<P>是的,但首先,我必须把赫尔辛基大学从<a href="https://automating-gis-processes.github.io/site/index.html" rel="nofollow noreferrer">automating GIS process</a>,这里是<a href="https://automating-gis-processes.github.io/site/notebooks/L3/nearest-neighbor-faster.html" rel="nofollow noreferrer">the source code</a>。下面是方法<br/>
首先,读取数据,例如,查找每栋建筑最近的公交车站</p>
<pre><code># Filepaths
stops = gpd.read_file('data/pt_stops_helsinki.gpkg')
buildings = read_gdf_from_zip('data/building_points_helsinki.zip')
</code></pre>
<p>定义函数,在这里,您可以调整<code>k_neighbors</code></p>
<pre><code>from sklearn.neighbors import BallTree
import numpy as np
def get_nearest(src_points, candidates, k_neighbors=1):
"""Find nearest neighbors for all source points from a set of candidate points"""
# Create tree from the candidate points
tree = BallTree(candidates, leaf_size=15, metric='haversine')
# Find closest points and distances
distances, indices = tree.query(src_points, k=k_neighbors)
# Transpose to get distances and indices into arrays
distances = distances.transpose()
indices = indices.transpose()
# Get closest indices and distances (i.e. array at index 0)
# note: for the second closest points, you would take index 1, etc.
closest = indices[0]
closest_dist = distances[0]
# Return indices and distances
return (closest, closest_dist)
def nearest_neighbor(left_gdf, right_gdf, return_dist=False):
"""
For each point in left_gdf, find closest point in right GeoDataFrame and return them.
NOTICE: Assumes that the input Points are in WGS84 projection (lat/lon).
"""
left_geom_col = left_gdf.geometry.name
right_geom_col = right_gdf.geometry.name
# Ensure that index in right gdf is formed of sequential numbers
right = right_gdf.copy().reset_index(drop=True)
# Parse coordinates from points and insert them into a numpy array as RADIANS
left_radians = np.array(left_gdf[left_geom_col].apply(lambda geom: (geom.x * np.pi / 180, geom.y * np.pi / 180)).to_list())
right_radians = np.array(right[right_geom_col].apply(lambda geom: (geom.x * np.pi / 180, geom.y * np.pi / 180)).to_list())
# Find the nearest points
# -
# closest ==> index in right_gdf that corresponds to the closest point
# dist ==> distance between the nearest neighbors (in meters)
closest, dist = get_nearest(src_points=left_radians, candidates=right_radians)
# Return points from right GeoDataFrame that are closest to points in left GeoDataFrame
closest_points = right.loc[closest]
# Ensure that the index corresponds the one in left_gdf
closest_points = closest_points.reset_index(drop=True)
# Add distance if requested
if return_dist:
# Convert to meters from radians
earth_radius = 6371000 # meters
closest_points['distance'] = dist * earth_radius
return closest_points
</code></pre>
<p>做最近邻分析</p>
<pre><code># Find closest public transport stop for each building and get also the distance based on haversine distance
# Note: haversine distance which is implemented here is a bit slower than using e.g. 'euclidean' metric
# but useful as we get the distance between points in meters
closest_stops = nearest_neighbor(buildings, stops, return_dist=True)
</code></pre>
<p>现在加入“从”和“到”数据帧</p>
<pre><code># Rename the geometry of closest stops gdf so that we can easily identify it
closest_stops = closest_stops.rename(columns={'geometry': 'closest_stop_geom'})
# Merge the datasets by index (for this, it is good to use '.join()' -function)
buildings = buildings.join(closest_stops)
</code></pre>