' Calculate Bivariate Moran >>> moran_bv = Moran_BV(x, y, w) plot >>> plot_moran_bv_simulation(moran_bv) >>> plt.show() customize plot >>> plot_moran_bv_simulation(moran_bv, ... fitline_kwds=dict(color='#4393c3')) >>> plt.show() """ # to set default as an empty dictionary that is later filled with defaults if fitline_kwds is None: fitline_kwds = dict() figsize = kwargs.pop('figsize', (7, 7)) # get fig and ax fig, ax = _create_moran_fig_ax(ax, figsize) # plot distribution shade = kwargs.pop('shade', True) color = kwargs.pop('color', splot_colors['moran_base']) sbn.kdeplot(moran_bv.sim, shade=shade, color=color, ax=ax, **kwargs) # customize plot fitline_kwds.setdefault('color', splot_colors['moran_fit']) ax.vlines(moran_bv.I, 0, 1, **fitline_kwds) ax.vlines(moran_bv.EI_sim, 0, 1) ax.set_title('Reference Distribution') ax.set_xlabel('Bivariate Moran I: ' + str(round(moran_bv.I, 2))) return fig, ax
[docs]def plot_moran_bv(moran_bv, scatter_kwds=None, fitline_kwds=None, **kwargs): """ Bivariate Moran's I simulated reference distribution and scatterplot. Parameters ---------- moran_bv : esda.moran.Moran_BV instance Values of Bivariate Moran's I Autocorrelation Statistics scatter_kwds : keyword arguments, optional Keywords used for creating and designing the scatter points. Default =None. fitline_kwds : keyword arguments, optional Keywords used for creating and designing the moran fitline and vertical fitline. Default =None. **kwargs : keyword arguments, optional Keywords used for creating and designing the figure, passed to seaborne.kdeplot. Returns ------- fig : Matplotlib Figure instance Bivariate moran scatterplot and reference distribution figure ax : matplotlib Axes instance Axes in which the figure is plotted Examples -------- Imports >>> import matplotlib.pyplot as plt >>> from pysal.lib.weights.contiguity import Queen >>> from pysal.lib import examples >>> import geopandas as gpd >>> from pysal.explore.esda.moran import Moran_BV >>> from pysal.viz.splot.esda import plot_moran_bv Load data and calculate weights >>> link_to_data = examples.get_path('Guerry.shp') >>> gdf = gpd.read_file(link_to_data) >>> x = gdf['Suicids'].values >>> y = gdf['Donatns'].values >>> w = Queen.from_dataframe(gdf) >>> w.transform = 'r' Calculate Bivariate Moran >>> moran_bv = Moran_BV(x, y, w) plot >>> plot_moran_bv(moran_bv) >>> plt.show() customize plot >>> plot_moran_bv(moran_bv, fitline_kwds=dict(color='#4393c3')) >>> plt.show() """ figsize = kwargs.pop('figsize', (10, 4)) fig, axs = plt.subplots(1, 2, figsize=figsize, subplot_kw={'aspect': 'equal'}) plot_moran_bv_simulation(moran_bv, ax=axs[0], fitline_kwds=fitline_kwds, **kwargs) moran_scatterplot(moran_bv, ax=axs[1],scatter_kwds=scatter_kwds, fitline_kwds=fitline_kwds) axs[0].set(aspect="auto") axs[1].set(aspect="auto") return fig, axs
def _moran_loc_scatterplot(moran_loc, zstandard=True, p=None, ax=None, scatter_kwds=None, fitline_kwds=None): """ Moran Scatterplot with option of coloring of Local Moran Statistics Parameters ---------- moran_loc : esda.moran.Moran_Local instance Values of Moran's I Local Autocorrelation Statistics p : float, optional If given, the p-value threshold for significance. Points will be colored by significance. By default it will not be colored. Default =None. ax : Matplotlib Axes instance, optional If given, the Moran plot will be created inside this axis. Default =None. scatter_kwds : keyword arguments, optional Keywords used for creating and designing the scatter points. Default =None. fitline_kwds : keyword arguments, optional Keywords used for creating and designing the moran fitline. Default =None. Returns ------- fig : Matplotlib Figure instance Moran Local scatterplot figure ax : matplotlib Axes instance Axes in which the figure is plotted Examples -------- Imports >>> import matplotlib.pyplot as plt >>> import geopandas as gpd >>> from pysal.lib.weights.contiguity import Queen >>> from pysal.lib import examples >>> from pysal.explore.esda.moran import Moran_Local >>> from pysal.viz.splot.esda import moran_scatterplot Load data and calculate Moran Local statistics >>> link = examples.get_path('Guerry.shp') >>> gdf = gpd.read_file(link) >>> y = gdf['Donatns'].values >>> w = Queen.from_dataframe(gdf) >>> w.transform = 'r' >>> m = Moran_Local(y, w) plot >>> moran_scatterplot(m) >>> plt.show() customize plot >>> moran_scatterplot(m, p=0.05, ... fitline_kwds=dict(color='#4393c3')) >>> plt.show() """ # to set default as an empty dictionary that is later filled with defaults if scatter_kwds is None: scatter_kwds = dict() if fitline_kwds is None: fitline_kwds = dict() if p is not None: if not isinstance(moran_loc, Moran_Local): raise ValueError("`moran_loc` is not a\n " + "esda.moran.Moran_Local instance") if 'color' in scatter_kwds or 'c' in scatter_kwds or 'cmap' in scatter_kwds: warnings.warn('To change the color use cmap with a colormap of 5,\n' + ' color defines the LISA category') # colors spots = moran_hot_cold_spots(moran_loc, p) hmap = colors.ListedColormap(['#bababa', '#d7191c', '#abd9e9', '#2c7bb6', '#fdae61']) # define customization scatter_kwds.setdefault('alpha', 0.6) scatter_kwds.setdefault('s', 40) fitline_kwds.setdefault('alpha', 0.9) # get fig and ax fig, ax = _create_moran_fig_ax(ax, figsize=(7,7)) # set labels ax.set_xlabel('Attribute') ax.set_ylabel('Spatial Lag') ax.set_title('Moran Local Scatterplot') # plot and set standards if zstandard is True: lag = lag_spatial(moran_loc.w, moran_loc.z) fit = OLS(moran_loc.z[:, None], lag[:, None]) # v- and hlines ax.axvline(0, alpha=0.5, color='k', linestyle='--') ax.axhline(0, alpha=0.5, color='k', linestyle='--') if p is not None: fitline_kwds.setdefault('color', 'k') scatter_kwds.setdefault('cmap', hmap) scatter_kwds.setdefault('c', spots) ax.plot(lag, fit.predy, **fitline_kwds) ax.scatter(moran_loc.z, fit.predy, **scatter_kwds) else: scatter_kwds.setdefault('color', splot_colors['moran_base']) fitline_kwds.setdefault('color', splot_colors['moran_fit']) ax.plot(lag, fit.predy, **fitline_kwds) ax.scatter(moran_loc.z, fit.predy, **scatter_kwds) else: lag = lag_spatial(moran_loc.w, moran_loc.y) b, a = np.polyfit(moran_loc.y, lag, 1) # dashed vert at mean of the attribute ax.vlines(moran_loc.y.mean(), lag.min(), lag.max(), alpha=0.5, linestyle='--') # dashed horizontal at mean of lagged attribute ax.hlines(lag.mean(), moran_loc.y.min(), moran_loc.y.max(), alpha=0.5, linestyle='--') if p is not None: fitline_kwds.setdefault('color', 'k') scatter_kwds.setdefault('cmap', hmap) scatter_kwds.setdefault('c', spots) ax.plot(moran_loc.y, a + b*moran_loc.y, **fitline_kwds) ax.scatter(moran_loc.y, lag, **scatter_kwds) else: scatter_kwds.setdefault('c', splot_colors['moran_base']) fitline_kwds.setdefault('color', splot_colors['moran_fit']) ax.plot(moran_loc.y, a + b*moran_loc.y, **fitline_kwds) ax.scatter(moran_loc.y, lag, **scatter_kwds) return fig, ax
[docs]def lisa_cluster(moran_loc, gdf, p=0.05, ax=None, legend=True, legend_kwds=None, **kwargs): """ Create a LISA Cluster map Parameters ---------- moran_loc : esda.moran.Moran_Local or Moran_Local_BV instance Values of Moran's Local Autocorrelation Statistic gdf : geopandas dataframe instance The Dataframe containing information to plot. Note that `gdf` will be modified, so calling functions should use a copy of the user provided `gdf`. (either using gdf.assign() or gdf.copy()) p : float, optional The p-value threshold for significance. Points will be colored by significance. ax : matplotlib Axes instance, optional Axes in which to plot the figure in multiple Axes layout. Default = None legend : boolean, optional If True, legend for maps will be depicted. Default = True legend_kwds : dict, optional Dictionary to control legend formatting options. Example: ``legend_kwds={'loc': 'upper left', 'bbox_to_anchor': (0.92, 1.05)}`` Default = None **kwargs : keyword arguments, optional Keywords designing and passed to geopandas.GeoDataFrame.plot(). Returns ------- fig : matplotlip Figure instance Figure of LISA cluster map ax : matplotlib Axes instance Axes in which the figure is plotted Examples -------- Imports >>> import matplotlib.pyplot as plt >>> from pysal.lib.weights.contiguity import Queen >>> from pysal.lib import examples >>> import geopandas as gpd >>> from pysal.explore.esda.moran import Moran_Local >>> from pysal.viz.splot.esda import lisa_cluster Data preparation and statistical analysis >>> link = examples.get_path('Guerry.shp') >>> gdf = gpd.read_file(link) >>> y = gdf['Donatns'].values >>> w = Queen.from_dataframe(gdf) >>> w.transform = 'r' >>> moran_loc = Moran_Local(y, w) Plotting >>> fig = lisa_cluster(moran_loc, gdf) >>> plt.show() """ # retrieve colors5 and labels from mask_local_auto _, colors5, _, labels = mask_local_auto(moran_loc, p=p) # define ListedColormap hmap = colors.ListedColormap(colors5) if ax is None: figsize = kwargs.pop('figsize', None) fig, ax = plt.subplots(1, figsize=figsize) else: fig = ax.get_figure() gdf.assign(cl=labels).plot(column='cl', categorical=True, k=2, cmap=hmap, linewidth=0.1, ax=ax, edgecolor='white', legend=legend, legend_kwds=legend_kwds, **kwargs) ax.set_axis_off() ax.set_aspect('equal') return fig, ax
[docs]def plot_local_autocorrelation(moran_loc, gdf, attribute, p=0.05, region_column=None, mask=None, mask_color='#636363', quadrant=None, legend=True, scheme='Quantiles', cmap='YlGnBu', figsize=(15, 4), scatter_kwds=None, fitline_kwds=None): ''' Produce three-plot visualisation of Moran Scatteprlot, LISA cluster and Choropleth maps, with Local Moran region and quadrant masking Parameters ---------- moran_loc : esda.moran.Moran_Local or Moran_Local_BV instance Values of Moran's Local Autocorrelation Statistic gdf : geopandas dataframe The Dataframe containing information to plot the two maps. attribute : str Column name of attribute which should be depicted in Choropleth map. p : float, optional The p-value threshold for significance. Points and polygons will be colored by significance. Default = 0.05. region_column: string, optional Column name containing mask region of interest. Default = None mask: str, optional Identifier or name of the region to highlight. Default = None mask_color: str, optional Color of mask. Default = '#636363' quadrant : int, optional Quadrant 1-4 in scatterplot masking values in LISA cluster and Choropleth maps. Default = None figsize: tuple, optional W, h of figure. Default = (15,4) legend: boolean, optional If True, legend for maps will be depicted. Default = True scheme: str, optional Name of PySAL classifier to be used. Default = 'Quantiles' cmap: str, optional Name of matplotlib colormap used for plotting the Choropleth. Default = 'YlGnBu' scatter_kwds : keyword arguments, optional Keywords used for creating and designing the scatter points. Default =None. fitline_kwds : keyword arguments, optional Keywords used for creating and designing the moran fitline in the scatterplot. Default =None. Returns ------- fig : Matplotlib figure instance Moran Scatterplot, LISA cluster map and Choropleth. axs : list of Matplotlib axes Lisat of Matplotlib axes plotted. Examples -------- Imports >>> import matplotlib.pyplot as plt >>> from pysal.lib.weights.contiguity import Queen >>> from pysal.lib import examples >>> import geopandas as gpd >>> from pysal.explore.esda.moran import Moran_Local >>> from pysal.viz.splot.esda import plot_local_autocorrelation Data preparation and analysis >>> link = examples.get_path('Guerry.shp') >>> gdf = gpd.read_file(link) >>> y = gdf['Donatns'].values >>> w = Queen.from_dataframe(gdf) >>> w.transform = 'r' >>> moran_loc = Moran_Local(y, w) Plotting with quadrant mask and region mask >>> fig = plot_local_autocorrelation(moran_loc, gdf, 'Donatns', p=0.05, ... region_column='Dprtmnt', ... mask=['Ain'], quadrant=1) >>> plt.show() ''' fig, axs = plt.subplots(1, 3, figsize=figsize, subplot_kw={'aspect': 'equal'}) # Moran Scatterplot moran_scatterplot(moran_loc, p=p, ax=axs[0], scatter_kwds=scatter_kwds, fitline_kwds=fitline_kwds) axs[0].set_aspect('auto') # Lisa cluster map # TODO: Fix legend_kwds: display boxes instead of points lisa_cluster(moran_loc, gdf, p=p, ax=axs[1], legend=legend, legend_kwds={'loc': 'upper left', 'bbox_to_anchor': (0.92, 1.05)}) axs[1].set_aspect('equal') # Choropleth for attribute gdf.plot(column=attribute, scheme=scheme, cmap=cmap, legend=legend, legend_kwds={'loc': 'upper left', 'bbox_to_anchor': (0.92, 1.05)}, ax=axs[2], alpha=1) axs[2].set_axis_off() axs[2].set_aspect('equal') # MASKING QUADRANT VALUES if quadrant is not None: # Quadrant masking in Scatterplot mask_angles = {1: 0, 2: 90, 3: 180, 4: 270} # rectangle angles # We don't want to change the axis data limits, so use the current ones xmin, xmax = axs[0].get_xlim() ymin, ymax = axs[0].get_ylim() # We are rotating, so we start from 0 degrees and # figured out the right dimensions for the rectangles for other angles mask_width = {1: abs(xmax), 2: abs(ymax), 3: abs(xmin), 4: abs(ymin)} mask_height = {1: abs(ymax), 2: abs(xmin), 3: abs(ymin), 4: abs(xmax)} axs[0].add_patch(patches.Rectangle((0, 0), width=mask_width[quadrant], height=mask_height[quadrant], angle=mask_angles[quadrant], color='#E5E5E5', zorder=-1, alpha=0.8)) # quadrant selection in maps non_quadrant = ~(moran_loc.q == quadrant) mask_quadrant = gdf[non_quadrant] df_quadrant = gdf.iloc[~non_quadrant] union2 = df_quadrant.unary_union.boundary # LISA Cluster mask and cluster boundary with warnings.catch_warnings(): # temorarily surpress geopandas warning warnings.filterwarnings('ignore', category=UserWarning) mask_quadrant.plot(column=attribute, scheme=scheme, color='white', ax=axs[1], alpha=0.7, zorder=1) gpd.GeoSeries([union2]).plot(linewidth=1, ax=axs[1], color='#E5E5E5') # CHOROPLETH MASK with warnings.catch_warnings(): # temorarily surpress geopandas warning warnings.filterwarnings('ignore', category=UserWarning) mask_quadrant.plot(column=attribute, scheme=scheme, color='white', ax=axs[2], alpha=0.7, zorder=1) gpd.GeoSeries([union2]).plot(linewidth=1, ax=axs[2], color='#E5E5E5') # REGION MASKING if region_column is not None: # masking inside axs[0] or Moran Scatterplot ix = gdf[region_column].isin(mask) df_mask = gdf[ix] x_mask = moran_loc.z[ix] y_mask = lag_spatial(moran_loc.w, moran_loc.z)[ix] axs[0].plot(x_mask, y_mask, color=mask_color, marker='o', markersize=14, alpha=.8, linestyle="None", zorder=-1) # masking inside axs[1] or Lisa cluster map union = df_mask.unary_union.boundary gpd.GeoSeries([union]).plot(linewidth=2, ax=axs[1], color=mask_color) # masking inside axs[2] or Chloropleth gpd.GeoSeries([union]).plot(linewidth=2, ax=axs[2], color=mask_color) return fig, axs
def _moran_loc_bv_scatterplot(moran_loc_bv, p=None, ax=None, scatter_kwds=None, fitline_kwds=None): """ Moran Bivariate Scatterplot with option of coloring of Local Moran Statistics Parameters ---------- moran_loc : esda.moran.Moran_Local_BV instance Values of Moran's I Local Autocorrelation Statistics p : float, optional If given, the p-value threshold for significance. Points will be colored by significance. By default it will not be colored. Default =None. ax : Matplotlib Axes instance, optional If given, the Moran plot will be created inside this axis. Default =None. scatter_kwds : keyword arguments, optional Keywords used for creating and designing the scatter points. Default =None. fitline_kwds : keyword arguments, optional Keywords used for creating and designing the moran fitline. Default =None. Returns ------- fig : Matplotlib Figure instance Bivariate Moran Local scatterplot figure ax : matplotlib Axes instance Axes in which the figure is plotted Examples -------- Imports >>> import matplotlib.pyplot as plt >>> import geopandas as gpd >>> from pysal.lib.weights.contiguity import Queen >>> from pysal.lib import examples >>> from pysal.explore.esda.moran import Moran_Local_BV >>> from pysal.viz.splot.esda import moran_scatterplot Load data and calculate Moran Local statistics >>> link = examples.get_path('Guerry.shp') >>> gdf = gpd.read_file(link) >>> x = gdf['Suicids'].values >>> y = gdf['Donatns'].values >>> w = Queen.from_dataframe(gdf) >>> w.transform = 'r' >>> m = Moran_Local_BV(x, y, w) Plot >>> moran_scatterplot(m) >>> plt.show() Customize plot >>> moran_scatterplot(m, p=0.05, ... fitline_kwds=dict(color='#4393c3'))) >>> plt.show() """ # to set default as an empty dictionary that is later filled with defaults if scatter_kwds is None: scatter_kwds = dict() if fitline_kwds is None: fitline_kwds = dict() if p is not None: if not isinstance(moran_loc_bv, Moran_Local_BV): raise ValueError("`moran_loc_bv` is not a\n" + "esda.moran.Moran_Local_BV instance") if 'color' in scatter_kwds or 'cmap' in scatter_kwds: warnings.warn("To change the color use cmap with a colormap of 5,\n" + "c defines the LISA category, color will interfere with c") # colors spots_bv = moran_hot_cold_spots(moran_loc_bv, p) hmap = colors.ListedColormap(['#bababa', '#d7191c', '#abd9e9', '#2c7bb6', '#fdae61']) # define customization scatter_kwds.setdefault('alpha', 0.6) scatter_kwds.setdefault('s', 40) fitline_kwds.setdefault('alpha', 0.9) # get fig and ax fig, ax = _create_moran_fig_ax(ax, figsize=(7,7)) # set labels ax.set_xlabel('Attribute') ax.set_ylabel('Spatial Lag') ax.set_title('Moran BV Local Scatterplot') # plot and set standards lag = lag_spatial(moran_loc_bv.w, moran_loc_bv.zy) fit = OLS(moran_loc_bv.zy[:, None], lag[:, None]) # v- and hlines ax.axvline(0, alpha=0.5, color='k', linestyle='--') ax.axhline(0, alpha=0.5, color='k', linestyle='--') if p is not None: fitline_kwds.setdefault('color', 'k') scatter_kwds.setdefault('cmap', hmap) scatter_kwds.setdefault('c', spots_bv) ax.plot(lag, fit.predy, **fitline_kwds) ax.scatter(moran_loc_bv.zx, fit.predy, **scatter_kwds) else: scatter_kwds.setdefault('color', splot_colors['moran_base']) fitline_kwds.setdefault('color', splot_colors['moran_fit']) ax.plot(lag, fit.predy, **fitline_kwds) ax.scatter(moran_loc_bv.zy, fit.predy, **scatter_kwds) return fig, ax
[docs]def moran_facet(moran_matrix, figsize=(16,12), scatter_bv_kwds=None, fitline_bv_kwds=None, scatter_glob_kwds=dict(color='#737373'), fitline_glob_kwds=None): """ Moran Facet visualization. Includes BV Morans and Global Morans on the diagonal. Parameters ---------- moran_matrix : esda.moran.Moran_BV_matrix instance Dictionary of Moran_BV objects figsize : tuple, optional W, h of figure. Default =(16,12) scatter_bv_kwds : keyword arguments, optional Keywords used for creating and designing the scatter points of off-diagonal Moran_BV plots. Default =None. fitline_bv_kwds : keyword arguments, optional Keywords used for creating and designing the moran fitline of off-diagonal Moran_BV plots. Default =None. scatter_glob_kwds : keyword arguments, optional Keywords used for creating and designing the scatter points of diagonal Moran plots. Default =None. fitline_glob_kwds : keyword arguments, optional Keywords used for creating and designing the moran fitline of diagonal Moran plots. Default =None. Returns ------- fig : Matplotlib Figure instance Bivariate Moran Local scatterplot figure axarr : matplotlib Axes instance Axes in which the figure is plotted Examples -------- Imports >>> import matplotlib.pyplot as plt >>> import pysal.lib as lp >>> import numpy as np >>> import geopandas as gpd >>> from pysal.explore.esda.moran import Moran_BV_matrix >>> from pysal.viz.splot.esda import moran_facet Load data and calculate Moran Local statistics >>> f = gpd.read_file(lp.examples.get_path("sids2.dbf")) >>> varnames = ['SIDR74', 'SIDR79', 'NWR74', 'NWR79'] >>> vars = [np.array(f[var]) for var in varnames] >>> w = lp.io.open(lp.examples.get_path("sids2.gal")).read() >>> moran_matrix = Moran_BV_matrix(vars, w, varnames = varnames) Plot >>> fig, axarr = moran_facet(moran_matrix) >>> plt.show() Customize plot >>> fig, axarr = moran_facet(moran_matrix, ... fitline_bv_kwds=dict(color='#4393c3')) >>> plt.show() """ nrows = int(np.sqrt(len(moran_matrix))) + 1 ncols = nrows fig, axarr = plt.subplots(nrows, ncols, figsize=figsize, sharey=True, sharex=True) fig.suptitle('Moran Facet') for row in range(nrows): for col in range(ncols): if row == col: global_m = Moran(moran_matrix[row, (row+1) % 4].zy, moran_matrix[row, (row+1) % 4].w) _moran_global_scatterplot(global_m, ax= axarr[row,col], scatter_kwds=scatter_glob_kwds, fitline_kwds=fitline_glob_kwds) axarr[row, col].set_facecolor('#d9d9d9') else: _moran_bv_scatterplot(moran_matrix[row,col], ax=axarr[row,col], scatter_kwds=scatter_bv_kwds, fitline_kwds=fitline_bv_kwds) axarr[row, col].spines['bottom'].set_visible(False) axarr[row, col].spines['left'].set_visible(False) if row == nrows - 1: axarr[row, col].set_xlabel(str( moran_matrix[(col+1)%4, col].varnames['x']).format(col)) axarr[row, col].spines['bottom'].set_visible(True) else: axarr[row, col].set_xlabel('') if col == 0: axarr[row, col].set_ylabel(('Spatial Lag of '+str( moran_matrix[row, (row+1)%4].varnames['y'])).format(row)) axarr[row, col].spines['left'].set_visible(True) else: axarr[row, col].set_ylabel('') axarr[row, col].set_title('') plt.tight_layout() return fig, axarr