A Pokémon Go scraper capable of scanning large area for Pokémon spawns over long period of time. Suitable for gathering data for further analysis.
Oh great, another map?
This is not a map, a map is included, but the main goal of this app is to gather data and put it in a database for further analysis.
How does it work?
worker.py gets a rectangle from start/end coordinates (configured in
config.py) and spawns n workers. Each of the workers use different accounts to scan their surrounding areas for Pokemon. To put it simply: you can scan an entire city for Pokemon. All gathered information is put into a database for further analysis.
worker.py is fully threaded and logs in again after X scans just to make sure the connection with the server is in a good state. It’s also capable of restarting workers that are misbehaving, so that the data-gathering process is uninterrupted.
There’s also a simple interface that displays active Pokemon on a map, and can generate nice-looking reports.
Here it is in action:
And here are workers together with their area of scan:
- multithreaded, multiple accounts at the same time
- aims at being very stable for long-term runs
- able to map entire city (or larger area) in real time
- gathers Pokémon and Gyms and stores in database
- reports for gathered data
Note: Pokeminer works with Python 3.5 only. Python 2.7 is not supported and is not compatible at all since I moved from threads to coroutines. Seriously, it’s 2016, Python 2.7 hasn’t been developed for 6 years, why don’t you upgrade already?
Create the database by running Python interpreter. Note that if you want more than 10 workers simultaneously running, SQLite is probably not the best choice.
$ python >>> import db >>> db.Base.metadata.create_all(db.get_engine())
config.py and modify as you wish. See wiki page for explanation on properties.
Run the worker:
Optionally run the live map interface and reporting system:
python web.py --host 127.0.0.1 --port 8000
How many workers do I need?
tl;dr: about 1.2 workers per km².
Longer version: there’s a set delay between each scan, and one spawn lasts for at least 15 minutes; so to avoid missing spawns there is a limit to the PPC (points per cycle) each worker can be assigned. As I’m writing this the scan delay is set to 10, so combining that with 15-minute spawn times gives a maximum of 90 PPC. You can check that value in worker.py’s status window.
And how many workers do you need? Let’s calculate that for a hexagonal grid:
overlap_area = (pi - 3/2*sqrt(3) *2) * 2 overlap_correction_factor ≈ 1.17
number_of_workers = (π * radius²) /( π * 70m²) * 1.17 * 10s / (15*60s) = (radius_in_km)² * 2.65
For example, a radius of 5.5km is around 95km² and with the formula above would be ~80 workers.
There are three reports, all available as web pages on the same server as the live map:
- Overall report, available at
- Single species report, available at
- Gym statistics page, available by running
Here’s what the overall report looks like:
The gyms statistics server is in a separate file, because it’s intended to be shared publicly as a webpage.