Identification and Classification of Routine Locations Using Anonymized Mobile Communication Data
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/100515 https://doi.org/10.3390/ijgi11040228 |
Resumo: | Digital location traces are a relevant source of insights into how citizens experience their cities. Previous works using call detail records (CDRs) tend to focus on modeling the spatial and temporal patterns of human mobility, not paying much attention to the semantics of places, thus failing to model and enhance the understanding of the motivations behind people’s mobility. In this paper, we applied a methodology for identifying individual users’ routine locations and propose an approach for attaching semantic meaning to these locations. Specifically, we used circular sectors that correspond to cellular antennas’ signal areas. In those areas, we found that all contained points of interest (POIs), extracted their most important attributes (opening hours, check-ins, category) and incorporated them into the classification. We conducted experiments with real-world data from Coimbra, Portugal, and the initial experimental results demonstrate the effectiveness of the proposed methodology to infer activities in the user’s routine areas. |
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Identification and Classification of Routine Locations Using Anonymized Mobile Communication Datacall detail recordsclustering algorithmshuman mobilitymeaningful placesmobile phone datapoints of interestDigital location traces are a relevant source of insights into how citizens experience their cities. Previous works using call detail records (CDRs) tend to focus on modeling the spatial and temporal patterns of human mobility, not paying much attention to the semantics of places, thus failing to model and enhance the understanding of the motivations behind people’s mobility. In this paper, we applied a methodology for identifying individual users’ routine locations and propose an approach for attaching semantic meaning to these locations. Specifically, we used circular sectors that correspond to cellular antennas’ signal areas. In those areas, we found that all contained points of interest (POIs), extracted their most important attributes (opening hours, check-ins, category) and incorporated them into the classification. We conducted experiments with real-world data from Coimbra, Portugal, and the initial experimental results demonstrate the effectiveness of the proposed methodology to infer activities in the user’s routine areas.2022info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/100515http://hdl.handle.net/10316/100515https://doi.org/10.3390/ijgi11040228eng2220-9964Ferreira, GonçaloAlves, AnaVeloso, MarcoBento, Carlosinfo: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:RCAAP2022-06-28T20:31:07Zoai:estudogeral.uc.pt:10316/100515Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:17:53.252904Repositório 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 |
Identification and Classification of Routine Locations Using Anonymized Mobile Communication Data |
title |
Identification and Classification of Routine Locations Using Anonymized Mobile Communication Data |
spellingShingle |
Identification and Classification of Routine Locations Using Anonymized Mobile Communication Data Ferreira, Gonçalo call detail records clustering algorithms human mobility meaningful places mobile phone data points of interest |
title_short |
Identification and Classification of Routine Locations Using Anonymized Mobile Communication Data |
title_full |
Identification and Classification of Routine Locations Using Anonymized Mobile Communication Data |
title_fullStr |
Identification and Classification of Routine Locations Using Anonymized Mobile Communication Data |
title_full_unstemmed |
Identification and Classification of Routine Locations Using Anonymized Mobile Communication Data |
title_sort |
Identification and Classification of Routine Locations Using Anonymized Mobile Communication Data |
author |
Ferreira, Gonçalo |
author_facet |
Ferreira, Gonçalo Alves, Ana Veloso, Marco Bento, Carlos |
author_role |
author |
author2 |
Alves, Ana Veloso, Marco Bento, Carlos |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Ferreira, Gonçalo Alves, Ana Veloso, Marco Bento, Carlos |
dc.subject.por.fl_str_mv |
call detail records clustering algorithms human mobility meaningful places mobile phone data points of interest |
topic |
call detail records clustering algorithms human mobility meaningful places mobile phone data points of interest |
description |
Digital location traces are a relevant source of insights into how citizens experience their cities. Previous works using call detail records (CDRs) tend to focus on modeling the spatial and temporal patterns of human mobility, not paying much attention to the semantics of places, thus failing to model and enhance the understanding of the motivations behind people’s mobility. In this paper, we applied a methodology for identifying individual users’ routine locations and propose an approach for attaching semantic meaning to these locations. Specifically, we used circular sectors that correspond to cellular antennas’ signal areas. In those areas, we found that all contained points of interest (POIs), extracted their most important attributes (opening hours, check-ins, category) and incorporated them into the classification. We conducted experiments with real-world data from Coimbra, Portugal, and the initial experimental results demonstrate the effectiveness of the proposed methodology to infer activities in the user’s routine areas. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/100515 http://hdl.handle.net/10316/100515 https://doi.org/10.3390/ijgi11040228 |
url |
http://hdl.handle.net/10316/100515 https://doi.org/10.3390/ijgi11040228 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2220-9964 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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 |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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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1799134074316521472 |