基于geopandas的精美地图绘制：geoplot
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前言
项目目标
本项目旨在介绍geoplot的地图绘图示例。
项目方法
在以下内容中，将基于geoplot官方示例展示地图绘图。
安装与导入库
!pip install geoplot -i https://pypi.mirrors.ustc.edu.cn/simple/Looking in indexes: https://pypi.mirrors.ustc.edu.cn/simple/
Collecting geoplot
Downloading https://mirrors.ustc.edu.cn/pypi/packages/49/52/f7fa2faaead12a6a11d34d812a9615575e1fccb50cfe628e0d801cebe237/geoplot-0.5.1-py3-none-any.whl (28 kB)
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Collecting contextily>=1.0.0
Downloading https://mirrors.ustc.edu.cn/pypi/packages/fb/46/07a029b73f9a5c7bbf9b538e6441c42014a448f335a1cc780616f2594bad/contextily-1.6.2-py3-none-any.whl (17 kB)
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Collecting mercantile
Downloading https://mirrors.ustc.edu.cn/pypi/packages/b2/d6/de0cc74f8d36976aeca0dd2e9cbf711882ff8e177495115fd82459afdc4d/mercantile-1.2.1-py3-none-any.whl (14 kB)
Collecting geopy
Downloading https://mirrors.ustc.edu.cn/pypi/packages/e5/15/cf2a69ade4b194aa524ac75112d5caac37414b20a3a03e6865dfe0bd1539/geopy-2.4.1-py3-none-any.whl (125 kB)
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Collecting geographiclib<3,>=1.52
Downloading https://mirrors.ustc.edu.cn/pypi/packages/9f/5a/a26132406f1f40cf51ea349a5f11b0a46cec02a2031ff82e391c2537247a/geographiclib-2.0-py3-none-any.whl (40 kB)
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Installing collected packages: mercantile, geographiclib, geopy, contextily, geoplot
Successfully installed contextily-1.6.2 geographiclib-2.0 geoplot-0.5.1 geopy-2.4.1 mercantile-1.2.1import geopandas as gpd
import geoplot as gplt
usa_cities = gpd.read_file(gplt.datasets.get_path('usa_cities'))
usa_cities.head()| id | POP_2010 | ELEV_IN_FT | STATE | geometry | |
|---|---|---|---|---|---|
| 0 | 53 | 40888.0 | 1611.0 | ND | POINT (-101.29627 48.23251) |
| 1 | 101 | 52838.0 | 830.0 | ND | POINT (-97.03285 47.92526) |
| 2 | 153 | 15427.0 | 1407.0 | ND | POINT (-98.70844 46.91054) |
| 3 | 177 | 105549.0 | 902.0 | ND | POINT (-96.78980 46.87719) |
| 4 | 192 | 17787.0 | 2411.0 | ND | POINT (-102.78962 46.87918) |
continental_usa_cities = usa_cities.query('STATE not in ["HI", "AK", "PR"]')
gplt.pointplot(continental_usa_cities)<Axes: >contiguous_usa = gpd.read_file(gplt.datasets.get_path('contiguous_usa'))
gplt.polyplot(contiguous_usa)<Axes: >ax = gplt.polyplot(contiguous_usa)
gplt.pointplot(continental_usa_cities, ax=ax)<Axes: >import geoplot.crs as gcrs
ax = gplt.polyplot(contiguous_usa, projection=gcrs.AlbersEqualArea())
gplt.pointplot(continental_usa_cities, ax=ax)<GeoAxes: >ax = gplt.polyplot(
contiguous_usa, projection=gcrs.AlbersEqualArea(),
edgecolor='white', facecolor='lightgray',
figsize=(12, 8)
)
gplt.pointplot(
continental_usa_cities, ax=ax, hue='ELEV_IN_FT', cmap='Blues',
scheme='quantiles',
scale='ELEV_IN_FT', limits=(1, 10),
legend=True, legend_var='scale',
legend_kwargs={'frameon': False},
legend_values=[-110, 1750, 3600, 5500, 7400],
legend_labels=['-110 feet', '1750 feet', '3600 feet', '5500 feet', '7400 feet']
)
ax.set_title('Cities in the Continental United States by Elevation', fontsize=16)Text(0.5, 1.0, 'Cities in the Continental United States by Elevation')gplt.choropleth(
contiguous_usa, hue='population', projection=gcrs.AlbersEqualArea(),
edgecolor='white', linewidth=1,
cmap='Greens', legend=True,
scheme='FisherJenks',
legend_labels=[
'<3 million', '3-6.7 million', '6.7-12.8 million',
'12.8-25 million', '25-37 million'
]
)<GeoAxes: >import geoplot.crs as gcrs
import matplotlib.pyplot as plt
import mapclassify as mc
scheme = mc.Quantiles(continental_usa_cities['POP_2010'], k=5)
proj = gcrs.AlbersEqualArea()
ax = gplt.polyplot(
contiguous_usa,
zorder=-1,
linewidth=1,
projection=proj,
edgecolor='white',
facecolor='lightgray',
figsize=(12, 12)
)
gplt.pointplot(
continental_usa_cities,
scale='POP_2010',
limits=(2, 30),
hue='POP_2010',
cmap='Blues',
scheme=scheme,
legend=True,
legend_var='scale',
legend_values=[8000000, 2000000, 1000000, 100000],
legend_labels=['8 million', '2 million', '1 million', '100 thousand'],
legend_kwargs={'frameon': False, 'loc': 'lower right'},
ax=ax
)
plt.title("Cities in the contiguous United States, 2010")Text(0.5, 1.0, 'Cities in the contiguous United States, 2010')import geopandas as gpd
import geoplot as gplt
import geoplot.crs as gcrs
import matplotlib.pyplot as plt
dc_roads = gpd.read_file(gplt.datasets.get_path('dc_roads'))
gplt.sankey(
dc_roads, projection=gcrs.AlbersEqualArea(),
scale='aadt', limits=(0.1, 10), color='black'
)
plt.title("Streets in Washington DC by Average Daily Traffic, 2015")Text(0.5, 1.0, 'Streets in Washington DC by Average Daily Traffic, 2015')import geopandas as gpd
import geoplot as gplt
import geoplot.crs as gcrs
import matplotlib.pyplot as plt
# load the data
nyc_boroughs = gpd.read_file(gplt.datasets.get_path('nyc_boroughs'))
tickets = gpd.read_file(gplt.datasets.get_path('nyc_parking_tickets'))
proj = gcrs.AlbersEqualArea(central_latitude=40.7128, central_longitude=-74.0059)
def plot_state_to_ax(state, ax):
gplt.choropleth(
tickets.set_index('id').loc[:, [state, 'geometry']],
hue=state, cmap='Blues',
linewidth=0.0, ax=ax
)
gplt.polyplot(
nyc_boroughs, edgecolor='black', linewidth=0.5, ax=ax
)
f, axarr = plt.subplots(2, 2, figsize=(12, 13), subplot_kw={'projection': proj})
plt.suptitle('Parking Tickets Issued to State by Precinct, 2016', fontsize=16)
plt.subplots_adjust(top=0.95)
plot_state_to_ax('ny', axarr[0][0])
axarr[0][0].set_title('New York (n=6,679,268)')
plot_state_to_ax('nj', axarr[0][1])
axarr[0][1].set_title('New Jersey (n=854,647)')
plot_state_to_ax('pa', axarr[1][0])
axarr[1][0].set_title('Pennsylvania (n=215,065)')
plot_state_to_ax('ct', axarr[1][1])
axarr[1][1].set_title('Connecticut (n=126,661)')Text(0.5, 1.0, 'Connecticut (n=126,661)')import pandas as pd
import geopandas as gpd
import geoplot as gplt
import geoplot.crs as gcrs
import matplotlib.pyplot as plt
import mapclassify as mc
# load the data
obesity_by_state = pd.read_csv(gplt.datasets.get_path('obesity_by_state'), sep='\t')
contiguous_usa = gpd.read_file(gplt.datasets.get_path('contiguous_usa'))
contiguous_usa['Obesity Rate'] = contiguous_usa['state'].map(
lambda state: obesity_by_state.query("State == @state").iloc[0]['Percent']
)
scheme = mc.Quantiles(contiguous_usa['Obesity Rate'], k=5)
ax = gplt.cartogram(
contiguous_usa,
scale='Obesity Rate', limits=(0.75, 1),
projection=gcrs.AlbersEqualArea(central_longitude=-98, central_latitude=39.5),
hue='Obesity Rate', cmap='Reds', scheme=scheme,
linewidth=0.5,
legend=True, legend_kwargs={'loc': 'lower right'}, legend_var='hue',
figsize=(12, 7)
)
gplt.polyplot(contiguous_usa, facecolor='lightgray', edgecolor='None', ax=ax)
plt.title("Adult Obesity Rate by State, 2013")Text(0.5, 1.0, 'Adult Obesity Rate by State, 2013')小结
虽然许久不更新，但geoplot依然能绘制颜值在线的地图，当然还有更多功能有待发掘
