利用Python绘制中国大陆人口热力图

https://mp.weixin.qq.com/s/Bh-L3syzJSHhLPZqzHvIyA

这篇文章给出了如何绘制中国人口密度图，但是运行存在一些问题，我在一些地方进行了修改。

本人使用的IDE是anaconda，因此事先在anaconda prompt 中安装Basemap包

conda install Basemap

新建文档，导入需要的包

import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from matplotlib.patches import Polygon
from matplotlib.colors import rgb2hex
import numpy as np
import pandas as pd

Basemap中不包括中国省界，需要在下面网站下载中国省界(https://gadm.org/download_country_v3.html)，点击Shapefile下载。

生成中国大陆省界图片。

plt.figure(figsize=(16,8))
m = Basemap(
    llcrnrlon=77,
    llcrnrlat=14,
    urcrnrlon=140,
    urcrnrlat=51,
    projection='lcc',
    lat_1=33,
    lat_2=45,
    lon_0=100
)
m.drawcountries(linewidth=1.5)
m.drawcoastlines()

m.readshapefile('gadm36_CHN_shp/gadm36_CHN_1', 'states', drawbounds=True)

去国家统计局网站下载人口各省(http://www.stats.gov.cn/tjsj/pcsj/rkpc/6rp/indexce.htm)，只需保留地区和总人口即可，保存为csv格式并改名为pop.csv。

读取数据，储存为dataframe格式，删去地名之中的空格，并设置地名为dataframe的index。

df = pd.read_csv('pop.csv')
new_index_list = []
for i in df["地区"]:
    i = i.replace(" ","")
    new_index_list.append(i)
new_index = {"region": new_index_list}
new_index = pd.DataFrame(new_index)
df = pd.concat([df,new_index], axis=1)
df = df.drop(["地区"], axis=1)
df.set_index("region", inplace=True)

将Basemap中的地区与我们下载的csv中的人口数据对应起来，建立字典。注意，Basemap中的地名与csv文件中的地名并不完全一样，需要进行一些处理。

provinces = m.states_info
statenames=[]
colors = {}
cmap = plt.cm.YlOrRd
vmax = 100000000
vmin = 3000000

for each_province in provinces:
    province_name = each_province['NL_NAME_1']
    p = province_name.split('|')
    if len(p) > 1:
        s = p[1]
    else:
        s = p[0]
    s = s[:2]
    if s == '黑龍':
        s = '黑龙江'
    if s == '内蒙':
        s = '内蒙古'
    statenames.append(s)
    pop = df['人口数'][s]
    colors[s] = cmap(np.sqrt((pop - vmin) / (vmax - vmin)))[:3]

最后画出图片即可

ax = plt.gca()
for nshape, seg in enumerate(m.states):
    color = rgb2hex(colors[statenames[nshape]])
    poly = Polygon(seg, facecolor=color, edgecolor=color)
    ax.add_patch(poly)

plt.show()

完整代码如下

# -*- coding: utf-8 -*-

import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from matplotlib.patches import Polygon
from matplotlib.colors import rgb2hex
import numpy as np
import pandas as pd

plt.figure(figsize=(16,8))
m = Basemap(
    llcrnrlon=77,
    llcrnrlat=14,
    urcrnrlon=140,
    urcrnrlat=51,
    projection='lcc',
    lat_1=33,
    lat_2=45,
    lon_0=100
)
m.drawcountries(linewidth=1.5)
m.drawcoastlines()

m.readshapefile('gadm36_CHN_shp/gadm36_CHN_1', 'states', drawbounds=True)

df = pd.read_csv('pop.csv')
new_index_list = []
for i in df["地区"]:
    i = i.replace(" ","")
    new_index_list.append(i)
new_index = {"region": new_index_list}
new_index = pd.DataFrame(new_index)
df = pd.concat([df,new_index], axis=1)
df = df.drop(["地区"], axis=1)
df.set_index("region", inplace=True)

provinces = m.states_info
statenames=[]
colors = {}
cmap = plt.cm.YlOrRd
vmax = 100000000
vmin = 3000000

for each_province in provinces:
    province_name = each_province['NL_NAME_1']
    p = province_name.split('|')
    if len(p) > 1:
        s = p[1]
    else:
        s = p[0]
    s = s[:2]
    if s == '黑龍':
        s = '黑龙江'
    if s == '内蒙':
        s = '内蒙古'
    statenames.append(s)
    pop = df['人口数'][s]
    colors[s] = cmap(np.sqrt((pop - vmin) / (vmax - vmin)))[:3]

ax = plt.gca()
for nshape, seg in enumerate(m.states):
    color = rgb2hex(colors[statenames[nshape]])
    poly = Polygon(seg, facecolor=color, edgecolor=color)
    ax.add_patch(poly)

plt.show()

转载自：https://blog.csdn.net/qq_41816368/article/details/80787415