Algorithms for crime prediction in smart cities through data mining
The concentration of police resources in conflict zones contributes to the reduction of crime in the region and the optimization of those resources. This paper presents the use of regression techniques to predict the number of criminal acts in Colombian municipalities. To this end, a set of data was...
- Autores:
-
Silva, Jesús
Romero Marin, Ligia Cielo
Jiménez González, Roberto
Larios, Omar
Barrantes, Fanny
Pineda, Omar
Manotas, Alberto
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7743
- Acceso en línea:
- https://hdl.handle.net/11323/7743
https://doi.org/10.1007/978-981-15-4875-8_45
https://repositorio.cuc.edu.co/
- Palabra clave:
- Public data
Data mining
Prediction of facts
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Algorithms for crime prediction in smart cities through data mining |
| title |
Algorithms for crime prediction in smart cities through data mining |
| spellingShingle |
Algorithms for crime prediction in smart cities through data mining Public data Data mining Prediction of facts |
| title_short |
Algorithms for crime prediction in smart cities through data mining |
| title_full |
Algorithms for crime prediction in smart cities through data mining |
| title_fullStr |
Algorithms for crime prediction in smart cities through data mining |
| title_full_unstemmed |
Algorithms for crime prediction in smart cities through data mining |
| title_sort |
Algorithms for crime prediction in smart cities through data mining |
| dc.creator.fl_str_mv |
Silva, Jesús Romero Marin, Ligia Cielo Jiménez González, Roberto Larios, Omar Barrantes, Fanny Pineda, Omar Manotas, Alberto |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesús Romero Marin, Ligia Cielo Jiménez González, Roberto Larios, Omar Barrantes, Fanny Pineda, Omar Manotas, Alberto |
| dc.subject.spa.fl_str_mv |
Public data Data mining Prediction of facts |
| topic |
Public data Data mining Prediction of facts |
| description |
The concentration of police resources in conflict zones contributes to the reduction of crime in the region and the optimization of those resources. This paper presents the use of regression techniques to predict the number of criminal acts in Colombian municipalities. To this end, a set of data was generated merging the data from the Guardia Civil with public data on the demographic structure and voting trends in the municipalities. The best regressor obtained (Random Forests) achieves a RRSE (Root Relative Squared Error) of 40.12% and opens the way to keep incorporating public data of another type with greater predictive power. In addition, M5Rules were used to interpret the results. |
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2021-01-21T13:40:27Z |
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eng |
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1. Zaharia, M., Xin, R.S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M.J., Ghodsi, A., Gonzalez, J., Shenker, S., Stoica, I.: Apache spark: a unified engine for big data processing. Comm. ACM 59(11), 56–65 (2016) 2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pp. 487–499 (1994) 3. Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55–60 (2014) 4. Hahsler, M., Karpienko, R.: Visualizing association rules in hierarchical groups. J. Bus. Econ. 87, 317–335 (2017) 5. Alves, L.G.A., Ribeiro, H.V., Rodrigues, F.A.: Crime prediction through urban metrics and statistical learning. Phys. A Stat. Mech. Appl. 505, 435–443 (2018) 6. Silverstein, C., Brin, S., Motwani, R., Ullman, J.: Scalable techniques for mining causal structures. Data Min. Knowl. Discov. 4(2–3), 163–192 (2000) 7. Amelec, V., Carmen, V.: Relationship between variables of performance social and financial of microfinance institutions. Adv. Sci. Lett. 21(6), 1931–1934 (2015) 8. Viloria, A., Lezama, O.B.P.: Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Procedia Comput. Sci. 151, 1201–1206 (2019) 9. Kamatkar, S.J., Kamble, A., Viloria, A., Hernández-Fernandez, L., Cali, E.G.: Database performance tuning and query optimization. In: International Conference on Data Mining and Big Data, pp. 3–11. Springer, Cham (2018) 10. Erlandsson, F., Brodka, P., Borg, A., Johnson, H.: Finding influential users in social media using association rule learning. Entropy 18, 164 (2016) 11. Baculo, M.J.C., Marzan, C.S. de Dios Bulos, R., Ruiz, C.: Geospatial-temporal analysis and classification of criminal data in Manila. In: Proceedings of 2nd IEEE International Conference on Computational Intelligence and Applications, pp. 6–11. IEEE (2017) 12. Viloria, A., et al.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019) 13. Clougherty, E., Clougherty, J., Liu, X., Brown, D.: Spatial and temporal analysis of sex crimes in Charlottesville, Virginia. In: Proceedings of IEEE Systems and Information Engineering Design Symposium, pp. 69–74. IEEE (2015) 14. Pineda, C.J.: Apuntes críticos: Visión Colombia 2019. Institución Universitaria Politécnico Grancolombiano (2016) 15. Torres, A.X.O.: Los derechos de los colombianos en el extranjero y de los extranjeros en Colombia. En mora de un enfoque integral. Vniversitas 57(117), 357–376 (2008) 16. Drucker, H.: Improving regressors using boosting techniques. In: Proceedings of the Fourteenth International Conference on Machine Learning, ICML ’97, San Francisco, CA, USA, pp. 107–115. Morgan Kaufmann Publishers Inc. (1997) 17. Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008) 18. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009) 19. Kang, H.-W., Kang, H.-B.: Prediction of crime occurrence from multimodal data using deep learning. PLoS ONE 12(4), e0176244 (2017) 20. Kianmehr, K., Alhajj, R.: Effectiveness of support vector machine for crime hot-spots prediction. Appl. Artif. Intell. 22(5), 433–458 (2008) 21. Leitão, J.C., Miotto, J.M., Gerlach, M., Altmann, E.G.: Is this scaling nonlinear? R. Soc. Open Sci. 3(7) (2016) |
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Silva, JesúsRomero Marin, Ligia CieloJiménez González, RobertoLarios, OmarBarrantes, FannyPineda, OmarManotas, Alberto2021-01-21T13:40:27Z2021-01-21T13:40:27Z2020https://hdl.handle.net/11323/7743https://doi.org/10.1007/978-981-15-4875-8_45Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The concentration of police resources in conflict zones contributes to the reduction of crime in the region and the optimization of those resources. This paper presents the use of regression techniques to predict the number of criminal acts in Colombian municipalities. To this end, a set of data was generated merging the data from the Guardia Civil with public data on the demographic structure and voting trends in the municipalities. The best regressor obtained (Random Forests) achieves a RRSE (Root Relative Squared Error) of 40.12% and opens the way to keep incorporating public data of another type with greater predictive power. In addition, M5Rules were used to interpret the results.Silva, JesúsRomero Marin, Ligia Cielo-will be generated-orcid-0000-0002-1216-4489-600Jiménez González, RobertoLarios, OmarBarrantes, FannyPineda, Omar-will be generated-orcid-0000-0002-8239-3906-600Manotas, Albertoapplication/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Smart Innovation, Systems and Technologieshttps://link.springer.com/chapter/10.1007/978-981-15-4875-8_45Public dataData miningPrediction of factsAlgorithms for crime prediction in smart cities through data miningArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Zaharia, M., Xin, R.S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M.J., Ghodsi, A., Gonzalez, J., Shenker, S., Stoica, I.: Apache spark: a unified engine for big data processing. Comm. ACM 59(11), 56–65 (2016)2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pp. 487–499 (1994)3. Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55–60 (2014)4. Hahsler, M., Karpienko, R.: Visualizing association rules in hierarchical groups. J. Bus. Econ. 87, 317–335 (2017)5. Alves, L.G.A., Ribeiro, H.V., Rodrigues, F.A.: Crime prediction through urban metrics and statistical learning. Phys. A Stat. Mech. Appl. 505, 435–443 (2018)6. Silverstein, C., Brin, S., Motwani, R., Ullman, J.: Scalable techniques for mining causal structures. Data Min. Knowl. Discov. 4(2–3), 163–192 (2000)7. Amelec, V., Carmen, V.: Relationship between variables of performance social and financial of microfinance institutions. Adv. Sci. Lett. 21(6), 1931–1934 (2015)8. Viloria, A., Lezama, O.B.P.: Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Procedia Comput. Sci. 151, 1201–1206 (2019)9. Kamatkar, S.J., Kamble, A., Viloria, A., Hernández-Fernandez, L., Cali, E.G.: Database performance tuning and query optimization. In: International Conference on Data Mining and Big Data, pp. 3–11. Springer, Cham (2018)10. Erlandsson, F., Brodka, P., Borg, A., Johnson, H.: Finding influential users in social media using association rule learning. Entropy 18, 164 (2016)11. Baculo, M.J.C., Marzan, C.S. de Dios Bulos, R., Ruiz, C.: Geospatial-temporal analysis and classification of criminal data in Manila. In: Proceedings of 2nd IEEE International Conference on Computational Intelligence and Applications, pp. 6–11. IEEE (2017)12. Viloria, A., et al.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019)13. Clougherty, E., Clougherty, J., Liu, X., Brown, D.: Spatial and temporal analysis of sex crimes in Charlottesville, Virginia. In: Proceedings of IEEE Systems and Information Engineering Design Symposium, pp. 69–74. IEEE (2015)14. Pineda, C.J.: Apuntes críticos: Visión Colombia 2019. Institución Universitaria Politécnico Grancolombiano (2016)15. Torres, A.X.O.: Los derechos de los colombianos en el extranjero y de los extranjeros en Colombia. En mora de un enfoque integral. Vniversitas 57(117), 357–376 (2008)16. Drucker, H.: Improving regressors using boosting techniques. In: Proceedings of the Fourteenth International Conference on Machine Learning, ICML ’97, San Francisco, CA, USA, pp. 107–115. Morgan Kaufmann Publishers Inc. (1997)17. Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)18. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)19. Kang, H.-W., Kang, H.-B.: Prediction of crime occurrence from multimodal data using deep learning. PLoS ONE 12(4), e0176244 (2017)20. Kianmehr, K., Alhajj, R.: Effectiveness of support vector machine for crime hot-spots prediction. Appl. Artif. Intell. 22(5), 433–458 (2008)21. Leitão, J.C., Miotto, J.M., Gerlach, M., Altmann, E.G.: Is this scaling nonlinear? R. Soc. Open Sci. 3(7) (2016)PublicationORIGINALAlgorithms for crime prediction in smart cities through data mining.pdfAlgorithms for crime prediction in smart cities through data mining.pdfapplication/pdf113075https://repositorio.cuc.edu.co/bitstreams/98a5bdd2-9f05-46fb-9ac3-d3a4e6f22c49/downloadc632f3e63f5a5f735d305c5cb1aa0d01MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/c012ea3c-a9af-4c55-b257-25c0f45ecc41/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/2a6680b6-06cd-47f9-b2a1-cfe5ac354705/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILAlgorithms for crime prediction in smart cities through data mining.pdf.jpgAlgorithms for crime prediction in smart cities through data 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