在Geopandas中过滤和合并特定距离内的两个数据帧中的点

Question

对于以下两个GeoPandas数据帧：

df1:

id  sMiddleLng  sMiddleLat  p1_sum  p2_sum  
0  325782  109.255034   34.691754     0.0     0.0   
1   84867  107.957177   33.958289     0.0     0.0   
2   13101  107.835338   33.739493     0.0     0.0   
3   92771  109.464280   33.980666     0.0     0.0   
4   86609  108.253830   33.963262     0.0     0.0   
geometry  
0  POINT (109.255033915 34.69175367)  
1  POINT (107.957177305 33.95828929)  
2    POINT (107.8353377 33.73949313)  
3   POINT (109.46428019 33.98066616)  
4  POINT (108.253830245 33.96326193)  

df2:

fnid  sMiddleLng  sMiddleLat  p1_sum  p2_sum  
0  361104  102.677887   36.686408     0.0     0.0   
1  276307  103.268356   36.425372     0.0     0.0   
2  334778  103.242125   36.605224     0.0     0.0   
3  205223  104.186869   36.206637     0.0     0.0   
4  167892  104.387566   36.091905     0.0     0.0   
geometry  
0  POINT (102.67788654685 36.68640780045)  
1  POINT (103.26835590025 36.42537187675)  
2   POINT (103.2421246007 36.60522388845)  
3    POINT (104.1868687253 36.2066370049)  
4   POINT (104.38756565315 36.0919047206)  

如何在id和geometry的基础上找到并合并来自另一个类似的地理数据帧df2到df1的所有点，这些点与df1和df2中的点之间的距离小于10 km？谢谢

计算距离的功能：

from math import radians, cos, sin, asin, sqrt
def haversine(lon1, lat1, lon2, lat2):
"""
Calculate the great circle distance between two points 
on the earth (specified in decimal degrees)
"""
# convert decimal degrees to radians 
lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
# haversine formula 
dlon = lon2 - lon1 
dlat = lat2 - lat1 
a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
c = 2 * asin(sqrt(a)) 
r = 6371 # Radius of earth in kilometers. Use 3956 for miles
return c * r

Answer 1

我的建议遵循以下逻辑：

1. 使用米作为单位(如WebMercator(将两个GeoDataFrame重新投影到投影中
2. 在其中一个数据集中的点周围临时创建10公里的缓冲区
3. 使用sjoin查找/合并重叠点

这可以按如下方式实现：

# Assuming your data uses WGS84 projection. only use the following line if crs has not been initialised
df1.crs = df2.crs = {'init': 'epsg:4326'} 
# Now convert the Dataframes to WebMercator
df2 = df2.to_crs({'init': 'epsg:3857'})
df1 = df1.to_crs({'init': 'epsg:3857'})
# Create a buffer with a radius of 10000 meters around each point in df2
df2.geometry = df2.geometry.buffer(10000)
# Join the two Dataframes and convert back to original projection
df3 = gpd.sjoin(df1, df2, how='left', op='intersects', lsuffix='_df1', rsuffix='_df2')
df3.to_crs({'init': 'epsg:4326'}) # or whatever was used originally

现在，您可以在一个方便的GeoDataFrame中获得有关连接点的信息。在给定数据的情况下，df2中的点与df1中的点相距10km内没有点。

此外，我不完全确定您希望以何种形式获得合并后的数据，所以只需相应地适应您的需求即可。