地理数据可视化的神奇组合：Python和Geopandas

pip install geopandas

import geopandas as gpd

# 读取Shapefile格式的地图数据
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

# 查看数据的前几行
print(world.head())

# 查看数据的列名
print(world.columns)

# 查看数据的几何类型
print(world.geom_type)

import matplotlib.pyplot as plt

# 绘制地图
world.plot()
plt.show()

# 自定义地图样式
world.plot(color='lightblue', edgecolor='black')
plt.title('World Map')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.show()

# 添加其他数据
cities = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))
world.plot()
cities.plot(marker='o', color='red', markersize=5)
plt.show()

from shapely.geometry import Point

# 创建一个点对象代表某个地点的经纬度
point = Point(-74.006, 40.7128)

# 空间查询，找出距离该点最近的城市
nearest_city = cities[cities.distance(point).idxmin()]
print("最近的城市是:", nearest_city['name'])

# 根据大陆进行分组
world_grouped = world.groupby('continent').agg({'geometry': 'union'})
world_grouped.plot()
plt.title('World Map Grouped by Continent')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.show()

# 空间缓冲区示例
buffered_area = world.geometry.buffer(5)
buffered_area.plot()
plt.title('Buffered World Map')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.show()

import folium

# 创建一个地图对象
m = folium.Map(location=[40.7128, -74.006], zoom_start=10)

# 添加城市标记
for idx, row in cities.iterrows():
    folium.Marker([row['latitude'], row['longitude']], popup=row['name']).add_to(m)

# 显示地图
m

# 创建一个地图对象
m = folium.Map(location=[40.7128, -74.006], zoom_start=10)

# 添加世界地图图层
folium.GeoJson(world).add_to(m)

# 添加城市图层
city_layer = folium.FeatureGroup(name='Cities')
for idx, row in cities.iterrows():
    folium.Marker([row['latitude'], row['longitude']], popup=row['name']).add_to(city_layer)
city_layer.add_to(m)

# 添加图层控制
folium.LayerControl().add_to(m)

# 显示地图
m

# 读取人口数据
population_data = pd.read_csv("population.csv")

# 根据城市名称将人口数据与城市数据合并
cities_with_population = pd.merge(cities, population_data, how='left', on='name')

# 在地图上绘制城市，并根据人口数量调整标记大小
m = folium.Map(location=[40.7128, -74.006], zoom_start=4)
for idx, row in cities_with_population.iterrows():
    folium.CircleMarker(location=[row['latitude'], row['longitude']], radius=row['population'] / 100000,
                        fill_color='blue', fill_opacity=0.6).add_to(m)
m

# 读取GDP和人口数据
gdp_data = pd.read_csv("gdp_data.csv")
population_data = pd.read_csv("population_data.csv")

# 将数据合并为一个DataFrame
world_data = pd.merge(world, gdp_data, how='left', left_on='name', right_on='Country Name')
world_data = pd.merge(world_data, population_data, how='left', left_on='name', right_on='Country Name')

# 计算人均GDP
world_data['GDP per capita'] = world_data['GDP (current US$)'] / world_data['Population']

# 绘制人均GDP地图
fig, ax = plt.subplots(1, 1, figsize=(10, 6))
world_data.plot(column='GDP per capita', cmap='OrRd', linewidth=0.8, ax=ax, edgecolor='0.8', legend=True)
ax.set_title('World GDP per Capita')
plt.show()

# 计算人口密度
world_data['Population Density'] = world_data['Population'] / world_data.geometry.area

# 绘制人口密度地图
fig, ax = plt.subplots(1, 1, figsize=(10, 6))
world_data.plot(column='Population Density', cmap='Blues', linewidth=0.8, ax=ax, edgecolor='0.8', legend=True)
ax.set_title('World Population Density')
plt.show()