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最近傍結合と距離条件

この質問では、このプロジェクトを参考にしています。

https://automating-gis-processes.github.io/site/master/notebooks/L3/nearest-neighbor-faster.html

2つのGeoDataFrameがあります。

建物：

             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)

バス停：

     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)

申し込み後

sklearn.neighborsインポートBallTree

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


closest_stops = nearest_neighbor(buildings, stops, return_dist=True)

各建物インデックスについて、最も近いバス停までの距離を取得します。

    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

各建物のすべてのバス停（複数の場合があります）が250メートル未満の距離にあるソリューションを探しています。

ご協力ありがとうございます。

pythonpandasscikit-learngeopandas
3
cincin21

250m以内で最も近いバス停を取得するには：

filtered_by_distance = closest_stops[closest_stops.distance < 250]
result = buildings.join(filtered_by_distance)

半径内のすべてのストップには、 BallTree.query_radius を使用する必要があります。ただし、メートルをラジアンに変換する必要があります。

0
roman