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Big Data Analytics to Improve the Decision-Making Process in Public Safety: A Case Study in Northeast Brazil

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Decision Support Systems VIII: Sustainable Data-Driven and Evidence-Based Decision Support (ICDSST 2018)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 313))

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Abstract

The concern about national security has increased over the years. The large number of crimes has brought a variety of serious problems to Brazil and other countries around the world. Therefore, the major challenge, especially in Brazil, faced by public safety is how best to analyze large amounts of data so as to identify the factors that influence how crimes evolve. Thus, this paper analyzes public safety in the northeast of Brazil and proposes a decision-making model based on Big Data Analytics. This model is a part of a framework that will support decision processes by identifying the most dangerous places based on correlating data on location and the number of crimes from a large volume of crime data.

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Author information

Authors and Affiliations

  1. Management Engineering Department, Universidade Federal de Pernambuco, Recife, Brazil

    Jean Gomes Turet & Ana Paula Cabral Seixas Costa

Authors
  1. Jean Gomes Turet
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  2. Ana Paula Cabral Seixas Costa
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Corresponding author

Correspondence to Jean Gomes Turet .

Editor information

Editors and Affiliations

  1. SimTech Simulation Technology, Graz, Austria

    Fatima Dargam

  2. Eastern Macedonia and Thrace Institute of Technology, Kavala, Greece

    Pavlos Delias

  3. University of Namur, Namur, Belgium

    Isabelle Linden

  4. ULB, Brussels, Belgium

    Bertrand Mareschal

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Turet, J.G., Costa, A.P.C.S. (2018). Big Data Analytics to Improve the Decision-Making Process in Public Safety: A Case Study in Northeast Brazil. In: Dargam, F., Delias, P., Linden, I., Mareschal, B. (eds) Decision Support Systems VIII: Sustainable Data-Driven and Evidence-Based Decision Support. ICDSST 2018. Lecture Notes in Business Information Processing, vol 313. Springer, Cham. https://doi.org/10.1007/978-3-319-90315-6_7

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  • DOI: https://doi.org/10.1007/978-3-319-90315-6_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90314-9

  • Online ISBN: 978-3-319-90315-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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