Python如何绘制土地利用图和类型转移桑基图

pip install pandas geopandas matplotlib plotly

import geopandas as gpd
import matplotlib.pyplot as plt

# 1. 读取地理数据
data = gpd.read_file('land_use.geojson')

# 2. 验证数据结构（打印前5行确认字段存在）
print(data.head())

# 3. 初始化画布
fig, ax = plt.subplots(1, 1, figsize=(12, 10))

# 4. 按指定列绘制分类图
data.plot(
    column='land_use_type',
    ax=ax,
    legend=True,
    legend_kwds={'label': "Land Use Types", 'orientation': "horizontal"},
    cmap='Set3'
)

# 5. 添加标注并展示
ax.set_title('Land Use and Land Cover Types Map', fontsize=15)
ax.set_xlabel('Longitude', fontsize=12)
ax.set_ylabel('Latitude', fontsize=12)
plt.show()

import pandas as pd
import matplotlib.pyplot as plt

# 饼状图：展示面积比例
data_pie = {
    '类型': ['森林', '农田', '城市', '水体', '草地'],
    '面积_km2': [150, 320, 100, 50, 80]
}
df_pie = pd.DataFrame(data_pie)
df_pie['比例'] = df_pie['面积_km2'] / df_pie['面积_km2'].sum()

plt.figure(figsize=(8, 6))
plt.pie(df_pie['比例'], labels=df_pie['类型'], autopct='%.1f%%', startangle=140)
plt.title('土地利用类型面积占比')
plt.axis('equal')
plt.show()

# 柱状图：展示覆盖类型频次（假设已读取CSV）
# df_csv = pd.read_csv('land_cover_data.csv')
# counts = df_csv['cover_type'].value_counts()
# plt.figure(figsize=(10, 6))
# plt.bar(counts.index, counts.values)
# plt.title('Land Cover Distribution')
# plt.xlabel('Cover Type')
# plt.ylabel('Count')
# plt.show()

import plotly.graph_objects as go

# 转移矩阵（行代表2014年类别，列代表2024年类别）
transfer_matrix = [
    [383.69, 3.51, 0.02, 7.19, 4.27, 0.24],
    [8.66, 166.07, 1.44, 63.02, 17.73, 39.24],
    [0.0, 0.88, 3.73, 0.09, 0.01, 0.23],
    [12.76, 29.42, 1.1, 79.35, 11.97, 25.42],
    [0.27, 3.73, 0.03, 1.64, 1.72, 0.94],
    [2.14, 4.39, 0.05, 11.69, 1.15, 2.75]
]
categories = ["林地", "建设用地", "水域", "耕地", "未利用地", "草地"]

# 拼接首尾年份标签
labels = [f"2014_{c}" for c in categories] + [f"2024_{c}" for c in categories]

source, target, value = [], [], []
for i in range(len(categories)):
    for j in range(len(categories)):
        if transfer_matrix[i][j] > 0:
            source.append(i)
            target.append(len(categories) + j)
            value.append(transfer_matrix[i][j])

colors = ["#228B22", "#D2691E", "#1E90FF", "#FFD700", "#A9A9A9", "#8FBC8F"]
node_colors = colors * 2

fig = go.Figure(data=[go.Sankey(
    node=dict(pad=15, thickness=20, line=dict(color="black", width=0.5),
              label=labels, color=node_colors),
    link=dict(source=source, target=target, value=value)
)])
fig.update_layout(title_text="2014年至2024年土地利用类型转移桑基图", font_size=10)
fig.show()