地图（三）利用python绘制等值区域地图

HsuHeinrich

发布于 2025-02-25 19:57:05

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地图（三）利用python绘制等值区域地图

等值区域地图（Choropleth Map）简介

等值区域地图通过颜色区别地图上不同区域的变量，便于在空间上进行变量的比较。

快速绘制

基于geopandas和geoplot

import geopandas as gpd
import geoplot as gplt

# 导入数据
geoData = gpd.read_file('https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/US-counties.geojson')
# 删除Alaska、Hawaii、Puerto Rico.
stateToRemove = ['02', '15', '72']
geoData = geoData[~geoData.STATE.isin(stateToRemove)]
geoData = geoData.explode(index_parts=True)

# 外部轮廓
gplt.polyplot(geoData, figsize=(20, 4));

import pandas as pd
import seaborn as sns

# 读取美国失业率数据，用来作为区域的数值变量
data = pd.read_csv('https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/unemployment-x.csv')

# 匹配数据
geoData['id'] = geoData['id'].astype('int64')
fullData = geoData.merge(data, left_on=['id'], right_on=['id'])
fullData.head()

import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1, figsize=(16, 12))

# 设置颜色方案
import mapclassify as mc
scheme = mc.Quantiles(fullData['rate'], k=10)

# 绘制choropleth
gplt.choropleth(fullData, 
    hue="rate", 
    linewidth=.1,
    scheme=scheme, cmap='inferno_r',
    legend=True,
    edgecolor='black',
    ax=ax
);

ax.set_title('Unemployment rate in US counties', fontsize=13);

基于plotly

import pandas as pd
import matplotlib.pyplot as plt
from urllib.request import urlopen
import json
import plotly.express as px

# 导入数据
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
                   dtype={"fips": str})


# 加载county的边界坐标
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
    counties = json.load(response)

# 绘制choropleth
fig = px.choropleth(df, 
    geojson=counties, 
    locations='fips', 
    color='unemp',
    color_continuous_scale="Viridis",
    range_color=(0, 12),
    scope="usa",
    labels={'unemp':'unemployment rate'}
)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})

# 修改图例
fig.update_layout(coloraxis_colorbar=dict(
    thicknessmode="pixels", thickness=10,
    lenmode="pixels", len=150,
    yanchor="top", y=0.8,
    ticks="outside", ticksuffix=" %",
    dtick=5
))

fig.show()