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Communication Dans Un Congrès Année : 2019

An on-line platform for supporting DSS-research collaboration (EWG-DSS Collab-Net Version 5)

Résumé

Over the past few years, a growing concern has taken place among authorities over crimes committed worldwide. It has not been different in Brazil. High crime rates have encouraged government authorities involved in public safety to identify solutions to minimize crimes considered VCA (Violent Crime Against Assets). In this context, one alternative to both plan and manage security is in the division of ISA (Integrated Security Areas) neighborhoods. However, the local government needs to identify the degree of crime severity of each ISA in order to establish prevention policies and, therefore, resources management, which will make it possible for the Secretariat for Social Defense and other entities involved to establish sets of actions intending to minimize the crime rate. Furthermore, it is evident that changes are constantly happening in the crime behavior tendencies in each ISA, requiring a different set of daily actions from authorities. Therefore, the purpose of this study is to analyze the crime behavior in the region under study based on data provided by the website “onde fui roubado” and classify each ISA according to the severity of the crime through algorithms of classification of machine learning. Data analysis will take place from the KDD methodology and Big Data tools will be used to handle the large volume of data. Thus, this paper presents a framework that will allow the identification of the crimes severity in each ISA based on Big Data and Machine Learning.
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Dates et versions

hal-02421562 , version 1 (20-12-2019)

Identifiants

  • HAL Id : hal-02421562 , version 1
  • OATAO : 24955

Citer

Jean Turet, Jadielson Moura, Ana Paula Cabral, Fatima Dargam, Pascale Zaraté, et al.. An on-line platform for supporting DSS-research collaboration (EWG-DSS Collab-Net Version 5). 5th International Conference on Decision Support Systems Technologies (ICDSST 2019), May 2019, Funchal, Madeira, Portugal. pp.186-192. ⟨hal-02421562⟩
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