def display_map3(gdf, clip, proj):
# Setup the Voronoi axes; this creates the Voronoi regions
ax = geoplot.voronoi(
gdf, # Define the GeoPandas DataFrame
hue='total_crimes', # df column used to color regions
clip=clip, # Define the voronoi clipping (map edge)
projection=proj, # Define the Projection
legend=True, # Create a legend
edgecolor='white', # Color of the voronoi boundaries
)
# Render the plot with a base map
geoplot.choropleth(
chicago, # Base Map
ax=ax, # Axis attribute we created above
extent=chicago.
total_bounds, # Set plotting boundaries to base map boundaries
edgecolor='black', # Color of base map's edges
linewidth=1, # Width of base map's edge lines
zorder=1 # Plot base map edges above the voronoi regions
)
def display_map1(gdf, clip, proj):
# Setup the Voronoi axes; this creates the Voronoi regions
ax = geoplot.voronoi(
gdf, # Define the GeoPandas DataFrame
#hue='values', # df column used to color regions
clip=clip, # Define the voronoi clipping (map edge)
projection=proj, # Define the Projection
#cmap = 'Reds', # color set
#k = None, # No. of discretized buckets to create
#legend = True, # Create a legend
#edgecolor = 'white', # Color of the voronoi boundaries
#linewidth = 0.01 # width of the voronoi boundary lines
)
# Render the plot with a base map
geoplot.polyplot(
chicago, # Base Map
ax=ax, # Axis attribute we created above
extent=chicago.
total_bounds, # Set plotting boundaries to base map boundaries
edgecolor='black', # Color of base map's edges
linewidth=1, # Width of base map's edge lines
zorder=1 # Plot base map edges above the voronoi regions
)
import quilt
from quilt.data.ResidentMario import geoplot_data
import geopandas as gpd
boroughs = gpd.read_file(geoplot_data.nyc_boroughs())
injurious_collisions = gpd.read_file(geoplot_data.nyc_injurious_collisions())
# A plot type using Voronoi tessellation: https://en.wikipedia.org/wiki/Voronoi_diagram
import geoplot as gplt
import matplotlib.pyplot as plt
f, axarr = plt.subplots(1, 2, figsize=(16, 8))
gplt.voronoi(injurious_collisions.head(1000), edgecolor='lightsteelblue', linewidth=0.5, ax=axarr[0])
gplt.polyplot(boroughs, linewidth=0.5, ax=axarr[0])
gplt.voronoi(injurious_collisions.head(1000), hue='NUMBER OF PERSONS INJURED', cmap='Reds',
edgecolor='white', clip=boroughs.geometry,
linewidth=0.5, categorical=True, ax=axarr[1])
gplt.polyplot(boroughs, linewidth=1, ax=axarr[1])
axarr[0].axis('off')
axarr[1].axis('off')
plt.suptitle("Injurious Car Crashes in New York City, 2016", fontsize=20, y=0.95)
plt.savefig("nyc-collisions-voronoi.png", bbox_inches='tight', pad_inches=0)
from shapely.geometry import Point
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib import cm, colors
df_map = gpd.GeoDataFrame.from_file('Virtual_Map1.shp')
df_city = pd.read_csv("Virtual_City.csv")
geom = gpd.GeoSeries(
[Point(x, y) for x, y in zip(df_city.long.values, df_city.lat.values)])
df_city = gpd.GeoDataFrame(df_city, geometry=geom)
#--------------------------------- (a)黑白沃罗诺伊图.----------------------------------------
ax1 = gplt.voronoi(
df_city, #projection=gcrs.AlbersEqualArea(),
clip=df_map,
linewidth=0.5,
#hue='orange', cmap='Reds',k=5,
legend=False,
edgecolor='k')
ax2 = gplt.pointplot(df_city, color='white', s=10, edgecolors='k',
ax=ax1) #zorder=2,
gplt.polyplot(df_map, edgecolor='none', facecolor='lightgray',
ax=ax1) #zorder=1,
#plt.savefig('沃罗诺伊地图2.pdf')
#--------------------------------- (b)彩色沃罗诺伊图.----------------------------------------
ax = gplt.voronoi(
df_city, #projection=gcrs.AlbersEqualArea(),
clip=df_map,
hue='city',
def test_clip_params_geometric(kwargs):
return voronoi(p_df, **kwargs).get_figure()
scale = query,
limits = (30, 30),
legend = True,
#legend_values = [-1,1],
#legend_labels = ['negative','positive'],
ax = Us_ax)
plt.title(str(title)+', USA')
Us_ax1 = gplt.polyplot(USA,linewidth=0.7
#,figsize = size
)
gplt.voronoi(Us_gdf,
hue = query,
cmap = 'rainbow',
clip = USA,
k = None,
linewidth = 0.1,
alpha = 0.9,
#scale = query,
#limits = (30, 30),
legend = True,ax = Us_ax1)
plt.title(str(title)+', USA')
Jp_ax =gplt.polyplot(world,linewidth=0.7)
gplt.pointplot(Jp_gdf,
hue = query,
cmap = 'rainbow',
k = 2,
alpha = 0.8,
scale = query,
limits = (30, 30),
# three-dimensional heatmap on it using ``kdeplot``.
ax = geoplot.kdeplot(collisions,
clip=boroughs.geometry,
shade=True,
cmap='Reds',
projection=geoplot.crs.AlbersEqualArea())
geoplot.polyplot(boroughs, ax=ax, zorder=1)
###############################################################################
# Alternatively, we may partition the space into neighborhoods automatically,
# using Voronoi tessellation. This is a good way of visually verifying whether
# or not a certain data column is spatially correlated.
ax = geoplot.voronoi(collisions.head(1000),
projection=geoplot.crs.AlbersEqualArea(),
clip=boroughs.simplify(0.001),
hue='NUMBER OF PERSONS INJURED',
cmap='Reds',
k=None,
legend=True,
edgecolor='white')
geoplot.polyplot(boroughs, edgecolor='black', zorder=1, ax=ax)
###############################################################################
# These are just some of the plots you can make with Geoplot. There are
# many other possibilities not covered in this brief introduction. For more
# examples, refer to the
# `Gallery <https://residentmario.github.io/geoplot/gallery/index.html>`_ in
# the Geoplot documentation.
geoplot.cartogram(df[df['continent'] == 'Africa'],
scale='pop_est', limits=(0.2, 1), figsize=(7, 8))
###############################################################################
# If we have data in the shape of points in space, we may generate a
# three-dimensional heatmap on it using ``kdeplot``. This example also
# demonstrates how easy it is to stack plots on top of one another.
ax = geoplot.kdeplot(injurious_collisions.sample(1000),
shade=True, shade_lowest=False,
clip=boroughs.geometry)
geoplot.polyplot(boroughs, ax=ax)
###############################################################################
# Alternatively, we may partition the space into neighborhoods automatically,
# using Voronoi tessellation.
ax = geoplot.voronoi(
injurious_collisions.sample(1000),
hue='NUMBER OF PERSONS INJURED', cmap='Reds', scheme='fisher_jenks',
clip=boroughs.geometry,
linewidth=0)
geoplot.polyplot(boroughs, ax=ax)
###############################################################################
# Again, these are just some of the plots you can make with Geoplot. There are
# several other possibilities not covered in this brief introduction. For more
# examples, refer to the
# `Gallery <https://residentmario.github.io/geoplot/gallery.html>`_ in the
# Geoplot documentation.
###############################################################################
# If we have data in the shape of points in space, we may generate a
# three-dimensional heatmap on it using ``kdeplot``. This example also
# demonstrates how easy it is to stack plots on top of one another.
ax = geoplot.kdeplot(injurious_collisions.sample(1000),
shade=True,
shade_lowest=False,
clip=boroughs.geometry)
geoplot.polyplot(boroughs, ax=ax)
###############################################################################
# Alternatively, we may partition the space into neighborhoods automatically,
# using Voronoi tessellation.
ax = geoplot.voronoi(injurious_collisions.sample(1000),
hue='NUMBER OF PERSONS INJURED',
cmap='Reds',
scheme='fisher_jenks',
clip=boroughs.geometry,
linewidth=0)
geoplot.polyplot(boroughs, ax=ax)
###############################################################################
# Again, these are just some of the plots you can make with Geoplot. There are
# several other possibilities not covered in this brief introduction. For more
# examples, refer to the
# `Gallery <https://residentmario.github.io/geoplot/gallery.html>`_ in the
# Geoplot documentation.
def geospatial_viz(geo_data_url,
point_data_url=None,
att_var=None,
map_type=None):
'''
function to visualize the attribute information in map. (eg, population in states)
geo_att_data: geodataframe that contains both geometry and attributes info
att_var: the attributes to be visualized in the map
map_type: string, the type of map to be viz. pointplot, choropleth, voronoi
if point_data = None, att_var must be from geo_data
'''
geo_data = gpd.read_file(geo_data_url)
print(geo_data.head())
if point_data_url == 'No point attribute data':
if att_var is None:
ax = gplt.polyplot(geo_data, figsize=(10, 5))
ax.set_title('plain map of continental USA', fontsize=16)
else:
if map_type == 'choropleth':
scheme = mc.FisherJenks(geo_data[att_var], k=5)
labels = scheme.get_legend_classes()
ax = gplt.polyplot(geo_data, projection=gcrs.AlbersEqualArea())
gplt.choropleth(geo_data,
hue=att_var,
edgecolor='white',
linewidth=1,
cmap='Blues',
legend=True,
scheme=scheme,
legend_labels=labels,
ax=ax)
ax.set_title('{} in the continental US'.format(att_var),
fontsize=16)
if map_type == "cartogram":
gplt.cartogram(geo_data,
scale=att_var,
edgecolor='black',
projection=gcrs.AlbersEqualArea())
else:
point_data = gpd.read_file(point_data_url)
scheme = mc.Quantiles(point_data[att_var], k=5)
labels = scheme.get_legend_classes()
if map_type == 'pointplot':
if isinstance(point_data.geometry[0],
shapely.geometry.point.Point):
ax = gplt.polyplot(geo_data,
edgecolor='white',
facecolor='lightgray',
figsize=(12, 8)
#projection = gcrs.AlbersEqualArea()
)
gplt.pointplot(point_data,
ax=ax,
hue=att_var,
cmap='Blues',
scheme=scheme,
scale=att_var,
legend=True,
legend_var='scale',
legend_kwargs={"loc": 'lower right'},
legend_labels=labels)
ax.set_title(
'Cities in the continental US, by population 2010',
fontsize=16)
else:
print('Geometry data type not valid')
if map_type == "voronoi":
# check uniqueness of coordinates
duplicates = point_data.geometry.duplicated()
point_data_unique = point_data[-duplicates]
proj = gplt.crs.AlbersEqualArea(central_longitude=-98,
central_latitude=39.5)
ax = gplt.voronoi(point_data_unique,
hue=att_var,
clip=geo_data,
projection=proj,
cmap='Blues',
legend=True,
edgecolor="white",
linewidth=0.01)
gplt.polyplot(geo_data,
ax=ax,
extent=geo_data.total_bounds,
edgecolor="black",
linewidth=1,
zorder=1)
plt.title("{} in US cities".format(att_var), fontsize=16)
hue=query,
cmap='plasma',
k=None,
alpha=0.8,
scale=query,
limits=(25, 25),
legend=True,
ax=Us_ax)
#plt.title(str(title)+', USA')
Us_ax1 = gplt.polyplot(USA, linewidth=0.7, figsize=(80, 60))
gplt.voronoi(Us_gdf,
figsize=(80, 60),
hue=query,
cmap='plasma',
clip=USA,
k=None,
linewidth=0.1,
alpha=0.9,
legend=True,
ax=Us_ax1)
#plt.title(str(title)+', USA')
Jp_ax = gplt.polyplot(world, linewidth=0.7)
gplt.pointplot(Jp_gdf,
hue=query,
cmap='plasma',
k=None,
alpha=0.8,
scale=query,
limits=(20, 20),
def test_clip_params_geometric(kwargs):
# ignore warning from changed GeoSeries.isna behavior
import warnings
with warnings.catch_warnings():
warnings.filterwarnings('ignore', 'GeoSeries.isna', UserWarning)
return voronoi(p_df, **kwargs).get_figure()
"""
Voronoi of Melbourne primary schools
====================================
This example shows a ``pointplot`` combined with a ``voronoi`` mapping primary schools in
Melbourne. Schools in outlying, less densely populated areas serve larger zones than those in
central Melbourne.
This example inspired by the `Melbourne Schools Zones Webmap <http://melbourneschoolzones.com/>`_.
"""
import geopandas as gpd
import geoplot as gplt
import geoplot.crs as gcrs
import pandas as pd
import matplotlib.pyplot as plt
melbourne = gpd.read_file(gplt.datasets.get_path('melbourne'))
melbourne_primary_schools = gpd.read_file(gplt.datasets.get_path('melbourne_schools'))\
.query('School_Type == "Primary"')
ax = gplt.voronoi(
melbourne_primary_schools, clip=melbourne, linewidth=0.5, edgecolor='white',
projection=gcrs.Mercator()
)
gplt.polyplot(melbourne, edgecolor='None', facecolor='lightgray', ax=ax)
gplt.pointplot(melbourne_primary_schools, color='black', ax=ax, s=1, extent=melbourne.total_bounds)
plt.title('Primary Schools in Greater Melbourne, 2018')
plt.savefig("melbourne-schools.png", bbox_inches='tight', pad_inches=0)
import quilt
from quilt.data.ResidentMario import geoplot_data
import geopandas as gpd
boroughs = gpd.read_file(geoplot_data.nyc_boroughs())
injurious_collisions = gpd.read_file(geoplot_data.nyc_injurious_collisions())
# A plot type using Voronoi tessellation: https://en.wikipedia.org/wiki/Voronoi_diagram
import geoplot as gplt
import matplotlib.pyplot as plt
f, axarr = plt.subplots(1, 2, figsize=(16, 8))
gplt.voronoi(injurious_collisions.head(1000),
edgecolor='lightsteelblue',
linewidth=0.5,
ax=axarr[0])
gplt.polyplot(boroughs, linewidth=0.5, ax=axarr[0])
gplt.voronoi(injurious_collisions.head(1000),
hue='NUMBER OF PERSONS INJURED',
cmap='Reds',
edgecolor='white',
clip=boroughs.geometry,
linewidth=0.5,
categorical=True,
ax=axarr[1])
gplt.polyplot(boroughs, linewidth=1, ax=axarr[1])
axarr[0].axis('off')
axarr[1].axis('off')
crs = {'init': 'epsg:4326'}
gdf = geopandas.GeoDataFrame(soldf, crs=crs, geometry=geometry)
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
USA = world.query('name == "United States"')
contiguous_usa = geopandas.read_file(
geoplot.datasets.get_path('contiguous_usa'))
proj = gcrs.AlbersEqualArea(central_longitude=-98, central_latitude=39.5)
with PdfPages('mapPlot.pdf') as pdf:
for t in time_ranges:
ax = geoplot.voronoi(gdf,
projection=proj,
clip=USA.simplify(0.01),
hue=t,
figsize=(10, 10),
cmap='Reds',
k=None,
legend=True,
edgecolor='white',
linewidth=0.01)
#plt.title('Predicted Future Similarity')
plt.title(t)
geoplot.polyplot(USA,
edgecolor='black',
zorder=1,
ax=ax,
linewidth=1,
extent=contiguous_usa.total_bounds)
plt.savefig(pdf, format='pdf')