TargetBanks

Database

Create new DB in SQL Server, name it "bkrob". Create a new sql login for the database, username="bkrob_adm", password="bkrob_adm". Set "bkrob_adm" user mapping to "bkrob" database. Give "bkrob_adm" user membership as db_owner.

SSIS Import

In the /data folder, import these data into the FFL and CrimeRate tables:

• ffl_TX_import.xlsx
• TX_Crimes_City_2016.xlsx

DATA Gatherings

Run python code in /data_parse/:

• get_banks_TX.py #parse FDIC data TX and enter into DB
• get_ratings.py #ratings and reviews from google api
• get_police_TX.py #parse policeone.com for TX police infos

DATA Clean Up

In the SQL table PoliceStations, replace Officers # if it seems way too high, also remove duplicates based on LAT data.

DATA FILL

Geocoding

Run python code in /data_fill/:

• banks_geocode.py #fill in lat/lng from google API geocode
• police_geocode.py
• ffl_geocode.py

Take, Count and Probabilities

Run python code in /data_fill/:

• take.py #Money that's available to take
• banks_closest.py # Closest police stations for each bank
• ffl_count.py # Number of closest FFL for each bank
• pdistance.py # Possibility of getting caught by distance to PoliceStations, based on a formula
• officers_rate.py # Number of police/pop. served per 1000

Sample Data

Create sample data from /sql/sample.sql.

Target on Sample Data

Run python script /data_fill/target.py