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 llibreContribució a congrés/conferènciaAvaluat per experts

13 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 originalAnglès
Títol de la publicacióProceedings of 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017
EditorAssociation for Computing Machinery
Pàgines113-118
Nombre de pàgines6
ISBN (electrònic)9781450352840
DOIs
Estat de la publicacióPublicada - 10 d’ag. 2017
Publicat externament
Esdeveniment2017 International Conference on Algorithms, Computing and Systems, ICACS 2017 - Jeju Island, Korea, Republic of
Durada: 10 d’ag. 201713 d’ag. 2017

Sèrie de publicacions

NomACM International Conference Proceeding Series
VolumPart F132084

Conferència

Conferència2017 International Conference on Algorithms, Computing and Systems, ICACS 2017
País/TerritoriKorea, Republic of
CiutatJeju Island
Període10/08/1713/08/17

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