如何使用Python和开放数据构建爱丁堡Beergardens的交互式地图

import pandas as pd
import requests
import wget
 
import folium
from folium.plugins import MarkerCluster

filename = wget.download("http://www.edinburgh.gov.uk/download/downloads/id/11854/tables_and_chairs_permits.csv")
 
df0 = pd.read_csv(filename, encoding = "ISO-8859-1")
df0.head()

# dropping duplicate entries
df1 = df0.loc[:, ['Premises Name', 'Premises Address']]
df1 = df1.drop_duplicates()

# in 2012: 280
print(df1.shape[0])

389

def query_address(address):
    """Return response from open streetmap.
    
    Parameter:
    address - address of establishment
    
    Returns:
    result - json, response from open street map
    """
    
    url = "https://nominatim.openstreetmap.org/search"
    parameters = {'q':'{}, Edinburgh'.format(address), 'format':'json'}
    
    response = requests.get(url, params=parameters)
    # don't want to raise an error to not stop the processing
    # print address instead for future inspection
    if response.status_code != 200:
        print("Error querying {}".format(address))
        result = {}
    else:
        result = response.json()
    return result
df1['json'] = df1['Premises Address'].map(lambda x: query_address(x))

# drop empty responses

df2 = df1[df1['json'].map(lambda d: len(d)) > 0].copy()
print(df2.shape[0])

374

# extract relevant fields from API response (json format)
df2['lat'] = df2['json'].map(lambda x: x[0]['lat'])
df2['lon'] = df2['json'].map(lambda x: x[0]['lon'])
df2['type'] = df2['json'].map(lambda x: x[0]['type'])

df2.type.value_counts()[:5]
cafe          84
pub           69
restaurant    66
tertiary      33
house         27
Name: type, dtype: int64

def define_category(mytype):
    if mytype in ['cafe', 'bakery', 'deli', 'ice_cream']:
        category = 1
    elif mytype in ['restaurant', 'pub', 'bar', 'fast_food']:
        category = 2
    else:
        category = 3
    return category

# assign category according to OpenStreetMap type

df2['category'] = df2['type'].map(lambda mytype: define_category(mytype))

def flag_premise(premisename, category):
    """Flag premise according to its name.
    
    Parameter:
    premisename - str
    
    Returns:
    ans - boolean
    """
    prem = str(premisename).lower()
    if ((category == 'coffeeshop'and ('caf' in prem
                                       or 'coffee' in prem
                                       or 'Tea' in str(premisename)
                                       or 'bake' in prem
                                       or 'bagel' in prem
                                       or 'roast' in prem))
         or
        (category == 'restaurant' and ('restaurant' in prem
                                       or 'bar ' in prem
                                       or 'tavern' in prem
                                       or 'cask' in prem
                                       or 'pizza' in prem
                                       or 'whisky' in prem
                                       or 'kitchen' in prem
                                       or 'Arms' in str(premisename)
                                       or 'Inn' in str(premisename)
                                       or 'Bar' in str(premisename)))):
        ans = True
    else:
        ans = False
    return ans
 
# flag coffee shops and restaurants according to their names
df2['is_coffeeshop'] = df2['Premises Name'].map(lambda x: flag_premise(x, category='coffeeshop'))
df2['is_restaurant'] = df2['Premises Name'].map(lambda x: flag_premise(x, category='restaurant'))

# show some differences between classification by name and by type returned by the API
df2.loc[(df2.is_coffeeshop) & (df2.type != 'cafe'), ['Premises Name', 'type']].head(10)

# re-set category if flagged as restaurant or coffeeshop through name
df2.loc[df2.is_restaurant, 'category'] = 2
df2.loc[df2.is_coffeeshop, 'category'] = 1

# central coordinates of Edinburgh
EDI_COORDINATES = (55.953251, -3.188267)
  
# create empty map zoomed in on Edinburgh
map = folium.Map(location=EDI_COORDINATES, zoom_start=12)
 
# add one markercluster per type to allow for individual toggling
coffeeshops = MarkerCluster(name='coffee shops').add_to(map)
restaurants = MarkerCluster(name='pubs and restaurants').add_to(map)
other = MarkerCluster(name='other').add_to(map)
 
# add coffeeshops to the map
for chairs in df2[df2.category == 1].iterrows():
    folium.Marker(location=[float(chairs[1]['lat']), float(chairs[1]['lon'])],
                  popup=chairs[1]['Premises Name'],
                 icon=folium.Icon(color='green', icon_color='white', icon='coffee', angle=0, prefix='fa'))\
    .add_to(coffeeshops)
    
# add pubs and restaurants to the map
for chairs in df2[df2.category == 2].iterrows():
    folium.Marker(location=[float(chairs[1]['lat']), float(chairs[1]['lon'])],
                  popup=chairs[1]['Premises Name'],
                 icon=folium.Icon(color='blue', icon='glass', prefix='fa'))\
    .add_to(restaurants)
    
# add other to the map
for chairs in df2[df2.category == 3].iterrows():
    folium.Marker(location=[float(chairs[1]['lat']), float(chairs[1]['lon'])],
                  popup=chairs[1]['Premises Name'],
                 icon=folium.Icon(color='gray', icon='question', prefix='fa'))\
    .add_to(other)
    
# enable toggling of data points
folium.LayerControl().add_to(map)    
    
display(map)

import os
import time
from selenium import webdriver
 
# save map
fn = 'beergarden_happiness_map.html'
tmpurl = 'file:///{path}/{mapfile}'.format(path=os.getcwd(),mapfile=fn)
map.save(fn)
 
# download screenshot of map
delay = 5
browser = webdriver.Firefox()
browser.get(tmpurl)
# give the map tiles some time to load
time.sleep(delay)
browser.save_screenshot('{mapname}.png'.format(mapname=fn.split('.')[0]))
browser.quit()