Comparing Regression Models to Predict Property Crime in High-Risk Lima Districts

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Crime continues to be an issue, in Metropolitan Lima, Peru affecting society. Our focus is on property crimes. We recognized the lack of studies on predicting these crimes. To tackle this problem, we used regression techniques such as XGBoost, Extra Tree, Support Vector, Bagging, Random Forest and A...

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Detalles Bibliográficos
Autores: Escobedo Neyra, María Cielo, Tapia Aquino, Cynthia, Lizet, Gutiérrez Cárdenas, Juan Manuel, Ayma, Víctor
Formato: artículo
Fecha de Publicación:2024
Institución:Universidad de Lima
Repositorio:ULIMA-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.ulima.edu.pe:20.500.12724/20201
Enlace del recurso:https://hdl.handle.net/20.500.12724/20201
https://doi.org/10.14569/IJACSA.2024.0150307
Nivel de acceso:acceso abierto
Materia:Pendiente
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spelling Escobedo Neyra, María CieloTapia Aquino, Cynthia, LizetGutiérrez Cárdenas, Juan ManuelAyma, VíctorGutiérrez Cárdenas, Juan ManuelEscobedo Neyra, María Cielo (Ingeniería de Sistemas)Tapia Aquino, Cynthia, Lizet (Ingeniería de Sistemas)2024-04-19T13:54:31Z2024-04-19T13:54:31Z2024Escobedo, M., Tapia, C., Gutierrez, J., & Ayma, V. (2024). Comparing Regression Models to Predict Property Crime in High-Risk Lima Districts. International Journal of Advanced Computer Science and Applications. https://doi.org/10.14569/IJACSA.2024.0150307.2156-5570https://hdl.handle.net/20.500.12724/20201International Journal of Advanced Computer Science and Applications0000000121541816https://doi.org/10.14569/IJACSA.2024.01503072-s2.0-85189935904Crime continues to be an issue, in Metropolitan Lima, Peru affecting society. Our focus is on property crimes. We recognized the lack of studies on predicting these crimes. To tackle this problem, we used regression techniques such as XGBoost, Extra Tree, Support Vector, Bagging, Random Forest and AdaBoost. Through GridsearchCV we optimized hyperparameters to enhance our research findings. The results showed that Extra Tree Regression stood out as the model with an R2 value of 0.79. Additionally, error metrics like MSE (185.43) RMSE (13.62) and MAE (10.47) were considered to evaluate the model's performance. Our approach considers time patterns in crime incidents. Contributes, to addressing the issue of insecurity in a meaningful way.application/htmlengScience and Information OrganizationGBurn:issn:2156-5570info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/4.0/Repositorio Institucional - UlimaUniversidad de Limareponame:ULIMA-Institucionalinstname:Universidad de Limainstacron:ULIMAPendienteComparing Regression Models to Predict Property Crime in High-Risk Lima Districtsinfo:eu-repo/semantics/articleArtículo en ScopusIngeniería de SistemasProfessor Member, Universidad de LimaOI20.500.12724/20201oai:repositorio.ulima.edu.pe:20.500.12724/202012024-11-08 16:16:04.995Repositorio Universidad de Limarepositorio@ulima.edu.pe
dc.title.en_EN.fl_str_mv Comparing Regression Models to Predict Property Crime in High-Risk Lima Districts
title Comparing Regression Models to Predict Property Crime in High-Risk Lima Districts
spellingShingle Comparing Regression Models to Predict Property Crime in High-Risk Lima Districts
Escobedo Neyra, María Cielo
Pendiente
title_short Comparing Regression Models to Predict Property Crime in High-Risk Lima Districts
title_full Comparing Regression Models to Predict Property Crime in High-Risk Lima Districts
title_fullStr Comparing Regression Models to Predict Property Crime in High-Risk Lima Districts
title_full_unstemmed Comparing Regression Models to Predict Property Crime in High-Risk Lima Districts
title_sort Comparing Regression Models to Predict Property Crime in High-Risk Lima Districts
author Escobedo Neyra, María Cielo
author_facet Escobedo Neyra, María Cielo
Tapia Aquino, Cynthia, Lizet
Gutiérrez Cárdenas, Juan Manuel
Ayma, Víctor
author_role author
author2 Tapia Aquino, Cynthia, Lizet
Gutiérrez Cárdenas, Juan Manuel
Ayma, Víctor
author2_role author
author
author
dc.contributor.other.none.fl_str_mv Gutiérrez Cárdenas, Juan Manuel
dc.contributor.student.none.fl_str_mv Escobedo Neyra, María Cielo (Ingeniería de Sistemas)
Tapia Aquino, Cynthia, Lizet (Ingeniería de Sistemas)
dc.contributor.author.fl_str_mv Escobedo Neyra, María Cielo
Tapia Aquino, Cynthia, Lizet
Gutiérrez Cárdenas, Juan Manuel
Ayma, Víctor
dc.subject.es_PE.fl_str_mv Pendiente
topic Pendiente
description Crime continues to be an issue, in Metropolitan Lima, Peru affecting society. Our focus is on property crimes. We recognized the lack of studies on predicting these crimes. To tackle this problem, we used regression techniques such as XGBoost, Extra Tree, Support Vector, Bagging, Random Forest and AdaBoost. Through GridsearchCV we optimized hyperparameters to enhance our research findings. The results showed that Extra Tree Regression stood out as the model with an R2 value of 0.79. Additionally, error metrics like MSE (185.43) RMSE (13.62) and MAE (10.47) were considered to evaluate the model's performance. Our approach considers time patterns in crime incidents. Contributes, to addressing the issue of insecurity in a meaningful way.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-04-19T13:54:31Z
dc.date.available.none.fl_str_mv 2024-04-19T13:54:31Z
dc.date.issued.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
dc.type.other.none.fl_str_mv Artículo en Scopus
format article
dc.identifier.citation.es_PE.fl_str_mv Escobedo, M., Tapia, C., Gutierrez, J., & Ayma, V. (2024). Comparing Regression Models to Predict Property Crime in High-Risk Lima Districts. International Journal of Advanced Computer Science and Applications. https://doi.org/10.14569/IJACSA.2024.0150307.
dc.identifier.issn.none.fl_str_mv 2156-5570
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12724/20201
dc.identifier.journal.none.fl_str_mv International Journal of Advanced Computer Science and Applications
dc.identifier.isni.none.fl_str_mv 0000000121541816
dc.identifier.doi.none.fl_str_mv https://doi.org/10.14569/IJACSA.2024.0150307
dc.identifier.scopusid.none.fl_str_mv 2-s2.0-85189935904
identifier_str_mv Escobedo, M., Tapia, C., Gutierrez, J., & Ayma, V. (2024). Comparing Regression Models to Predict Property Crime in High-Risk Lima Districts. International Journal of Advanced Computer Science and Applications. https://doi.org/10.14569/IJACSA.2024.0150307.
2156-5570
International Journal of Advanced Computer Science and Applications
0000000121541816
2-s2.0-85189935904
url https://hdl.handle.net/20.500.12724/20201
https://doi.org/10.14569/IJACSA.2024.0150307
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv urn:issn:2156-5570
dc.rights.*.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.uri.*.fl_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.format.none.fl_str_mv application/html
dc.publisher.none.fl_str_mv Science and Information Organization
dc.publisher.country.none.fl_str_mv GB
publisher.none.fl_str_mv Science and Information Organization
dc.source.none.fl_str_mv Repositorio Institucional - Ulima
Universidad de Lima
reponame:ULIMA-Institucional
instname:Universidad de Lima
instacron:ULIMA
instname_str Universidad de Lima
instacron_str ULIMA
institution ULIMA
reponame_str ULIMA-Institucional
collection ULIMA-Institucional
repository.name.fl_str_mv Repositorio Universidad de Lima
repository.mail.fl_str_mv repositorio@ulima.edu.pe
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Comparing Regression Models to Predict Property Crime in High-Risk Lima Districts
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