Google Location Crawler: POI Data Collection Solution

First, why do you have to use a proxy IP to get Google location data?

Engaged in data collection know, Google Maps this thing is particularly sensitive to crawlers. You take your own home broadband connection to climb, within half an hour will give you IP black. At this time, you have to rely on proxy IP torisk-sharing, as if fighting a guerrilla war, shooting one shot and changing places.

Ordinary proxy IPs are easy to reveal, especially server room IPs, which Google can recognize at a glance. This is the time to useResidential Agents, masquerading as a real user operation. For example, with ipipgo's dynamic residential IP, each request automatically change IP, the success rate can be doubled several times.

Second, hand to teach you to build a crawler shield

Let's start with a practical configuration plan:

import requests
from itertools import cycle

 Proxy interface for ipipgo
proxy_list = [
    'http://user:pass@gateway.ipipgo.com:8000',
    'http://user:pass@gateway.ipipgo.com:8001'.
     Prepare at least 20 rotating IPs
]

proxy_pool = cycle(proxy_list)

def get_poi(keyword).
    proxy = next(proxy_pool)
    try: response = requests.get(keyword): proxy = next(proxy_pool)
        response = requests.get(
            'https://www.google.com/maps/search/'+keyword, proxies={'http': proxy, 'https': proxy, 'https': proxy
            proxies={'http': proxy, 'https': proxy}, timeout=10
            timeout=10
        )
         Add the parsing logic here
        return data
    except Exception as e.
        print(f'{proxy} hung, next one')
        return get_poi(keyword)

Focus on these three points:

1. Request intervalsDon't be too regular, preferably with random delays (1-3 seconds)
2. User-AgentTo match the real browser version
3. Captcha processingWe need to prepare a coding platform for backup

Proxy IP selection to avoid the pit guide

There are all kinds of agent types on the market, so I'll give you a comparison table:

Highlighted here are ipipgo'sDynamic Residential AgentsThe actual test can run Google Maps API to catch 800-1000 data per hour stably. Their IP pool is updated quickly, but also with automatic authentication, without the old toss account password.

IV. Practical Frequently Asked Questions QA

Q：Why was I blocked even though I used a proxy?
A: Check three things: 1. whether the request header with browser fingerprints 2. whether the IP is shared by more than one person 3. whether the operation behavior is too mechanical

Q: What can I do if I can't get up to speed on acquisition?
A: It is recommended to use a combination of asynchronous concatenation + multithreading, but pay attention to the concurrency limit of each sub-account of ipipgo (no more than 5 threads is recommended)

Q: What should I do if there is always an error in data parsing?
A: Google page structure often changes, it is recommended to use xpath and regular double insurance, or on the third-party parsing libraries such as pyquery

V. Essential skills for high-level players

Share a cold trick: useGeolocation Binding. For example, if you climb a cafe in New York, you will exclusively use local residential IPs in New York. ipipgo supports IP positioning at the specified city level, so that the POI data collected is more accurate and you can avoid triggering geographic detection.

Here's another trick for you to set the parameters: in the request URL add&hl=en&gl=USThese two parameters, forced to return the English results, the data format is more standardized and easy to parse.

Finally, to remind the novice: do not buy cheap junk proxy, was blocked IP is a small matter, or perhaps the entire collection project have to rewrite. Use ipipgo this kind of professional service provider, although spend more money, but save time cost enough to return to the capital.