Uso da análise de cluster para o estudo da criminalidade no Estado do Rio de Janeiro

Translated title of the contribution: Use of cluster analysis to study crime in the State of Rio de Janeiro

Max William Coelho Moreira de Oliveira, Miguel Fernández Pérez, Aldo Fernández Pérez, Wagner Santos, Antonio Costa Neto

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This article aims to construct clusters based on historical data of thefts in the State of Rio de Janeiro, aiming to identify possible similarities among the records. Monthly quantities of vehicle thefts, robberies on public transportation, pedestrian robberies, cell phone thefts, cargo thefts, and robberies at commercial establishments were selected. Using these records, the k-means algorithm was employed to build clusters, resulting in two subsets of records. These subsets present distinct characteristics and are valuable for analyzing the interaction between different types of thefts in a disaggregated manner, thus avoiding statistical fallacies. Additionally, we propose a classification model that establishes criteria for assigning scenarios to a specific cluster. This model can assist in developing more effective strategies in public security, and in the use of human and logistical resources.

Translated title of the contributionUse of cluster analysis to study crime in the State of Rio de Janeiro
Original languagePortuguese
Title of host publicationProceedings of the 22nd LACCEI International Multi-Conference for Engineering, Education and Technology
Subtitle of host publicationSustainable Engineering for a Diverse, Equitable, and Inclusive Future at the Service of Education, Research, and Industry for a Society 5.0., LACCEI 2024
PublisherLatin American and Caribbean Consortium of Engineering Institutions
ISBN (Electronic)9786289520781
DOIs
StatePublished - 2024
Event22nd LACCEI International Multi-Conference for Engineering, Education and Technology, LACCEI 2024 - Hybrid, San Jose, Costa Rica
Duration: 17 Jul 202419 Jul 2024

Publication series

NameProceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
ISSN (Electronic)2414-6390

Conference

Conference22nd LACCEI International Multi-Conference for Engineering, Education and Technology, LACCEI 2024
Country/TerritoryCosta Rica
CityHybrid, San Jose
Period17/07/2419/07/24

Keywords

  • Classification
  • Clusters
  • Correlation
  • Dimensional reduction
  • Machine Learning
  • Public security

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