使用Python编程语言中的Geopandas库来读取多边形形状文件。首先，需要使用Shapely库计算每个多边形的中心点，然后使用OSMnx库下载所需区域的街道网络。接下来，使用OSMnx库中的方向计算功能来计算每个路段的方向，并将路段方向分配给跨越多个多边形的任何路段。然后，将每个多边形中的路段方向分组，以计算该多边形的平均方向和方向分散度。最后，将熵计算公式应用于每个多边形的路段方向分布，以计算该多边形的网络熵值。示例代码如下：

import geopandas as gpd from shapely.geometry import Point import osmnx as ox import numpy as np

Read in shapefile and calculate centroid

polygons = gpd.read_file('shapefile.shp') polygons['centroid'] = polygons.centroid

Download street network using OSMnx

G = ox.graph_from_place('City, State, Country', network_type='all')

Calculate street network orientation

orientations = ox.get_edge_bearings(G)

Assign edge orientations to polygons

for i in range(len(polygons)): polygon = polygons.iloc[i] edges = ox.get_polygon_nodes(G, polygon.geometry.exterior.coords) orientations_subset = orientations.loc[edges] orientations_subset = orientations_subset[~orientations_subset.isna()]

if len(orientations_subset) > 0:
    orientations_mean = np.mean(orientations_subset)
    orientations_std = np.std(orientations_subset)
    polygon['orientation'] = orientations_mean
    polygon['orientation_std'] = orientations_std
else:
    polygon['orientation'] = np.nan
    polygon['orientation_std'] = np.nan

Calculate network entropy for each polygon

for i in range(len(polygons)): polygon = polygons.iloc[i] angles = orientations.loc[edges_subset] if len(angles) > 1: freqs = np.histogram(angles, bins=36, range=(0, 360))[0] freqs = freqs / sum(freqs) entropy = -sum([freqs[j] * np.log2(freqs[j]) for j in range(len(freqs)) if freqs[j] > 0]) polygon['entropy'] = entropy else: polygon['entropy'] = np.nan

Save results to shapefile

polygons.to_file('result.shp')