Intelligent Dashboards to Monitor the Occurrences in Smart Cities – A Portuguese Case Study
DOI:
https://doi.org/10.4108/eetsc.v6i4.2796Keywords:
Big Data, Data Science, Smart Cities, Business Intelligence, Intervention RequestsAbstract
This article concerns the needed response by the Professional Fire Brigade Regiment (FBR) in the city of Lisbon. To solve and answer the question "How to improve FBR intervention requests when an emergency is detected?" the project aims to create a functional prototype containing interactive dashboards allowing the analysis of indicators that improve decision capacity. As results attest, 58% of false alarms are cancelled even after the emergency and rescue means have been activated to the location. About 97% of the suspended requests are not cancelled before the means are sent. The number of records of occurrences tends to increase over the 8 years of study. Sunday is the weekday with the highest number of associated records, with 23.33%, specifically at 9 am and 8 pm. Autumn is the season with more occurrences, with 26.51%. More than 50% of the occurrences are in the administrative services closing time and more than 50% of the registrations send only one vehicle to the place. These indicators aim to understand if these variables are probabilistically associated with requests for interventions to be able to anticipate these scenarios and help in decision-making whenever necessary.
Downloads
References
The United Nations. World Population Prospects 2022. https://doi.org/978-92-1-148373-4.
Bibri S. Big Data Science and Analytics for Smart Sustainable Urbanism. Springer Cham; 2019. DOI: https://doi.org/10.1007/978-3-030-17312-8
Weiss M, Bernardes R, Consoni F. Cidades inteligentes como nova prática para o gerenciamento dos serviços e infraestruturas urbanos: a experiência da cidade de Porto Alegre. Urbe Revista Brasileira de Gestão Urbana 2015;7. https://doi.org/10.1590/2175-3369.007.003.AO01. DOI: https://doi.org/10.1590/2175-3369.007.003.AO01
Anthopoulos LG. Understanding Smart Cities: A Tool for Smart Government or an Industrial Trick? vol. 22. Springer Cham; 2017. DOI: https://doi.org/10.1007/978-3-319-57015-0
Scheps S. Business Intelligence For Dummies. John Wiley & Sons, Inc.; 2008.
Steele B, Chandler J, Reddy S. Algorithms for Data Science. Cham: Springer International Publishing; 2016. https://doi.org/10.1007/978-3- 319-45797-0.
Cady F. The Data Science Handbook. John Wiley & Sons, Inc; 2017. DOI: https://doi.org/10.1002/9781119092919
Hurwitz J, Nugent A, Halper F, Kaufman M. Big Data For Dummies. 2013.
Giffinger R, Fertner C, Kramar H, Kalasek R, Milanović N, Meijers E. Smart cities - Ranking of European medium-sized cities. 2007.
Perera C, Zaslavsky A, Christen P, Georgakopoulos D. Context Aware Computing for The Internet of Things: A Survey. IEEE Communications Surveys & Tutorials 2014;16:414–54. https://doi.org/10.1109/SURV.2013.042313.0019 7. DOI: https://doi.org/10.1109/SURV.2013.042313.00197
ANEPC. PLANO NACIONAL DE EMERGÊNCIA DE PROTEÇÃO CIVIL. n.d.
Ahsaan S, Mourya A. Prognostic Modelling for Smart cities using Smart Agents and IoT: A Proposed Solution for Sustainable Development. EAI Endorsed Transactions on Smart Cities 2018:169916. https://doi.org/10.4108/eai.13-5- 2021.169916. DOI: https://doi.org/10.4108/eai.13-5-2021.169916
Luís B. Elvas, Sandra P. Gonçalves, João C. Ferreira, Ana Madureira. Data Fusion and Visualization towards City Disaster Management: Lisbon Case Study. EAI Endorsed Transactions on Smart Cities 2022;6:e3. https://doi.org/10.4108/eetsc.v6i18.1374. DOI: https://doi.org/10.4108/eetsc.v6i18.1374
Nesmachnow S, Baña S, Massobrio R. A distributed platform for big data analysis in smart cities: combining Intelligent Transportation Systems and socioeconomic data for Montevideo, Uruguay. EAI Endorsed Transactions on Smart Cities 2017;2:153478. https://doi.org/10.4108/eai.19-12-2017.153478. DOI: https://doi.org/10.4108/eai.19-12-2017.153478
Fernandes M. Cidades Inteligentes: Um novo paradigma urbano - Estudo de caso da cidade do Porto. Católica Porto Business School, 2016.
Durand A. Cidades Inteligentes - Análise de um estudo de caso, 82. Instituto Politécnico de Setúbal, 2013.
Hillenbrand K. Boston Equips Firefighters with Hazard Data 2016.
Patton E, Appelbaum S. The Case for Case Studies in Management Research. Management Research News 2003;26:60–71. https://doi.org/10.1108/01409170310783484. DOI: https://doi.org/10.1108/01409170310783484
Webster M, Sell J. Laboratory Experiments in the Social Sciences. 1st ed. Academic Press; 2007. DOI: https://doi.org/10.1002/9781405165518.wbeos0800
Kimball R, Ross M. The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, Third Edition. 3rd ed. John Wiley & Sons, Inc.; 2002.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Rita Silva, Maria Silva, Gustavo Caldas, Filipe Portela, Henrique Santos
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
