使用Python绘制中国省份管网老化分布地图（PNG/SVG）

import os
import geopandas as gpd
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
from matplotlib.patheffects import withStroke
from matplotlib.colors import Normalize, LinearSegmentedColormap
from matplotlib.cm import ScalarMappable

# ==============================
# 配置参数
# ==============================
PLOT_CONFIG = {
    'figsize': (8, 7),
    'cmap': 'warm_africa',                       # ✅ 自定义暖色调（米黄→橙→深红棕）
    'linewidth': 0.6,
    'edgecolor': 'black',
    'missing_color': '#D9CDB4',
    'title': '中国主要省份排水管网老化比例分布',   # ✅ 更新标题
    'title_fontsize': 14,
    'label_fontsize': 9,
    'dpi': 300
}

# ==============================
# 1 读取中国地图
# ==============================
def clean_province_name(name):
    suffixes = ["省", "市", "自治区", "壮族", "回族", "维吾尔", "特别行政区"]
    for suffix in suffixes:
        name = name.replace(suffix, "")
    return name

geojson_path = "china_province.geojson"
if not os.path.exists(geojson_path):
    raise FileNotFoundError(f"找不到文件: {geojson_path}")

china = gpd.read_file(geojson_path)
china["name"] = china["name"].apply(clean_province_name)

# ==============================
# 2 模拟数据：各省老化比例（示例）
# ==============================
data = {
    "黑龙江": 0.60,
    "吉林": 0.60,
    "辽宁": 0.60,
    "贵州": 0.60,
    "北京": 0.40,
    "上海": 0.35,
    "广东": 0.30,
    "山西": 0.25,
    "山东": 0.20,
    "江苏": 0.20,
    "浙江": 0.20,
    "湖南": 0.25,
    "江西": 0.20,
    "福建": 0.20,
    "广西": 0.20
}

df = pd.DataFrame(list(data.items()), columns=["name", "value"])

# ==============================
# 3 合并数据
# ==============================
china = china.merge(df, on="name", how="left")

# 数据验证
merged_data = china.dropna(subset=["value"])
missing_provinces = set(df['name']) - set(merged_data['name'])
if missing_provinces:
    print(f"警告: 以下省份在地图中未找到: {missing_provinces}")

# ==============================
# 3.5 注册自定义配色
# ==============================
warm_colors = [
    "#F5E6C8",   # 浅米黄（低值）
    "#F0C070",   # 浅橙黄
    "#E8913A",   # 橙色
    "#C95C1A",   # 深橙
    "#8B2500",   # 深红棕（高值）
]
warm_cmap = LinearSegmentedColormap.from_list("warm_africa", warm_colors, N=256)
plt.colormaps.register(warm_cmap, name="warm_africa", force=True)

# ==============================
# 4 绘图
# ==============================
fig, ax = plt.subplots(figsize=PLOT_CONFIG['figsize'])

vmin, vmax = 0.0, 1.0

china.plot(
    column="value",
    cmap=PLOT_CONFIG['cmap'],
    linewidth=PLOT_CONFIG['linewidth'],
    edgecolor=PLOT_CONFIG['edgecolor'],
    vmin=vmin,
    vmax=vmax,
    legend=False,
    missing_kwds={
        "color": PLOT_CONFIG['missing_color'],
        "label": "暂无数据",
        "hatch": "///"
    },
    ax=ax
)

# 手动添加 colorbar
norm = Normalize(vmin=vmin, vmax=vmax)
sm = ScalarMappable(cmap=PLOT_CONFIG['cmap'], norm=norm)
sm.set_array([])

cbar = fig.colorbar(
    sm, ax=ax,
    orientation='vertical',
    shrink=0.5,
    pad=0.02,
    aspect=20,
    fraction=0.03
)
cbar.set_label('老化比例', fontsize=9, labelpad=6)
cbar.set_ticks([0, 0.2, 0.4, 0.6, 0.8, 1.0])
cbar.ax.yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f"{int(x * 100)}%"))
cbar.ax.tick_params(labelsize=8)

# ==============================
# 5 添加百分比标签
# ==============================
for idx, row in china.dropna(subset=["value"]).iterrows():
    if row.geometry and not row.geometry.is_empty:
        point = row.geometry.representative_point()
        ax.text(
            point.x, point.y,
            f"{row['value'] * 100:.0f}%",
            ha="center", va="center",
            fontsize=PLOT_CONFIG['label_fontsize'],
            color='white',
            weight='bold',
            path_effects=[withStroke(linewidth=2, foreground='black')]
        )

# ==============================
# 6 美化与标题
# ==============================
plt.rcParams['font.sans-serif'] = ['SimHei', 'PingFang SC', 'Microsoft YaHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False

ax.set_title(
    PLOT_CONFIG['title'],
    fontsize=PLOT_CONFIG['title_fontsize'],
    fontweight='bold',
    pad=12
)
ax.axis("off")

fig.text(
    0.5, 0.01,
    '注：仅展示部分省份数据，灰色区域表示暂无数据',
    ha='center', fontsize=7, color='#888888', style='italic'
)

plt.tight_layout()

# ==============================
# 7 输出图片
# ==============================
plt.savefig("china_map.png", dpi=PLOT_CONFIG['dpi'], bbox_inches="tight")
plt.savefig("china_map.svg", bbox_inches="tight")
plt.show()

pip install geopandas pandas matplotlib