Crime prediction and monitoring in Porto, Portugal, using machine learning, spatial and text analytics

Detalhes bibliográficos
Autor(a) principal: Saraiva, Miguel
Data de Publicação: 2022
Outros Autores: Matijosaitiene, Irina, Mishra, Saloni, Amante, Ana
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/10216/144949
Resumo: Crimes are a common societal concern impacting quality of life and economic growth. Despite the global decrease in crime statistics, specific types of crime and feelings of insecurity, have often increased, leading safety and security agencies with the need to apply novel approaches and advanced systems to better predict and prevent occurrences. The use of geospatial technologies, combined with data mining and machine learning techniques allows for significant advances in the criminology of place. In this study, official police data from Porto, in Portugal, between 2016 and 2018, was georeferenced and treated using spatial analysis methods, which allowed the identification of spatial patterns and relevant hotspots. Then, machine learning processes were applied for space-time pattern mining. Using lasso regression analysis, significance for crime variables were found, with random forest and decision tree supporting the important variable selection. Lastly, tweets related to insecurity were collected and topic modeling and sentiment analysis was performed. Together, these methods assist interpretation of patterns, prediction and ultimately, performance of both police and planning professionals.
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spelling Crime prediction and monitoring in Porto, Portugal, using machine learning, spatial and text analyticsGeografiaGeographyCrimes are a common societal concern impacting quality of life and economic growth. Despite the global decrease in crime statistics, specific types of crime and feelings of insecurity, have often increased, leading safety and security agencies with the need to apply novel approaches and advanced systems to better predict and prevent occurrences. The use of geospatial technologies, combined with data mining and machine learning techniques allows for significant advances in the criminology of place. In this study, official police data from Porto, in Portugal, between 2016 and 2018, was georeferenced and treated using spatial analysis methods, which allowed the identification of spatial patterns and relevant hotspots. Then, machine learning processes were applied for space-time pattern mining. Using lasso regression analysis, significance for crime variables were found, with random forest and decision tree supporting the important variable selection. Lastly, tweets related to insecurity were collected and topic modeling and sentiment analysis was performed. Together, these methods assist interpretation of patterns, prediction and ultimately, performance of both police and planning professionals.20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10216/144949eng10.3390/ijgi11070400Saraiva, MiguelMatijosaitiene, IrinaMishra, SaloniAmante, Anainfo: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:RCAAP2023-11-29T15:08:29Zoai:repositorio-aberto.up.pt:10216/144949Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:16:35.553306Repositório 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 Crime prediction and monitoring in Porto, Portugal, using machine learning, spatial and text analytics
title Crime prediction and monitoring in Porto, Portugal, using machine learning, spatial and text analytics
spellingShingle Crime prediction and monitoring in Porto, Portugal, using machine learning, spatial and text analytics
Saraiva, Miguel
Geografia
Geography
title_short Crime prediction and monitoring in Porto, Portugal, using machine learning, spatial and text analytics
title_full Crime prediction and monitoring in Porto, Portugal, using machine learning, spatial and text analytics
title_fullStr Crime prediction and monitoring in Porto, Portugal, using machine learning, spatial and text analytics
title_full_unstemmed Crime prediction and monitoring in Porto, Portugal, using machine learning, spatial and text analytics
title_sort Crime prediction and monitoring in Porto, Portugal, using machine learning, spatial and text analytics
author Saraiva, Miguel
author_facet Saraiva, Miguel
Matijosaitiene, Irina
Mishra, Saloni
Amante, Ana
author_role author
author2 Matijosaitiene, Irina
Mishra, Saloni
Amante, Ana
author2_role author
author
author
dc.contributor.author.fl_str_mv Saraiva, Miguel
Matijosaitiene, Irina
Mishra, Saloni
Amante, Ana
dc.subject.por.fl_str_mv Geografia
Geography
topic Geografia
Geography
description Crimes are a common societal concern impacting quality of life and economic growth. Despite the global decrease in crime statistics, specific types of crime and feelings of insecurity, have often increased, leading safety and security agencies with the need to apply novel approaches and advanced systems to better predict and prevent occurrences. The use of geospatial technologies, combined with data mining and machine learning techniques allows for significant advances in the criminology of place. In this study, official police data from Porto, in Portugal, between 2016 and 2018, was georeferenced and treated using spatial analysis methods, which allowed the identification of spatial patterns and relevant hotspots. Then, machine learning processes were applied for space-time pattern mining. Using lasso regression analysis, significance for crime variables were found, with random forest and decision tree supporting the important variable selection. Lastly, tweets related to insecurity were collected and topic modeling and sentiment analysis was performed. Together, these methods assist interpretation of patterns, prediction and ultimately, performance of both police and planning professionals.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-01-01T00:00:00Z
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dc.identifier.uri.fl_str_mv https://hdl.handle.net/10216/144949
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dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.3390/ijgi11070400
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repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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