A data model for doing geospatial analysis and regular analytics on Pokemon Go data.
These blog posts help to explain what Pokelyzer is, and what it can be used for.
- The Original Concept
- Finding Hotpots for Locally Rare Pokemon Using Tableau
- Help Others Find Rare Pokemon Nearby
See the wiki page for installation instructions.
If you already have the database running, ensure all of the patches below since your installation date have been applied. I wish there were better version control for databases, but this is the best solution I have for now. If you know of a better one, let me know.
To use the webhook listener, I recommend doing a fresh installation of PokemonGo-Map using the instructions prodvided in the wiki . Just rename your current directory to something else and do the install. No data will be lost from Pokelyzer since that's stored separately from the PokemonGo-Map database.
This is a patch that makes the Pokelyzer capable of accepting data automatically pushed by PokemonGo-Map via its webhood interface.
First, we'll drop the Name column from the spotted_pokemon table. This is redundant now because of our pokemon_info table. We also update the _meta table with the new schema version.
INSERT INTO _meta (db_version, last_update) VALUES ('v1.0-alpha', '2016-07-31');
ALTER TABLE spotted_pokemon DROP COLUMN name;
This patch adds information that makes it easier to debug issues with the database. Specifically, I have added version tracking so that all records stored record the version of the schema they were stored with, along with a timestamp of when each record was stored.
To apply this patch, get the SQL from the 2016-07-30-metaPatch.sql file in the patches folder above and apply it to your database. There's no need to stop your web server to apply this.
This patch adds an extra column that gives us the ability to assign different Pokemon records to different "eras" - a very useful thing to have when doing historical analysis, especially since the recent changes that switched around all the nests.
It's a bit of a longer one, so see the guide here: http://www.whackdata.com/2016/07/29/the-era-of-eras-pokemon-go-pokelyzer/
Thanks again to @zenthere for supplying the SQL for this as well!
A big thanks to @zenthere for fixing a bug in my jitter calculation, and for creating a beautiful solution to the fact that a lot of rows in the database were duplicates. (Also note, if you don't apply this patch you'll receive a "there is no unique or exclusion constraint" error.) To apply this patch, shut down your map server and execute the following SQL to remove all current duplicates and put a constraint on any new ones:
DELETE FROM spotted_pokemon USING spotted_pokemon sp2
WHERE spotted_pokemon.encounter_id = sp2.encounter_id AND spotted_pokemon.id > sp2.id;
ALTER TABLE spotted_pokemon ADD CONSTRAINT encounter_id_unique UNIQUE (encounter_id);Then update your customLog.py file with the one from the Pokelyzer v0.3-alpha release.
Start your server back up!
I've added a table for doing analysis using various Pokemon properties, such as type, classification, weight and height. To patch an existing database to support this, first drop the existing pokemon_info table from pgAdmin or using:
DROP TABLE public.pokemon_info;And then use the same Restore feature you used to load the database initially to load the pokemon_into_table_patch.tar file available in patches folder above.
If you loaded the database backup file before this time (commit bd81308), run the following command to patch your existing database. It fixes an issue with joining tables in Tableau.
ALTER TABLE date_dimension ALTER COLUMN date_key TYPE integer USING (date_key::integer);