Adjacency features

adjacency_features.create_poi_index(poi_gdf, path)

Creates spatial index containing the pois given.

Parameters

• poi_gdf (geopandas.GeoDataFrame) – Contains pois to be stored in the index

• path (str) – Path to save the index

Returns

None

adjacency_features.get_classes_in_neighbors_bln(poi_gdf, poi_index_path, nlabels, label_map, k)

Creates a features array. For each poi p (each row) the array will contain 1 (True) in column c, if there is at least one poi of category c among the k nearest neighbors of p.

Parameters

• poi_gdf (geopandas.GeoDataFrame) – Contains pois for which the features will be created

• poi_index_path (str) – Path to the stored index

• nlabels (int) – Number of poi categories

• label_map (list) – A list containing the labels of the train pois

• k (int) – Number of nearest neighbors to take into account

Returns

The features array of shape (n_samples, n_features), here (len(poi_gdf), nlabels)

Return type

numpy.ndarray

adjacency_features.get_classes_in_neighbors_cnt(poi_gdf, poi_index_path, nlabels, label_map, k)

Creates a features array. For each poi p (each row) the array will contain an integer in column c, representing the number of pois of category c among the k nearest neighbors of p.

Parameters

• poi_gdf (geopandas.GeoDataFrame) – Contains pois for which the features will be created

• poi_index_path (str) – Path to the stored index

• nlabels (int) – Number of poi categories

• label_map (list) – A list containing the labels of the train pois

• k (int) – Number of nearest neighbors to take into account

Returns

The features array of shape (n_samples, n_features), here (len(poi_gdf), nlabels)

Return type

numpy.ndarray

adjacency_features.get_classes_in_radius_bln(poi_gdf, poi_index_path, nlabels, label_map, thr)

Creates a features array. For each poi p (each row) the array will contain 1 (True) in column c, if there is at least one poi of category c inside p’s defined radius.

Parameters

• poi_gdf (geopandas.GeoDataFrame) – Contains pois for which the features will be created

• poi_index_path (str) – Path to the stored index

• nlabels (int) – Number of poi categories

• label_map (list) – A list containing the labels of the train pois

• thr (float) – Radius to be searched (in meters)

Returns

The features array of shape (n_samples, n_features), here (len(poi_gdf), nlabels)

Return type

numpy.ndarray

adjacency_features.get_classes_in_radius_cnt(poi_gdf, poi_index_path, nlabels, label_map, thr)

Creates a features array. For each poi p (each row) the array will contain an integer in column c, representing the number of pois of category c inside p’s defined radius.

Parameters

• poi_gdf (geopandas.GeoDataFrame) – Contains pois for which the features will be created

• poi_index_path (str) – Path to the stored index

• nlabels (int) – Number of poi categories

• label_map (list) – A list containing the labels of the train pois

• thr (float) – Radius to be searched (in meters)

Returns

The features array of shape (n_samples, n_features), here (len(poi_gdf), nlabels)

Return type

numpy.ndarray

adjacency_features.get_classes_in_street_and_radius_bln(poi_gdf, street_gdf, pois_by_street, nlabels, label_map, geometry_map, thr)

Creates a features array. For each poi p, the nearest street to p is identified and the pois of this street are kept. These pois are then filtered and only those which are inside p’s defined radius are considered (e.g. a set of pois P). Finally, for each poi p (each row) the array will contain 1 (True) in column c, if there is at least one poi of category c among pois in P.

Parameters

• poi_gdf (geopandas.GeoDataFrame) – Contains pois for which the features will be created

• street_gdf (geopandas.GeoDataFrame) – Contains all streets extracted from OSM, along with their geometries

• pois_by_street (dict) – Has streets ids as keys and a list containing the pois which belong to each street as values

• nlabels (int) – Number of poi categories

• label_map (list) – A list containing the labels of the train pois

• geometry_map (list) – A list containing the geometries of the train pois

• thr (float) – Radius to be searched (in meters)

Returns

The features array of shape (n_samples, n_features), here (len(poi_gdf), nlabels)

Return type

numpy.ndarray

adjacency_features.get_classes_in_street_and_radius_cnt(poi_gdf, street_gdf, pois_by_street, nlabels, label_map, geometry_map, thr)

Creates a features array. For each poi p, the nearest street to p is identified and the pois of this street are kept. These pois are then filtered and only those which are inside p’s defined radius are considered (e.g. a set of pois P). Finally, for each poi p (each row) the array will contain an integer in column c, representing the number of pois of category c among pois in P.

Parameters

• poi_gdf (geopandas.GeoDataFrame) – Contains pois for which the features will be created

• street_gdf (geopandas.GeoDataFrame) – Contains all streets extracted from OSM, along with their geometries

• pois_by_street (dict) – Has streets ids as keys and a list containing the pois which belong to each street as values

• nlabels (int) – Number of poi categories

• label_map (list) – A list containing the labels of the train pois

• geometry_map (list) – A list containing the geometries of the train pois

• thr (float) – Radius to be searched (in meters)

Returns

The features array of shape (n_samples, n_features), here (len(poi_gdf), nlabels)

Return type

numpy.ndarray