Crime patteRn MachINe Learning: a framework to uncover crimes patterns using logic-based relational machine learning techniques.
This repository presents the CRiMINaL a framework to uncover crimes patterns using logic-based relational machine learning techniques. CRiMINaL addresses the city public security problem by collecting crime data from existing crowd-sourcing systems and automatically induce patterns with relational machine learning. Experimental results conducted with real data evidentiate CRiMINaL as a suitable and promising tool to assist police departments on crime prevention.
CRiMINaL/code/data/raw_data/model_data/
- README.md
The following pipeline is a real case-of-study addressed in the city of Niterói, RJ, Brazil.
All data retrieved and processed during the execution of the pipeline can be found in /data/raw_data/.
- Data Retrieval
A1_ext.R retrieves the crowd-source information from Onde Fui Roubado website.
input: none
output: ondefuiroubado_occurrences n-m. TimeStamp k.csv
A2_ext retrieves relevant public locations (e.g. banks, stores, attractions, hospitals etc) from Open Street Maps.
input: none
output: geo_places.csv
- Data Cleaning and Discretizations
B_cln.R filters the Niterói occurrences from the other.
input: ondefuiroubado_occurrences n-m. TimeStamp k.csv
output: Niteroi_occurrences.csv
C_cln.R convert the georeferences (latitude and longitude) to its official neighborhood.
input: Niteroi_occurrences.csv
output: Niteroi_occurrences_suburb.csv
D1_cln.R and D2_cln.R merge the ocurrences with the nearby relevant public locations.
D1_cln:
input: Niteroi_occurrences.csv ; geo_places.csv
output: nearby_location.csv
D2_cln:
input: nearby_location.csv
output: nearby_locations_plus_neighbourhoods.csv
E_cln.R convert the continuous date and time into a discrete date and time.
input: nearby_location.csv
output: time_discretize_nit.csv
F_nrm.R build clauses from data.
input: time_discretize_suburb_updated.csv
output: siac.f ; siac.b ; siac.n
- Learning the Model
yap -f G_lrn.pl > model.txt