def geoplot(canvas):
obesity = pd.read_csv(gplt.datasets.get_path("obesity_by_state"), sep="\t")
contiguous_usa = gpd.read_file(gplt.datasets.get_path("contiguous_usa"))
result = contiguous_usa.set_index("state").join(obesity.set_index("State"))
# https://stackoverflow.com/a/51621986/6622587
ax = canvas.figure.subplots(subplot_kw={"projection": gcrs.AlbersEqualArea()})
gplt.cartogram(result, scale="Percent", projection=gcrs.AlbersEqualArea(), ax=ax)
def test_cartogram(self):
try:
gplt.cartogram(dataframe_gaussian_polys, scale='hue_var', projection=gcrs.PlateCarree(), facecolor='white')
gplt.cartogram(dataframe_gaussian_polys, scale='hue_var',
projection=gcrs.PlateCarree(), legend_kwargs={'fancybox': False})
finally:
plt.close()
def test_cartogram(self):
try:
gplt.cartogram(dataframe_gaussian_polys, scale='hue_var', projection=gcrs.PlateCarree(), facecolor='white')
gplt.cartogram(dataframe_gaussian_polys, scale='hue_var',
projection=gcrs.PlateCarree(), legend_kwargs={'fancybox': False})
finally:
plt.close()
def test_cartogram(self, projection, scale_dataset, hue_vars, legend_vars,
trace):
kwargs = {
'projection': projection,
'scale': scale_dataset,
'trace': trace
}
kwargs = {**kwargs, **hue_vars, **legend_vars}
try:
gplt.cartogram(gaussian_polys, **kwargs)
finally:
plt.close()
def create_cartogram(filename):
"""
This function produces a cartogram map.
"""
data_raw = gpd.read_file(filename)
map = gplt.cartogram(data_raw,
projection=gcrs.Robinson(),
scale=data_raw['NUMPOINTS'],
hue=data_raw['NUMPOINTS'],
cmap='Reds',
k=5,
figsize=(30, 15)
)
return map
def main():
data_dir = "./data/au-cities.csv"
world = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))
cities = gpd.read_file(gpd.datasets.get_path("naturalearth_cities"))
print(world.head())
ax = gp.polyplot(world, projection = gp.crs.Orthographic())
ax.outline_patch.set_visible(True)
#Graphs a choropleth of gdp / population
gdp_per_person = world["gdp_md_est"] / world["pop_est"]
scheme = mapclassify.Quantiles(gdp_per_person, k = 5)
gp.choropleth(world, hue = gdp_per_person, scheme = scheme,
cmap = "Greens")
print(world.head())
#Graphs population size by establishing area size
#to the African continent
africa = world[world.continent == "Africa"]
ax = gp.cartogram(africa, scale = "pop_est", limits = (0.2, 1),
edgecolor = "black")
gp.polyplot(africa, edgecolor = "black", ax = ax)
plt.show()
'Massachusetts', 'New Hampshire', 'Rhode Island', 'Vermont', 'Alabama',
'Florida', 'Georgia', 'Mississippi', 'South Carolina', 'Illinois',
'Indiana', 'Kentucky', 'North Carolina', 'Ohio', 'Tennessee', 'Virginia',
'Wisconsin', 'West Virginia', 'Delaware', 'District of Columbia',
'Maryland', 'New Jersey', 'New York', 'Pennsylvania', 'Maine', 'Michigan'
]
contiguous_usa['Obesity Rate'] = contiguous_usa['State'].map(
lambda state: obesity.query("State == @state").iloc[0]['Percent'])
# Plot the data.
import geoplot as gplt
import geoplot.crs as gcrs
import matplotlib.pyplot as plt
ax = gplt.cartogram(contiguous_usa,
scale='Obesity Rate',
projection=gcrs.AlbersEqualArea(central_longitude=-98,
central_latitude=39.5),
limits=(0.75, 1),
linewidth=0.5,
hue='Obesity Rate',
cmap='Reds',
k=5,
trace_kwargs={'linewidth': 0.5},
legend=True,
legend_kwargs={'loc': 'lower right'},
legend_var='hue',
figsize=(12, 12))
plt.title("Adult Obesity Rate by State, 2013")
plt.savefig("obesity.png", bbox_inches='tight', pad_inches=0.1)
# you want to use color as a visual variable, specify a ``choropleth``. Here
# we sort GDP per person by country into five buckets by color.
geoplot.choropleth(world,
hue=world['gdp_md_est'] / world['pop_est'],
cmap='Greens',
figsize=(8, 4))
###############################################################################
# If you want to use size as a visual variable, use a ``cartogram``. Here are
# population estimates for countries in Africa.
africa = world.query('continent == "Africa"')
ax = geoplot.cartogram(africa,
scale='pop_est',
limits=(0.2, 1),
edgecolor='None',
figsize=(7, 8))
geoplot.polyplot(africa, edgecolor='gray', ax=ax)
###############################################################################
# If we have data in the shape of points in space, we may generate a
# 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)
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)
ax.set_global()
ax.outline_patch.set_visible(True)
###############################################################################
# ``polyplot`` is trivial and can only plot the geometries you pass to it. If
# you want to use color as a visual variable, specify a ``choropleth``. Here
# we sort GDP per person by country into five buckets by color.
geoplot.choropleth(df, hue='gdp_pp', cmap='Greens', figsize=(8, 4))
###############################################################################
# If you want to use size as a visual variable, you want a ``cartogram``. Here
# are population estimates for countries in Africa.
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,
geomap = geomap.drop(['cartodb_id', 'regiao', 'nome_uf', 'geocodigo', 'simounao'],
axis=1)
# Order Columns
geomap = geomap[['uf_05', 'População', 'Médicos', 'geometry']]
# Rename Columns
geomap.columns = ['UF', 'População', 'Médicos', 'geometry']
import geoplot
import geoplot.crs as gcrs
# Plot cartogram
geoplot.cartogram(geomap,
scale='População',
hue='Médicos',
k=None,
cmap='coolwarm')
# Add Labels
plt.title('População por Médicos')
plt.legend()
plt.xticks([])
plt.yticks([])
# Save file
plt.savefig(path + 'cartogram.png', dpi=600)
# Show the plot
plt.show()
import pandas as pd
import matplotlib.pyplot as plt
df_map = GeoDataFrame.from_file('Virtual_Map1.shp')
df_city=pd.read_csv("Virtual_City.csv")
df=pd.merge(right=df_map, left=df_city,how='right',on="country")
df=GeoDataFrame(df)
#----------------------------------orange----------------------------------------------------
ax = geoplot.cartogram(
df,
scale='orange', limits=(0.5, 0.9),
#projection=gcrs.AlbersEqualArea(central_longitude=-98, central_latitude=39.5),
hue='orange', cmap='Reds', k=10,
linewidth=0.5,
legend=True, legend_kwargs={'loc': 'center',
'bbox_to_anchor':(1.15, 0, 0, 1),
'title':'value',
'edgecolor':'white'}, legend_var='hue',
figsize=(5, 5)
)
geoplot.polyplot(df, facecolor='lightgray', edgecolor='white', ax=ax)
#plt.title("Adult Obesity Rate by State, 2013")
#plt.savefig("cartogram_orange.pdf",bbox_inches='tight', pad_inches=0.1)
#-----------------------------------apple---------------------------------------------------
ax = geoplot.cartogram(
df,
scale='apple', limits=(0.5, 0.9),
#projection=gcrs.AlbersEqualArea(central_longitude=-98, central_latitude=39.5),
def test_cartogram(self):
try:
gplt.cartogram(series_gaussian_polys, scale=list_hue_values)
gplt.cartogram(dataframe_gaussian_polys, scale=list_hue_values)
gplt.cartogram(dataframe_gaussian_polys, hue=list_hue_values, scale=list_hue_values)
gplt.cartogram(dataframe_gaussian_polys, hue=series_hue_values, scale=list_hue_values)
gplt.cartogram(dataframe_gaussian_polys, hue=map_hue_values(), scale=list_hue_values)
gplt.cartogram(dataframe_gaussian_polys, hue='hue_var', scale=list_hue_values)
finally:
plt.close('all')
#border
border = gplt.polyplot(
df=covidMap,
projection=gcrs.Mercator(), #reproject
edgecolor='gray', #border
facecolor='gainsboro') #fill of polygon
#area and color
gplt.cartogram(
df=covidMap, #map
scheme=mc.EqualInterval(covidMap.DeathsPer100k, k=3),
cmap=plt.get_cmap('YlGnBu', 3), #palette
hue="DeathsPer100k", #var for color
scale='CasesPer100k', #var for resize
limits=(0.3, 1), #limits cartogram polygons
edgecolor='None', #no border
legend=True,
legend_var='hue', #legend of what
legend_kwargs={
'bbox_to_anchor': (0.1, 0.4), #location
'frameon': True, #with frame?
'markeredgecolor': 'k',
'title': "DeathsPer100k"
},
ax=border)
#%%
# mapOfPoints
import geoplot.crs as gcrs #activating!
#borders
countyBorders = gplt.polyplot(
data_cases_cleaned_day = data_cases_cleaned_day.fillna(0)
# print(data_cases_cleaned_day)
contiguous_usa['cases'] = contiguous_usa['state'].map(get_cases)
contiguous_usa['deaths'] = contiguous_usa['state'].map(get_deaths)
m, n = i % 2, i // 2
try:
ax = gplt.cartogram(
contiguous_usa,
scale='cases',
projection=gcrs.AlbersEqualArea(central_longitude=-98,
central_latitude=39.5),
hue='deaths',
cmap='YlOrRd',
#norm=colors.Normalize(vmin=0, vmax=18000),
scheme=scheme,
linewidth=0.5,
legend=True,
legend_kwargs={'loc': 'lower right'},
legend_var='hue',
figsize=(16, 20),
# ax = axes[m][n]
)
gplt.polyplot(contiguous_usa,
facecolor='lightgray',
edgecolor='None',
ax=ax)
except:
pass
plt.title(
import geopandas as gpd
from quilt.data.ResidentMario import geoplot_data
obesity = geoplot_data.obesity_by_state()
contiguous_usa = gpd.read_file(geoplot_data.contiguous_usa())
contiguous_usa['Obesity Rate'] = contiguous_usa['State'].map(
lambda state: obesity.query("State == @state").iloc[0]['Percent']
)
# Plot the data.
import geoplot as gplt
import geoplot.crs as gcrs
import matplotlib.pyplot as plt
ax = gplt.cartogram(contiguous_usa, scale='Obesity Rate',
projection=gcrs.AlbersEqualArea(central_longitude=-98, central_latitude=39.5),
limits=(0.75, 1),
linewidth=0.5,
hue='Obesity Rate',
cmap='Reds',
k=5,
trace_kwargs={'linewidth': 0.5},
legend=True,
legend_kwargs={'loc': 'lower right'},
legend_var='hue',
figsize=(12, 12))
plt.title("Adult Obesity Rate by State, 2013")
plt.savefig("obesity.png", bbox_inches='tight', pad_inches=0.1)
ax = geoplot.polyplot(df, projection=gcrs.Orthographic(), figsize=(8, 4))
ax.set_global()
ax.outline_patch.set_visible(True)
###############################################################################
# ``polyplot`` is trivial and can only plot the geometries you pass to it. If
# you want to use color as a visual variable, specify a ``choropleth``. Here
# we sort GDP per person by country into five buckets by color.
geoplot.choropleth(df, hue='gdp_pp', cmap='Greens', figsize=(8, 4))
###############################################################################
# If you want to use size as a visual variable, you want a ``cartogram``. Here
# are population estimates for countries in Africa.
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.
# Shape the data.
obesity = pd.read_csv("../../data/obesity/obesity_by_state.tsv", sep='\t')
usa = gpd.read_file("../../data/united_states/usa.geojson")
continental_usa = usa[~usa['adm1_code'].isin(['USA-3517', 'USA-3563'])]
continental_usa['State'] = [
'Minnesota', 'Montana', 'North Dakota', 'Idaho', 'Washington', 'Arizona',
'California', 'Colorado', 'Nevada', 'New Mexico', 'Oregon', 'Utah',
'Wyoming', 'Arkansas', 'Iowa', 'Kansas', 'Missouri', 'Nebraska',
'Oklahoma', 'South Dakota', 'Louisiana', 'Texas', 'Connecticut',
'Massachusetts', 'New Hampshire', 'Rhode Island', 'Vermont', 'Alabama',
'Florida', 'Georgia', 'Mississippi', 'South Carolina', 'Illinois',
'Indiana', 'Kentucky', 'North Carolina', 'Ohio', 'Tennessee', 'Virginia',
'Wisconsin', 'West Virginia', 'Delaware', 'District of Columbia',
'Maryland', 'New Jersey', 'New York', 'Pennsylvania', 'Maine', 'Michigan'
]
continental_usa['Obesity Rate'] = continental_usa['State'].map(
lambda state: obesity.query("State == @state").iloc[0]['Percent'])
# Plot the data.
ax = gplt.cartogram(continental_usa,
scale='Obesity Rate',
projection=gcrs.AlbersEqualArea(central_longitude=-98,
central_latitude=39.5),
limits=(0.75, 1),
linewidth=0.5,
facecolor='steelblue',
trace_kwargs={'linewidth': 0.5})
ax.set_ylim((-1597757.3894385984, 1457718.4893930717))
plt.title("Adult Obesity Rate by State, 2013")
plt.savefig("obesity.png", bbox_inches='tight', pad_inches=0.1)
def test_cartogram(self):
try:
gplt.cartogram(series_gaussian_polys, scale=list_hue_values)
gplt.cartogram(dataframe_gaussian_polys, scale=list_hue_values)
gplt.cartogram(dataframe_gaussian_polys,
hue=list_hue_values,
scale=list_hue_values)
gplt.cartogram(dataframe_gaussian_polys,
hue=series_hue_values,
scale=list_hue_values)
gplt.cartogram(dataframe_gaussian_polys,
hue=map_hue_values(),
scale=list_hue_values)
gplt.cartogram(dataframe_gaussian_polys,
hue='hue_var',
scale=list_hue_values)
finally:
plt.close('all')
print(i + 1, float(frac))
wien['street'] = wien.BEZNR.map(district_dict)**(1 / 2)
# %%
def identity_scale(minval, maxval):
def scalar(val):
return val
return scalar
f, ax = plt.subplots(1, figsize=(20, 20))
geoplot.polyplot(wien, ax=ax, color='green', alpha=0.1)
ax = geoplot.cartogram(wien,
scale='street',
scale_func=identity_scale,
limits=(0, 1),
edgecolor='None',
figsize=(7, 8),
ax=ax,
color='black',
alpha=0.7)
geoplot.polyplot(wien, edgecolor='gray', ax=ax)
list(wien)
# %%
