Part II : Extracting Fourquare Data¶

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Part 1 Extracting Street Addresses & Coordinates
Part 3 , Exploratory Data Analysis
Part 4, Clustering and Visualising
Part 5, Conclusion & Discussion

1. Set up environment¶

In [9]:

import pandas as pd
import numpy as np
import folium
import requests
import re

In [2]:

streetData = pd.read_csv('./streetData_Midcoordinates.csv')

2. Basic Folium Map visualisation¶

In [3]:

streetData.head()

Out[3]:

	Street	MidLatitude	MidLongitude
0	Charlotte Andersens vei	59.940584	10.696497
1	Heggelibakken	59.938909	10.692733
2	Forskningsveien	59.943733	10.713100
3	Risveien	59.946870	10.704020
4	Sandermosveien	60.019786	10.793857

In [5]:

#1.2.1 Make map with street info
latitude = 59.9133301
longitude = 10.7389701
map_oslo = folium.Map(location=[latitude, longitude], zoom_start=10)

# add markers to map
for lat, lng, street , in zip(streetData['MidLatitude'], streetData['MidLongitude'],
                                                      streetData['Street']):
    
    #Create pop-up label to display
    label = '{}'.format(street)#neighborhood, borough originally
    label = folium.Popup(label, parse_html=True)
    folium.CircleMarker(
        [lat, lng],
        radius=2,#Change radius of circle marker
        popup=label,
        color='blue',
        fill=False,
        #fill_color='#3186cc',
        fill_opacity=0.7,
        parse_html=False).add_to(map_oslo)  
    
map_oslo

Out[5]: