在GIS进行空间分析时经常会需要计算最短路径，我也是最近在计算DPC的时候有这方面的需求，刚开始直接是用面的中心点求得距离，但其对不规则或空洞面很不友好。所以今天跟大家分享一下基于Geopandas和Shapely计算矢量面最短路径，这里的最短即点/边的最短！

原创作者：RS迷途小书童

博客地址：https://blog.csdn.net/m0_56729804?type=blog

# -*- coding: utf-8 -*-
"""
@Time ： 2024/7/3 9:52
@Auth ： RS迷途小书童
@File ：Vectors Data Short Distance.py
@IDE ：PyCharm
@Purpose：检查两个要素的最短路径
@Web：博客地址:https://blog.csdn.net/m0_56729804
"""
from shapely.geometry import Polygon
from shapely.ops import nearest_points
import matplotlib.pyplot as plt
import geopandas as gpd


def check_short_distance_between_vector(shp_path=r"Y:\1.shp"):
    """
    :param shp_path: 输入面矢量文件，默认计算前两个要素的最短路径
    :return: 最近点1，最近点2，最短路径
    """
    gdf = gpd.read_file(shp_path)
    list_geometry = []
    for index, row in gdf.iterrows():  # 循环矢量属性表
        geometry = row['geometry']
        list_geometry.append(geometry)
    # polygon1 = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)])
    # polygon2 = Polygon([(2, 2), (3, 2), (3, 3), (2, 3)])
    polygon1, polygon2 = list_geometry[0], list_geometry[1]
    point1, point2 = nearest_points(polygon1, polygon2)  # 使用 nearest_points 函数找到两个多边形之间的最近点
    distance = point1.distance(point2)  # 计算两点之间的距离
    print(f"The shortest path between the two polygons is {distance} units.")
    print(f"The points are {point1} and {point2}.")
    fig, ax = plt.subplots()  # 可视化两个多边形和最短路径
    x1, y1 = polygon1.exterior.xy
    x2, y2 = polygon2.exterior.xy
    ax.plot(x1, y1, color='blue', label='Polygon 1')  # 绘制多边形
    ax.plot(x2, y2, color='green', label='Polygon 2')
    ax.plot([point1.x, point2.x], [point1.y, point2.y], color='red', linestyle='--', label='Shortest Path')  # 绘制最短路径
    ax.scatter([point1.x, point2.x], [point1.y, point2.y], color='red')
    ax.legend()
    plt.xlabel('X')
    plt.ylabel('Y')
    plt.title('Shortest Path between Two Polygons')
    plt.grid(True)
    plt.show()
    return point1, point2, distance


if __name__ == "__main__":
    check_short_distance_between_vector()