In [2]:
import osmnx
import pandas as pd
import geopandas as gpd
import folium
import shapely
In [3]:
PLACE_NAME = "Pune, Maharashtra, India"
graph_drive = osmnx.graph_from_place(PLACE_NAME,
                                     network_type="drive") #network_type="drive"

figure, ax2 = osmnx.plot_graph(graph_drive)
No description has been provided for this image
In [4]:
edges_drive = osmnx.graph_to_gdfs(graph_drive, 
                                  nodes=False, 
                                  edges=True)
In [5]:
edges_sub = edges_drive[['highway',
                         'length',
                         'geometry',
                         'lanes',
                         'name',
                         'bridge']]

bridge = edges_sub[(edges_sub['bridge'] == 'yes') | (edges_sub['bridge'] == 'viaduct')]

bridge.to_csv('Pune_Bridges.csv')

bridge
Out[5]:
highway length geometry lanes name bridge
u v key
245645414 245645415 0 residential 27.500 LINESTRING (73.81082 18.45942, 73.81108 18.45939) NaN Sinhagad Road yes
245645415 245645414 0 residential 27.500 LINESTRING (73.81108 18.45939, 73.81082 18.45942) NaN Sinhagad Road yes
245645447 245645615 0 trunk 1250.661 LINESTRING (73.85807 18.46246, 73.85809 18.462... 2 Sadguru Shankar Maharaj Uddanpul yes
245645724 1224905721 0 trunk 117.146 LINESTRING (73.85710 18.47709, 73.85705 18.477... 2 Satara Road yes
245645855 248170961 0 primary 1220.570 LINESTRING (73.88661 18.48993, 73.88652 18.490... NaN Kondhwa Marg yes
... ... ... ... ... ... ... ... ...
12109702231 1330411956 0 trunk 238.154 LINESTRING (73.90105 18.50650, 73.90081 18.506... 3 Solapur Road yes
12124231127 2954430480 0 primary 703.291 LINESTRING (73.89033 18.55518, 73.89031 18.555... NaN Chhatrapati Shahu Maharaj Flyover yes
12154760541 12154760540 0 primary 564.150 LINESTRING (73.82818 18.48588, 73.82819 18.485... 3 Sinhagad Road Flyover yes
12203546213 2171089142 0 trunk 232.650 LINESTRING (73.85510 18.53027, 73.85502 18.530... 1 H. K. Firodia Bridge yes
12287362347 7065593407 0 trunk 160.377 LINESTRING (73.89877 18.50671, 73.89912 18.506... 3 Solapur Road yes

637 rows × 6 columns

In [6]:
pub_trans = osmnx.features_from_place(PLACE_NAME,
                                      {'public_transport': True,
                                       'railway': True})

pub_trans.to_csv('Pune_Pub_Trans.csv')

pub_trans
Out[6]:
bus highway name name:mr public_transport geometry crossing:barrier railway addr:city addr:housenumber ... horse motor_vehicle website building:levels height min_height min_level industrial landuse level
element_type osmid
node 245646238 yes bus_stop Sutar Davakhana सुतार दवाखाना stop_position POINT (73.80753 18.50410) NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
245647406 NaN NaN NaN NaN NaN POINT (73.89727 18.52719) full level_crossing NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
248156037 yes bus_stop Deccan Corner डेक्कन कॉर्नर stop_position POINT (73.84115 18.51338) NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
343304315 NaN NaN Pune Junction NaN station POINT (73.87441 18.52888) NaN station Pune Near Jahangir Hospital ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
531393992 NaN NaN NaN NaN NaN POINT (73.85521 18.56592) NaN level_crossing NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
way 1319646961 NaN NaN Purple Line NaN NaN LINESTRING (73.85876 18.52639, 73.85947 18.526... NaN subway NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1319646962 NaN NaN Pune Metro Line 1 पुणे मेट्रो मार्गिका १ NaN LINESTRING (73.85734 18.49888, 73.85752 18.49973) NaN subway NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1319646963 NaN NaN Pune Metro Line 1 पुणे मेट्रो मार्गिका १ NaN LINESTRING (73.85722 18.49891, 73.85741 18.49975) NaN subway NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1322447614 NaN NaN NaN NaN NaN LINESTRING (73.85400 18.57090, 73.85418 18.570... NaN disused NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1322447615 NaN NaN NaN NaN NaN LINESTRING (73.85477 18.57111, 73.85492 18.57115) NaN disused NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

1018 rows × 90 columns

In [7]:
metro = pub_trans[pub_trans['railway'] == 'subway']

metro = metro[metro['bridge'] == 'yes']
In [8]:
hotel = gpd.GeoDataFrame({'id':['Blue_Diamond_Hotel'],
                          'geometry':[shapely.Point(73.88632931059034,18.538349158201086)]},
                         crs='EPSG:4326')

swach = gpd.GeoDataFrame({'id':['SWaCH_Pune'],
                          'geometry':[shapely.Point(73.85579554559784, 18.50359639546184)]},
                         crs='EPSG:4326')

rannniti = gpd.GeoDataFrame({'id':['RannNiti'],
                             'geometry':[shapely.Point(73.9000271246573, 18.539212389758735)]},
                            crs='EPSG:4326')

yutak = gpd.GeoDataFrame({'id':['YUTAK'],
                          'geometry':[shapely.Point(73.7848050733244, 18.648347408214402)]},
                         crs='EPSG:4326')

dest = pd.concat([hotel, swach, rannniti, yutak], ignore_index=True)

Locations of road and metro flyovers relative to where we will be on our itinerary from Monday to Wednesday¶

In [10]:
flyover = bridge.explore(color = 'red', popup = True)
metro.explore (m = flyover, color = 'green', popup = True)
dest.explore(m = flyover, marker_type = 'marker', popup = True)
Out[10]:
Make this Notebook Trusted to load map: File -> Trust Notebook

=====================================================================================================================

In [12]:
atm = pd.read_excel('ATM.xlsx')
atm['geometry'] = atm.apply(lambda row: shapely.geometry.Point(row['longitude'], row['latitude']), axis=1)
atm = gpd.GeoDataFrame(atm, columns=['name', 'address', 'geometry'], crs='epsg:4326')
atm = atm.set_geometry('geometry')

bank = pd.read_excel('Banks.xlsx')
bank['geometry'] = bank.apply(lambda row: shapely.geometry.Point(row['longitude'], row['latitude']), axis=1)
bank = gpd.GeoDataFrame(bank, columns=['name', 'address', 'geometry'], crs='epsg:4326')
bank = bank.set_geometry('geometry')

blood = pd.read_excel('Blood Bank.xlsx')
blood['geometry'] = blood.apply(lambda row: shapely.geometry.Point(row['longitude'], row['latitude']), axis=1)
blood = gpd.GeoDataFrame(blood, columns=['name', 'address', 'geometry'], crs='epsg:4326')
blood = blood.set_geometry('geometry')

garden = pd.read_excel('Garden.xlsx')
garden['geometry'] = garden.apply(lambda row: shapely.geometry.Point(row['longitude'], row['latitude']), axis=1)
garden = gpd.GeoDataFrame(garden, columns=['name', 'address', 'geometry'], crs='epsg:4326')
garden = garden.set_geometry('geometry')

college = pd.read_excel('GIS-College Details in Pune.xlsx')
college['geometry'] = college.apply(lambda row: shapely.geometry.Point(row['longitude'], row['latitude']), axis=1)
college = gpd.GeoDataFrame(college, columns=['name', 'address', 'geometry'], crs='epsg:4326')
college = college.set_geometry('geometry')

govt = pd.read_excel('Government Offices.xlsx')
govt['geometry'] = govt.apply(lambda row: shapely.geometry.Point(row['longitude'], row['latitude']), axis=1)
govt = gpd.GeoDataFrame(govt, columns=['name', 'address', 'geometry'], crs='epsg:4326')
govt = govt.set_geometry('geometry')

lib = pd.read_excel('Library.xlsx')
lib['geometry'] = lib.apply(lambda row: shapely.geometry.Point(row['longitude'], row['latitude']), axis=1)
lib = gpd.GeoDataFrame(lib, columns=['name', 'address', 'geometry'], crs='epsg:4326')
lib = lib.set_geometry('geometry')

med_store = pd.read_excel('Medical Stores.xlsx')
med_store['geometry'] = med_store.apply(lambda row: shapely.geometry.Point(row['longitude'], row['latitude']), axis=1)
med_store = gpd.GeoDataFrame(med_store, columns=['name', 'address', 'geometry'], crs='epsg:4326')
med_store = med_store.set_geometry('geometry')

parking = pd.read_excel('Parking.xlsx')
parking['geometry'] = parking.apply(lambda row: shapely.geometry.Point(row['longitude'], row['latitude']), axis=1)
parking = gpd.GeoDataFrame(parking, columns=['name', 'address', 'geometry'], crs='epsg:4326')
parking = parking.set_geometry('geometry')

pmc_office = pd.read_excel('PMC Offices.xlsx')
pmc_office['geometry'] = pmc_office.apply(lambda row: shapely.geometry.Point(row['longitude'], row['latitude']), axis=1)
pmc_office = gpd.GeoDataFrame(pmc_office, columns=['name', 'address', 'geometry'], crs='epsg:4326')
pmc_office = pmc_office.set_geometry('geometry')

sport = pd.read_excel('PMC Sport Complex.xlsx')
sport['geometry'] = sport.apply(lambda row: shapely.geometry.Point(row['longitude'], row['latitude']), axis=1)
sport = gpd.GeoDataFrame(sport, columns=['name', 'address', 'geometry'], crs='epsg:4326')
sport = sport.set_geometry('geometry')

ward_office = pd.read_excel('PMC Ward Offices.xlsx')
ward_office['geometry'] = ward_office.apply(lambda row: shapely.geometry.Point(row['longitude'], row['latitude']), axis=1)
ward_office = gpd.GeoDataFrame(ward_office, columns=['name', 'address', 'geometry'], crs='epsg:4326')
ward_office = ward_office.set_geometry('geometry')

police = pd.read_excel('Police Stations.xlsx')
police['geometry'] = police.apply(lambda row: shapely.geometry.Point(row['longitude'], row['latitude']), axis=1)
police = gpd.GeoDataFrame(police, columns=['name', 'address', 'geometry'], crs='epsg:4326')
police = police.set_geometry('geometry')

post = pd.read_excel('Post Office.xlsx')
post['geometry'] = post.apply(lambda row: shapely.geometry.Point(row['longitude'], row['latitude']), axis=1)
post = gpd.GeoDataFrame(post, columns=['name', 'address', 'geometry'], crs='epsg:4326')
post = post.set_geometry('geometry')

resto = pd.read_excel('Restaurants.xlsx')
resto['geometry'] = resto.apply(lambda row: shapely.geometry.Point(row['longitude'], row['latitude']), axis=1)
resto = gpd.GeoDataFrame(resto, columns=['name', 'address', 'geometry'], crs='epsg:4326')
resto = resto.set_geometry('geometry')

C:\Users\Khalis\anaconda3\Lib\site-packages\openpyxl\packaging\core.py:99: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
  now = datetime.datetime.utcnow()
C:\Users\Khalis\anaconda3\Lib\site-packages\openpyxl\packaging\core.py:99: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
  now = datetime.datetime.utcnow()
C:\Users\Khalis\anaconda3\Lib\site-packages\openpyxl\packaging\core.py:99: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
  now = datetime.datetime.utcnow()
C:\Users\Khalis\anaconda3\Lib\site-packages\openpyxl\packaging\core.py:99: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
  now = datetime.datetime.utcnow()
C:\Users\Khalis\anaconda3\Lib\site-packages\openpyxl\packaging\core.py:99: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
  now = datetime.datetime.utcnow()
C:\Users\Khalis\anaconda3\Lib\site-packages\openpyxl\packaging\core.py:99: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
  now = datetime.datetime.utcnow()
C:\Users\Khalis\anaconda3\Lib\site-packages\openpyxl\packaging\core.py:99: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
  now = datetime.datetime.utcnow()
C:\Users\Khalis\anaconda3\Lib\site-packages\openpyxl\packaging\core.py:99: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
  now = datetime.datetime.utcnow()
C:\Users\Khalis\anaconda3\Lib\site-packages\openpyxl\packaging\core.py:99: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
  now = datetime.datetime.utcnow()
C:\Users\Khalis\anaconda3\Lib\site-packages\openpyxl\packaging\core.py:99: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
  now = datetime.datetime.utcnow()
C:\Users\Khalis\anaconda3\Lib\site-packages\openpyxl\packaging\core.py:99: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
  now = datetime.datetime.utcnow()
C:\Users\Khalis\anaconda3\Lib\site-packages\openpyxl\packaging\core.py:99: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
  now = datetime.datetime.utcnow()
C:\Users\Khalis\anaconda3\Lib\site-packages\openpyxl\packaging\core.py:99: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
  now = datetime.datetime.utcnow()
C:\Users\Khalis\anaconda3\Lib\site-packages\openpyxl\packaging\core.py:99: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
  now = datetime.datetime.utcnow()
C:\Users\Khalis\anaconda3\Lib\site-packages\openpyxl\packaging\core.py:99: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
  now = datetime.datetime.utcnow()

ATMs mapped with road and metro flyovers¶

In [14]:
flyover = bridge.explore(color = 'red', popup = True)
metro.explore (m = flyover, color = 'green', popup = True)
atm.explore(m = flyover, marker_type = 'marker', popup = True)
Out[14]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Banks mapped with road and metro flyovers¶

In [16]:
flyover = bridge.explore(color = 'red', popup = True)
metro.explore (m = flyover, color = 'green', popup = True)
bank.explore(m = flyover, marker_type = 'marker', popup = True)
Out[16]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Blood Banks mapped with road and metro flyovers¶

In [18]:
flyover = bridge.explore(color = 'red', popup = True)
metro.explore (m = flyover, color = 'green', popup = True)
blood.explore(m = flyover, marker_type = 'marker', popup = True)
Out[18]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Gardens mapped with road and metro flyovers¶

In [20]:
flyover = bridge.explore(color = 'red', popup = True)
metro.explore (m = flyover, color = 'green', popup = True)
garden.explore(m = flyover, marker_type = 'marker', popup = True)
Out[20]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Colleges mapped with road and metro flyovers¶

In [22]:
flyover = bridge.explore(color = 'red', popup = True)
metro.explore (m = flyover, color = 'green', popup = True)
college.explore(m = flyover, marker_type = 'marker', popup = True)
Out[22]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Libraries mapped with road and metro flyovers¶

In [24]:
flyover = bridge.explore(color = 'red', popup = True)
metro.explore (m = flyover, color = 'green', popup = True)
lib.explore(m = flyover, marker_type = 'marker', popup = True)
Out[24]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Medical Stores mapped with road and metro flyovers¶

In [26]:
flyover = bridge.explore(color = 'red', popup = True)
metro.explore (m = flyover, color = 'green', popup = True)
med_store.explore(m = flyover, marker_type = 'marker', popup = True)
Out[26]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Parking Spaces mapped with road and metro flyovers¶

In [28]:
flyover = bridge.explore(color = 'red', popup = True)
metro.explore (m = flyover, color = 'green', popup = True)
parking.explore(m = flyover, marker_type = 'marker', popup = True)
Out[28]:
Make this Notebook Trusted to load map: File -> Trust Notebook

PMC Offices mapped with road and metro flyovers¶

In [30]:
flyover = bridge.explore(color = 'red', popup = True)
metro.explore (m = flyover, color = 'green', popup = True)
pmc_office.explore(m = flyover, marker_type = 'marker', popup = True)
Out[30]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Other Govt Offices mapped with road and metro flyovers¶

In [32]:
flyover = bridge.explore(color = 'red', popup = True)
metro.explore (m = flyover, color = 'green', popup = True)
govt.explore(m = flyover, marker_type = 'marker', popup = True)
Out[32]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Sports Areas mapped with road and metro flyovers¶

In [34]:
flyover = bridge.explore(color = 'red', popup = True)
metro.explore (m = flyover, color = 'green', popup = True)
sport.explore(m = flyover, marker_type = 'marker', popup = True)
Out[34]:
Make this Notebook Trusted to load map: File -> Trust Notebook

PMC Ward Offices mapped with road and metro flyovers¶

In [36]:
flyover = bridge.explore(color = 'red', popup = True)
metro.explore (m = flyover, color = 'green', popup = True)
ward_office.explore(m = flyover, marker_type = 'marker', popup = True)
Out[36]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Police Post mapped with road and metro flyovers¶

In [38]:
flyover = bridge.explore(color = 'red', popup = True)
metro.explore (m = flyover, color = 'green', popup = True)
police.explore(m = flyover, marker_type = 'marker', popup = True)
Out[38]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Postal Offices mapped with road and metro flyovers¶

In [40]:
flyover = bridge.explore(color = 'red', popup = True)
metro.explore (m = flyover, color = 'green', popup = True)
post.explore(m = flyover, marker_type = 'marker', popup = True)
Out[40]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Restaurants mapped with road and metro flyovers¶

In [42]:
flyover = bridge.explore(color = 'red', popup = True)
metro.explore (m = flyover, color = 'green', popup = True)
resto.explore(m = flyover, marker_type = 'marker', popup = True)
Out[42]:
Make this Notebook Trusted to load map: File -> Trust Notebook