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CRiMINaL is a framework to uncover crimes patterns using logic-based relational machine learning techniques develop by UFF eScience group

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CRiMINaL

Crime patteRn MachINe Learning: a framework to uncover crimes patterns using logic-based relational machine learning techniques.

Overview

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.

Repository Structure

  • CRiMINaL/
    • code/
    • data/
      • raw_data/
      • model_data/
    • README.md

Quick Start Guide

Prerequisits

Pipeline Execution

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

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CRiMINaL is a framework to uncover crimes patterns using logic-based relational machine learning techniques develop by UFF eScience group

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