根据存储在 csv 中的地点/商店名称和城市检索完整地址和地理编码

Question

我有一个包含 2 个字段的 csv 文件，store_name 和 city。一个城市可以有多家商店。
我想要一个包含 5 个字段的输出 csv，store_name、city、address、latitude、longitude。

例如，如果 csv 的一个条目是Starbucks, Chicago，我希望输出 csv 包含 5 个字段（如上所述）中的所有信息：
Starbucks, Chicago, "200 S Michigan Ave, Chicago, IL 60604, USA", 41.8164613, -87.8127855,
Starbucks, Chicago, "8 N Michigan Ave, Chicago, IL 60602, USA", 41.8164613, -87.8127855
其余的结果以此类推。

我曾尝试使用 Nomanitim 通过 GeoPy 来解决此问题，然后再使其与 Google Maps API 一起使用。虽然我不知道解决这个问题的最佳方法是什么。请注意，源 csv 中有一百万个这样的条目，但是一旦它起作用，购买 API 密钥就不是问题了。

我确实尝试使用 Pandas 仅使用 Nominatim 进行地理编码，但这只会在每个条目的输出 csv 中创建一个结果。我想按照上面示例中的说明获取每个结果。不知道如何实现。

from geopy.geocoders import Nominatim
import csv, sys
import pandas as pd
import keys

in_file = str(sys.argv[1])
out_file = str('gc_' + in_file)
timeout = int(sys.argv[2])

nominatim = Nominatim(user_agent=your_key_here, timeout=timeout)

def gc(address):
    name = str(address['store_name'])
    city = str(address['city'])
    add_concat = name + ", " + city
    location = nominatim.geocode(add_concat)
    if location != None:
        print(f'geocoded record {address.name}: {city}')
        located = pd.Series({
            'lat': location.latitude,
            'lng': location.longitude,
        })
    else:
        print(f'failed to geolocate record {address.name}: {city}')
        located = pd.Series({
            'lat': 'null',
            'lng': 'null',
        })
    return located

print('opening input.')
reader = pd.read_csv(in_file, header=0)
print('geocoding addresses.')
reader = reader.merge(reader.apply(lambda add: gc(add), axis=1), left_index=True, right_index=True)
print(f'writing to {out_file}.')
reader.to_csv(out_file, encoding='utf-8', index=False)
print('done.')

Answer 1

您可以为此目的使用reverse geocoding。根据官方文档here，这是一种将地理坐标转换为人类可读地址的方法。

我在我的一个项目中使用了以下功能，它仍在工作。您可以根据自己的要求对其进行修改。

import requests

GCODE_URL = 'https://maps.googleapis.com/maps/api/geocode/json?'
GCODE_KEY = 'YOUR API KEY' 

def reverse_gcode(location):
    location = str(location).replace(' ','+')
    nav_req = 'address={}&key={}'.format(location,GCODE_KEY)
    request = GCODE_URL + nav_req
    result = requests.get(request)
    data = result.json()
    status = data['status']

    geo_location = {}
    if str(status) == "OK":
       sizeofjson = len(data['results'][0]['address_components'])
       for i in range(sizeofjson):
           sizeoftype = len(data['results'][0]['address_components'][i]['types'])
           if sizeoftype == 3:
              geo_location[data['results'][0]['address_components'][i]['types'][2]] = data['results'][0]['address_components'][i]['long_name']

           else:
              if data['results'][0]['address_components'][i]['types'][0] == 'administrative_area_level_1':
                 geo_location['state'] = data['results'][0]['address_components'][i]['long_name']

              elif data['results'][0]['address_components'][i]['types'][0] == 'administrative_area_level_2':
                   geo_location['city'] = data['results'][0]['address_components'][i]['long_name']
                   geo_location['town'] = geo_location['city']

               else:
                   geo_location[data['results'][0]['address_components'][i]['types'][0] ]= data['results'][0]['address_components'][i]['long_name']

       formatted_address = data['results'][0]['formatted_address']
       geo_location['lat'] = data['results'][0]['geometry']['location']['lat']
       geo_location['lang']= data['results'][0]['geometry']['location']['lng']
       geo_location['formatted_address']=formatted_address

       return geo_location

print(reverse_gcode("Starbucks, Chicago"))

输出将采用json 格式，如下所示：

{'street_number': '8', 'town': 'Cook County', 'locality': 'Chicago', 'city': 'Cook County', 'lat': 41.882413, 'neighborhood': 'Chicago Loop', 'route': 'North Michigan Avenue', 'lang': -87.62468799999999, 'postal_code': '60602', 'country': 'United States', 'formatted_address': '8 N Michigan Ave, Chicago, IL 60602, USA', 'state': 'Illinois'}