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

Producció científica: Capítol de llibre › Contribució a congrés/conferència › Avaluat per experts

14 Cites (Scopus)

Resum

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	Anglès
Títol de la publicació	Proceedings of 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017
Editor	Association for Computing Machinery
Pàgines	113-118
Nombre de pàgines	6
ISBN (electrònic)	9781450352840
DOIs	https://doi.org/10.1145/3127942.3127959
Estat de la publicació	Publicada - 10 d’ag. 2017
Publicat externament	Sí
Esdeveniment	2017 International Conference on Algorithms, Computing and Systems, ICACS 2017 - Jeju Island, Korea, Republic of Durada: 10 d’ag. 2017 → 13 d’ag. 2017

Sèrie de publicacions

Nom	ACM International Conference Proceeding Series
Volum	Part F132084

Conferència

Conferència	2017 International Conference on Algorithms, Computing and Systems, ICACS 2017
País/Territori	Korea, Republic of
Ciutat	Jeju Island
Període	10/08/17 → 13/08/17

SDG de les Nacions Unides

Aquest resultat contribueix als següents objectius de desenvolupament sostenible.

Accés al document

10.1145/3127942.3127959

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Com citar-ho

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. In Proceedings of 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017 (pàg. 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",

}

Marzan, CS, De Dios Bulos, R, Baculo, MJC & Ruiz, C 2017, Time series analysis and crime pattern forecasting of city crime data. in Proceedings of 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017. ACM International Conference Proceeding Series, vol. Part F132084, Association for Computing Machinery, pàg. 113-118, 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017, Jeju Island, Korea, Republic of, 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. pàg. 113-118 (ACM International Conference Proceeding Series; Vol. Part F132084).

Producció científica: Capítol de llibre › Contribució a congrés/conferència › Avaluat per experts

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.

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U2 - 10.1145/3127942.3127959

DO - 10.1145/3127942.3127959

M3 - Conference contribution

AN - SCOPUS:85039034490

T3 - ACM International Conference Proceeding Series

SP - 113

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

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ER -

Time series analysis and crime pattern forecasting of city crime data

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