Algorithms for Crime Prediction in Smart Cities Through Data Mining

Jesús Silva; Ligia Romero; Roberto Jiménez González; Omar Larios; Fanny Barrantes; Omar Bonerge Pineda Lezama; Alberto Manotas

doi:10.1007/978-981-15-4875-8_45

Algorithms for Crime Prediction in Smart Cities Through Data Mining

Jesús Silva
, Ligia Romero
, Roberto Jiménez González
, Omar Larios
, Fanny Barrantes
, Omar Bonerge Pineda Lezama
, Alberto Manotas

Escuela de Postgrado

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

2 Citas (Scopus)

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	Developments and Advances in Defense and Security - Proceedings of MICRADS 2020
Editores	Álvaro Rocha, Manolo Paredes-Calderón, Teresa Guarda
Editorial	Springer
Páginas	519-527
Número de páginas	9
ISBN (versión impresa)	9789811548741
DOI	https://doi.org/10.1007/978-981-15-4875-8_45
Estado	Publicada - 2020
Evento	Multidisciplinary International Conference of Research Applied to Defense and Security, MICRADS 2020 - Quito, Ecuador Duración: 13 may. 2020 → 15 may. 2020

Serie de la publicación

Nombre	Smart Innovation, Systems and Technologies
Volumen	181
ISSN (versión impresa)	2190-3018
ISSN (versión digital)	2190-3026

Conferencia

Conferencia	Multidisciplinary International Conference of Research Applied to Defense and Security, MICRADS 2020
País/Territorio	Ecuador
Ciudad	Quito
Período	13/05/20 → 15/05/20

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

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10.1007/978-981-15-4875-8_45

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Silva, J., Romero, L., González, R. J., Larios, O., Barrantes, F., Lezama, O. B. P., & Manotas, A. (2020). Algorithms for Crime Prediction in Smart Cities Through Data Mining. En Á. Rocha, M. Paredes-Calderón, & T. Guarda (Eds.), Developments and Advances in Defense and Security - Proceedings of MICRADS 2020 (pp. 519-527). (Smart Innovation, Systems and Technologies; Vol. 181). Springer. https://doi.org/10.1007/978-981-15-4875-8_45

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title = "Algorithms for Crime Prediction in Smart Cities Through Data Mining",

abstract = "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.",

keywords = "Data mining, Prediction of facts, Public data",

author = "Jes{\'u}s Silva and Ligia Romero and Gonz{\'a}lez, \{Roberto Jim{\'e}nez\} and Omar Larios and Fanny Barrantes and Lezama, \{Omar Bonerge Pineda\} and Alberto Manotas",

note = "Publisher Copyright: {\textcopyright} 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; Multidisciplinary International Conference of Research Applied to Defense and Security, MICRADS 2020 ; Conference date: 13-05-2020 Through 15-05-2020",

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Silva, J, Romero, L, González, RJ, Larios, O, Barrantes, F, Lezama, OBP & Manotas, A 2020, Algorithms for Crime Prediction in Smart Cities Through Data Mining. En Á Rocha, M Paredes-Calderón & T Guarda (eds.), Developments and Advances in Defense and Security - Proceedings of MICRADS 2020. Smart Innovation, Systems and Technologies, vol. 181, Springer, pp. 519-527, Multidisciplinary International Conference of Research Applied to Defense and Security, MICRADS 2020, Quito, Ecuador, 13/05/20. https://doi.org/10.1007/978-981-15-4875-8_45

Algorithms for Crime Prediction in Smart Cities Through Data Mining. / Silva, Jesús; Romero, Ligia; González, Roberto Jiménez et al.
Developments and Advances in Defense and Security - Proceedings of MICRADS 2020. ed. / Álvaro Rocha; Manolo Paredes-Calderón; Teresa Guarda. Springer, 2020. p. 519-527 (Smart Innovation, Systems and Technologies; Vol. 181).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Algorithms for Crime Prediction in Smart Cities Through Data Mining

AU - Silva, Jesús

AU - Romero, Ligia

AU - González, Roberto Jiménez

AU - Larios, Omar

AU - Barrantes, Fanny

AU - Lezama, Omar Bonerge Pineda

AU - Manotas, Alberto

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Data mining

KW - Prediction of facts

KW - Public data

UR - https://www.scopus.com/pages/publications/85085206595

U2 - 10.1007/978-981-15-4875-8_45

DO - 10.1007/978-981-15-4875-8_45

M3 - Contribución a la conferencia

AN - SCOPUS:85085206595

SN - 9789811548741

T3 - Smart Innovation, Systems and Technologies

SP - 519

EP - 527

BT - Developments and Advances in Defense and Security - Proceedings of MICRADS 2020

A2 - Rocha, Álvaro

A2 - Paredes-Calderón, Manolo

A2 - Guarda, Teresa

PB - Springer

T2 - Multidisciplinary International Conference of Research Applied to Defense and Security, MICRADS 2020

Y2 - 13 May 2020 through 15 May 2020

ER -

Silva J, Romero L, González RJ, Larios O, Barrantes F, Lezama OBP et al. Algorithms for Crime Prediction in Smart Cities Through Data Mining. En Rocha Á, Paredes-Calderón M, Guarda T, editores, Developments and Advances in Defense and Security - Proceedings of MICRADS 2020. Springer. 2020. p. 519-527. (Smart Innovation, Systems and Technologies). doi: 10.1007/978-981-15-4875-8_45

Algorithms for Crime Prediction in Smart Cities Through Data Mining

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