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

Posgraduate school

Universidad Peruana de Ciencias Aplicadas
Universidad de la Costa
Universidad Tecnológica Centroamericana
Corporación Universitaria Minuto de Dios

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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.

Original language	English
Title of host publication	Developments and Advances in Defense and Security - Proceedings of MICRADS 2020
Editors	Álvaro Rocha, Manolo Paredes-Calderón, Teresa Guarda
Publisher	Springer
Pages	519-527
Number of pages	9
ISBN (Print)	9789811548741
DOIs	https://doi.org/10.1007/978-981-15-4875-8_45
State	Published - 2020
Event	Multidisciplinary International Conference of Research Applied to Defense and Security, MICRADS 2020 - Quito, Ecuador Duration: 13 May 2020 → 15 May 2020

Publication series

Name	Smart Innovation, Systems and Technologies
Volume	181
ISSN (Print)	2190-3018
ISSN (Electronic)	2190-3026

Conference

Conference	Multidisciplinary International Conference of Research Applied to Defense and Security, MICRADS 2020
Country/Territory	Ecuador
City	Quito
Period	13/05/20 → 15/05/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities
SDG 16 Peace, Justice and Strong Institutions

Keywords

Data mining
Prediction of facts
Public data

Access to Document

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

Cite this

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. In 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

@inproceedings{630b6df8e3f04c7a869ffeefde56a2dd,

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. in 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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. In Rocha Á, Paredes-Calderón M, Guarda T, editors, 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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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