sc 22(18): e3

Research Article

Data Fusion and Visualization towards City Disaster Management: Lisbon Case Study

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  • @ARTICLE{10.4108/eetsc.v6i18.1374,
        author={Lu\^{\i}s B. Elvas and Sandra P. Gon\`{e}alves and Jo\"{a}o C. Ferreira and Ana Madureira},
        title={Data Fusion and Visualization towards City Disaster Management: Lisbon Case Study},
        journal={EAI Endorsed Transactions on Smart Cities},
        volume={6},
        number={18},
        publisher={EAI},
        journal_a={SC},
        year={2022},
        month={6},
        keywords={Disaster Management, Data mining, Smart City, CRISP-DM},
        doi={10.4108/eetsc.v6i18.1374}
    }
    
  • Luís B. Elvas
    Sandra P. Gonçalves
    João C. Ferreira
    Ana Madureira
    Year: 2022
    Data Fusion and Visualization towards City Disaster Management: Lisbon Case Study
    SC
    EAI
    DOI: 10.4108/eetsc.v6i18.1374
Luís B. Elvas1,*, Sandra P. Gonçalves2, João C. Ferreira1, Ana Madureira3
  • 1: Instituto Universitário de Lisboa (ISCTE-IUL)
  • 2: Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, Lisboa, Portugal
  • 3: Instituto Superior de Engenharia do Porto – P.PORTO
*Contact email: luis.elvas@iscte.pt

Abstract

INTRODUCTION: Due to the high level of unpredictability and the complexity of the information requirements, disaster management operations are information demanding. Emergency response planners should organize response operations efficiently and assign rescue teams to particular catastrophe areas with a high possibility of surviving. Making decisions becomes more difficult when the information provided is heterogeneous, out of date, and often fragmented. OBJECTIVES: In this research work a data fusion of different information sources and a data visualization process was applied to provide a big picture about the disruptive events in a city. This high-level knowledge is important for emergency management authorities. This holistic process for managing, processing, and analysing the seven Vs (Volume, Velocity, Variety, Variability, Veracity, Visualization, and Value) in order to generate actionable insights for disaster management. METHODS: A CRISP-DM methodology over smart city-data was applied. The fusion approach was introduced to merge different data sources. RESULTS: A set of visual tools in dashboards were produced to support the city municipality management process. Visualization of big picture based on different data available is the proposed work. CONCLUSION: Through this research, it was verified that there are temporal and spatial patterns of occurrences that affected the city of Lisbon, with some types of occurrences having a higher incidence in certain periods of the year, such as floods and collapses that occur when there are high levels of precipitation. On the other hand, it was verified that the downtown area of the city is the most affected area.