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地图（八）利用python绘制散点地图

散点地图（Scatter Map）简介

散点地图可以在地图上呈现数据点，根据散点的性质对当前区域进行可视化分析。

快速绘制

1. 基于pyecharts的散点地图（Scatter Map）

   参考：通过Pyecharts实现【微博签到中国】可视化作品

   from pyecharts.charts import *
   from pyecharts import options as opts
   import pandas as pdgeo = Geo(init_opts=opts.InitOpts(theme='dark', bg_color='#000000', width='1000px', height='800px'))# 导入数据
   df = pd.read_csv('https://raw.githubusercontent.com/HsuHeinrich/data-collection/master/graph/weibo.csv')# 将数据分为强中弱三类
   weak, strong, normal = [], [], []
   for idx, row in df.iterrows():if row.num < 10:weak.append((idx, row.num))geo.add_coordinate(idx, row.lon, row.lat)elif 10 <= row.num < 30:normal.append((idx, row.num))geo.add_coordinate(idx, row.lon, row.lat)elif row.num >= 30:strong.append((idx, row.num))geo.add_coordinate(idx, row.lon, row.lat)# 设置地图
   geo.add_schema(maptype="china", is_roam=False, zoom=1.2,itemstyle_opts=opts.ItemStyleOpts(color="#000000", border_color="#1E90FF"),emphasis_label_opts=opts.LabelOpts(is_show=False),emphasis_itemstyle_opts=opts.ItemStyleOpts(color="#323c48"))# 添加数据
   geo.add("弱",weak,type_='scatter',
   #         is_selected=True,symbol_size=1,is_large=True,itemstyle_opts=opts.ItemStyleOpts(color="#1E90FF"))geo.add("中",normal,type_='scatter',
   #         is_selected=True,symbol_size=1,is_large=True,itemstyle_opts=opts.ItemStyleOpts(color="#00FFFF"))geo.add("强",strong,type_='scatter',
   #         is_selected=True,symbol_size=1,is_large=True,itemstyle_opts=opts.ItemStyleOpts(color="#E1FFFF"))# 关闭标签
   geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))# 标题
   geo.set_global_opts(title_opts=opts.TitleOpts(title="微博签到点亮中国", pos_top='top', pos_left='center'),tooltip_opts=opts.TooltipOpts(is_show=False),legend_opts=opts.LegendOpts(is_show=True, pos_left='left', orient='vertical'))# 添加依赖，scatterGL需要使用
   geo.js_dependencies.add("echarts-gl")# 更改图表类型
   geo.options['series'][0]['type'] = 'scatterGL'
   geo.options['series'][1]['type'] = 'scatterGL'
   geo.options['series'][2]['type'] = 'scatterGL'geo.render_notebook()
   

   

2. 基于pyecharts的涟漪散点地图（Effect Scatter Map）

   import pandas as pd
   import matplotlib.pyplot as plt
   import cartopy.crs as ccrs
   import cartopy.feature as cfeature
   import pandas as pd
   import numpy as np# 导入数据
   data = pd.read_csv('https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/TweetSurfData.csv', sep=";")# 绘制气泡地图# 初始布局
   fig = plt.figure(figsize=(15,10))# 背景地图
   ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
   ax.set_extent([-180, 180, -65, 80], crs=ccrs.PlateCarree())
   ax.add_feature(cfeature.LAND, facecolor='grey', alpha=0.3)
   ax.add_feature(cfeature.OCEAN, facecolor='#A6CAE0')
   ax.add_feature(cfeature.COASTLINE, linewidth=0.1, edgecolor="white")# 根据大陆为每个点准备一种颜色
   data['labels_enc'] = pd.factorize(data['homecontinent'])[0]# 每个位置添加一个点
   scatter = ax.scatter(data['homelon'], data['homelat'], s=data['n']/6,alpha=0.4, c=data['labels_enc'], cmap="Set1",transform=ccrs.PlateCarree()) # 著作信息
   plt.text( -175, -62,'Where people talk about #Surf\n\nData collected on twitter by @R_Graph_Gallery during 300 days\nPlot realized with Python and the Basemap library', ha='left', va='bottom', size=9, color='#555555' )plt.show()
   

3. 基于plotly的数据栅格化的散点地图（Rasterization Scatter Map）

   import pandas as pd
   import datashader as ds
   from colorcet import fire
   import datashader.transfer_functions as tf
   import plotly.express as px# 导入数据
   df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/uber-rides-data1.csv')
   dff = df.query('Lat < 40.82').query('Lat > 40.70').query('Lon > -74.02').query('Lon < -73.91')cvs = ds.Canvas(plot_width=1000, plot_height=1000)
   agg = cvs.points(dff, x='Lon', y='Lat')# 获取纬度和经度的坐标
   coords_lat, coords_lon = agg.coords['Lat'].values, agg.coords['Lon'].values
   # 图像的角点，需要传递给mapbox
   coordinates = [[coords_lon[0], coords_lat[0]],[coords_lon[-1], coords_lat[0]],[coords_lon[-1], coords_lat[-1]],[coords_lon[0], coords_lat[-1]]]# 将agg图形进行着色，颜色映射采用fire配色方案，然后生成图片格式
   img = tf.shade(agg, cmap=fire)[::-1].to_pil()# 绘制基于MapBox的散点图
   fig = px.scatter_mapbox(dff[:1], lat='Lat', lon='Lon', zoom=12)# 将 datashader 图像添加为 mapbox 图层图像
   fig.update_layout(mapbox_style="carto-darkmatter",mapbox_layers = [{"sourcetype": "image","source": img,"coordinates": coordinates}]
   )
   fig.show()
   

   

总结

以上利用pyecharts绘制了散点地图和具有涟漪效应的散点地图，利用plotly绘制了具有数据栅格化的散点地图。

共勉～