Identifying patterns in city municipality incident reports by Lisbon citizens: A data-driven analysis

Bibliographic Details
Main Author: Sousa, Pedro Miguel Silva de
Publication Date: 2024
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10071/33571
Summary: In a scenario of escalating urbanization, major cities face the challenge of providing optimal conditions for their residents. This work focuses on the significance of obtaining and analysing information regarding urban occurrences, as such knowledge is crucial in forecasting occurrences and efficiently allocating municipal resources. The primary objective of this dissertation is to provide data and forecasts that can empower municipal authorities to make informed decisions and proactively manage critical areas of the city. The research is based on data collected through the “Na Minha Rua” application, made available by the Lisbon City Council. By exploring this source of information, the aim is to identify patterns in urban occurrences, mapping areas prone to specific issues. This study seeks to contribute to a more effective approach in urban management, enabling the anticipation of needs and the implementation of preventive measures. By addressing urban dynamics through data analysis, this work offers valuable insights for optimizing the safety and quality of the urban environment. By understanding and anticipating incidents, municipal authorities can enhance their response, thus promoting a safer and more efficient city. This work represents a contribution to an informed and proactive approach to urban management, aiming to improve the quality of life for citizens in the city of Lisbon.
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spelling Identifying patterns in city municipality incident reports by Lisbon citizens: A data-driven analysisUrban incident managementOccurrencesSmart CitiesAnálise de dados -- Data analysisData qualityGestão de incidentes urbanosOcorrênciasCidades inteligentesIn a scenario of escalating urbanization, major cities face the challenge of providing optimal conditions for their residents. This work focuses on the significance of obtaining and analysing information regarding urban occurrences, as such knowledge is crucial in forecasting occurrences and efficiently allocating municipal resources. The primary objective of this dissertation is to provide data and forecasts that can empower municipal authorities to make informed decisions and proactively manage critical areas of the city. The research is based on data collected through the “Na Minha Rua” application, made available by the Lisbon City Council. By exploring this source of information, the aim is to identify patterns in urban occurrences, mapping areas prone to specific issues. This study seeks to contribute to a more effective approach in urban management, enabling the anticipation of needs and the implementation of preventive measures. By addressing urban dynamics through data analysis, this work offers valuable insights for optimizing the safety and quality of the urban environment. By understanding and anticipating incidents, municipal authorities can enhance their response, thus promoting a safer and more efficient city. This work represents a contribution to an informed and proactive approach to urban management, aiming to improve the quality of life for citizens in the city of Lisbon.Num cenário de crescente urbanização, as grandes cidades enfrentam o desafio de proporcionar condições ideais aos seus residentes. Este trabalho centra-se na importância da aquisição e análise de informações relativas a ocorrências urbanas, uma vez que este conhecimento é crucial na previsão de ocorrências e na eficiente alocação de recursos municipais. O objetivo primordial desta dissertação é fornecer dados e previsões que possam instrumentalizar as autoridades municipais na tomada de decisões informadas e na gestão proativa de áreas críticas da cidade. A pesquisa baseia-se nos dados recolhidos através da aplicação “Na Minha Rua”, disponibilizados pela Câmara Municipal de Lisboa. Ao explorar esta fonte de informação, pretende-se identificar padrões em ocorrências urbanas, mapeando áreas propensas a problemas específicos. Este estudo visa contribuir para uma abordagem mais eficaz na gestão urbana, possibilitando a antecipação de necessidades e a implementação das respetivas medidas preventivas. Ao abordar a dinâmica urbana através da análise de dados, este trabalho oferece informações de valor para otimizar a segurança e a qualidade do ambiente urbano. Ao compreender e antecipar ocorrências, as autoridades municipais podem aprimorar a sua resposta aos mesmos, promovendo assim uma cidade mais segura e eficiente. Este trabalho representa um contributo para uma abordagem informada e proativa na gestão urbana, visando melhorar a qualidade de vida dos cidadãos na cidade de Lisboa.2025-03-05T15:44:11Z2024-11-21T00:00:00Z2024-11-212024-09info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10071/33571TID:203756819engSousa, Pedro Miguel Silva deinfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-03-09T01:17:15Zoai:repositorio.iscte-iul.pt:10071/33571Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T01:13:56.417304Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Identifying patterns in city municipality incident reports by Lisbon citizens: A data-driven analysis
title Identifying patterns in city municipality incident reports by Lisbon citizens: A data-driven analysis
spellingShingle Identifying patterns in city municipality incident reports by Lisbon citizens: A data-driven analysis
Sousa, Pedro Miguel Silva de
Urban incident management
Occurrences
Smart Cities
Análise de dados -- Data analysis
Data quality
Gestão de incidentes urbanos
Ocorrências
Cidades inteligentes
title_short Identifying patterns in city municipality incident reports by Lisbon citizens: A data-driven analysis
title_full Identifying patterns in city municipality incident reports by Lisbon citizens: A data-driven analysis
title_fullStr Identifying patterns in city municipality incident reports by Lisbon citizens: A data-driven analysis
title_full_unstemmed Identifying patterns in city municipality incident reports by Lisbon citizens: A data-driven analysis
title_sort Identifying patterns in city municipality incident reports by Lisbon citizens: A data-driven analysis
author Sousa, Pedro Miguel Silva de
author_facet Sousa, Pedro Miguel Silva de
author_role author
dc.contributor.author.fl_str_mv Sousa, Pedro Miguel Silva de
dc.subject.por.fl_str_mv Urban incident management
Occurrences
Smart Cities
Análise de dados -- Data analysis
Data quality
Gestão de incidentes urbanos
Ocorrências
Cidades inteligentes
topic Urban incident management
Occurrences
Smart Cities
Análise de dados -- Data analysis
Data quality
Gestão de incidentes urbanos
Ocorrências
Cidades inteligentes
description In a scenario of escalating urbanization, major cities face the challenge of providing optimal conditions for their residents. This work focuses on the significance of obtaining and analysing information regarding urban occurrences, as such knowledge is crucial in forecasting occurrences and efficiently allocating municipal resources. The primary objective of this dissertation is to provide data and forecasts that can empower municipal authorities to make informed decisions and proactively manage critical areas of the city. The research is based on data collected through the “Na Minha Rua” application, made available by the Lisbon City Council. By exploring this source of information, the aim is to identify patterns in urban occurrences, mapping areas prone to specific issues. This study seeks to contribute to a more effective approach in urban management, enabling the anticipation of needs and the implementation of preventive measures. By addressing urban dynamics through data analysis, this work offers valuable insights for optimizing the safety and quality of the urban environment. By understanding and anticipating incidents, municipal authorities can enhance their response, thus promoting a safer and more efficient city. This work represents a contribution to an informed and proactive approach to urban management, aiming to improve the quality of life for citizens in the city of Lisbon.
publishDate 2024
dc.date.none.fl_str_mv 2024-11-21T00:00:00Z
2024-11-21
2024-09
2025-03-05T15:44:11Z
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dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
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