地图（八）利用python绘制散点地图_python cartopy 绘制地图散点-优快云博客

本文链接：https://blog.youkuaiyun.com/weixin_39293132/article/details/146879438

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

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 pd

geo = 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()

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()

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()