Geospatial-temporal analysis andclassification of criminal data in Manila

Maria Jeseca C. Baculo; Charlie S. Marzan; Remedios De Dios Bulos; Conrado Ruiz

doi:10.1109/CIAPP.2017.8167050

Geospatial-temporal analysis andclassification of criminal data in Manila

Maria Jeseca C. Baculo, Charlie S. Marzan, Remedios De Dios Bulos, Conrado Ruiz

Producción científica: Capítulo del libro › Contribución a congreso/conferencia › revisión exhaustiva

17 Citas (Scopus)

Resumen

The use of technology on criminal data has proven to be a valuable tool in forecasting criminal activity. Crime prediction is one of the approaches that help reduce and deter crimes. In this paper, we perform geospatial analysis using the kernel density estimation in ArcGIS 10 to identify the spatiotemporal hotspots in Manila, the most densely populated city in the Philippines. We also compared the performance measures of the BayesNet, Naïve Bayes, J48, Decision Stump, and Random Forest classifiers in predicting possible crime activities. The results presented in this paper aim to provide insights on crime patterns as well as help law enforcement agencies design and implement approaches to respond to criminal activities.

Idioma original	Inglés
Título de la publicación alojada	2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	6-11
Número de páginas	6
ISBN (versión digital)	9781538620304
DOI	https://doi.org/10.1109/CIAPP.2017.8167050
Estado	Publicada - 4 dic 2017
Publicado de forma externa	Sí
Evento	2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017 - Beijing, China Duración: 8 sept 2017 → 11 sept 2017

Serie de la publicación

Nombre	2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017
Volumen	2017-January

Conferencia

Conferencia	2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017
País/Territorio	China
Ciudad	Beijing
Período	8/09/17 → 11/09/17

ODS de las Naciones Unidas

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10.1109/CIAPP.2017.8167050

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Cómo citar

Baculo, M. J. C., Marzan, C. S., De Dios Bulos, R., & Ruiz, C. (2017). Geospatial-temporal analysis andclassification of criminal data in Manila. En 2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017 (pp. 6-11). (2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CIAPP.2017.8167050

Baculo, Maria Jeseca C. ; Marzan, Charlie S. ; De Dios Bulos, Remedios et al. / Geospatial-temporal analysis andclassification of criminal data in Manila. 2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 6-11 (2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017).

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title = "Geospatial-temporal analysis andclassification of criminal data in Manila",

abstract = "The use of technology on criminal data has proven to be a valuable tool in forecasting criminal activity. Crime prediction is one of the approaches that help reduce and deter crimes. In this paper, we perform geospatial analysis using the kernel density estimation in ArcGIS 10 to identify the spatiotemporal hotspots in Manila, the most densely populated city in the Philippines. We also compared the performance measures of the BayesNet, Na{\"i}ve Bayes, J48, Decision Stump, and Random Forest classifiers in predicting possible crime activities. The results presented in this paper aim to provide insights on crime patterns as well as help law enforcement agencies design and implement approaches to respond to criminal activities.",

keywords = "Classifiers, Crime analysis, Predictive methods",

author = "Baculo, {Maria Jeseca C.} and Marzan, {Charlie S.} and {De Dios Bulos}, Remedios and Conrado Ruiz",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017 ; Conference date: 08-09-2017 Through 11-09-2017",

year = "2017",

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doi = "10.1109/CIAPP.2017.8167050",

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series = "2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Baculo, MJC, Marzan, CS, De Dios Bulos, R & Ruiz, C 2017, Geospatial-temporal analysis andclassification of criminal data in Manila. En 2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017. 2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 6-11, 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017, Beijing, China, 8/09/17. https://doi.org/10.1109/CIAPP.2017.8167050

Geospatial-temporal analysis andclassification of criminal data in Manila. / Baculo, Maria Jeseca C.; Marzan, Charlie S.; De Dios Bulos, Remedios et al.
2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 6-11 (2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017; Vol. 2017-January).

Producción científica: Capítulo del libro › Contribución a congreso/conferencia › revisión exhaustiva

TY - GEN

T1 - Geospatial-temporal analysis andclassification of criminal data in Manila

AU - Baculo, Maria Jeseca C.

AU - Marzan, Charlie S.

AU - De Dios Bulos, Remedios

AU - Ruiz, Conrado

PY - 2017/12/4

Y1 - 2017/12/4

N2 - The use of technology on criminal data has proven to be a valuable tool in forecasting criminal activity. Crime prediction is one of the approaches that help reduce and deter crimes. In this paper, we perform geospatial analysis using the kernel density estimation in ArcGIS 10 to identify the spatiotemporal hotspots in Manila, the most densely populated city in the Philippines. We also compared the performance measures of the BayesNet, Naïve Bayes, J48, Decision Stump, and Random Forest classifiers in predicting possible crime activities. The results presented in this paper aim to provide insights on crime patterns as well as help law enforcement agencies design and implement approaches to respond to criminal activities.

AB - The use of technology on criminal data has proven to be a valuable tool in forecasting criminal activity. Crime prediction is one of the approaches that help reduce and deter crimes. In this paper, we perform geospatial analysis using the kernel density estimation in ArcGIS 10 to identify the spatiotemporal hotspots in Manila, the most densely populated city in the Philippines. We also compared the performance measures of the BayesNet, Naïve Bayes, J48, Decision Stump, and Random Forest classifiers in predicting possible crime activities. The results presented in this paper aim to provide insights on crime patterns as well as help law enforcement agencies design and implement approaches to respond to criminal activities.

KW - Classifiers

KW - Crime analysis

KW - Predictive methods

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U2 - 10.1109/CIAPP.2017.8167050

DO - 10.1109/CIAPP.2017.8167050

M3 - Conference contribution

AN - SCOPUS:85043457649

T3 - 2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017

SP - 6

EP - 11

BT - 2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017

Y2 - 8 September 2017 through 11 September 2017

ER -

Baculo MJC, Marzan CS, De Dios Bulos R, Ruiz C. Geospatial-temporal analysis andclassification of criminal data in Manila. En 2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 6-11. (2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017). doi: 10.1109/CIAPP.2017.8167050

Geospatial-temporal analysis andclassification of criminal data in Manila

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