[Python] Basemap

╰半橙微兮° 2023-10-18 14:16 133阅读 0赞

参考：[https://blog.csdn.net/sinat\_18665801/article/details/82291067\#1\_\_15][https_blog.csdn.net_sinat_18665801_article_details_82291067_1_15]

#### 附世界地图： ####

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMzc0NjA0_size_16_color_FFFFFF_t_70][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMzc0NjA0_size_16_color_FFFFFF_t_70 1][]

# 该2行包括必要的basemap和matplotlib库------世界地图
    from mpl_toolkits.basemap import Basemap    
    import matplotlib.pyplot as plt
    
    # plt.figure(figsize=(10,8))
    # map = Basemap()
    map=Basemap(
        llcrnrlon=77,
        llcrnrlat=14,
        urcrnrlon=140,
        urcrnrlat=51,
        projection='lcc',
        lat_1=33,
        lat_2=45,
        lon_0=100
    )#中国地图
    map.drawcoastlines()#海岸线
    map.drawcountries(linewidth=1.5)#国界线
    map.readshapefile('gadm36_CHN_shp/gadm36_CHN_1', 'states', drawbounds=True)
    plt.show()
    plt.savefig('momo.png')

####  ####

#### 美国地图及人口热力图 ####

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMzc0NjA0_size_16_color_FFFFFF_t_70 2][]

from mpl_toolkits.basemap import Basemap
    import matplotlib.pyplot as plt
    import pandas as pd
    import numpy as np
    from matplotlib import cm
    
    # 绘制基础地图，选择绘制的区域，因为是绘制美国地图，故选取如下经纬度，lat_0和lon_0是地图中心的维度和经度
    
    map = Basemap(projection='stere',lat_0=90,lon_0=-105,\
                llcrnrlat=23.41 ,urcrnrlat=45.44,\
                llcrnrlon=-118.67,urcrnrlon=-64.52,\
                rsphere=6371200.,resolution='l',area_thresh=10000)#美国地图
    # map = Basemap(projection='stere', 
    #               lat_0=0, lon_0=280,
    #               llcrnrlon=73.33, 
    #               llcrnrlat=3.51, 
    #               urcrnrlon=112.16, 
    #               urcrnrlat=53.123)
    # map=Basemap(
    #     llcrnrlon=77,
    #     llcrnrlat=14,
    #     urcrnrlon=140,
    #     urcrnrlat=51,
    #     projection='lcc',
    #     lat_1=33,
    #     lat_2=45,
    #     lon_0=100
    # )#中国地图
    
    map.drawmapboundary()   # 绘制边界
    #map.fillcontinents()   # 填充大陆，发现填充之后无法显示散点图，应该是被覆盖了
    map.drawstates()        # 绘制州
    map.drawcoastlines()    # 绘制海岸线
    map.drawcountries()     # 绘制国家
    # map.drawcounties()      # 绘制县
    
    parallels = np.arange(0.,90,10.) 
    map.drawparallels(parallels,labels=[1,0,0,0],fontsize=10) # 绘制纬线
    
    meridians = np.arange(-110.,-60.,10.)
    map.drawmeridians(meridians,labels=[0,0,0,1],fontsize=10) # 绘制经线
    
    
    posi=pd.read_csv("C:\\Users\\Downloads\\datasets-master\\2014_us_cities.csv") # 读取数据
    
    ## 原始数据有3228组数据，我只选择了180个城市的数据
    lat = np.array(posi["lat"][1:1100])                        # 获取维度之维度值
    lon = np.array(posi["lon"][1:1100])                        # 获取经度值
    pop = np.array(posi["pop"][1:1100],dtype=float)    # 获取人口数，转化为numpy浮点型
    
    size=(pop/np.max(pop))*1000     # 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
    x,y = map(lon,lat)
    
    # plt.text(x, y, 'Lagos',fontsize=12,fontweight='bold',
    #                     ha='left',va='bottom',color='k')
    # 
    # 
    # x, y = map(lon[0], lat[0])
    # 
    # plt.text(x, y, 'Barcelona',fontsize=12,fontweight='bold',
    #                     ha='left',va='center',color='k',
    #                     bbox=dict(facecolor='b', alpha=0.2))
    #                     
    
    # plt.scatter(x,y,s=size,cmap=cm.hsv,edgecolors=None,facecolors='c')
    
    # plt.scatter(x,y,s=size,cmap=cm.hsv) # 使用matplotlib的散点图绘制函数
    
    map.scatter(x,y,s=size)     # 也可以使用Basemap的methord本身的scatter
    plt.title('Population distribution in America')
    plt.show()

数据下载地址：[https://github.com/plotly/datasets/blob/master/2014\_us\_cities.csv][https_github.com_plotly_datasets_blob_master_2014_us_cities.csv]

[https_blog.csdn.net_sinat_18665801_article_details_82291067_1_15]: https://blog.csdn.net/sinat_18665801/article/details/82291067#1__15
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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMzc0NjA0_size_16_color_FFFFFF_t_70 1]: https://img-blog.csdnimg.cn/20190130165827829.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMzc0NjA0,size_16,color_FFFFFF,t_70
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[https_github.com_plotly_datasets_blob_master_2014_us_cities.csv]: https://github.com/plotly/datasets/blob/master/2014_us_cities.csv