Data-driven approach for incident management in a smart city

Detalhes bibliográficos
Autor(a) principal: Elvas, L. B.
Data de Publicação: 2020
Outros Autores: Marreiros, C. F., Dinis, J. M., Pereira, M. C., Martins, A. L., Ferreira, J. C.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10071/21128
Resumo: Buildings in Lisbon are often the victim of several types of events (such as accidents, fires, collapses, etc.). This study aims to apply a data-driven approach towards knowledge extraction from past incident data, nowadays available in the context of a Smart City. We apply a Cross Industry Standard Process for Data Mining (CRISP-DM) approach to perform incident management of the city of Lisbon. From this data-driven process, a descriptive and predictive analysis of an events dataset provided by the Lisbon Municipality was possible, together with other data obtained from the public domain, such as the temperature and humidity on the day of the events. The dataset provided contains events from 2011 to 2018 for the municipality of Lisbon. This data mining approach over past data identified patterns that provide useful knowledge for city incident managers. Additionally, the forecasts can be used for better city planning, and data correlations of variables can provide information about the most important variables towards those incidents. This approach is fundamental in the context of smart cities, where sensors and data can be used to improve citizens’ quality of life. Smart Cities allow the collecting of data from different systems, and for the case of disruptive events, these data allow us to understand them and their cascading effects better.
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spelling Data-driven approach for incident management in a smart cityBuildingsIncident managementKnowledge extractionSmart citiesCritical infrastructuresCRISP-DMBuildings in Lisbon are often the victim of several types of events (such as accidents, fires, collapses, etc.). This study aims to apply a data-driven approach towards knowledge extraction from past incident data, nowadays available in the context of a Smart City. We apply a Cross Industry Standard Process for Data Mining (CRISP-DM) approach to perform incident management of the city of Lisbon. From this data-driven process, a descriptive and predictive analysis of an events dataset provided by the Lisbon Municipality was possible, together with other data obtained from the public domain, such as the temperature and humidity on the day of the events. The dataset provided contains events from 2011 to 2018 for the municipality of Lisbon. This data mining approach over past data identified patterns that provide useful knowledge for city incident managers. Additionally, the forecasts can be used for better city planning, and data correlations of variables can provide information about the most important variables towards those incidents. This approach is fundamental in the context of smart cities, where sensors and data can be used to improve citizens’ quality of life. Smart Cities allow the collecting of data from different systems, and for the case of disruptive events, these data allow us to understand them and their cascading effects better.MDPI2021-01-06T16:52:43Z2020-01-01T00:00:00Z20202021-06-09T14:26:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/21128eng2076-341710.3390/app10228281Elvas, L. B.Marreiros, C. F.Dinis, J. M.Pereira, M. C.Martins, A. L.Ferreira, J. C.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-09T18:01:06Zoai:repositorio.iscte-iul.pt:10071/21128Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:32:34.368938Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Data-driven approach for incident management in a smart city
title Data-driven approach for incident management in a smart city
spellingShingle Data-driven approach for incident management in a smart city
Elvas, L. B.
Buildings
Incident management
Knowledge extraction
Smart cities
Critical infrastructures
CRISP-DM
title_short Data-driven approach for incident management in a smart city
title_full Data-driven approach for incident management in a smart city
title_fullStr Data-driven approach for incident management in a smart city
title_full_unstemmed Data-driven approach for incident management in a smart city
title_sort Data-driven approach for incident management in a smart city
author Elvas, L. B.
author_facet Elvas, L. B.
Marreiros, C. F.
Dinis, J. M.
Pereira, M. C.
Martins, A. L.
Ferreira, J. C.
author_role author
author2 Marreiros, C. F.
Dinis, J. M.
Pereira, M. C.
Martins, A. L.
Ferreira, J. C.
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Elvas, L. B.
Marreiros, C. F.
Dinis, J. M.
Pereira, M. C.
Martins, A. L.
Ferreira, J. C.
dc.subject.por.fl_str_mv Buildings
Incident management
Knowledge extraction
Smart cities
Critical infrastructures
CRISP-DM
topic Buildings
Incident management
Knowledge extraction
Smart cities
Critical infrastructures
CRISP-DM
description Buildings in Lisbon are often the victim of several types of events (such as accidents, fires, collapses, etc.). This study aims to apply a data-driven approach towards knowledge extraction from past incident data, nowadays available in the context of a Smart City. We apply a Cross Industry Standard Process for Data Mining (CRISP-DM) approach to perform incident management of the city of Lisbon. From this data-driven process, a descriptive and predictive analysis of an events dataset provided by the Lisbon Municipality was possible, together with other data obtained from the public domain, such as the temperature and humidity on the day of the events. The dataset provided contains events from 2011 to 2018 for the municipality of Lisbon. This data mining approach over past data identified patterns that provide useful knowledge for city incident managers. Additionally, the forecasts can be used for better city planning, and data correlations of variables can provide information about the most important variables towards those incidents. This approach is fundamental in the context of smart cities, where sensors and data can be used to improve citizens’ quality of life. Smart Cities allow the collecting of data from different systems, and for the case of disruptive events, these data allow us to understand them and their cascading effects better.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-01T00:00:00Z
2020
2021-01-06T16:52:43Z
2021-06-09T14:26:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10071/21128
url http://hdl.handle.net/10071/21128
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2076-3417
10.3390/app10228281
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dc.publisher.none.fl_str_mv MDPI
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dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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