Spatio-temporal probabilistic modeling based on Gaussian mixture models and neural gas theory for prediction of criminal activity
Criminal risk models are used to assist security forces both in the identification of zones with high of criminal activity for better resource allocation and prediction of future criminal events for the prevention of new crimes. In this sense, spatio-temporal models are widely employed by their capa...
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| Outros autores: | , , |
| Formato: | article |
| Idioma: | inglés |
| Publicado: |
2017
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| Subjects: | |
| Acceso en liña: | http://ridda2.utp.ac.pa/handle/123456789/6153 |
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| _version_ | 1869652463099838464 |
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| author | Jaramillo, Francisco |
| author2 | L. Quintero, Vanessa Perez, Aramis Orchard, Marcos |
| author2_role | author author author |
| author_browse | Jaramillo, Francisco L. Quintero, Vanessa Orchard, Marcos Perez, Aramis |
| author_facet | Jaramillo, Francisco L. Quintero, Vanessa Perez, Aramis Orchard, Marcos |
| author_role | author |
| collection | Repositorio Institucional de documento digitales de acceso abierto de la UTP |
| dc.contributor.none.fl_str_mv | L. Quintero, Vanessa |
| dc.creator.none.fl_str_mv | Jaramillo, Francisco L. Quintero, Vanessa Perez, Aramis Orchard, Marcos |
| dc.date.none.fl_str_mv | 2017-08-18 2017-08-18 2019-07-02T17:47:23Z 2019-07-02T17:47:23Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://ridda2.utp.ac.pa/handle/123456789/6153 |
| dc.language.none.fl_str_mv | eng eng |
| dc.publisher.none.fl_str_mv | Annual Conference of the Prognostics and Health Management Society 2017 Annual Conference of the Prognostics and Health Management Society 2017 |
| dc.rights.none.fl_str_mv | https://creativecommons.org/licenses/by-nc-sa/4.0/ info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:Repositorio Institucional de documento digitales de acceso abierto de la UTP instname:Universidad Tecnológica de Panamá instacron:U Tecnológica de Panamá |
| dc.subject.none.fl_str_mv | Gaussian Mixture Model criminal risk characterization Neural Gas Theory Gaussian Mixture Model criminal risk characterization Neural Gas Theory |
| dc.title.none.fl_str_mv | Spatio-temporal probabilistic modeling based on Gaussian mixture models and neural gas theory for prediction of criminal activity |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| description | Criminal risk models are used to assist security forces both in the identification of zones with high of criminal activity for better resource allocation and prediction of future criminal events for the prevention of new crimes. In this sense, spatio-temporal models are widely employed by their capacity of characterizing the criminal risk inside of a zone of interest and updating the model to new crime data. This paper improves an existing method based on spatio-temporal probabilistic risk functions. The spatial probabilistic characterization uses geo-referenced information of criminal incidents related to public services to approximate a risk function based on a Gaussian Mixture Model (GMM). The temporal characterization is supported by Importance Sampling methods and Neural Gas theory to incorporate the information from new measurements, in a recursive manner, updating the spatial probabilistic risk function. Finally, we propose a prediction scheme for criminal activity that also uses Neural Gas Theory, in conjunction with hypothetical future criminal events sampled from a GMM that characterizes the spatial distribution associated with recent criminal activity. The time index related to each hypothetical future crime event is probabilistically characterized using an exponential distribution. Results using real data and the defined performance indexes show an improvement both in the temporal updating as well as the proposed prediction approach. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | lrtest_55b348a92571da5830ab45fca8bb86a0 |
| instacron_str | U Tecnológica de Panamá |
| institution | U Tecnológica de Panamá |
| instname_str | Universidad Tecnológica de Panamá |
| language | eng |
| network_acronym_str | lrtest |
| network_name_str | lr |
| oai_identifier_str | oai:ridda2.utp.ac.pa:123456789/6153 |
| publishDate | 2017 |
| publishDateSort | 2017 |
| publisher.none.fl_str_mv | Annual Conference of the Prognostics and Health Management Society 2017 Annual Conference of the Prognostics and Health Management Society 2017 |
| reponame_str | Repositorio Institucional de documento digitales de acceso abierto de la UTP |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | https://creativecommons.org/licenses/by-nc-sa/4.0/ |
| spelling | Spatio-temporal probabilistic modeling based on Gaussian mixture models and neural gas theory for prediction of criminal activityJaramillo, FranciscoL. Quintero, VanessaPerez, AramisOrchard, MarcosGaussian Mixture Modelcriminal risk characterizationNeural Gas TheoryGaussian Mixture Modelcriminal risk characterizationNeural Gas TheoryCriminal risk models are used to assist security forces both in the identification of zones with high of criminal activity for better resource allocation and prediction of future criminal events for the prevention of new crimes. In this sense, spatio-temporal models are widely employed by their capacity of characterizing the criminal risk inside of a zone of interest and updating the model to new crime data. This paper improves an existing method based on spatio-temporal probabilistic risk functions. The spatial probabilistic characterization uses geo-referenced information of criminal incidents related to public services to approximate a risk function based on a Gaussian Mixture Model (GMM). The temporal characterization is supported by Importance Sampling methods and Neural Gas theory to incorporate the information from new measurements, in a recursive manner, updating the spatial probabilistic risk function. Finally, we propose a prediction scheme for criminal activity that also uses Neural Gas Theory, in conjunction with hypothetical future criminal events sampled from a GMM that characterizes the spatial distribution associated with recent criminal activity. The time index related to each hypothetical future crime event is probabilistically characterized using an exponential distribution. Results using real data and the defined performance indexes show an improvement both in the temporal updating as well as the proposed prediction approach.Criminal risk models are used to assist security forces both in the identification of zones with high of criminal activity for better resource allocation and prediction of future criminal events for the prevention of new crimes. In this sense, spatio-temporal models are widely employed by their capacity of characterizing the criminal risk inside of a zone of interest and updating the model to new crime data. This paper improves an existing method based on spatio-temporal probabilistic risk functions. The spatial probabilistic characterization uses geo-referenced information of criminal incidents related to public services to approximate a risk function based on a Gaussian Mixture Model (GMM). The temporal characterization is supported by Importance Sampling methods and Neural Gas theory to incorporate the information from new measurements, in a recursive manner, updating the spatial probabilistic risk function. Finally, we propose a prediction scheme for criminal activity that also uses Neural Gas Theory, in conjunction with hypothetical future criminal events sampled from a GMM that characterizes the spatial distribution associated with recent criminal activity. The time index related to each hypothetical future crime event is probabilistically characterized using an exponential distribution. Results using real data and the defined performance indexes show an improvement both in the temporal updating as well as the proposed prediction approach.Annual Conference of the Prognostics and Health Management Society 2017Annual Conference of the Prognostics and Health Management Society 2017L. Quintero, Vanessa2019-07-02T17:47:23Z2019-07-02T17:47:23Z2017-08-182017-08-18info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://ridda2.utp.ac.pa/handle/123456789/6153engenghttps://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessreponame:Repositorio Institucional de documento digitales de acceso abierto de la UTPinstname:Universidad Tecnológica de Panamáinstacron:U Tecnológica de Panamáoai:ridda2.utp.ac.pa:123456789/61532021-07-06T15:34:53Z |
| spellingShingle | Spatio-temporal probabilistic modeling based on Gaussian mixture models and neural gas theory for prediction of criminal activity Jaramillo, Francisco Gaussian Mixture Model criminal risk characterization Neural Gas Theory Gaussian Mixture Model criminal risk characterization Neural Gas Theory |
| status_str | publishedVersion |
| title | Spatio-temporal probabilistic modeling based on Gaussian mixture models and neural gas theory for prediction of criminal activity |
| title_full | Spatio-temporal probabilistic modeling based on Gaussian mixture models and neural gas theory for prediction of criminal activity |
| title_fullStr | Spatio-temporal probabilistic modeling based on Gaussian mixture models and neural gas theory for prediction of criminal activity |
| title_full_unstemmed | Spatio-temporal probabilistic modeling based on Gaussian mixture models and neural gas theory for prediction of criminal activity |
| title_short | Spatio-temporal probabilistic modeling based on Gaussian mixture models and neural gas theory for prediction of criminal activity |
| title_sort | Spatio-temporal probabilistic modeling based on Gaussian mixture models and neural gas theory for prediction of criminal activity |
| topic | Gaussian Mixture Model criminal risk characterization Neural Gas Theory Gaussian Mixture Model criminal risk characterization Neural Gas Theory |
| url | http://ridda2.utp.ac.pa/handle/123456789/6153 |