A twist in intimate partner violence risk assessment tools: gauging the contribution of exogenous and historical variables

Gender violence is a problem that affects millions of people worldwide. Among its many manifestations Intimate Partner Violence (IPV) is one of the most common. In Spain, a police monitoring protocol has been developed to minimize recidivism in IPV cases. This protocol is complemented by VioGén, an...

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Detalles Bibliográficos
Autores: Quijano Sánchez, Lara, Liberatore, Federico, Rodríguez Lorenzo, Guillermo, Lillo, Rosa E., González-Álvarez, José L.
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/717610
Acceso en línea:http://hdl.handle.net/10486/717610
https://dx.doi.org/10.1016/j.knosys.2021.107586
Access Level:acceso abierto
Palabra clave:Police Risk Assessment
Reassault Risk Assessment
Machine Learning
Viogén System
Intimate Partner Violence
Gender Violence
Informática
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spelling A twist in intimate partner violence risk assessment tools: gauging the contribution of exogenous and historical variablesQuijano Sánchez, LaraLiberatore, FedericoRodríguez Lorenzo, GuillermoLillo, Rosa E.González-Álvarez, José L.Police Risk AssessmentReassault Risk AssessmentMachine LearningViogén SystemIntimate Partner ViolenceGender ViolenceInformáticaGender violence is a problem that affects millions of people worldwide. Among its many manifestations Intimate Partner Violence (IPV) is one of the most common. In Spain, a police monitoring protocol has been developed to minimize recidivism in IPV cases. This protocol is complemented by VioGén, an Intimate Partner Violence Risk Assessment Tool (IPVRAT) created by the Spanish State Secretariat for Security of the Ministry of Interior (SES) for risk prediction. VioGén’s goal is to help the authorities determine what security and safety measures are most suitable. This paper improves on the current version of VioGén by introducing a model based on machine learning and data science and by studying the predictive value of exogenous and historical variables. The model is fitted on an anonymized database provided by SES and extracted from VioGén. This database includes the 2-year evolution of 46,047 new cases of IPV violence reported between October 2016 and December 2017, making it the largest database analyzed in the field. Obtained results show a clear improvement in the predictive capabilities of the new model against the original system, where it would have corrected more than 25% of the infra-protected cases, while improving the overall accuracy at the same time. Finally, lessons learned from the performed study and experiments are reported to aid in the design of future IPVRAT. In particular, insights show that IPVRAT should not treat cases statically as the incorporation of information regarding their evolution improves significantly the model’s performanceThe research of Quijano-Sánchez was conducted with financial support from the Spanish Ministry of Science and Innovation, grant PID2019-108965GB-I00. The research of Liberatore is partially funded by the European Commission’s Horizon 2020 research and innovation programme under the Marie SklodowskaCurie, grant number MSCA-RISE 691161 (GEO-SAFE), and the Government of Spain, grant MTM2015-65803-RElsevierDepartamento de Ingeniería QuímicaEscuela Politécnica SuperiorUAM. Departamento de Ingeniería Química20212021-10-13research articlehttp://purl.org/coar/resource_type/c_2df8fbb1AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/717610https://dx.doi.org/10.1016/j.knosys.2021.107586reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/7176102026-06-23T12:46:27Z
dc.title.none.fl_str_mv A twist in intimate partner violence risk assessment tools: gauging the contribution of exogenous and historical variables
title A twist in intimate partner violence risk assessment tools: gauging the contribution of exogenous and historical variables
spellingShingle A twist in intimate partner violence risk assessment tools: gauging the contribution of exogenous and historical variables
Quijano Sánchez, Lara
Police Risk Assessment
Reassault Risk Assessment
Machine Learning
Viogén System
Intimate Partner Violence
Gender Violence
Informática
title_short A twist in intimate partner violence risk assessment tools: gauging the contribution of exogenous and historical variables
title_full A twist in intimate partner violence risk assessment tools: gauging the contribution of exogenous and historical variables
title_fullStr A twist in intimate partner violence risk assessment tools: gauging the contribution of exogenous and historical variables
title_full_unstemmed A twist in intimate partner violence risk assessment tools: gauging the contribution of exogenous and historical variables
title_sort A twist in intimate partner violence risk assessment tools: gauging the contribution of exogenous and historical variables
dc.creator.none.fl_str_mv Quijano Sánchez, Lara
Liberatore, Federico
Rodríguez Lorenzo, Guillermo
Lillo, Rosa E.
González-Álvarez, José L.
author Quijano Sánchez, Lara
author_facet Quijano Sánchez, Lara
Liberatore, Federico
Rodríguez Lorenzo, Guillermo
Lillo, Rosa E.
González-Álvarez, José L.
author_role author
author2 Liberatore, Federico
Rodríguez Lorenzo, Guillermo
Lillo, Rosa E.
González-Álvarez, José L.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Química
Escuela Politécnica Superior
UAM. Departamento de Ingeniería Química
dc.subject.none.fl_str_mv Police Risk Assessment
Reassault Risk Assessment
Machine Learning
Viogén System
Intimate Partner Violence
Gender Violence
Informática
topic Police Risk Assessment
Reassault Risk Assessment
Machine Learning
Viogén System
Intimate Partner Violence
Gender Violence
Informática
description Gender violence is a problem that affects millions of people worldwide. Among its many manifestations Intimate Partner Violence (IPV) is one of the most common. In Spain, a police monitoring protocol has been developed to minimize recidivism in IPV cases. This protocol is complemented by VioGén, an Intimate Partner Violence Risk Assessment Tool (IPVRAT) created by the Spanish State Secretariat for Security of the Ministry of Interior (SES) for risk prediction. VioGén’s goal is to help the authorities determine what security and safety measures are most suitable. This paper improves on the current version of VioGén by introducing a model based on machine learning and data science and by studying the predictive value of exogenous and historical variables. The model is fitted on an anonymized database provided by SES and extracted from VioGén. This database includes the 2-year evolution of 46,047 new cases of IPV violence reported between October 2016 and December 2017, making it the largest database analyzed in the field. Obtained results show a clear improvement in the predictive capabilities of the new model against the original system, where it would have corrected more than 25% of the infra-protected cases, while improving the overall accuracy at the same time. Finally, lessons learned from the performed study and experiments are reported to aid in the design of future IPVRAT. In particular, insights show that IPVRAT should not treat cases statically as the incorporation of information regarding their evolution improves significantly the model’s performance
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-10-13
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/717610
https://dx.doi.org/10.1016/j.knosys.2021.107586
url http://hdl.handle.net/10486/717610
https://dx.doi.org/10.1016/j.knosys.2021.107586
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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