For our swimming route we chose a path through the following coordinates.
52.370631, 4.912138
52.366824, 4.906239
52.365795, 4.908870
52.364477, 4.901910
52.362811, 4.902780
52.363131, 4.904473
52.362023, 4.905010
52.363891, 4.914505
52.363334, 4.916339
52.365548, 4.921682
52.366120, 4.921192
52.367123, 4.918531
52.368649, 4.919936
52.367450, 4.924110
52.371675, 4.912920
52.372894, 4.913767
52.373418, 4.915827(52.373418, 4.915827)Creating the lists for our dataframe of coordinates
longitude = ['52.370631', '52.366824', '52.365795', '52.364477', '52.362811',
'52.363131', '52.362023', '52.363891', '52.363334', '52.365548',
'52.366120', '52.367123', '52.368649', '52.367450', '52.371675',
'52.372894', '52.373418']
latitude = ['4.912138', '4.906239', '4.908870', '4.901910', '4.902780',
'4.904473', '4.905010', '4.914505', '4.916339', '4.921682',
'4.921192', '4.918531', '4.919936', '4.924110', '4.912920',
'4.913767', '4.915827']
#print(len(latitude))
#print(coordinates)17Below we calculate the center of the nodes of the swimming route.
import pandas as pd
import plotly as pl
import osmnx as ox
import pickle
df = pd.DataFrame(longitude)
columns = ['longitude']
df.columns = columns
df.insert(1, 'latitude', latitude)
# print(df)
longnum = pd.to_numeric(longitude)
latnum = pd.to_numeric(latitude)
df.insert(0, 'longnum', longnum)
df.insert(1, 'latnum', latnum)
print(df)
avg_lon = (df.longnum.sum() / len(longnum))
avg_lat = (df.latnum.sum() / len(latnum))
start_swim = (df.longnum[0], df.latnum[0])
finish_swim = (df.longnum[16], df.latnum[16])
# center of the nodes of the swimming route
center = (avg_lon, avg_lat)
print(center)
# print(start_swim)
# print(df.longnum[16]) longnum latnum longitude latitude
0 52.370631 4.912138 52.370631 4.912138
1 52.366824 4.906239 52.366824 4.906239
2 52.365795 4.908870 52.365795 4.908870
3 52.364477 4.901910 52.364477 4.901910
4 52.362811 4.902780 52.362811 4.902780
5 52.363131 4.904473 52.363131 4.904473
6 52.362023 4.905010 52.362023 4.905010
7 52.363891 4.914505 52.363891 4.914505
8 52.363334 4.916339 52.363334 4.916339
9 52.365548 4.921682 52.365548 4.921682
10 52.366120 4.921192 52.366120 4.921192
11 52.367123 4.918531 52.367123 4.918531
12 52.368649 4.919936 52.368649 4.919936
13 52.367450 4.924110 52.367450 4.924110
14 52.371675 4.912920 52.371675 4.912920
15 52.372894 4.913767 52.372894 4.913767
16 52.373418 4.915827 52.373418 4.915827
(52.36681141176471, 4.9129546470588235)import osmnx as ox
import pickle
import matplotlib.pyplot as plt
import networkx as nx
import gpxpy
import gpxpy.gpx
import codecs
import requests
import geopandas as gpd
import geopy.distance as geo
import json
import plotly as py
from shapely.geometry import Point, Polygon
import plotly.express as pxcenter = (avg_lon, avg_lat)
tags = {'highway':['bus_stop'], 'public_transport':['stop_area', 'stop_position']}
tags_10min = {'amenity':['cafe', 'restaurant', 'bar', 'pub']}
tags1 = {'building': True} # 'waterway': True
gdf = ox.features.features_from_point(center, tags = tags, dist = 1300) # = {'public_transport':['stop_area','stop_position', 'platform']}, dist = 1000)
gdf.head()
gdf.info()
gdf1 = ox.features.features_from_point(center, tags = tags1, dist = 50)
gdf1.head()
gdf1.info()
gdf2 = ox.features.features_from_point(finish_swim, tags = tags_10min, dist = 50)
gdf2.head()
gdf2.info()
link = "https://maps.amsterdam.nl/open_geodata/geojson_lnglat.php?KAARTLAAG=TRAMMETRO_PUNTEN_2022&THEMA=trammetro"
link_answer = requests.get(link)
gdf_link_answer = gpd.read_file(link_answer.text)
gdf_link_answer['distance'] = gdf_link_answer['geometry'].apply(lambda stop: geo.great_circle(finish_swim, (stop.y, stop.x)).meters)
stops = gdf_link_answer.nsmallest(5, 'distance')
print(stops)<class 'geopandas.geodataframe.GeoDataFrame'>
MultiIndex: 201 entries, ('node', 46350382) to ('node', 10208418533)
Data columns (total 34 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 name 201 non-null object
1 public_transport 201 non-null object
2 railway 86 non-null object
3 tram 75 non-null object
4 wheelchair 74 non-null object
5 wikidata 71 non-null object
6 geometry 201 non-null geometry
7 bus 117 non-null object
8 source 1 non-null object
9 note 3 non-null object
10 ref:IFOPT 40 non-null object
11 level 1 non-null object
12 toilets:wheelchair 3 non-null object
13 network 8 non-null object
14 station 6 non-null object
15 subway 8 non-null object
16 zone 25 non-null object
17 name:de 26 non-null object
18 ref 8 non-null object
19 train 5 non-null object
20 name:es 1 non-null object
21 name:nl 2 non-null object
22 old_name 6 non-null object
23 bench 42 non-null object
24 bin 34 non-null object
25 highway 59 non-null object
26 lit 23 non-null object
27 operator 7 non-null object
28 shelter 49 non-null object
29 tactile_paving 27 non-null object
30 covered 1 non-null object
31 wheelchair:description 1 non-null object
32 check_date:shelter 2 non-null object
33 website 1 non-null object
dtypes: geometry(1), object(33)
memory usage: 63.9+ KB
<class 'geopandas.geodataframe.GeoDataFrame'>
MultiIndex: 18 entries, ('way', 26525263) to ('way', 860196314)
Data columns (total 15 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 geometry 18 non-null geometry
1 nodes 18 non-null object
2 building 18 non-null object
3 name 4 non-null object
4 ref:bag 15 non-null object
5 shop 1 non-null object
6 source 15 non-null object
7 source:date 15 non-null object
8 start_date 15 non-null object
9 wikidata 2 non-null object
10 wikimedia_commons 2 non-null object
11 height 1 non-null object
12 name:de 2 non-null object
13 name:nl 1 non-null object
14 wikipedia:nl 1 non-null object
dtypes: geometry(1), object(14)
memory usage: 3.1+ KB
Naam Modaliteit Lijn Lijn_select RADIUS \
121 Muziekgebouw Bimhuis Tram 26 26 5
122 Kattenburgerstraat Tram 26 26 5
76 Artis Tram 14 14 5
34 Eerste Coehoornstraat Tram 7 7 5
223 Plantage Lepellaan Tram 14 14 5
Label geometry distance
121 26 - Muziekgebouw Bimhuis POINT (4.91302 52.37725) 466.432472
122 26 - Kattenburgerstraat POINT (4.92158 52.37612) 492.763524
76 14 - Artis POINT (4.91117 52.36663) 818.541501
34 7 - Eerste Coehoornstraat POINT (4.92646 52.36827) 921.032540
223 14 - Plantage Lepellaan POINT (4.91548 52.36513) 921.818880 cityswim = ox.graph.graph_from_point(center, dist=1300,
network_type='all', dist_type='network')
north = float(avg_lat)+.1
south = float(avg_lat)-.1
west = float(avg_lon)-.1
east = float(avg_lon)+.1
edges_map = Polygon([(west, north), (east, north), (east, south),
(west, south), (west, north)])
water = ox.geometries.geometries_from_bbox(north, south, east,
west, tags={'natural' : 'water'})
water = water.clip(edges_map)
fig, ax = plt.subplots(figsize=(10, 10))
ax.set_aspect('equal')
ax.set_facecolor('white')
ox.plot_graph(cityswim, edge_color='grey',
node_size=0,
show=False, close=False, ax=ax,)
gdf.plot(ax=ax, markersize=20, color="red") # closest bus stops
stops.plot(ax=ax, markersize=20, color="orange")
gdf1.plot(ax=ax, color="purple") # We determine a 50m radius for the close
# buildings in order to ssign the HQ of the race.
gdf2.plot(ax=ax, color = "dark green")
water.plot(ax=ax, color="lightblue")
plt.savefig("publictransport.png")
We used the available data to find the capacity of public transport. We have 5 tram stops wihtin 1 km. The tram passes about 5-10 times and hour so averaging 7.5 times an hour. A tram has 60 seats and a 125 standing spaces [@(GVB, 2023; Wikipedia-bijdragers, 2022)]. The capacity of public transport is calculated below.
# Our endpoint and startpoint were assigned earlier as
# finish_swim and start_swim
# The center coordinates are calculated above as avg_lon, avg_lat
time_seconds = 10*60
speed = 4*1000/3600
wdistance = time_seconds * speed
print(wdistance)
# we calculate centrality for the street network around the swimming route
# and include the nodes at 666m distance.
# we use network_type is all to include the bridges around Marine terrain
central_finish = ox.graph.graph_from_point(finish_swim, dist = wdistance,
network_type='all')
central_start = ox.graph.graph_from_point(start_swim, dist = wdistance,
network_type='all')
#find nearest nodes to the coordinates of the finish and headquarter
closest_finish = ox.distance.nearest_nodes(central_finish, df.longnum[16],
df.latnum[16], return_dist=True)
print(closest_finish)
closest_start = ox.distance.nearest_nodes(central_start, df.longnum[0],
df.latnum[0], return_dist=True)
print(closest_start)
centrality_finish = nx.closeness_centrality(central_finish, 46366390)
centrality_start = nx.closeness_centrality(central_start, 46365861)
print("The centrality of the finish is", centrality_finish,
", and the centrality of the start is", centrality_start)666.6666666666667
(46366390, 6823208.798913781)
(46365861, 6823644.953020242)
The centrality of the finish is 0.0 , and the centrality of the start is 0.0390860200493787