Basemap绘制中国地图

python/folium绘制中国人口数量热力图(HeatMap)

这篇博文主要实现用Pyhon,Matplotlib/Basemap绘制中国地图，主要是各省份行政图（轮廓图），地形图和人口分布图，其中人口分布可以嵌入到上述图形中。

更多详细的函数介绍请参考

http://matplotlib.org/basemap/users/geography.html

程序1，各省份轮廓图

import time start = time.clock() from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt from matplotlib.patches import Polygon fig = plt.figure() ax1 = fig.add_axes([0.1,0.1,0.8,0.8]) map = Basemap(llcrnrlon=80.33, llcrnrlat=3.01, urcrnrlon=138.16, urcrnrlat=56.123, resolution='h', projection='cass', lat_0 = 42.5,lon_0=120,ax=ax1) shp_info = map.readshapefile("D:\\GoogleDownload\\CHN_adm_shp\\CHN_adm1",'states',drawbounds=True) # CHN_adm1的数据是中国各省区域 for info, shp in zip(map.states_info, map.states): proid = info['NAME_1'] # 可以用notepad打开CHN_adm1.csv文件，可以知道'NAME_1'代表各省的名称 if proid == 'Guangdong': poly = Polygon(shp,facecolor='g',edgecolor='c', lw=3) # 绘制广东省区域 ax1.add_patch(poly) map.shadedrelief() # 绘制阴暗的浮雕图 map.drawcoastlines() end=time.clock() print(end-start) plt.show() 

程序2，地形图

import time start = time.clock() from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt from matplotlib.patches import Polygon import pandas as pd import numpy as np posi=pd.read_excel("D:\\Files\\datasets\\2015Cities-CHINA.xlsx") #读取中国城市数据 lat = np.array(posi["lat"][0:120]) # 获取维度之维度值 lon = np.array(posi["lon"][0:120]) # 获取经度值 pop = np.array(posi["pop"][0:120],dtype=float) size=(pop/np.max(pop))*100 map = Basemap(llcrnrlon=80.33, llcrnrlat=3.01, urcrnrlon=138.16, urcrnrlat=56.123, resolution='h', projection='cass', lat_0 = 42.5,lon_0=120) map.readshapefile("D:\\GoogleDownload\\CHN_adm_shp\\CHN_adm1",'states',drawbounds=True) map.etopo() # 绘制地形图，浮雕样式 map.drawcoastlines() x,y = map(lon[2],lat[2]) # 北京市坐标，经纬度坐标转换为该map的坐标 a,b = map(lon,lat) # map.scatter(a,b,s=size) # 取消注释此行即可获得中国各地区人口分布示意图 map.scatter(x,y,s=200,marker='*',facecolors='r',edgecolors='r') # 绘制首都 end=time.clock() print(end-start) plt.show() 

程序3，人口分布图

 import urllib import numpy as np import matplotlib matplotlib.rcParams['toolbar'] = 'None' import matplotlib.pyplot as plt from mpl_toolkits.basemap import Basemap from matplotlib.animation import FuncAnimation import pandas as pd import seaborn as sns # posi=pd.read_csv("D:\\Files\\datasets\\CitiesLatLon_China.csv") posi=pd.read_excel("D:\\Files\\datasets\\2015Cities-CHINA.xlsx") lat = np.array(posi["lat"][0:120]) # 获取维度之维度值 lon = np.array(posi["lon"][0:120]) # 获取经度值 pop = np.array(posi["pop"][0:120],dtype=float) # 获取人口数，转化为numpy浮点型 gdp = np.array(posi["GDP"][0:120],dtype=float) # 获取人口数，转化为numpy浮点型 size=(pop/np.max(pop))*100 # 绘制散点图时图形的大小，如果之前pop不转换为浮点型会没有大小不一的效果 # size=(gdp/np.max(gdp))*100 # 绘制散点图时图形的大小，如果之前pop不转换为浮点型会没有大小不一的效果 map = Basemap(projection='stere', lat_0=35, lon_0=110, llcrnrlon=82.33, llcrnrlat=3.01, urcrnrlon=138.16, urcrnrlat=53.123,resolution='l',area_thresh=10000,rsphere=.) map.drawcoastlines() map.drawcountries() map.drawcounties() map.readshapefile("D:\\GoogleDownload\\CHN_adm_shp\\CHN_adm1",'states',drawbounds=True) map.drawmapboundary() parallels = np.arange(0.,90,10.) map.drawparallels(parallels,labels=[1,0,0,0],fontsize=10) # 绘制纬线 meridians = np.arange(80.,140.,10.) map.drawmeridians(meridians,labels=[0,0,0,1],fontsize=10) # 绘制经线 x,y = map(lon,lat) # map.scatter(x,y,edgecolors='r',facecolors='r',marker='*',s=320) map.scatter(x,y,s=size) plt.title("Population Distribution in China") plt.show()