Time series analysis and crime pattern forecasting of city crime data

College

College of Computer Studies

Department/Unit

Software Technology

Document Type

Conference Proceeding

Source Title

ACM International Conference Proceeding Series

Volume

Part F132084

First Page

113

Last Page

118

Publication Date

8-10-2017

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. © 2017 Association for Computing Machinery.

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Digitial Object Identifier (DOI)

10.1145/3127942.3127959

Disciplines

Computer Sciences

Keywords

Crime analysis; Crime forecasting; Crime—Data processing

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