Python 带距离条件的最近邻连接

在这个问题上，我指的是这个项目：

我们有两个GeodataFrame：

建筑物：

             name                   geometry
0            None  POINT (24.85584 60.20727)
1     Uimastadion  POINT (24.93045 60.18882)
2            None  POINT (24.95113 60.16994)
3  Hartwall Arena  POINT (24.92918 60.20570)

及巴士站:

     stop_name   stop_lat   stop_lon  stop_id                   geometry
0  Ritarihuone  60.169460  24.956670  1010102  POINT (24.95667 60.16946)
1   Kirkkokatu  60.171270  24.956570  1010103  POINT (24.95657 60.17127)
2   Kirkkokatu  60.170293  24.956721  1010104  POINT (24.95672 60.17029)
3    Vironkatu  60.172580  24.956554  1010105  POINT (24.95655 60.17258)

申请后

sklearn.com导入BallTree

我们得到每个建筑指数到最近公共汽车站的距离：

    stop_name    stop_lat   stop_lon    stop_id                 geometry      distance
0   Muusantori   60.207490  24.857450   1304138 POINT (24.85745 60.20749)   180.521584
1   Eläintarha   60.192490  24.930840   1171120 POINT (24.93084 60.19249)   372.665221
2   Senaatintori 60.169010  24.950460   1020450 POINT (24.95046 60.16901)   119.425777
3   Veturitie    60.206610  24.929680   1174112 POINT (24.92968 60.20661)   106.762619

我正在寻找解决方案，使每栋建筑的每一个公交车站（可能不止一个）距离低于250米

---

谢谢您的帮助。

要在250米内到达最近的公共汽车站：

按距离过滤的距离=最近的距离[最近的距离.距离<250]
结果=建筑物。连接（按距离过滤）

---

对于需要使用的半径范围内的所有挡块。但是您需要将米转换为弧度。

这里有一种方法，可以重复使用BallTree所做的工作，就像前面提到的那样，但是使用

query\u radius

。此外，它不是函数格式，但您仍然可以轻松更改它

from sklearn.neighbors import BallTree
import numpy as np
import pandas as pd
## here I start with buildings and stops as loaded in the link provided

# variable in meter you can change
radius_max = 250 # meters
# another parameter, in case you want to do with Mars radius ^^
earth_radius = 6371000  # meters

# similar to the method with apply in the tutorial 
# to create left_radians and right_radians, but faster
candidates = np.vstack([stops['geometry'].x.to_numpy(), 
                        stops['geometry'].y.to_numpy()]).T*np.pi/180
src_points = np.vstack([buildings['geometry'].x.to_numpy(), 
                        buildings['geometry'].y.to_numpy()]).T*np.pi/180

# Create tree from the candidate points
tree = BallTree(candidates, leaf_size=15, metric='haversine')
# use query_radius instead
ind_radius, dist_radius = tree.query_radius(src_points, 
                                            r=radius_max/earth_radius, 
                                            return_distance=True)

现在，您可以操纵结果以获得所需内容

# create a dataframe build with
# index based on row position of the building in buildings
# column row_stop is the row position of the stop
# dist is the distance
closest_dist = pd.concat([pd.Series(ind_radius).explode().rename('row_stop'), 
                          pd.Series(dist_radius).explode().rename('dist')*earth_radius], 
                         axis=1)
print (closest_dist.head())
#  row_stop     dist
#0     1131  180.522
#1      NaN      NaN
#2       64  174.744
#2       61  119.426
#3      532  106.763

# merge the dataframe created above with the original data stops
# to get names, id, ... note: the index must be reset as in closest_dist
# it is position based
closest_stop = closest_dist.merge(stops.reset_index(drop=True), 
                                  left_on='row_stop', right_index=True, how='left')
print (closest_stop.head())
#  row_stop     dist     stop_name  stop_lat  stop_lon    stop_id  \
#0     1131  180.522    Muusantori  60.20749  24.85745  1304138.0   
#1      NaN      NaN           NaN       NaN       NaN        NaN   
#2       64  174.744  Senaatintori  60.16896  24.94983  1020455.0   
#2       61  119.426  Senaatintori  60.16901  24.95046  1020450.0   
#3      532  106.763     Veturitie  60.20661  24.92968  1174112.0   
#
#                    geometry  
#0  POINT (24.85745 60.20749)  
#1                       None  
#2  POINT (24.94983 60.16896)  
#2  POINT (24.95046 60.16901)  
#3  POINT (24.92968 60.20661) 

最后连接回建筑物

# join buildings with reset_index with 
# closest_stop as index in closest_stop are position based
final_df = buildings.reset_index(drop=True).join(closest_stop, rsuffix='_stop')
print (final_df.head(10))
#              name                   geometry row_stop     dist     stop_name  \
# 0            None  POINT (24.85584 60.20727)     1131  180.522    Muusantori   
# 1     Uimastadion  POINT (24.93045 60.18882)      NaN      NaN           NaN   
# 2            None  POINT (24.95113 60.16994)       64  174.744  Senaatintori   
# 2            None  POINT (24.95113 60.16994)       61  119.426  Senaatintori   
# 3  Hartwall Arena  POINT (24.92918 60.20570)      532  106.763     Veturitie   

#    stop_lat  stop_lon    stop_id              geometry_stop  
# 0  60.20749  24.85745  1304138.0  POINT (24.85745 60.20749)  
# 1       NaN       NaN        NaN                       None  
# 2  60.16896  24.94983  1020455.0  POINT (24.94983 60.16896)  
# 2  60.16901  24.95046  1020450.0  POINT (24.95046 60.16901)  
# 3  60.20661  24.92968  1174112.0  POINT (24.92968 60.20661)  

---

所以，我有一个奇怪的问题——计算的距离是错误的。我交换了geopandas数据帧中的纬度和经度字段，距离更好，但它们仍然相差半米到一米（这仍然是不正确的）。以前有人用balltree遇到过这个问题吗？

谢谢，我会在balltree上阅读。query\u radius，第一个解决方案不会像我希望的那样在每个公交车站（可能不止一个）都起作用。这种问题非常适合。@s.k是和否，因为GIS工具不会在巨大的数据集上工作，这就是我寻求严格的Python-ish解决方案的原因。GIS工具是最早接受大数据的工具之一，所以我不确定你的评论是什么意思。我投票结束这个问题，因为它属于GIS。seHi@PaulH，我参考了一些Python工具，如scikit learn，numpy，它们可以帮助解决这个问题（和其他类似的问题，不仅仅是空间上的）。如果没有人帮忙，我可以把它移到gis.se。结束困难的问题并不是所有问题的答案……好东西，给我一天时间来测试它，然后我会在它工作时勾选它为正确的！：）在最后一个DF中，为什么索引为0和2x 2的建筑“没有”？而索引为#1的建筑在250米半径范围内没有公交车站，对吗？@cincin21所以对于无，我在打开建筑数据框时看到有一行没有名字（无），所以这就是为什么最后也没有。对于第1行是的，所有nan表示在限定范围内没有停止（在本例中为250米）@cincin21实际上，如果你在你的问题中看到你打印的

最近的_停止

在最后，那一行第1，距离是372，因此它确认了在这里找到的结果，只是想确保我理解一切！）谢谢你，干得好！这并不能回答这个问题。一旦你有足够的钱，你将能够；相反-

# join buildings with reset_index with 
# closest_stop as index in closest_stop are position based
final_df = buildings.reset_index(drop=True).join(closest_stop, rsuffix='_stop')
print (final_df.head(10))
#              name                   geometry row_stop     dist     stop_name  \
# 0            None  POINT (24.85584 60.20727)     1131  180.522    Muusantori   
# 1     Uimastadion  POINT (24.93045 60.18882)      NaN      NaN           NaN   
# 2            None  POINT (24.95113 60.16994)       64  174.744  Senaatintori   
# 2            None  POINT (24.95113 60.16994)       61  119.426  Senaatintori   
# 3  Hartwall Arena  POINT (24.92918 60.20570)      532  106.763     Veturitie   

#    stop_lat  stop_lon    stop_id              geometry_stop  
# 0  60.20749  24.85745  1304138.0  POINT (24.85745 60.20749)  
# 1       NaN       NaN        NaN                       None  
# 2  60.16896  24.94983  1020455.0  POINT (24.94983 60.16896)  
# 2  60.16901  24.95046  1020450.0  POINT (24.95046 60.16901)  
# 3  60.20661  24.92968  1174112.0  POINT (24.92968 60.20661)