Question

我有一个包含100多个多边形的geopandas文件，一个稀疏集（其中约有10个）具有感兴趣的值。对我来说，是否有一种简单的方法可以根据最近的非零多边形的值为剩余的90+个多边形分配一个值？

提前谢谢

Answer 1

以下代码指示一种算法，该算法将基于质心使用空间连接（最近邻），将“无值”的多边形连接到具有有效值的最接近的多边形。

请注意，该代码是您所需代码的草稿；它表示一般算法，但您需要根据数据和变量来完成功能。

# gdf with all the polygons
gdf1 = gpd.read_file(...)

# calculate a column with the centroid geometry; 
# it will be used later.
# note: this is not the active gdf geometry at this stage
gdf1['geometry_pt'] = gdf1['geometry'].centroid

# set the point geometry as the active geometry
gdf1.set_geometry('geometry_pt')

# filter out the gdf in two gdfs, with/without the value you want
gdf1_yesval = gdf1.loc[gdf1['field1'] != 0]
gdf1_noval  = gdf1.loc[gdf1['field1'] == 0]

# perform a spatial join to assign the closest value to the points with no value
# for this, apply the code in the link below
gdf1_noval_joined = gdf1_noval.apply(...  nearest... gdf1_yesval... )

# do the necessary column operations in the joined gdf 
# to update your desired columns with values from the spatially joined gdf
gdf1_noval_joined['field1'] = gdf1_noval_joined['joinedfieldA']

# delete the unnecessary columns in the joined gdf
gdf1_noval_joined.drop(columns=['joinedfieldA', 'joinedfieldB'], inplace=True)

# concatenate the two gdfs to make one
df2 = pd.concat([gdf1_yesval, gdf1_noval_joined])

# convert it into a gdf again
gdf2 = gpd.GeoDataFrame(df2, geometry='geometry')

解释最近邻居连接功能的链接为：https://gis.stackexchange.com/q/222315/93912

希望有帮助。

geopandas颜色最接近的形状相同的颜色

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