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

Research output: Book chapter › Conference contribution › peer-review

15 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017
Publisher	Association for Computing Machinery
Pages	113-118
Number of pages	6
ISBN (Electronic)	9781450352840
DOIs	https://doi.org/10.1145/3127942.3127959
Publication status	Published - 10 Aug 2017
Externally published	Yes
Event	2017 International Conference on Algorithms, Computing and Systems, ICACS 2017 - Jeju Island, Korea, Republic of Duration: 10 Aug 2017 → 13 Aug 2017

Publication series

Name	ACM International Conference Proceeding Series
Volume	Part F132084

Conference

Conference	2017 International Conference on Algorithms, Computing and Systems, ICACS 2017
Country/Territory	Korea, Republic of
City	Jeju Island
Period	10/08/17 → 13/08/17

UN SDGs

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

SDG 16 Peace, Justice and Strong Institutions

Keywords

Association rule mining
Crime analysis
Time series forecasting methods.

Access to Document

10.1145/3127942.3127959

Cite this

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 (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",

}

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, pp. 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. p. 113-118 (ACM International Conference Proceeding Series; Vol. Part F132084).

Research output: Book chapter › Conference contribution › peer-review

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

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

Y2 - 10 August 2017 through 13 August 2017

ER -

Time series analysis and crime pattern forecasting of city crime data

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this