Banca de DEFESA: ADELSON DIAS DE ARAÚJO JÚNIOR

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
DISCENTE : ADELSON DIAS DE ARAÚJO JÚNIOR
DATA : 30/09/2019
HORA: 10:30
LOCAL: B321 - IMD
TÍTULO:

Predspot: Predicting Crime Hotspots with Machine Learning

PALAVRAS-CHAVES:

predictive policing, hotspots forecasting, machine learning, spatiotemporal features.

PÁGINAS: 81
GRANDE ÁREA: Ciências Exatas e da Terra
ÁREA: Ciência da Computação
SUBÁREA: Matemática da Computação
ESPECIALIDADE: Modelos Analíticos e de Simulação
RESUMO:

Smarter cities are largely adopting data infrastructure and analysis to improve decision making for public safety issues. Although traditional hotspot policing methods have shown benefits in reducing crime, previous studies suggest that the adoption of predictive techniques can produce more accurate estimates for future crime concentration. In this work we propose a framework to generate future hotspots using spatiotemporal features and other geographic information from OpenStreetMap. We implemented an open source Python-package called predspot to support efficient hotspots prediction following the steps suggested in the framework. To evaluate the predictive approach against the traditional methodology implemented by Natal’s police department, we compared two crime mapping methods (KGrid and KDE) and two efficient machine learning algorithms (Random Forest and Gradient Boosting) in twelve crime scenarios, considering burglary, violent and drugs crimes. The results indicate that our predictive approach estimate hotspots 1.6-5.1 times better than the analysts baseline. A feature importance analysis were extracted from the models to account with how much the selected variables helped the predictions and to discuss the modelling strategy we conducted.

MEMBROS DA BANCA:
Interna - 2524467 - MARJORY CRISTIANY DA COSTA ABREU
Externo ao Programa - 2859562 - LEONARDO CESAR TEONACIO BEZERRA
Externa à Instituição - OURANIA KOUNADI