From 52978bfd98c84e82022cf1e183e03d4a8dc23283 Mon Sep 17 00:00:00 2001
From: Nie <yni@eonerc.rwth-aachen.de>
Date: Mon, 6 Feb 2023 11:22:04 +0100
Subject: [PATCH] tool and example for network generation
---
network/district.py | 492 ++++
network/overpass.geojson | 5245 ++++++++++++++++++++++++++++++++++++++
2 files changed, 5737 insertions(+)
create mode 100644 network/district.py
create mode 100644 network/overpass.geojson
diff --git a/network/district.py b/network/district.py
new file mode 100644
index 0000000..79df426
--- /dev/null
+++ b/network/district.py
@@ -0,0 +1,492 @@
+"""Develop on the project uesgraph from RWTH Aachen University,
+E.ON Energy Research Center, Institute for Energy Efficient Buildings and
+Indoor Climate.
+The district object used a updated coordinate system with the unit of meter.
+"""
+import os
+import numpy as np
+import networkx as nx
+import pyproj
+import shapely.geometry as sg
+import shapely.ops as ops
+from shapely.prepared import prep
+import geopandas as gpd
+import matplotlib.pyplot as plt
+import momepy
+from itertools import combinations
+
+
+def latlon2abs(geometry, lat1, lat2):
+ """Convert a geometry object from lat/lon to absolute coords.
+
+ Taken from http://gis.stackexchange.com/questions/127607/
+
+ Parameters
+ ----------
+ geometry : a shapely geometry object
+ lat1 : float
+ First reference latitude
+ lat 2 : float
+ Second reference latitude
+
+ Returns
+ -------
+ converted : a shapely geometry object
+ """
+ proj_4326_crs = pyproj.crs.CRS("epsg:4326")
+ proj_from_latlat_crs = pyproj.crs.CRS(proj="aea", lat_1=lat1, lat_2=lat2)
+ transformer = pyproj.Transformer.from_crs(
+ proj_4326_crs,
+ proj_from_latlat_crs,
+ always_xy=True).transform
+
+ converted = ops.transform(transformer, geometry)
+
+ return converted
+
+
+def shapely_insert_point(point, line):
+ """Add a shapely point into shapely linestring. The point should be
+ contained by the line. In the transformation of roads in map could change
+ the form of road, in some situation the intersection point of two road
+ is not described in the linestring. After the transformation the
+ information is loss. The handling of the data could use Shapely methods
+ and attributes, it provides lot of analysis tool.
+ This method is not finished, because the crs transformation method could
+ avoid the problem. Remain this method just for backup and would be
+ deleted for stable version."""
+ if line.contains(point):
+ # if contains, means the point is already on the line, nothing need
+ # to be changed.
+ print('the line contains point')
+ else:
+ pass
+
+
+def shapely_split_street(street, other_pt_list=None):
+ """The osm generated street contains multiple points, this method split
+ the street into multipel lines with these points. So that by generation
+ of network, the whole street could be changed into edge."""
+ shapely_pt_list = []
+ # print(street)
+ if isinstance(street, sg.LineString):
+ for point in street.coords:
+ shapely_pt_list.append(sg.Point(point))
+ elif isinstance(street, sg.MultiLineString):
+ for line in street:
+ for point in line.coords:
+ shapely_pt_list.append(sg.Point(point))
+
+ if other_pt_list is not None:
+ shapely_pt_list += other_pt_list
+
+ for pt in shapely_pt_list:
+ split_list = ops.split(street, pt)
+ street = sg.MultiLineString(split_list.geoms)
+ # print(street)
+ return street
+
+
+def nx_search_subgraph(graph, node_list):
+ """Use networkx method to find a subgraph containing all nodes (
+ buildings) in between. The code comes from
+ https://stackoverflow.com/questions/61929188/get-networkx-subgraph-containing-all-nodes-in-between
+ """
+ # paths = {}
+ # for nodes in combinations(node_list, r=2):
+ # paths[nodes] = nx.shortest_path_length(graph, *nodes)
+ #
+ # max_path = max(paths.items(), key=lambda x: x[1])[0]
+ # longest_induced_path = nx.shortest_path(graph, *max_path)
+
+ H = nx.MultiGraph()
+ for nodes in combinations(node_list, r=2):
+ shortest_path = nx.shortest_path(graph, *nodes, weight='mm_len')
+ sG = nx.subgraph(graph, shortest_path)
+ H = nx.compose(sG, H)
+ # nx.draw(H, {n: [n[0], n[1]] for n in list(H.nodes)})
+ # plt.show()
+
+ return H
+
+
+class District(nx.Graph):
+ """
+ Contains buildings and streets, could generate the minimum spanning
+ tree for energy network.
+ Buildings are polygon.
+ Streets are multiple lines.
+ """
+
+ def __init__(self):
+ super().__init__()
+ self.buildings = None
+ self.streets = None
+
+ self.min_lat = None
+ self.min_lon = None
+ self.max_lat = None
+ self.max_lon = None
+
+ def read_osm(self, osm_file):
+ """using geopands to import buildings and streets from OpenStreetMap
+ data"""
+ roh_data = gpd.read_file((osm_file))
+
+ # Filter buildings with building column. If some specific building
+ # type like garage are not wanted, filter condition could be changed.
+ self.buildings = roh_data[roh_data['building'].notnull()]
+ # print(self.buildings)
+
+ # Filter roads, same as building dataframe, the specific road type
+ # could be used as additional condition.
+ self.streets = roh_data[roh_data['building'].isnull()]
+ # print(self.streets)
+
+ def crs_trans(self, adj_precision=False):
+ """Transform the coordination reference system into germany center
+ reference system, which use meter as the unit."""
+ self.buildings = self.buildings.to_crs(25832)
+ self.streets = self.streets.to_crs(25832)
+
+ # adjust precision for each point. The precision could cause problem
+ # at finding a point on the line. This way doesn't work as well. Just
+ # save as a backup.
+ if adj_precision:
+ for idx, bld in self.buildings.iterrows():
+ geojson = sg.mapping(bld['geometry'])
+ geojson['coordinates'] = np.round(np.array(
+ geojson['coordinates']), 2)
+ self.buildings['geometry'][idx] = sg.shape(geojson)
+
+ for idx, street in self.streets.iterrows():
+ geojson = sg.mapping(street['geometry'])
+ geojson['coordinates'] = np.round(np.array(
+ geojson['coordinates']), 2)
+ self.streets['geometry'][idx] = sg.shape(geojson)
+
+ def adj_pos(self):
+ """adjust the canvas position. Using the minimum latitude and
+ longitude as the zero point."""
+ min_lat_list = []
+ min_lon_list = []
+ max_lat_list = []
+ max_lon_list = []
+
+ for idx, bld in self.buildings.iterrows():
+ min_lat_list.append(bld['geometry'].bounds[0])
+ min_lon_list.append(bld['geometry'].bounds[1])
+ max_lat_list.append(bld['geometry'].bounds[2])
+ max_lon_list.append(bld['geometry'].bounds[3])
+
+ for idx, street in self.streets.iterrows():
+ min_lat_list.append(street['geometry'].bounds[0])
+ min_lon_list.append(street['geometry'].bounds[1])
+ max_lat_list.append(street['geometry'].bounds[2])
+ max_lon_list.append(street['geometry'].bounds[3])
+
+ self.min_lat = min(min_lat_list)
+ self.min_lon = min(min_lon_list)
+ self.max_lat = max(max_lat_list)
+ self.max_lon = max(max_lon_list)
+ # print(self.min_lat)
+ # print(self.min_lon)
+ # print(self.max_lat)
+ # print(self.max_lon)
+
+ # Adjust the coordinate of points in Polygon and LineString
+ for idx, bld in self.buildings.iterrows():
+ coords = bld['geometry'].exterior.coords[:]
+ for point_idx in range(len(coords)):
+ coords[point_idx] = (coords[point_idx][0] - self.min_lat,
+ coords[point_idx][1] - self.min_lon)
+ self.buildings['geometry'][idx] = sg.Polygon(coords)
+
+ for idx, street in self.streets.iterrows():
+ coords = street['geometry'].coords[:]
+ for point_idx in range(len(coords)):
+ coords[point_idx] = (coords[point_idx][0] - self.min_lat,
+ coords[point_idx][1] - self.min_lon)
+ self.streets['geometry'][idx] = sg.LineString(coords)
+
+ def line_to_multi(self):
+ """Turn the shapely LineString into MultiLineString, whose content
+ Lines are only consist of two point. By generation of networkx object
+ the lineString object might loss the information of points in line"""
+ for idx, street in self.streets.iterrows():
+ if isinstance(street['geometry'], sg.LineString):
+ self.streets['geometry'][idx] = \
+ shapely_split_street(street['geometry'])
+ elif isinstance(street['geometry'], sg.MultiLineString):
+ new_seg_list = []
+ for line in street['geometry']:
+ line_seg = shapely_split_street(line)
+ new_seg_list += list(line_seg.geoms)
+ self.streets['geometry'][idx] = sg.MultiLineString(new_seg_list)
+
+ def merge_streets(self, other_streets_list=None):
+ """merge streets into a multilinestring."""
+ street_list = []
+ for street in self.streets['geometry']:
+ if isinstance(street, sg.MultiLineString):
+ for lines in street:
+ street_list.append(lines)
+ elif isinstance(street, sg.LineString):
+ street_list.append(street)
+
+ if other_streets_list is not None:
+ for other_street in other_streets_list:
+ street_list.append(other_street)
+ return street_list
+ else:
+ return ops.linemerge(street_list)
+
+ def gen_network(self):
+ """Using the buildings and roads geographical information to generate
+ the shortest network, which is along the roads and connect all the
+ building to a possible network. In this method, the methods from
+ shapely for analysing graph are used."""
+
+ all_street = self.merge_streets()
+
+ # split the streets with the nearest points. Since the points might
+ # not on the line because of precision issue, using the two
+ # preliminary lines to split the original streets. Save the point on
+ # street to split the street later.
+ bld_street_list = []
+ bld_pt_list = []
+ bld_node_list = []
+ street_pt_list = []
+ for bld in self.buildings.centroid:
+ point_list = list(ops.nearest_points(all_street, bld))
+ bld_pt_list.append(point_list[1])
+ bld_node_list.append((point_list[1].x, point_list[1].y))
+
+ for idx, street_line in self.streets.iterrows():
+ street = None
+ seg_idx = 1
+ if isinstance(street_line['geometry'], sg.LineString):
+ if point_list[0].buffer(0.01).intersection(
+ street_line['geometry']):
+ point_left = sg.Point(list(point_list[0].buffer(
+ 0.01).intersection(
+ street_line['geometry'].parallel_offset(
+ distance=0.001, side='left')).coords[0]))
+ point_right = sg.Point(list(point_list[0].buffer(
+ 0.01).intersection(
+ street_line['geometry'].parallel_offset(
+ distance=0.001, side='right')).coords[0]))
+
+ pre_street_left = sg.LineString([point_left, point_list[1]])
+ pre_street_right = sg.LineString(
+ [point_right, point_list[1]])
+ if pre_street_left.crosses(street_line['geometry']):
+ split_list = ops.split(street_line['geometry'],
+ pre_street_left)
+ street = sg.MultiLineString(split_list.geoms)
+ elif pre_street_right.crosses(street_line['geometry']):
+ split_list = ops.split(street_line['geometry'],
+ pre_street_right)
+ street = sg.MultiLineString(split_list.geoms)
+ elif isinstance(street_line['geometry'], sg.MultiLineString):
+ line_seg_list = []
+ connect_street = False
+ seg_idx = 0
+ for line in range(len(street_line['geometry'])):
+ if point_list[0].buffer(0.01).intersection(
+ street_line['geometry'][line]):
+ connect_street = True
+ seg_idx = line + 1
+ point_left = sg.Point(list(point_list[0].buffer(
+ 0.01).intersection(
+ street_line['geometry'][line].parallel_offset(
+ distance=0.001, side='left')).coords[0]))
+ point_right = sg.Point(list(point_list[0].buffer(
+ 0.01).intersection(
+ street_line['geometry'][line].parallel_offset(
+ distance=0.001, side='right')).coords[0]))
+
+ pre_street_left = sg.LineString(
+ [point_left, point_list[1]])
+ pre_street_right = sg.LineString(
+ [point_right, point_list[1]])
+ if pre_street_left.crosses(street_line['geometry'][line]):
+ split_list = ops.split(street_line['geometry'][line],
+ pre_street_left)
+ line_seg_list += split_list.geoms
+ elif pre_street_right.crosses(
+ street_line['geometry'][line]):
+ split_list = ops.split(street_line['geometry'][line],
+ pre_street_right)
+ line_seg_list += split_list.geoms
+ # else:
+ # street = None
+ else:
+ line_seg_list.append(street_line['geometry'][line])
+
+ if connect_street:
+ street = sg.MultiLineString(line_seg_list)
+
+ if street:
+ street_pt = sg.Point(list(list(street.geoms)[
+ seg_idx].coords)[0])
+ street_pt_list.append(street_pt)
+ bld_street = sg.LineString((point_list[1], street_pt))
+ bld_street_list.append(bld_street)
+ self.streets['geometry'][idx] = street
+
+ self.line_to_multi()
+ print(bld_street_list)
+ print('##')
+ new_streets = self.merge_streets(bld_street_list)
+ street_df = gpd.GeoDataFrame({'geometry': new_streets},
+ crs=self.streets.crs,
+ geometry='geometry')
+
+ # plot the street segments
+ ##########################################
+ # fig, axs = plt.subplots(figsize=(12, 6))
+ # for geom in new_streets:
+ # xs, ys = geom.xy
+ # axs.plot(xs, ys)
+ # plt.show()
+
+ # use networkx to generate subgraph
+ # The package momepy could change the geopandas dataframe into a
+ # networkx object. The approach method for primal would only save the
+ # start and end point of the lines. These codes are not used in the
+ # current use case.
+ G = momepy.gdf_to_nx(street_df, approach="primal")
+ print(G.edges.data())
+ sG = nx_search_subgraph(G, bld_node_list)
+ # nodes, edges, sw = momepy.nx_to_gdf(sG, points=True, lines=True,
+ # spatial_weights=True)
+
+ # print(nodes)
+ # print(edges)
+ # print(edges.length)
+ # f, ax = plt.subplots(figsize=(12, 6))
+ # nodes.plot(ax=ax, column='geometry', cmap='tab20b',
+ # zorder=2)
+ # edges.plot(ax=ax, color='lightgrey', zorder=1)
+ # ax.set_axis_off()
+ # plt.show()
+
+ return sG
+
+ # not used, might be deleted later
+ def search_nearest_pt(self):
+ """find the nearest point of streets and each building. Merge the
+ streets at first and find the nearest point with shapely method."""
+ all_street = self.merge_streets()
+
+ # generate empty gdf to store the network points and the lines
+ # between building and street.
+ points_df = gpd.GeoDataFrame(crs=self.streets.crs, columns=['geometry'],
+ geometry='geometry')
+ lines_df = gpd.GeoDataFrame(crs=self.streets.crs, columns=['geometry'],
+ geometry='geometry')
+
+ # search the nearest point for buildings and streets.
+ for bld in self.buildings.centroid:
+ # point_list = list(ops.nearest_points(bld, all_street))
+ point_list = list(ops.nearest_points(all_street, bld))
+
+ # The nearest point is not on the line of street because of
+ # precision issues. Using buffer and intersection to find another
+ # point on the line.
+ # point_list[1] = sg.Point(list(point_list[1].buffer(
+ # 0.0001).intersection(all_street).coords[0]))
+ # point_list[1] = sg.Point(list(all_street.intersection(point_list[1].buffer(
+ # 0.0001)).coords[0]))
+ new_point_bld = gpd.GeoDataFrame(crs=self.streets.crs,
+ columns=['geometry'],
+ geometry=[point_list[0]])
+ new_point_street = gpd.GeoDataFrame(crs=self.streets.crs,
+ columns=['geometry'],
+ geometry=[point_list[1]])
+ new_line = gpd.GeoDataFrame(crs=self.streets.crs,
+ columns=['geometry'],
+ geometry=[sg.LineString(point_list)])
+ points_df = points_df.append(new_point_bld)
+ points_df = points_df.append(new_point_street)
+ lines_df = lines_df.append(new_line)
+
+ if all_street.contains(point_list[0]):
+ print('find point 1')
+ if all_street.contains(point_list[1]):
+ print('find point 2')
+ if point_list[0].within(all_street.buffer(0.0000001)):
+ print('find point 3')
+ if point_list[1].within(all_street.buffer(0.0000001)):
+ print('find point 4')
+ # print(type(list(point_list[1].buffer(0.001).intersection(
+ # all_street).coords[0])))
+
+ return points_df, lines_df
+
+ # not used, might be deleted later
+ def crs_to_meter(self):
+ """using pyproj to turn the coordinate reference system (crs) to
+ absolute distance, which could be used to calculate the length of
+ network and area of buildings."""
+ for idx, bld in self.buildings.iterrows():
+ self.buildings['geometry'][idx] = \
+ latlon2abs(bld['geometry'], bld['geometry'].exterior.coords[0][
+ 1], bld['geometry'].exterior.coords[1][1])
+
+ # Todo: The code is not right for roads, because the intersection
+ # information of line segments is lost after coordinate transformation
+ # it could be solved with shapely function ops.linemerge(lines),
+ # or add the intersect points into the road information
+ # The following codes are the pitch for the transformations problem.
+ # for idx_1, street_1 in self.streets.iterrows():
+ # for idx_2, street_2 in self.streets.iterrows():
+ # if isinstance(self.streets['geometry'][idx_1].intersection(
+ # self.streets['geometry'][idx_2]), Point):
+ # shapely_insert_point(self.streets['geometry'][idx_1
+ # ].intersection(
+ # self.streets['geometry'][idx_2]), self.streets[
+ # 'geometry'][idx_1])
+
+ for idx, street in self.streets.iterrows():
+ self.streets['geometry'][idx] = \
+ latlon2abs(street['geometry'], street['geometry'].coords[0][
+ 1], street['geometry'].coords[1][1])
+
+
+if __name__ == "__main__":
+ base_path = os.path.dirname(__file__)
+ map_file = os.path.join(base_path, 'overpass.geojson')
+
+ d = District()
+ d.read_osm(map_file)
+
+ # reducing the number of buildings and streets for test
+ d.buildings = d.buildings[0:6]
+
+ # handling map
+ #####################
+ d.crs_trans()
+ d.adj_pos()
+
+ # generating network
+ #####################
+ G = d.gen_network()
+
+ # Plotting streets and building
+ #####################
+ # f, ax = plt.subplots(1, 1, figsize=(12, 6))
+ # d.streets.plot(color="k", ax=ax, aspect=1)
+ # # d.buildings.plot(color="k", ax=ax, aspect=1)
+ # d.buildings.centroid.plot(color="k", ax=ax, aspect=1)
+ # plt.show()
+
+ # Plotting generated network with/without streets
+ ######################
+ positions = {n: [n[0], n[1]] for n in list(G.nodes)}
+ f, ax = plt.subplots(1, 1, figsize=(40, 20))
+ # f, ax = plt.subplots(1, 1, figsize=(12, 6))
+ # d.streets.plot(color="k", ax=ax)
+ nx.draw(G, positions, ax=ax, node_size=50)
+ plt.show()
diff --git a/network/overpass.geojson b/network/overpass.geojson
new file mode 100644
index 0000000..bd38fdf
--- /dev/null
+++ b/network/overpass.geojson
@@ -0,0 +1,5245 @@
+{
+ "type": "FeatureCollection",
+ "generator": "overpass-ide",
+ "copyright": "The data included in this document is from www.openstreetmap.org. The data is made available under ODbL.",
+ "timestamp": "2022-12-01T17:52:39Z",
+ "features": [
+ {
+ "type": "Feature",
+ "properties": {
+ "@id": "way/95924050",
+ "addr:city": "Aachen",
+ "addr:country": "DE",
+ "addr:housenumber": "13",
+ "addr:postcode": "52074",
+ "addr:street": "Hörnstieg",
+ "building": "apartments",
+ "building:levels": "2",
+ "roof:colour": "grey",
+ "roof:shape": "gabled"
+ },
+ "geometry": {
+ "type": "Polygon",
+ "coordinates": [
+ [
+ [
+ 6.0573793,
+ 50.7827378
+ ],
+ [
+ 6.0573579,
+ 50.7826604
+ ],
+ [
+ 6.0575392,
+ 50.7826397
+ ],
+ [
+ 6.0575601,
+ 50.7827181
+ ],
+ [
+ 6.0574735,
+ 50.7827274
+ ],
+ [
+ 6.0573793,
+ 50.7827378
+ ]
+ ]
+ ]
+ },
+ "id": "way/95924050"
+ },
+ {
+ "type": "Feature",
+ "properties": {
+ "@id": "way/95924062",
+ "addr:city": "Aachen",
+ "addr:country": "DE",
+ "addr:housenumber": "67",
+ "addr:postcode": "52074",
+ "addr:street": "Hörnhang",
+ "building": "apartments",
+ "building:levels": "2",
+ "roof:colour": "grey",
+ "roof:shape": "gabled"
+ },
+ "geometry": {
+ "type": "Polygon",
+ "coordinates": [
+ [
+ [
+ 6.0575937,
+ 50.7830167
+ ],
+ [
+ 6.0574623,
+ 50.7830317
+ ],
+ [
+ 6.0574512,
+ 50.7829917
+ ],
+ [
+ 6.0574422,
+ 50.782955
+ ],
+ [
+ 6.0575728,
+ 50.7829402
+ ],
+ [
+ 6.0575937,
+ 50.7830167
+ ]
+ ]
+ ]
+ },
+ "id": "way/95924062"
+ },
+ {
+ "type": "Feature",
+ "properties": {
+ "@id": "way/104657385",
+ "addr:city": "Aachen",
+ "addr:country": "DE",
+ "addr:housenumber": "72",
+ "addr:postcode": "52074",
+ "addr:street": "Seffenter Weg",
+ "building": "yes"
+ },
+ "geometry": {
+ "type": "Polygon",
+ "coordinates": [
+ [
+ [
+ 6.0586729,
+ 50.7841951
+ ],
+ [
+ 6.058547,
+ 50.7842368
+ ],
+ [
+ 6.0584408,
+ 50.7841081
+ ],
+ [
+ 6.0585112,
+ 50.7840848
+ ],
+ [
+ 6.0585668,
+ 50.7840664
+ ],
+ [
+ 6.0586729,
+ 50.7841951
+ ]
+ ]
+ ]
+ },
+ "id": "way/104657385"
+ },
+ {
+ "type": "Feature",
+ "properties": {
+ "@id": "way/104657387",
+ "addr:city": "Aachen",
+ "addr:country": "DE",
+ "addr:housenumber": "60",
+ "addr:postcode": "52074",
+ "addr:street": "Seffenter Weg",
+ "building": "yes"
+ },
+ "geometry": {
+ "type": "Polygon",
+ "coordinates": [
+ [
+ [
+ 6.0599303,
+ 50.7836083
+ ],
+ [
+ 6.0598128,
+ 50.7836675
+ ],
+ [
+ 6.0596683,
+ 50.7835522
+ ],
+ [
+ 6.0597352,
+ 50.7835185
+ ],
+ [
+ 6.0597858,
+ 50.783493
+ ],
+ [
+ 6.0599303,
+ 50.7836083
+ ]
+ ]
+ ]
+ },
+ "id": "way/104657387"
+ },
+ {
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--
GitLab