Time series analysis and crime pattern forecasting of city crime data

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

doi:10.1145/3127942.3127959

Time series analysis and crime pattern forecasting of city crime data

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

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

15 Citas (Scopus)

Resumen

Crime analysis using data mining techniques have been a possible solution to aid law enforcement officers to mitigate crime related problems. In this paper, a geospatial data analysis was conducted for detecting the hotspots of criminal activities in Manila City, Philippines. The crime records of 2012-2016 which were manually collected were geocoded and the map was generated using ArcGIS version 10. Association rules mining using Apriori algorithm was also performed on discovering frequent patterns to help the police officers to form a preventive action. This analyzed the different crimes and predicted the chance of each crime that can recur. In addition, analysis of various time series forecasting methods such as Linear Regression, Gaussian Processes, Multilayer Perceptron, and SMOreg to predict future trends of crime was performed. This work provides a solution to help the officers to build a crime controlling strategy to prevent crimes in the future.

Idioma original	Inglés
Título de la publicación alojada	Proceedings of 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017
Editorial	Association for Computing Machinery
Páginas	113-118
Número de páginas	6
ISBN (versión digital)	9781450352840
DOI	https://doi.org/10.1145/3127942.3127959
Estado	Publicada - 10 ago 2017
Publicado de forma externa	Sí
Evento	2017 International Conference on Algorithms, Computing and Systems, ICACS 2017 - Jeju Island, República de Corea Duración: 10 ago 2017 → 13 ago 2017

Serie de la publicación

Nombre	ACM International Conference Proceeding Series
Volumen	Part F132084

Conferencia

Conferencia	2017 International Conference on Algorithms, Computing and Systems, ICACS 2017
País/Territorio	República de Corea
Ciudad	Jeju Island
Período	10/08/17 → 13/08/17

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 16: Paz, justicia e instituciones sólidas

Acceder al documento

10.1145/3127942.3127959

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Enlace a la publicación en Scopus

Cómo citar

Marzan, C. S., De Dios Bulos, R., Baculo, M. J. C., & Ruiz, C. (2017). Time series analysis and crime pattern forecasting of city crime data. En Proceedings of 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017 (pp. 113-118). (ACM International Conference Proceeding Series; Vol. Part F132084). Association for Computing Machinery. https://doi.org/10.1145/3127942.3127959

@inproceedings{eeb257a99c5446b08070807cf26cfc99,

title = "Time series analysis and crime pattern forecasting of city crime data",

abstract = "Crime analysis using data mining techniques have been a possible solution to aid law enforcement officers to mitigate crime related problems. In this paper, a geospatial data analysis was conducted for detecting the hotspots of criminal activities in Manila City, Philippines. The crime records of 2012-2016 which were manually collected were geocoded and the map was generated using ArcGIS version 10. Association rules mining using Apriori algorithm was also performed on discovering frequent patterns to help the police officers to form a preventive action. This analyzed the different crimes and predicted the chance of each crime that can recur. In addition, analysis of various time series forecasting methods such as Linear Regression, Gaussian Processes, Multilayer Perceptron, and SMOreg to predict future trends of crime was performed. This work provides a solution to help the officers to build a crime controlling strategy to prevent crimes in the future.",

keywords = "Association rule mining, Crime analysis, Time series forecasting methods.",

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

note = "Publisher Copyright: {\textcopyright} 2017 Association for Computing Machinery.; 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017 ; Conference date: 10-08-2017 Through 13-08-2017",

year = "2017",

month = aug,

day = "10",

doi = "10.1145/3127942.3127959",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "113--118",

booktitle = "Proceedings of 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017",

address = "United States",

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Marzan, CS, De Dios Bulos, R, Baculo, MJC & Ruiz, C 2017, Time series analysis and crime pattern forecasting of city crime data. En Proceedings of 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017. ACM International Conference Proceeding Series, vol. Part F132084, Association for Computing Machinery, pp. 113-118, 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017, Jeju Island, República de Corea, 10/08/17. https://doi.org/10.1145/3127942.3127959

Time series analysis and crime pattern forecasting of city crime data. / Marzan, Charlie S.; De Dios Bulos, Remedios; Baculo, Maria Jeseca C. et al.
Proceedings of 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017. Association for Computing Machinery, 2017. p. 113-118 (ACM International Conference Proceeding Series; Vol. Part F132084).

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

TY - GEN

T1 - Time series analysis and crime pattern forecasting of city crime data

AU - Marzan, Charlie S.

AU - De Dios Bulos, Remedios

AU - Baculo, Maria Jeseca C.

AU - Ruiz, Conrado

PY - 2017/8/10

Y1 - 2017/8/10

N2 - Crime analysis using data mining techniques have been a possible solution to aid law enforcement officers to mitigate crime related problems. In this paper, a geospatial data analysis was conducted for detecting the hotspots of criminal activities in Manila City, Philippines. The crime records of 2012-2016 which were manually collected were geocoded and the map was generated using ArcGIS version 10. Association rules mining using Apriori algorithm was also performed on discovering frequent patterns to help the police officers to form a preventive action. This analyzed the different crimes and predicted the chance of each crime that can recur. In addition, analysis of various time series forecasting methods such as Linear Regression, Gaussian Processes, Multilayer Perceptron, and SMOreg to predict future trends of crime was performed. This work provides a solution to help the officers to build a crime controlling strategy to prevent crimes in the future.

AB - Crime analysis using data mining techniques have been a possible solution to aid law enforcement officers to mitigate crime related problems. In this paper, a geospatial data analysis was conducted for detecting the hotspots of criminal activities in Manila City, Philippines. The crime records of 2012-2016 which were manually collected were geocoded and the map was generated using ArcGIS version 10. Association rules mining using Apriori algorithm was also performed on discovering frequent patterns to help the police officers to form a preventive action. This analyzed the different crimes and predicted the chance of each crime that can recur. In addition, analysis of various time series forecasting methods such as Linear Regression, Gaussian Processes, Multilayer Perceptron, and SMOreg to predict future trends of crime was performed. This work provides a solution to help the officers to build a crime controlling strategy to prevent crimes in the future.

KW - Association rule mining

KW - Crime analysis

KW - Time series forecasting methods.

UR - https://www.scopus.com/pages/publications/85039034490

U2 - 10.1145/3127942.3127959

DO - 10.1145/3127942.3127959

M3 - Conference contribution

AN - SCOPUS:85039034490

T3 - ACM International Conference Proceeding Series

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EP - 118

BT - Proceedings of 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017

PB - Association for Computing Machinery

T2 - 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017

Y2 - 10 August 2017 through 13 August 2017

ER -

Time series analysis and crime pattern forecasting of city crime data

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