Python如何制作地理信息地图？folium可视化技巧

location

zoom_start

location=[39.9, 116.4]

popup

folium.GeoJson

folium.Polygon

import folium  # 创建一个地图对象，中心点设在上海东方明珠附近，初始缩放级别为15 m = folium.Map(location=[31.233334, 121.49388], zoom_start=15)  # 添加一个标记点，并附带点击弹窗信息 folium.Marker(     location=[31.233334, 121.49388],     popup="<b>东方明珠广播电视塔</b><br>上海的地标性建筑",     icon=folium.Icon(color="red", icon="info-sign") ).add_to(m)  # 将地图保存为HTML文件 m.save("shanghai_landmark_map.html")

shanghai_landmark_map.html

folium.plugins.MarkerCluster

MarkerCluster

from folium.plugins import MarkerCluster import pandas as pd import folium  # 假设你有一个包含经纬度数据的DataFrame # df = pd.read_csv('your_data.csv') # 模拟一些数据 data = {     'lat': [31.23, 31.235, 31.24, 31.232, 31.238, 31.245, 31.236],     'lon': [121.49, 121.495, 121.50, 121.492, 121.498, 121.505, 121.494],     'name': ['点A', '点B', '点C', '点D', '点E', '点F', '点G'] } df = pd.DataFrame(data)  m = folium.Map(location=[31.235, 121.495], zoom_start=13)  # 创建一个MarkerCluster实例 marker_cluster = MarkerCluster().add_to(m)  # 遍历数据，将每个点添加到MarkerCluster中 for idx, row in df.iterrows():     folium.Marker(         location=[row['lat'], row['lon']],         popup=row['name']     ).add_to(marker_cluster)  m.save("clustered_points_map.html")

shapely

geopandas

simplify

tiles

import folium  m = folium.Map(location=[39.9, 116.4], zoom_start=12, tiles="Stamen Toner") folium.Marker(location=[39.9096045, 116.397228], popup="天安门").add_to(m) m.save("tiananmen_toner_map.html")

{z}/{x}/{y}

name

attr

overlay

import folium  m = folium.Map(location=[34.0522, -118.2437], zoom_start=10)  # 添加一个自定义的瓦片图层，这里以一个虚构的ArcGIS在线服务为例 # 实际使用时需要替换为有效的瓦片服务URL folium.TileLayer(     tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}',     attr='Esri World Imagery',     name='Esri Satellite',     overlay=True,     control=True ).add_to(m)  # 添加一个图层控制器，方便用户切换底图和叠加层 folium.LayerControl().add_to(m)  m.save("custom_tile_map.html")

folium.LayerControl()

plugins.HeatMap

from folium.plugins import HeatMap import folium import numpy as np  m = folium.Map(location=[40.730610, -73.935242], zoom_start=12)  # 模拟一些纽约市的出租车上下车点数据 # 格式为 [纬度, 经度, 权重（可选）] data = (     np.random.normal(loc=[40.73, -73.93], scale=[0.02, 0.02], size=(1000, 2)) ).tolist()  # 如果数据量很大，可以考虑只取一部分或者抽样 # data = data[:500]  HeatMap(data).add_to(m)  m.save("nyc_heatmap.html")

folium.Choropleth

import folium import pandas as pd import json  m = folium.Map(location=[37.0902, -95.7129], zoom_start=4)  # 假设你有一个GeoJSON文件，包含了美国各州的边界 # 实际使用时需要加载真实的GeoJSON文件 # with open('us_states.json', 'r', encoding='utf-8') as f: #     states_geojson = json.load(f)  # 模拟一个简化的GeoJSON结构 states_geojson = {     "type": "FeatureCollection",     "features": [         {"type": "Feature", "properties": {"name": "California", "id": "CA"}, "geometry": {"type": "Polygon", "coordinates": [[[-120, 35], [-122, 37], [-121, 39], [-118, 36], [-120, 35]]]}},         {"type": "Feature", "properties": {"name": "Texas", "id": "TX"}, "geometry": {"type": "Polygon", "coordinates": [[[-103, 30], [-100, 32], [-97, 30], [-103, 30]]]}},         # ... 更多州的简化几何     ] }   # 假设你有一个DataFrame，包含了各州的某个统计数据 data = {     'id': ['CA', 'TX'],     'value': [500, 300] # 模拟的统计值 } df_data = pd.DataFrame(data)  folium.Choropleth(     geo_data=states_geojson,     name='choropleth',     data=df_data,     columns=['id', 'value'],     key_on='feature.properties.id', # GeoJSON中用于匹配的字段     fill_color='YlGn', # 颜色方案，例如 'YlGn', 'BuPu', 'RdPu'     fill_opacity=0.7,     line_opacity=0.2,     legend_name='统计值分布' ).add_to(m)  folium.LayerControl().add_to(m) # 添加图层控制器  m.save("us_states_choropleth.html")