---

基本介绍

1. 可视化分析

通过绘制数据的分布图，可以直观地观察数据是否在空间上均匀分布。

使用Python的matplotlib和Basemap库：

import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap# 假设数据格式为 [(lat1, lon1), (lat2, lon2), ...]
data = [(34.05, -118.25), (40.71, -74.01), (37.77, -122.42), (47.61, -122.33)]# 创建地图
m = Basemap(projection='merc', llcrnrlat=-80, urcrnrlat=80, llcrnrlon=-180, urcrnrlon=180, resolution='c')
m.drawcoastlines()
m.drawcountries()
m.drawstates()# 绘制数据点
lats = [lat for lat, lon in data]
lons = [lon for lat, lon in data]
x, y = m(lons, lats)
m.scatter(x, y, 10, marker='o', color='red')plt.show()

2. 统计检验

可以使用统计方法来检验数据的空间分布是否均匀。

使用Python的scipy库进行Kolmogorov-Smirnov检验：

from scipy.stats import ks_2samp
import numpy as np# 假设数据格式为 [(lat1, lon1), (lat2, lon2), ...]
data = [(34.05, -118.25), (40.71, -74.01), (37.77, -122.42), (47.61, -122.33)]# 提取纬度和经度
lats = [lat for lat, lon in data]
lons = [lon for lat, lon in data]# 生成均匀分布的随机数据
uniform_lats = np.random.uniform(min(lats), max(lats), len(lats))
uniform_lons = np.random.uniform(min(lons), max(lons), len(lons))# 进行Kolmogorov-Smirnov检验
ks_lat = ks_2samp(lats, uniform_lats)
ks_lon = ks_2samp(lons, uniform_lons)print("纬度的KS检验结果：", ks_lat)
print("经度的KS检验结果：", ks_lon)

3. 空间分析技术

可以使用空间分析技术，如核密度估计（Kernel Density Estimation, KDE）来评估数据的分布。

使用Python的geopandas和sklearn库进行核密度估计：

import geopandas as gpd
from shapely.geometry import Point
from sklearn.neighbors import KernelDensity
import numpy as np# 假设数据格式为 [(lat1, lon1), (lat2, lon2), ...]
data = [(34.05, -118.25), (40.71, -74.01), (37.77, -122.42), (47.61, -122.33)]# 创建GeoDataFrame
geometry = [Point(lon, lat) for lat, lon in data]
gdf = gpd.GeoDataFrame(geometry=geometry)# 提取坐标
coords = np.vstack([gdf.geometry.x, gdf.geometry.y]).T# 进行核密度估计
kde = KernelDensity(bandwidth=0.05, metric='euclidean')
kde.fit(coords)# 生成网格点
xmin, ymin, xmax, ymax = gdf.total_bounds
xx, yy = np.meshgrid(np.linspace(xmin, xmax, 100), np.linspace(ymin, ymax, 100))
grid_points = np.vstack([xx.ravel(), yy.ravel()]).T# 计算密度
log_density = kde.score_samples(grid_points)
density = np.exp(log_density).reshape(xx.shape)# 绘制密度图
plt.imshow(density, extent=(xmin, xmax, ymin, ymax), origin='lower', cmap='viridis')
plt.colorbar()
plt.scatter(coords[:, 0], coords[:, 1], c='red', s=10)
plt.show()

---

调用函数

1. 可视化分析函数

import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemapdef plot_spatial_distribution(data):"""绘制数据的空间分布图:param data: 包含经纬度坐标的数据列表，格式为 [(lat1, lon1), (lat2, lon2), ...]"""# 创建地图m = Basemap(projection='merc', llcrnrlat=-80, urcrnrlat=80, llcrnrlon=-180, urcrnrlon=180, resolution='c')m.drawcoastlines()m.drawcountries()m.drawstates()# 绘制数据点lats = [lat for lat, lon in data]lons = [lon for lat, lon in data]x, y = m(lons, lats)m.scatter(x, y, 10, marker='o', color='red')plt.show()# 示例调用
data = [(34.05, -118.25), (40.71, -74.01), (37.77, -122.42), (47.61, -122.33)]
plot_spatial_distribution(data)

2. 统计检验函数

from scipy.stats import ks_2samp
import numpy as npdef perform_ks_test(data):"""对数据进行Kolmogorov-Smirnov检验，判断其空间分布是否均匀:param data: 包含经纬度坐标的数据列表，格式为 [(lat1, lon1), (lat2, lon2), ...]:return: 纬度和经度的KS检验结果"""# 提取纬度和经度lats = [lat for lat, lon in data]lons = [lon for lat, lon in data]# 生成均匀分布的随机数据uniform_lats = np.random.uniform(min(lats), max(lats), len(lats))uniform_lons = np.random.uniform(min(lons), max(lons), len(lons))# 进行Kolmogorov-Smirnov检验ks_lat = ks_2samp(lats, uniform_lats)ks_lon = ks_2samp(lons, uniform_lons)return ks_lat, ks_lon# 示例调用
data = [(34.05, -118.25), (40.71, -74.01), (37.77, -122.42), (47.61, -122.33)]
ks_lat, ks_lon = perform_ks_test(data)
print("纬度的KS检验结果：", ks_lat)
print("经度的KS检验结果：", ks_lon)

3. 空间分析技术函数

import geopandas as gpd
from shapely.geometry import Point
from sklearn.neighbors import KernelDensity
import numpy as np
import matplotlib.pyplot as pltdef perform_kde(data):"""对数据进行核密度估计，评估其空间分布:param data: 包含经纬度坐标的数据列表，格式为 [(lat1, lon1), (lat2, lon2), ...]"""# 创建GeoDataFramegeometry = [Point(lon, lat) for lat, lon in data]gdf = gpd.GeoDataFrame(geometry=geometry)# 提取坐标coords = np.vstack([gdf.geometry.x, gdf.geometry.y]).T# 进行核密度估计kde = KernelDensity(bandwidth=0.05, metric='euclidean')kde.fit(coords)# 生成网格点xmin, ymin, xmax, ymax = gdf.total_boundsxx, yy = np.meshgrid(np.linspace(xmin, xmax, 100), np.linspace(ymin, ymax, 100))grid_points = np.vstack([xx.ravel(), yy.ravel()]).T# 计算密度log_density = kde.score_samples(grid_points)density = np.exp(log_density).reshape(xx.shape)# 绘制密度图plt.imshow(density, extent=(xmin, xmax, ymin, ymax), origin='lower', cmap='viridis')plt.colorbar()plt.scatter(coords[:, 0], coords[:, 1], c='red', s=10)plt.show()# 示例调用
data = [(34.05, -118.25), (40.71, -74.01), (37.77, -122.42), (47.61, -122.33)]
perform_kde(data)

–

*本文思路可行性有待讨论，目前仅供参考，请酌情使用