Data science techniques applied to analysis of incidents registered by the 1-1-2 Canary Islands emergencyservices

  1. Carlos Pérez Gonzalez 1
  2. Marcos Colebrook 1
  3. José Luis Roda-García 1
  4. Carlos Rosa Remedios
  5. Teno González Dos Santos
  1. 1 Universidad de La Laguna
    info

    Universidad de La Laguna

    San Cristobal de La Laguna, España

    GRID grid.10041.34

Proceedings:
29th European Conference on Operational Research (EURO2018). Valencia, July 8-11 2018

ISBN: 978-84-09-02938-9

Year of publication: 2018

Pages: 272

Type: Conference paper

Export: RIS
Author's full text: lockOpen access editor

Abstract

The study of alerts received in the emergency services is a very important issue in order to know different aspects of the time and spatialdistribution of alerts in a given region. In particular, the Emergency andSecurity Coordinating Center (CECOES) 1-1-2 of the Government ofthe Canary Islands records detailed information about the incidents thatare reported by the citizens through phone calls. Due to the high volume of information generated over the time in this process, it is neededto apply big data techniques to obtain statistical measures and results ofinterest. We have used the statistical software R and different libraries(Shiny, Highcharts, Highmaps) to present the data information in different interactive dashboards (time series charts to analyze the timeevolution, tree classification of the sanitary emergencies, geospatialrepresentations of incidents density distribution, etc.) and to proposeseveral predictive and classification models. In this work, we illustratesome illustrative and valuable results in studying the incidents in theregion during the last years.